GlobWetland FINAL TECHNICAL REPORT by kdl17033

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									                                                    GlobWetland
                                                    FINAL TECHNICAL REPORT

Submitted to:                                                Prepared by:
Marc Paganini                                                MDA Geospatial Inc.
ESRIN, European Space Agency (ESA)                           20 Colonnade Rd, Suite 110
Via Galileo Galilei                                          Ottawa, ON K2E 7M6, CANADA
I-00044 Frascati, ITALY                                      Tel: +1 613 727 1027
Tel: +011 36.06.941.80




                                             Protected - Business Information

    This document is provided pursuant to ESRIN Contract No. 17815/03/I-LG. The information contained herein is
    owned by Atlantis Scientific. ESA shall have the right to duplicate, use, or disclose this data to the extent provided
    in the Contract. This restriction does not limit ESA’s right to use the information contained in such data if it is
    obtained from another source.
                                       CHANGE RECORD


ISSUE   DATE                      PAGE(S)                         DESCRIPTION                         RELEASE

1.0     Dec 05, 2008




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                                          TABLE OF CONTENTS

1    INTRODUCTION................................................................................................................ 1-1
     1.1  Scope of Document.................................................................................................... 1-1
     1.2  Reference Documents ................................................................................................ 1-1
     1.3  Acronym List ............................................................................................................. 1-3

2    BACKGROUND .................................................................................................................. 2-1
     2.1 Ramsar Convention.................................................................................................... 2-1
     2.2 Geo-Spatial Information Needs for Wetland Management ....................................... 2-2
     2.3 Role of Earth Observation Technologies ................................................................... 2-5

3    PROJECT OBJECTIVES .................................................................................................. 3-1

4    STUDY LOGIC.................................................................................................................... 4-1
     4.1 User Driven Approach ............................................................................................... 4-1
     4.2 User List..................................................................................................................... 4-2

5    USER REQUIREMENTS ................................................................................................... 5-1

6    GLOBWETLAND PRODUCTS ........................................................................................ 6-1
     6.1 Globwetland Products Description ............................................................................ 6-1
     6.2 Globwetland Product examples ................................................................................. 6-6
     6.3 Globwetland Product List – all delivered products.................................................. 6-10

7    TECHNICAL SOLUTION ................................................................................................. 7-1
     7.1 Globwetland Information System .............................................................................. 7-1
     7.2 Globwetland Methodology ........................................................................................ 7-5
     7.3 Earth Observation Data Used to produce products .................................................. 7-21

8    VALIDATION OF THE GLOBWETLAND PRODUCTS ............................................. 8-1
     8.1  Validation approach ................................................................................................... 8-1
     8.2  Sample Validation Report.......................................................................................... 8-4
     8.3  Expected accuracy of products................................................................................... 8-9

9    USER ASSESSMENT ......................................................................................................... 9-1
     9.1  Greece – Nature Habitat mapping updating............................................................... 9-2
     9.2  Portugal – finely detailed wetland maps .................................................................... 9-3
     9.3  Lake Chad – Assessment of water quality parameters............................................... 9-3

10   USER TRAINING AND CAPACITY BUILDING......................................................... 10-1
     10.1 Field work carried out with end users...................................................................... 10-1
     10.2 African Capacity Building ....................................................................................... 10-7


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          10.3      European Capacity Building .................................................................................. 10-10
          10.4      User Handbook ...................................................................................................... 10-12

11        DISSEMINATION ACTIVITIES .................................................................................... 11-1
          11.1 Globwetland Website............................................................................................... 11-1
          11.2 Globwetland Symposium......................................................................................... 11-3
          11.3 Globwetland Video .................................................................................................. 11-6
          11.4 Globwetland per country / site reports ..................................................................... 11-7
          11.5 COP-9 ...................................................................................................................... 11-9
          11.6 COP-10 .................................................................................................................. 11-11

12        CONCLUSION AND FUTURE PERSPECTIVES ........................................................ 12-1

13        PROJECT TEAM.............................................................................................................. 13-1

DISTRIBUTION LIST........................................................................................................................1




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                                                               LIST OF FIGURES


Figure 1. Strategic approach to wetland inventory ............................................................................................................2-3
Figure 2. Various wetland types in a river basins ..............................................................................................................2-6
Figure 3. Nominal map scale as a function of the area to be mapped ................................................................................2-9
Figure 4. CD Label for Globwetland products...................................................................................................................6-7
Figure 5. Jewel case CD insert...........................................................................................................................................6-7
Figure 6. Folder structure for product delivery to end users ..............................................................................................6-8
Figure 7. Output Products – Land Use Land Cover...........................................................................................................6-9
Figure 8. LULC over the Tejo Estuary, Portugal (1:25,000 scale) ....................................................................................7-6
Figure 9. LULC Processing Methodology .........................................................................................................................7-7
Figure 10. Change detection showing significant loss of forest areas in the Lake Bogoria catchment, Kenya:
                   (a) Landsat TM data from 1986; (b) Landsat ETM+ acquired in 2003; (c) change analysis,
                   blue polygons showing extent of lost forest ........................................................................................7-11
Figure 11. Water Cycle Regime map over Creston Valley, Canada ................................................................................7-14
Figure 12: Water Quality Parameters (a) absolute suspended sediments in Axios Delta, Greece (b)
                   chlorophyll-a relative concentration values, Saint Lucia, South Africa ..............................................7-17
Figure 13: Identification and delineation of wetlands, La Brenne, France. The blue polypons depict the
                   location of identified wetlands extracted through the analysis of SPOT-5 and SAR data...................7-19
Figure 14. Lake Surface Temperature – derived from AATSR (10-10-2007)...................................................................9-3
Figure 15. Chlorophyll Concentration – Derived from MERIS (05-11-2007)...................................................................9-4
Figure 16. Total Suspended Matter/Yellow Matter - MERIS (05-11-2007)......................................................................9-5
Figure 17. (Z90) Signal Depth– MERIS (17-10-2007) – related to “Secchi Depth” .........................................................9-6
Figure 18. Vexcel Canada staff collect field data together with the Algerian National and Local Authorities. ...............10-3
Figure 19. Field data collection in Finland with the National Focal Point.......................................................................10-3
Figure 20. Data Collection in Valle Averto, Italy. ...........................................................................................................10-4
Figure 21. Data Collection in Greece ..............................................................................................................................10-4
Figure 21. Data Collection in Caps et Marais d’Opale, France. ......................................................................................10-5
Figure 22. Field data collection – Bogoria, Kenya ..........................................................................................................10-5
Figure 23. Data collection planning - Lisbon, Portugal ...................................................................................................10-6
Figure 24. Field data collection, Lesotho.........................................................................................................................10-6
Figure 25. Field data collection, Lagunas de Villafafilla, Spain ......................................................................................10-7
Figure 26. Participants from South Africa, Netherlands, Egypt, Chad, Senegal, Algeria, Kenya and Canada
                   during the April 24-28, 2006 Globwetland workshop held at the Naivasha Training Institute
                   in Naivasha, Kenya. ............................................................................................................................10-8
Figure 27. African Training Kit provided by ESA to African Seminar participants ........................................................10-9
Figure 28. Participants from Canada, The Netherlands, Italy, Portugal, Greece, Finland, France, and Spain
                   during the Oct 16-18, 2006 Globwetland workshop held at ESA ESRIN in Frascati, Italy. .............10-12
Figure 29. Globwetland User Handbook – professionally printed for broad distribution..............................................10-13
Figure 30. Globwetland Website – Present on the web since 2004 .................................................................................11-2
Figure 31. Globwetland Symposium call for abstracts. ...................................................................................................11-3
Figure 32. Lead paper published in special issue of the Journal of Environmental Management....................................11-4
Figure 33. Globwetland Symposium Chair, Dr. Nick Davidson addresses questions during a Plenary Session..............11-5


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                                  contained herein is subject to the restrictions on the title page of this document.
Figure 34. Globwetland Video: Earth Observation for Wetland Management................................................................ 11-6
Figure 35. Globwetland Final Report .............................................................................................................................. 11-7
Figure 36. National focus in Final Report – Algeria ....................................................................................................... 11-8
Figure 37. Site specific focus in final Globwetland Report ............................................................................................. 11-8
Figure 38. COP-09 Side Event agenda and speakers, Nov 11, 2008 COP-09 Kampala, Uganda ................................... 11-9
Figure 39: French national Focal Point Marie Claude Ximenes presents France’s assessment of the
                  Globwetland products ....................................................................................................................... 11-10
Figure 40. Side Event agenda and speakers, Nov 04, 2008 COP-10 Changwon, South Korea..................................... 11-11
Figure 41. Globwetland project team members in 2004.................................................................................................. 13-1




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                                 contained herein is subject to the restrictions on the title page of this document.
                                                                LIST OF TABLES


Table 1.: Globwetland Reference documents ....................................................................................................................1-2
Table 2. Overview of user information needs and requirements where EO technology may contribute............................2-4
Table 3. Mapping scales and MMU relationship...............................................................................................................2-8
Table 4. Mapping scale and MMU to determine satellite suitability ...............................................................................2-10
Table 5. Comparing sensor resolution to minimum mapping units..................................................................................2-11
Table 6. List of Globwetland National Focal Points.........................................................................................................4-2
Table 7. Core and Specific Components specified in ITT .................................................................................................5-3
Table 8. Total number of Core and Specific Products requested by country.....................................................................5-4
Table 9. Detailed list of requested products for each Globwetland site.............................................................................5-5
Table 10. Summary list of Core and Specific Products .....................................................................................................6-1
Table 11. Complete list of all delivered Globwetland products.......................................................................................6-10
Table 12. LULC challenges and solutions .........................................................................................................................7-8
Table 13. Change Detection methods applied ...................................................................................................................7-8
Table 14. Water Cycle Regime Classes and definitions ....................................................................................................7-9
Table 15. EO data selection considerations for the Land Use Land Cover product ........................................................7-22
Table 16. EO data selection considerations for the Change Detection product ...............................................................7-23
Table 17. EO data selection considerations for the Water Cycle Regime product ..........................................................7-24
Table 18. Satellite data collected and used to derive information products for Globwetland end users ..........................7-26
Table 19.Confusion Matrix – Land Use and Land Cover - Réserve Naturelle du Lac des Oiseaux, Algeria ....................8-6
Table 20.Confusion Matrix for – Long and Short Term Change Detection - Réserve Naturelle du Lac du Béni
                   Bélaïd, Algeria ......................................................................................................................................8-7
Table 21.Qualitative assessment by Algerian end user ......................................................................................................8-8
Table 22. Support of conservation activities through Globwetland ...................................................................................9-2
Table 23. Wetland sites visited by Globwetland Team....................................................................................................10-2
Table 24. Summary statistics on www.globwetland.org web visits since 2004. ..............................................................11-2




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1 INTRODUCTION


1.1 Scope of Document

This document has been prepared upon the completion of all Globwetland project activities. The
purpose of this document is to provide an overview of all activities in the three phases of the
Globwetland program thus far:

      Phase 1: Confirmation of end user requirements and prototyping of products

      Phase 2: Delivery of Service Cases for Core and Specific products

      Project Extension: Regional scale water quality and environmental change detection products
         for the Mekong River Commission and the Lake Chad Basin Commission

This document will provide a summary of all activities that occurred throughout these three phases
of the Globwetland project, which spans from January 2004 to December 2008.


1.2 Reference Documents

The following lists all documents which have been delivered under the Globwetland project.




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                                                          Table 1. Globwetland Reference documents
                            Document Name                                                                                           Publish Date
                            REQUIREMENTS BASELINE
                                   Contact ESA                                                                                          01-Jun-04
                            REQUIREMENTS BASELINE APPENDIX C
                                   Contact ESA                                                                                          01-Jun-04
                            DATA COLLECTION PLAN
                                   Contact ESA                                                                                         25-Aug-04
                            TECHNICAL SPECIFICATION
                                   Contact ESA                                                                                          27-Oct-04
                            DESIGN ENGINEERING PLAN
                                   Contact ESA                                                                                          27-Oct-04
                            ACCEPTANCE TEST PLAN
                                   Contact ESA                                                                                         09-May-05
  PROJECT DELIVERABLES




                            PROTOTYPE VALIDATION AND ASSESSMENT
                                   Contact ESA                                                                                         10-May-05
                            DESIGN JUSTIFICATION FILE
                                   Contact ESA                                                                                          13-Jun-05
                            DESIGN DEFINITION FILE
                                   Contact ESA                                                                                          04-Jul-05
                            SERVICE DEPLOYMENT AND ASSESSMENT PLAN
                                   Contact ESA                                                                                          28-Jul-05
                            SERVICE QUALITY ASSURANCE PLAN
                                   Contact ESA                                                                                          28-Jul-05
                            SPECIFIC COMPONENTS REVIEW
                                   Contact ESA                                                                                         08-Mar-07
                            DESIGN JUSTIFICATION FILE PRELIMNARY DESIGN AND CRITICAL DESIGN
                                   Contact ESA                                                                                         11-May-07
                            GLOBWETLAND EXTENSION REQUIREMENTS BASELINE
                                   Contact ESA                                                                                          24-Oct-07
                            GLOBWETLAND EXTENSION DESIGN JUSTIFICATION FILE
                                   Contact ESA                                                                                          24-Oct-07
                            Global wetlands surveyed from space
                                   http://www.esa.int/esaEO/SEM0PFXLDMD_index_0.html                                                   24-Nov-03
                            Wetlands satellite mapping scheme yielding first results
                                   http://www.esa.int/esaEO/SEMT952DU8E_index_0.html
  PUBLISHED NEWS ARTICLES




                                                                                                                                        10-Oct-05
                            African wetland managers armed with new technology
                                   http://www.esa.int/esaEO/SEMECNOFGLE_index_0.html                                                   02-May-06
                            ESAC Chair Hartmut Graßl talks about the role of space technology in wetland conservation
                                   http://www.esa.int/esaEO/SEME290CYTE_index_0.html                                                    30-Oct-06
                            Ramsar and remote-sensing experts tackle threats to wetlands
                                   http://www.esa.int/esaEO/SEMVA8PFHTE_index_0.html                                                   07-Nov-06
                            Connection between health of wetlands and humans in focus
                                   http://www.esa.int/esaCP/SEM9EUOR4CF_Protecting_0.html                                              01-Feb-08
                            Value of satellites recognised for conserving wetlands
                                   http://dup.esrin.esa.it/news/news171.asp                                                            18-Nov-08
                            What information do wetland managers really need?
  TECHNICAL PAPERS




                                   http://www.globwetland.org/news_links/links/Backscatter_Volume-14_Number-1.pdf                      01-Mar-03
                            Monitoring and Assessment of Wetlands using Earth Observation
                                   http://www.globwetland.org/news_links/links/433855kj.pdf                                             01-Oct-06
                            Monitoring and assessment of wetlands using Earth Observation: The GlobWetland project: in Journal of
                            Environmental Management
                                   http://dx.doi.org/10.1016/j.jenvman.2007.07.037                                                      01-Jul-08
                            Globwetland News Letter - April 2004
                                   http://www.globwetland.org/news_links/links/GW_Newsletter_April2004.pdf                             01-Apr-04
  NEWS-LETTERS




                            Globwetland News Letter - September 2004
                                   http://www.globwetland.org/news_links/links/GW_Newsletter_September2004.pdf                         01-Sep-04
                            Globwetland News Letter - October 2005
                                   http://www.globwetland.org/news_links/links/GW_Newsletter_Oct2005.pdf                                01-Oct-05
                            Globwetland News Letter - April 2006
                                   http://www.globwetland.org/news_links/links/GW_Newsletter_Apr2006.pdf                               01-Apr-06
                            Globwetland Invitation to Tender Documentation (produced by ESA)
ESA ITT
                                   http://www.globwetland.org/news_links/links/ESA+ITT+for+GlobWetland.pdf                              01-Jul-05




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1.3 Acronym List

 AP       Alternating Polarization
 ASAR     Advanced Synthetic Aperture Radar
 ASTER    Advanced Spaceborne Thermal Emission and Reflection Radiometer
 ATP      Acceptance Test Plan
 CD       Compact Disc
 CLC      Corine Land Cover
 COP      Conference Of the Parties
 COP-10   Conference Of the Parties - 2005 COP meeting held in Uganda
 COP-9    Conference Of the Parties - 2008 COP meeting held in South Korea
 COTS     Commercial Off The Shelf Software
 DCP      Data Collection Plan
 DDF      Design Definition File
 DEM      Digital Elevation Model
 DEP      Design Engineering Plan
 DIMAP    File format used primarily by SPOT
 DJF      Design Justification File
 DUE      Data User Element
 DUP      Data User Programme
 DVD      Digital Video Disc
 EC       European Commission
 EEA      European Environmental Agency
 EO       Earth Observation
 EOEP     Earth Observation Enveloppe Programme
 ESA      European Space Agency
 ESRIN    European Space Reseach Institute
 ERS-1    European Remote Sensing Satellite - 1
 ERS-2    European Remote Sensing Satellite - 2
 ETM      Enhanced Thematic Mapper
 GIS      Geographic Information System
 GPS      Global Positioning System
 HH       Horizontal send; Horizontal receive
 HV       Horizontal send; Vertical receive
 IAHS     International Association of Hydrological Sciences
 INSAR    Interferommetric SAR
 ITT      Invitation to Tender




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LANDSAT   Land Remote-Sensing Satellite
LCBC      Lake Chad Basin Commission
LULC      Land Use Land Cover
MERIS     Medium Resolution Imaging Spectrometer
MMU       Minimum Mapping Unit
MODIS     Moderate Resolution Imaging Spectroradiometer
MRC       Mekong River Commission
NDVI      Normalized Difference Vegetation index
NFP       National Focal Point
NGO       Non Governmental Organization
NRDI      Normalized Radar Difference Index
PDA       Portable Digital Assistant
PVAR      Prototype Validation and Assessment Report
RB        Requirements Baseline
RIS       Ramsar Information Sheet
SAR       Synthetic Aperture Radar
SDAP      Service Delivery and Assessment Plan
SPOT      Système Pour l'Observation de la Terre
SQAP      Service Quality and Assessment Plan
SRTM      Shuttle Radar Topographic Mission
STRP      Scientific and Technical Review Panel
SWO       Statement Of Work
TESEO     Treaty Enforcement Services using Earth Observation
TIGER     Earth Observation for improved water management in Africa
TM        Thematic Mapper
TS        Technical Specification
UN        United Nations
WCR       Water Cycle Regime




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2 BACKGROUND


2.1 Ramsar Convention

Abundant water makes wetlands the most biologically diverse ecosystems on Earth, more productive
even than tropical rainforests. Unlike rainforests, they are scattered across the world, providing regional
flood and erosion prevention, water purification and nutrient recycling.

For much of the 20th Century, wetlands were drained or otherwise degraded. However, growing
understanding of the vital importance of wetlands led to the signing in 1971 of the Ramsar Convention
on Wetlands. As of December 2008 more than 1,744 wetlands have been designated as Wetlands of
International Importance, a total area of 162 million hectares. The Convention's 156 national signatories
commit to maintaining the ecological character and are obliged to reporting on the state of listed
wetlands they have designated.

In this context, the overall objective of the Ramsar Convention, signed in 1971, is “the conservation and
wise use of wetlands by national action and international cooperation as a means to achieving
sustainable development throughout the world”. The wise use concept is understood by the Convention
as “the sustainable utilisation for the benefit of humankind in a way compatible with the maintenance of
the natural properties of the ecosystem”. This complex and challenging task requires all the national and
international bodies involved in the implementation of the Convention to rely on suitable information to
better understand wetland areas, their internal processes and their significance in the global environment,
to manage efficiently wetland areas so that they may yield the greatest continuous benefit to present and
future generations, to inform the general public and policy makers of the importance of wetlands and
promote their conservation and protection worldwide. In this context, existing and future EO technology
may play an important role.

Earth's wetlands are havens for wildlife and vital to the water cycle, but they are also under threat. An
ESA-led initiative aims to develop a global wetland information service based on Earth Observation for
conservation efforts. The Globwetland project has delivered products across four continents.




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                       contained herein is subject to the restrictions on the title page of this document.
2.2 Geo-Spatial Information Needs for Wetland Management

Actors involved in the implementation of the Ramsar Convention, who may take advantage of EO
technology, range from International Agencies, National Focal Points, scientists, non-governmental
organisations, wetland managers and local communities. However, the type of information required by
those categories of users may vary significantly depending on their role within the implementation of the
Convention.

Table 2 provides an overview of the different geo-information products that can be derived from EO data
in support of the Ramsar community. In this table, the Ramsar Community have been categorised in
terms of the scope of their organisations as global, regional, national or local. Moreover, user
requirements have been split into two main groups: global and local. This responds to the logic that
regional and national organisations usually meet their information needs by aggregating local
information, whereas, on the contrary, some requirements of "global" organisations cannot be easily
fulfilled by a simple collection of local data.

As far as the global information needs are concerned, the global nature of EO data renders EO
technology a unique tool to provide global information to users on a regular basis. Concerning the
information needs at local scale, EO technology represents an efficient source of continuous and
synoptic information not only of the wetland sites but also of the entire basins that supply water to the
wetlands. This provides novel capabilities to users, who may take advantage of EO technology, for
instance, to extend inventory information and monitoring activities through the catchment areas of
wetlands (as a tool to identify and monitor threats upstream in the catchment area that could potentially
damage the wetland site).

The user needs expressed in Table 2 represent a hierarchy of increasingly detailed information, which
must be spatially organised to address the management objectives for the wetlands concerned. The
Ramsar Convention has adopted a four level approach to organize the necessary wetland inventory
framework, as shown in Figure 1, and this approach assists managers to focus resources at the level that
matters most. For example, a wetland manager with objectives to manage a flood plain with seasonal
grazing and rice cultivation needs to know the incoming water flows and levels both spatially and
temporally. In order to do this the manager assesses the cumulative effect of land cover and use in the
supporting catchment, and needs to obtain low resolution information about the land cover and changes
across the catchment, but also needs high resolution data about the surrounding local landscape,
contours, flood level envelope and land use and any changes, to assess adaptive responses or to negotiate
with land owners about changes.




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              Figure 1. Strategic approach to wetland inventory




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                Table 2. Overview of user information needs and requirements where EO technology may contribute

     Scope        End-user                              User Requirements where EO can contribute
     Global       Ramsar Bureau;                        Global extent of wetlands ant their temporal variations (seasonal, multi-year) as
                  UN agencies;                          an input for global environmental models (carbon, methane production, etc.);
                  International NGOs;
                  International Research Org.;          Global monitoring of wetlands with respect to global environmental changes;
                  Scientific community;                 Global inventory of wetlands1;

     Regional     Regional policy makers (e.g.,         Inventorying and base mapping2:
                  EC);                                   Wetland boundaries (e.g., size and variation);
                  Regional Developing Agencies           Land cover/use of the wetland site and the corresponding catchment area;
                  (e.g., the African Development         Digital Elevation Model of the wetland site and the corresponding
                  Bank);                                catchment area;
                  Regional Environmental                 Water regime, (e.g., periodicity, extent of flooding);
                  Agencies (e.g., EEA);                  Water chemistry (e.g., colour, transparency);
                                                         Biota (vegetation zones and structure);
     National     National Focal Points;                 Location of potential threats to the wetland (in the wetland site and the
                  Related National Ministries;          corresponding catchment area);
                  National Implementing Agencies;        Additional information: e.g., infrastructures;
                  National NGOs;
                                                        Assessment activities:
     Local        Scientific community3;                 Estimation of biological (e.g., vegetation condition), physical (e.g., water
                  Local administrative authorities;     table), and chemical parameters (e.g., chlorophyll), which characterise the
                  Local wetland managers;               ecological condition of a wetland;
                  Local basin authorities;
                  Local NGOs;                           Monitoring activities:
                  Land owners;
                                                         Identification and monitoring of changes in the biological, physical, and
                  Local communities;
                                                        chemical condition of the wetland site;
                  Farmers associations4;
                                                         Identification and monitoring of threats in the wetland site and the
                  Fishing associations;
                                                        corresponding catchment area, which may affect the wetland condition (e.g.,
                                                        alien species, overgrazing, urban expansion, agricultural activities, industrial
                                                        pollutants, etc.).
                                                         Rapid reaction to catastrophic events (e.g., floods, pollution emergencies);

                                                        Implementation of management (e.g., rehabilitation) plans:
                                                         Base information for inventorying and as a basis for planning and decision
                                                        making (e.g., base maps);
                                                         Change analysis to monitor the efficiency of the undertaken actions and
                                                        impact assessment;




1
  By aggregating local information (see "inventorying and base mapping" below).
2
  Regional and national inventories are based on the aggregation of local information. Therefore, in the table we report the information needs
at local level.
3
   The Scientific community has been included under both global and local subdivisions, to distinguish between the research activity
focusing on understanding global issues (e.g., influence of wetlands in the global environment) and the research work aimed at better
understanding single wetlands and their processes.
4
  It is worth noting that some of the requirements mentioned in Table 1 under a certain category (e.g., Local) may not fulfill the information
needs of some of the corresponding users: (e.g., farming organizations do not require a wetland inventory, but, for instance, just information
about potential threats that may affect their activity).




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                               contained herein is subject to the restrictions on the title page of this document.
2.3 Role of Earth Observation Technologies

In the last few decades, EO technology has proven a powerful tool to monitor and assess the Earth
surface and its atmosphere in a regular basis. EO satellites, with increasing capabilities in terms of
spatial, temporal and spectral resolution, allow a more efficient, reliable and affordable monitoring of the
environment over time at global, regional and local scales. This establishes EO technology as a
fundamental tool to support the Convention Parties and other related national and international bodies
involved in the implementation of the Ramsar Convention.

Three main areas can be pointed out, where EO technology may contribute to achieve the objectives of
the Ramsar Convention:

Increasing scientific knowledge: The collection and analysis of short-term and long-term data and
information to better understand wetlands and their physical, biological and chemical components, such
as soil, water, plants, animals and nutrients, and the interactions between them, as well as the influence
of wetlands in the global environment.

Supporting the efficient management of wetland areas: The collection of short-term and long-term data
and information to allow the efficient inventory, assessment and monitoring of wetland sites and the
entire catchment area, as well as the effective support to the development and implementation of
restoration or rehabilitation plans.

Contributing to improve the performance of the Convention: The use of EO technology may enhance
the reporting mechanisms of the Convention and may strengthen the ability of the responsible bodies to
monitor treaty-relevant behaviour as well as instances of non-compliance. It may also contribute to the
creation of common data sets and information systems and may help in harmonizing methodologies,
procedures and formats for the gathering and analysis of information required for better decision-
making.

In order to demonstrate at a large scale the above mentioned capabilities, ESA launched in 2003 the
GlobWetland project (www.globwetland.org). The project aims at developing and demonstrating an EO-
based information service to support wetland managers and national authorities in responding to the
requirements of the Ramsar Convention. The project involves 50 different wetlands distributed in 21
countries worldwide and relies on the direct collaboration of several regional, national and local
conservation authorities and wetland managers.

The wide range of technologies now available for wetland inventory assessment and monitoring allows
wetland managers to improve their understanding of the spatial and temporal characteristics of their
wetlands. A key question wetland managers should ask is which approaches should be used and how
sustainable are these?

The GlobWetland Project has addressed the inventory needs of single wetland sites. It is important to
take this information into the context of the overall national level inventory.


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Key questions for managers often begin with: what can remote sensing do for me?

More detailed questions include:

1.     How much does it cost?
2.     How can it help with monitoring changes in and around wetlands?
3.     What exactly is it possible to see in images?
4.     Can maps be created from images to use in the field?

2.3.1 Wetlands and their catchment context

Wetlands are distributed from the top of the catchment to the deltas at the bottom, indicating clearly the
networked interdependence of water resources. Wetlands are often indicators of substantive groundwater
resources and fluvial buffering, in particular, important in sediment and nutrient trapping. Knowledge of
wetland location has particular relevance for water supply and runoff interception in many African
countries, because whole communities are directly dependent upon the biological and physical filtering
and water supply buffering capacity of larger wetlands, the best studied example being Nakivubo
swamp, Kampala, acting informally as the city’s waste water treatment facility.




                                      Figure 2. Various wetland types in a river basins




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Watersheds (river basins) are basic units for wetland management, often crossing national boundaries
demarcated within many wetlands and major rivers of the region. It is essential that adequate knowledge
is available about national and trans-national wetlands to promote cooperation between countries to
achieve effective wetland management.

Currently the official poverty reduction programmes of countries (e.g. stated within World Bank Poverty
Reduction Strategy Papers) rarely integrate the environment, and especially wetlands, within an
integrated water resource management strategy, as part of national action plans. Knowledge of the extent
and nature of national wetland resources is essential, both to evaluate their worth and to plan their
management priorities. This is the task of a national scale inventory, which needs to map the entire
landscape to identify the river basin structure within which wetlands are located. The mapping
assessment and monitoring of individual wetlands should be clearly linked to their river basin context, to
enable rational decisions to be taken about their management.


2.3.2 The issue of scale and mapping units


2.3.3 Minimum Mapping Unit (MMU)

Mapping land use and land cover requires the combination of ground truth data (information about the
land cover or land use on the ground) with the analysis of satellite images. As the required mapping
scale differs between sites, the resolution of the EO data used varies accordingly. Additionally, the
physical structure of the land cover and the required thematic classification will vary between the sites.

Since there is a relationship between mapping scale, resolution of the satellite data and thematic
classification, the size of the observational units to be measured in the field will be mainly determined
by the required mapping scale. An area measure that is applied for mapping land cover and land use is
the minimum mapping unit (MMU).

   The MMU can be defined as the smallest size area entity to be mapped as a discrete area.

In the case of Globwetland product generation, a discrete area was considered to contain relatively
homogenous land cover. With regard to the MMU, “homogeneous” land cover refers to land cover that
is uniformly representative of an observational point. For example, when a 2 ha plot has been identified
as grass type “A”, this area will include many other species besides grass type “A”. It is essential for the
observer to be able to assert that grass type “A” is sufficiently abundant in
a given mapping unit (relative to other species) in order to be objectively mapped in a reasonably similar
way by a second party. The cover of a species is defined as the proportion of ground occupied by vertical
projection. Cover is normally expressed as a percentage and the maximum cover of any one species is
100 percent. For classification purposes, it is helpful to have an estimation of cover in the field. Any
class that is smaller in area than the specified minimum mapping unit (MMU) threshold will be ignored,


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and therefore does not have to be mapped. This concept is critical in understanding the physical limits of
detection using EO satellites.

                                       Table 3. Mapping scales and MMU relationship

               Mapping scale            1:250,000                  1:50,000                 1:25,000           1,10,000
               Size of MMU                 20                         5                        3                  1
               on ground (ha)
               Size of MMU        0.2 (450m x 450m)             0.05 (225m x             0.03 (170m x        0.01 (100m x
              on ground (km2)                                      225m)                    170m)               100m)



   It might happen that the practical size of the MMU varies by class, e.g. a rare class will require a
   smaller MMU; any exceptions should be taken into consideration. In addition, it may be difficult to
   define the MMU for long, thin polygons like riparian zones, where it may be more appropriate to
   define a minimum width than a minimum area. However, in no case may the MMU be smaller than
   the minimum area that must be observed in order to establish the best represented class.

   As a field class might appear to physically extend much further away from the observational point
   than in reality, we recommend in this handbook that wetland managers should walk or measure the
   area using high resolution aerial imagery to determine its homogeneity and actual size before
   mapping using satellite imagery.


2.3.4 Scale vs Resolution

   A number of factors must be considered during the construction of a thematic map generated from
   EO data. In general these can be categorized as:

          Size of the process of interest
          Data availability
          Extent of study area
          Time frame of interest

   In the following discussion, we assume that the data availability and time frame issues have been
   addressed. This implies the user must balance the size of the process to be monitored with the extent
   of the area to be mapped. As an example, if one is interested in monitoring vegetation types the
   required scale would be significantly finer than if one is interested in fire mapping. Conversely, the
   extent of the study area would be much greater for the fire map.

   The problem of scale must be addressed since there is no natural scale at which ecological
   phenomena occur. All systems have a characteristic variability in terms of spatial, temporal and
   organizational scales. The scale can be defined as the spatial or temporal dimension of an object or
   process and is generally characterized by the grain (finest level of resolution possible) and extent


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   (size or time duration) of that process. Resolution is based upon the radiometric and geometric
   precision of the sensor and its ability to resolve features. Therefore, the optimum resolution is
   obtained when the grain size is equivalent to the scale of the process.

   An accurate map is the foundation for the aggregation of all geospatial information. This foundation
   is essential to the success of all subsequent analysis. The spatial accuracy necessary for the base map
   depends on two considerations:

   Scale. Until recently, map scale was determined by the size of the area of Earth to be covered and the
   size of the piece of paper available for the map. The size of the piece of paper is no longer a
   constraint but it is still not practical to map very large areas at very large scales. Excessive
   information may create useless clutter, whereas a map with too little detail conveys little information.
   The following figure provides guidance regarding the required scale.




                          Figure 3. Nominal map scale as a function of the area to be mapped

   Geometric precision: Environmental decision making can generally be done at the scales and
   precision levels shown in the above figure. However, when important property boundaries,
   administrative boundaries, and infrastructure must be considered, greater precision is required.


2.3.5 EO Sensor capabilities and mapping scales

A relationship can be established in terms of the size of wetlands and the scale at which they can be
mapped. This is a function of their relative size (surface area) and their distribution (heterogeneous vs.
homogeneous). The size of the elements inside wetlands can be used to determine what sensors can be
used for detection and mapping of wetland features, and how this translates into a map scale.




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Threshold for detection
In general, several pixels are necessary to detect an object: at least 4 pixels are necessary, except for very
contrasted objects, for example a pond in the medium of meadows. However the identification of an
object requires 3 times more pixels than its detection, that is to say 12 pixels at least. Depending on the
complexity of the features being mapped, a number as high as 28 pixels could be required for successful
identification in an image.

Threshold of cartography
In cartographic terms, there is a physical limit at which items can be visually separated by the human eye
on a printed map. A conservative estimate for minimal threshold is 2 x 2 mm. The corresponding size of
the features depends on the scale of the map. For example, to the 1: 25 000 an object of 2*2mm has a
surface of 0.25 ha (2,500 sq. meters).

Based on the concepts of MMU and thresholds for detection and identification, the following guidelines
are presented in terms of mapping scales and capabilities for detection of wetlands using satellite
imagery. Starting from the principle of the minimum detectable size of features on a printed map (as
described above in the section thresholds for cartography), we can determine the minimum number of
pixels required to detect this unit. A simple check is then performed to test if the resolutions of certain
satellites meet this criterion by taking the square root of the minimum area to be mapped and comparing
it to the sensor’s resolution. The following table compares various detection settings at different
mapping scales (2x2mm to 7x7mm) to calculate the maximum possible size of a sensor’s pixel
resolution if 12 or 28 pixels are required to detect and identify features on a satellite image.


                                         Table 4. Mapping scale and MMU to determine satellite suitability
    Map Scale (enter desired value)                             10,000
    Limit of cartographic output (mm)                           2*2            2     3*3            3     4*4            4     5*5            5     6*6            6     7*7            7
    Corresponding area (ha)                                            0.04                 0.09                 0.16                 0.25                 0.36                 0.49
    Min. amount of pixls required for feature identification     12           28     12            28      12           28      12           28      12           28      12           28
    Maximum possible size of pixel for Sensor                    5.8          3.8    8.7           5.7    11.5          7.6    14.4          9.4    17.3          11.3   20.2          13.2


    Map Scale (enter desired value)                             25,000
    Limit of cartographic output (mm)                           2*2            2     3*3            3     4*4            4     5*5            5     6*6            6     7*7            7
    Corresponding area (ha)                                            0.25                 0.56                 1.00                 1.56                 2.25                 3.06
    Min. amount of pixls required for feature identification     12           28     12            28      12           28      12           28      12           28      12           28
    Maximum possible size of pixel for Sensor                   14.4          9.4    21.7          14.2   28.9          18.9   36.1          23.6   43.3          28.3   50.5          33.1


    Map Scale (enter desired value)                             50,000
    Limit of cartographic output (mm)                           2*2            2     3*3            3     4*4            4     5*5            5     6*6            6     7*7            7
    Corresponding area (ha)                                            1.00                 2.25                 4.00                 6.25                 9.00                12.25
    Min. amount of pixls required for feature identification     12           28     12            28      12           28      12           28      12           28      12           28
    Maximum possible size of pixel for Sensor                   28.9          18.9   43.3          28.3   57.7          37.8   72.2          47.2   86.6          56.7   101.0         66.1


    Map Scale (enter desired value)                            100,000
    Limit of cartographic output (mm)                           2*2            2     3*3            3     4*4            4     5*5            5     6*6            6     7*7            7
    Corresponding area (ha)                                            4.00                 9.00                16.00                25.00                36.00                49.00
    Min. amount of pixls required for feature identification     12           28     12            28      12           28      12           28      12           28      12           28
    Maximum possible size of pixel for Sensor                   57.7          37.8   86.6          56.7   115.5         75.6   144.3         94.5   173.2     113.4      202.1     132.3




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  Using the table above, the resolution of EO satellites can be compared to determine their suitability for detecting and
  identification of features at the stated scales. The following example is based on a desired mapping scale of 1:25,000. The
  resulting MMU at 2x2mm to 7x7mm in terms of hectares is then calculated; based on a minimum detection threshold of
  12x12 or 28x28 pixels, a minimum pixel resolution is tabulated. A simple cross check is then used to determine if the sensor
  resolution meets the requirements at different thresholds. It should be noted that this assumes visual interpretation
  approaches, in which case black and white (panchromatic) data can be used in most cases. However, manual interpretation as
  well as semi-automated approaches benefit greatly from multi-spectral information to extract and separate different land
  cover information. Wetland managers need to take this into consideration when selecting data sources, and the minimum
  resolution at which multi-spectral information is provided.

                                                       Table 5. Comparing sensor resolution to minimum mapping units
                                    Map Scale (enter desired value)                            25,000
                                    Limit of cartographic output (mm)                          2*2            2    3*3          3    4*4          4    5*5          5    6*6          6    7*7          7
                                    Corresponding area (ha)                                          0.250              0.563             1.000             1.563             2.250             3.063
                                    Min. amount of pixls required for feature identification    12           28    12           28   12           28   12           28   12           28   12           28
                                    Maximum possible size of pixel for Sensor                  14.4          9.4   21.7      14.2    28.9     18.9     36.1     23.6     43.3     28.3     50.5     33.1



Satellite Sensors Pixel size (m)                      Surface area (ha)                                                   Is mapping possible at specified scale?
                                                                                                12           28    12           28   12           28   12           28   12           28   12           28
Quickbird                     0.6                            0.00004                           yes           yes   yes       yes     yes      yes      yes      yes      yes      yes      yes      yes
Ikonos                          1                            0.00010                           yes           yes   yes       yes     yes      yes      yes      yes      yes      yes      yes      yes
Spot 5                        2.5                            0.00063                           yes           yes   yes       yes     yes      yes      yes      yes      yes      yes      yes      yes
IRS                             5                            0.00250                           yes           yes   yes       yes     yes      yes      yes      yes      yes      yes      yes      yes
Spot 4 Pan                     10                            0.01000                           yes           no    yes       yes     yes      yes      yes      yes      yes      yes      yes      yes
Landsat 7 Pan                  15                            0.02250                           no            no    yes       no      yes      yes      yes      yes      yes      yes      yes      yes
Spot 4 MS                      20                            0.04000                           no            no    yes       no      yes      no       yes      yes      yes      yes      yes      yes
Landsat 7 TM                   30                            0.09000                           no            no    no        no      no       no       yes      no       yes      no       yes      yes
MERIS                         300                            9.00000                           no            no    no        no      no       no       no       no       no       no       no       no




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2.3.6 Ramsar reporting requirements

One of the key requirements in the Ramsar site designation process is to provide a map (along with a
standardised information sheet) to the Ramsar Secretariat. At a minimum the map must have had an
outer boundary of the proposed wetland site marked and an area calculated. The recommendation for
production of maps is defined by the Convention in the box below (Box 1). Following designation,
the principal need is to supply formal updates to the Ramsar Sites Information Sheet every six years
following first designation of the Ramsar Site. This will normally require updating of the Site map to
show land use and land cover changes inside the Site boundary, but also if possible, the changes in
the surrounding catchment – see Box 2.

BOX 1: Guidance on map production
(see http://www.ramsar.org/ris/key_ris.htm#maps)
Maps provided by a Contracting Party on designation of a Ramsar site should, as far as possible, and as high priority
attributes:
i) be prepared to professional cartographic standards: maps not prepared to professional cartographic standards are problematic, since even
moderately-opaque hand-drawn site boundaries or cross-hatching (e.g., to indicate zonation) often obscure other important map features.
Although coloured annotations may appear distinguishable from the underlying map features on the map original, it is important to
remember that most colours cannot be differentiated in any black and white photocopies. Such additional information should be provided
on additional outline maps;
ii) show the Ramsar Site in its natural or modified environment and should be within the scale ranges specified below, depending upon the
size of the site;
iii) clearly show the boundary of the Ramsar site, and distinguish this from any existing or proposed buffer zones;
iv) if the site is adjacent to, or now includes, a previously designated Ramsar site, the (former or active) boundaries of all of such sites
should be shown, making clear the current status of all such previously designated areas;
v) include a key or legend that clearly identifies the boundary and each other category of feature shown on the map and relevant to the
designation of the site; and
vi) show the map's scale, an indication of geographical coordinates (latitude and longitude), an indication of compass bearing (north arrow)
and, if possible, information on the map's projection. The map (or a companion map) should also show the position of several other
features if feasible.


The most suitable map or set of maps for the designation of a Ramsar site will also clearly show the following, although
provision of such information is of lower priority than the attributes listed in paragraph 3 above:
i) basic topographical information;
ii) the boundaries of relevant protected area designations and administrative boundaries (e.g., province, district, etc.);
iii) clearly delineated wetland and non-wetland parts of the site, and depiction of the wetland boundary with respect to the site's boundary,
especially where the wetland extends beyond the site being designated. Where available, information on the distribution of the main
wetland habitat types and key hydrological features is also useful. Where there is substantial seasonal variation in the extent of the wetland,
separate maps showing the wetland extent in the wet and in the dry seasons are helpful;
iv) major landmarks (towns, roads, etc.); and
v) distribution of land uses in the same catchment




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The GlobWetland Project provided wetland managers with the capacity to provide much enhanced
vector-based maps to the Ramsar Secretariat, showing not only the outer Ramsar Site boundary, but
also internal details of land cover, and any changes in ecological character. This means that
monitoring of site status would also be possible, on a three year cycle, and suitable for inclusion in
Site reports sent to the national Ramsar Administrative Authority within each Contracting Party.

At present National Reports sent to the Secretariat in advance of each Conference of the Parties
(COPs) do not include Site level details, but summarize key changes in the overall status of sites.
However, in the near future, Contracting Parties will increasingly use measures of management
effectiveness, which will include a spatial reporting obligation. Therefore, the GlobWetland Project
is a good basis to explore the relative costs and effort required to support the provision of
information on spatial change in land cover, land use and water regime.

BOX 2: RIS Updates: Extract from Ramsar Information Sheet
(http://www.ramsar.org/ris/key_ris_e.doc)
For RIS updates only, changes to the site since its designation or earlier update:
a) Site boundary and area

     The Ramsar site boundary and site area are unchanged: 
     or
     If the site boundary has changed:
       i) the boundary has been delineated more accurately ; or
       ii) the boundary has been extended      ; or
       iii) the boundary has been restricted**     
       and/or

     If the site area has changed:
       i) the area has been measured more accurately ; or
       ii) the area has been extended   ; or
       iii) the area has been reduced** 

** Important note: If the boundary and/or area of the designated site is being restricted/reduced, the Contracting Party
should have followed the procedures established by the Conference of the Parties in the Annex to COP9 Resolution IX.6
and provided a report in line with paragraph 28 of that Annex, prior to the submission of an updated RIS.
b) Describe briefly any major changes to the ecological character of the Ramsar site, including in the application
of the Criteria, since the previous RIS for the site:




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3 PROJECT OBJECTIVES

The stated objectives of the Globwetland project were originally stated in the ESA issued Invitation
to Tender (ITT) documentation. They fall into two categories, short and long term objectives:

Short Term Objectives:
In the short-term, the primary objective of the project consists of developing and demonstrating a
standardised information service, based on EO technology, aimed at supporting national and local
authorities, NGOs and wetland managers to implement the Ramsar Convention and to manage
wetlands efficiently. In this context, GlobWetland addresses mainly the information needs of the
following user sectors:
    1. At national level: National Focal Points of the Ramsar Convention, Ministries of the
        Environment, National Environmental Agencies, etc.;
    2. At local level: Wetland managers, local authorities, NGOs, national park managers, etc.

Long Term Objectives:
In the long term, this project aims at fostering the operational use of EO within the user community.
In this context, the Globwetland project has included the following elements:
    1. Develop and maintain a “GlobWetland Database” as a basis for future developments and as a
         reference for the user community;
    2. Derive clear user-oriented guidelines for the use of EO data within the Ramsar Convention.
         These guidelines were collected in a dedicated User Handbook which was distributed to the
         Ramsar community;
    3. Foster the integration of EO-derived products and services within the user’s traditional
         working procedures by implementing training and capacity building activities, especially in
         Africa;
    4. Promote the use of EO within the user community by implementing a user-oriented
         promotion plan.




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4 STUDY LOGIC


4.1 User Driven Approach

The Globwetland products were designed to meet the needs of wetland managers, and were defined
during a feasibility project known as TESEO-wetlands for the European Space Agency and the
Ramsar Secretariat. Based on the recommendations of the TESEO project, a summary list of
potential products of interest was produced with some basic information on scale and expected
information content. ESA prepared its Invitation to Tender documentation based on its interaction
with the Ramsar Secretariat, primarily through the relations established in working on the Ramsar
Convention’s Scientific and Technical Review Panel (STRP). As a result of this initial project, ESA
prepared documents to procure services and products under its Globwetland Project. The
procurement documentation included initial Requirements information collected directly from the
National Focal Points (NFP’s) which detailed the information being requested by the end user in
terms of what could be offered by the Globwetland Project.

Once the commercial companies were selected by ESA to execute the Globwetland project, the
Globwetland project team took over the responsibility to communicate with the end users to best
meet their requirements. The initial project activity consisted of completing the Requirements
Baseline, which essentially confirmed the initial requirements information collected in the
procurement documentation. Following this information gathering exercise, the Technical
Specification was used to confirm the expected product deliveries to the Globwetland end users.

To foster the end user driven approach further, ESA designed the implementation of the project to
include the generation of prototype products, which could be used to collect feedback on the utility
of the products in the initial stages of project execution. This approach was applied in Phase I and
Phase II of the main Globwetland project, as well as the Contract Extension.



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To summarize, the Globwetland project has been focused on a user driven approach based on the
following elements of the overall program:

      1. The TESEO project collected initial information from end users and the Ramsar Secretariat
         to assist in the design of the overall Globwetland programme;
      2. ESA co-designed the Globwetland project with input from the Ramsar Secretariat, primarily
         through its interaction in the Ramsar lead Scientific and Technical Review Panel (STRP)
      3. The procurement documentation prepared by ESA included a rich set of end user
         requirements information to be used by the chosen project team in execution of the project;
      4. All phases of the Globwetland project included communication with end users to confirm the
         requested products
      5. All phases of the Globwetland project included prototyping with end user feedback as critical
         elements for the acceptance not only of the products, but also the methodology used to derive
         them – which was then replicated for other Globwetland users

4.2 User List

                                             Table 6. List of Globwetland National Focal Points
           Country                     Organisation                           Contact point                Web Information
           Algeria                     Ministry of Agriculture and Fisheries, Mr. Ammar Boumezbeur         http://www.minagri-algeria.org/
                                       Directorate General of Forests

           Egypt                       Egyptian Environmental Affairs          Mr. Moustafa M. Fouda       http://www.eeaa.gov.eg/
                                       Agency,Nature Conservation Sector

           Kenya                       Kenya Wildlife Service, Wetlands        Mr. Anderson Koyo           http://www.kws.org/
                                       Programme
           Senegal                     Centre de Suivi Ecologique              Ms. Moussa Sall             http://www.cse.sn/
           South Africa                Biodiversity Planning Department of     Mr. Edward Netshithothole   http://www.deat.gov.za/
                                       Environmental Affairs and Tourism
                                       (DEAT)
           Cameroon, Niger, Nigeria,   Lake Chad Basin Commission              Mr. Muhammad Sani Adamu     http://www.cblt.org/
           Chad
           Austria                     Institute of Ecology and                Mr. Gert Michael Steiner    http://www.univie.ac.at/IECB/
                                       Conservation, Biology of the Wien
                                       University (IECB)
           Finland                     The Finnish Environment Institute       Ms. Minna Kallio            http://www. environment.fi
                                       (SYKE)
           France                      Institut Francais de lEnvironnement     Mr. Laurent Duhautois       http://www.ifen.fr/
                                       (IFEN)
           Greece                      Ministry of the Environment and         Mr. George Zalidis          http://www.minenv.gr/
                                       Public Works
           Italy                       Italian Ministry for Environment and    Mr. Angelo Ciasca           http://www.minambiente.it/
                                       Territory;
           Portugal                    Institute For Nature Conservation       Mr. João Farinha            http://www.icn.pt/
                                       (ICN)
           Russia                      Ministry of Ecology and Land Use of     Ms.Tatiana Minaeva          http://www.wwf.ru/
                                       Moscow region
           Spain                       Ministerio de Medio Ambiente            Mr. Jos Ramon Picatoste     http://www.mma.es/
           Switzerland                 Swiss Federal Institute for Agri-       Mr. Andreas Grunig          http://www.admin.ch/sar/fal
                                       Ecology and Land Use Research
           Canada                      Canadian Wildlife Service,              Mr. Robert Hélie            http://www.cws-scf.ec.gc.ca/
           Ecuador                     Ministry of Environment, Biodiversity   Mr. Sergio Lasso            http://www.ambiente.gov.ec/
                                       and Protected Areas Directorate




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5 USER REQUIREMENTS

The User Requirements were initially documented in the ESA ITT, and then further refined in the
Globwetland Project Deliverable, the Requirements Baseline and Technical Specification. These
documents include the products that were

The purpose of the Requirements Baseline (RB) was to document a common understanding of the
stated user requirements, and establish the starting point for the work done in the GlobWetland
project. The RB was the first deliverable from Task 1 of the project, which addressed the
“Requirements Engineering”. Task 1 included a series of work packages that assisted the project
team in systematically evaluating and proposing options for generating information products that met
the stated objectives of the GlobWetland project. This approach was critical to ensure success, since
there were over 17 National Focal Points (NFPs) representing more than 21 different countries in the
Americas (Canada and Ecuador), Europe (and Russia), and Africa. The Requirement Baseline
Document also analyzed the sites to be included in the final project, based on the requirement by
ESA to narrow the number of sites from 61 down to 50.

The Project team’s starting point for the information needs analysis included the information that the
participating countries provided to ESA in the form of the User Requirements Documentation, which
was included in the ITT’s SOW. These forms have been collected and collated through the NFPs,
who worked in close collaboration with the end users in question within their national user groups
for wetlands. The information contained in these forms includes background information on the
rationale for the country’s interests and motivation for participating in the project, the specific
information about the proposed wetland sites, as well as a preliminary description of the information
products that they would like to obtain through the GlobWetland project.

Based on the information described above, the project team applied a methodology that considers
several variables in evaluating the various wetland sites that have been proposed by the NFPs. In
broad terms, the following high level variables were applied to prioritize the selection of the 50 (out
of 61) sites:


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                     contained herein is subject to the restrictions on the title page of this document.
                       Geographical position
                       Wetland typology
                       Technical feasibility
                       Availability of ancillary data
                       Ramsar Promotion


      Once the RB was drafted, the relevant sections were extracted, and sent to the end users so that
      they could review the contents, and provide any clarifications. Most importantly, the section on
      the selection of the test sites was also shared with the end users, so that they could see the
      specific and overall rationale for including / excluding certain proposed wetland sites. The NFPs
      were given 10 days to respond, and provide their “approval” to the project team. Contentious
      issues were dealt with by providing clarifications through a telephone meeting or teleconference.

      The user requirements were re-formatted into the User Requirement table format originally
      collected by ESA to prepare the Statement of Work. For each of the countries and wetland sites
      proposed, the revised Requirements, which included quite a few recommendations for tradeoffs,
      were sent to the end users along with some explanatory text summarizing the contents of the
      tables sent. This last interaction was then finalized with the end users, who reviewed the
      document, and advised if they were in agreement with the proposed changes. The outcomes of
      these discussions were then updated the final Requirements Baseline Document.

  Given the complexity of the project, and the myriad of information requirements stated by the17
  NFPs, the approach adopted by the Project team was to standardize the product offering for the
  end users (see page 6 of 24 of ESA’s GlobWetland SOW). In analyzing the User Requirements
  Documentation, the Project team thus considered what broad category the stated information
  requirements fell into in terms of the overall components to be developed and provided for the
  end users. These information products are expressed as either “Core” or “Specific” components of
  the GlobWetland Information Service. The following Table lists the proposed Core and Specific
  Components, including what information products fall under them.




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                           Use, duplication, or disclosure of this document or any of the information
                        contained herein is subject to the restrictions on the title page of this document.
                                 Table 7. Core and Specific Components specified in ITT

                     Core Components                                     Specific Components
                a.   Geo-referenced base maps;                   a.   Wetlands Identification, delimitation,
                     including land cover and land-                   and attributes (wetland types)
                     use information;                            b.   Topography Dynamics (coastline) and
                b.   Long term and short term                         subsidence monitoring
                     change analyses;                            c.   Digital Elevation Models (DEMs)
                c.   Water cycle / regime                        d.   Mapping the location of peatland fires
                     monitoring                                  e.   Biophysical parameters
                                                                             i. Water colour
                                                                            ii. Water Sediment
                                                                           iii. Chlorophyll concentration
                                                                           iv. Evapotranspiration
                                                                            v. Water surface tempature



Based on the Requirement Baseline process and finalization with the end users, the project team
documented the planned Service Cases Delivery for each country/site. Some summary tables are
presented here showing the overall totals by country. In total, 114 Core and 33 Specific products
were requested for a total of 147 products.




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                         Use, duplication, or disclosure of this document or any of the information
                      contained herein is subject to the restrictions on the title page of this document.
                     Table 8. Total number of Core and Specific Products requested by country




                                                                               Total Specific
                                                             Total Core




                                                                                                      Total
                               Country
                               Austria                  0                 1                     1
                               Cameroon                 2                 0                     2
                               Chad                     2                 0                     2
                               Nigeria                  2                 0                     2
                               Sénégal                  2                 0                     2
                               Switzerland              0                 1                     1
                               Niger                    3                 0                     3
                               Algeria                  4                 0                     4
                               Egypt                    3                 1                     4
                               Italy                    4                 0                     4
                               Canada                   6                 0                     6
                               Ecuador                  6                 0                     6
                               Portugal                 6                 0                     6
                               Finland                  8                 0                     8
                               Russia                   2                 2                     4
                               South Africa             8                 6                     14
                               Kenya                    9                 6                     15
                               Greece                  12                 4                     16
                               Spain                   15                 5                     20
                               France                  20                 7                     27
                                                       114                33                    147




A more detailed list showing the specific products and sites who requested them is presented in the
following table.




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                        Use, duplication, or disclosure of this document or any of the information
                     contained herein is subject to the restrictions on the title page of this document.
                                    Table 9. Detailed list of requested products for each Globwetland site



                        Wetland sites                                 Core Components                                                                                                                                           Specific Components




                                                                                                                   Long and short term change detection




                                                                                                                                                                                                                                      Topography dynamics (coastline) and
                                                                                                                                                                                       Wetlands Identification, Delimitation,
                                                                          Georeferenced Base map including




                                                                                                                                                                                                                                                                                 Digital Elevation Models (DEMs)
                                                                                                                                                                                       and attributes (wetland types)




                                                                                                                                                                                                                                                                                                                       Mapping of Peatland fires




                                                                                                                                                                                                                                                                                                                                                       Biophysical parameters
                                                                                                                                                                                                                                      subsidence monitoring
                                                                          Land cover / land-use




                                                                                                                                                              Water Cycle Regime
Country        Wetland name
Algeria        Réserve Naturelle du Lac de Béni Bélaïd                1                                       1
Algeria        La Réserve Naturelle du Lac des oiseauok               1                                       1
Austria        Tamsweger Moore                                                                                                                                                     1
Cameroon       Waza Logone                                            1                                       1
Canada         Creston Valley                                         1                                       1                                           1
Canada         Lac Saint Pierre                                       1                                       1
Canada         Shepody Bay                                                                                                                                1
Chad           Lake Lere                                              1                                       1
Ecuador        Abras de Mantequilla                                   1                                                                                   1
Ecuador        La Segua                                               1                                                                                   1
Ecuador        Reserva Biológica Limoncocha                           1                                                                                   1
Egypt          Lake Burullus                                          1                                       1                                           1                                                                                                                                                                                        1
Finland        Keonsuo Mire                                           1                                       1
Finland        Liminganlahti Bay Area                                 1                                       1
Finland        Siikalahti bay                                         1                                       1
Finland        Vanhankaupunginlahti and Laajalahti Bays               1                                       1
France         La Brenne                                              1                                       1                                           1                        1
France         Camargue                                               1                                       1                                           1                        1
France         Caps et Marais d'Opale                                 1                                       1                                           1                        1
France         Forêt d'Orient                                         1                                       1                                           1                        1
France         Littoral audois                                        1                                       1                                           1                        1
France         Marais du Cotentin et du Bessin, Baie des Veys         1                                       1                                           1                        1
France         Massif central                                         1                                                                                   1                        1
Greece         Amvrakikos gulf                                        1                                       1                                           1                                                                                                                                                                                        1
Greece         Aokios, Loudias, Aliakmon Delta                        1                                       1                                           1                                                                                                                                                                                        1
Greece         Kotychi lagoons                                        1                                       1                                           1                                                                                                                                                                                        1
Greece         Artificial Lake Kerkini                                1                                       1                                           1                                                                                                                                                                                        1
Italy          Lake Sabaudia (part of Laguna Pontine)                 1                                       1
Italy          Laguna di Venezia: Valle Averto                        1                                       1
Kenya          Lake Bogoria                                           1                                       1                                           1                                                                                                                 1                                                                      1
Kenya          Lake Naivasha                                          1                                       1                                           1                                                                                                                 1                                                                      1
Kenya          Lake Nakuru                                            1                                       1                                           1                                                                                                                 1                                                                      1
Niger          Lac Tchad                                              1                                       1                                           1
Nigeria        Sambisa Game Reserve                                   1                                       1
Portugal       Estuário do Tejo                                       1                                                                                   1
Portugal       Lagoa da Albufeira                                     1                                                                                   1
Portugal       Lagoa de St. André et Lagoa de Sancha                  1                                                                                   1
Russia         Central Meschera                                                                               1                                                                                                                                                                                                    1
Russia         Dubna Lowland Wetlands                                                                         1                                                                                                                                                                                                    1
Sénégal        Bassin du Ndiaël                                       1                                       1
South Africa   Maloti-Drakensberg Transfrontier Park                  1                                       1                                                                    1                                                                                        1
South Africa   Nyl floodplain                                         1                                       1                                                                    1
South Africa   P.E.I. Nature Reserve Park                             1                                       1                                                                    1                                                                                        1
South Africa   St. Lucia System                                       1                                       1                                                                    1
Spain          Delta del Ebro                                         1                                       1                                           1                                                                       1
Spain          Lagunas de Villafáfila                                 1                                       1                                           1
Spain          Las Tablas de Daimiel                                  1                                       1                                           1
Spain          Mar Menor                                              1                                       1                                           1                                                                       1                                                                                                                1
Spain          Ria de Mundaka-Guernika                                1                                       1                                           1                                                                       1                                                                                                                1
Switzerland    Zürichsee                                                                                                                                                           1
                                                                147                        45                      40                                         29                                           13                                                    3                              5                                  2                   10
               Core / Specific Totals                                                                        114                                                                                                                                                            33




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                                    Use, duplication, or disclosure of this document or any of the information
                                 contained herein is subject to the restrictions on the title page of this document.
6 GLOBWETLAND PRODUCTS


6.1 Globwetland Products Description

One of the main objectives of the Globwetland project was to deliver EO based products for
evaluation by the wetland community in support of the Ramsar convention. The products were
designed to meet the needs of wetland managers, and were defined during a feasibility project known
as TESEO-wetlands for the European Space Agency and the Ramsar Secretariat. The products can be
divided into two main categories: Core and Specific products. The former represents the basic set of
common geo-information that was generated wetland sites that requested them and include three
main layers of information: land-use and land-cover map, a long-term change-analysis map and a
water cycle map. The latter represents a number of site-specific maps generated in response to
precise requests from wetland managers to better monitor and assess different local conditions. They
include a large range of geo-information products including water quality parameters, topographic
(coastal) dynamics, and Digital Elevation Models (DEMs), among others. In all cases, the methods
applied were validated using in situ data to ensure the products respect the accuracy required by the
wetland managers. A short description of each products is given below.

                                 Table 10. Summary list of Core and Specific Products

                CORE PRODUCTS                              SPECIFIC PRODUCTS
                Land Use and Land Cover Map,               Wetland Identification and Delineation
                including wetland types
                Change Detection Map                       Topographic Dynamics in Coastal
                Water Cycle Regime                         Digital Elevation Models
                                                           Peatland Fire Scar Mapping
                                                           Biophysical Parameters




                                                                                                           6-1

                        Use, duplication, or disclosure of this document or any of the information
                     contained herein is subject to the restrictions on the title page of this document.
CORE PRODUCTS: Land Use and Land Cover Map, including wetland types
The Land Use Land Cover (LULC) map includes a detailed
classification of all land parcels within the area of interest at
different scales depending on the wetland size (1:25,000 or
1:50,000). The LULC maps include classes of interest defined
by the end users, which are presented using a standardized
classification scheme based on the Corine Land Cover system
(EC, 1993), which has been adapted to the needs expressed by
wetland manager incorporating the Ramsar wetlands
classification system. In particular, the thematic information
provided follows a 4 level nomenclature where the fist level
distinguish between water, natural areas and artificial surfaces
and the most detailed one includes the single wetland
typologies defined by the Ramsar Convention. Maps are geo-
referenced to the corresponding national systems and present
(after validation) overall thematic accuracies ranging from 85%
to 95%.
CORE PRODUCTS: Change Detection Map
The purpose of this product is to provide a historical
comparison of the land use and the land cover in the wetland
site and its surroundings between today and a reference date in
the past. This may provide wetland managers with a synoptic
view of the main changes occurred in the areas of interest in
the last 20 years and more due to natural and anthropogenic
factors. It is worth noting the EO data archives include imagery
acquired from the 60s providing a unique source of information
to assess the historical evolution of wetlands worldwide. This
type of information represents an optimum complement to the
above LULC map, as change analysis provides wetland
managers with the possibility to identify threats affecting the
site and its impacts in the ecosystem over time.




6-2

                             Use, duplication, or disclosure of this document or any of the information
                          contained herein is subject to the restrictions on the title page of this document.
CORE PRODUCTS: Water Cycle Regime
The third layer of the core data set complement the above two products
providing an overview of the annual variations of the water table over the
wetland site. In particular, this layer, provide at scale 1:50,000 and better,
shows the minimum and maximum water extent of the water table,
including open water bodies and inundated vegetation, during a
hydrological year. This product, when generated over several years,
provides wetland mangers with a unique monitoring capacity to
characterize the water cycle and identify variations that may affect the
overall ecosystem.




SPECIFIC PRODUCTS: Wetland Identification and Delineation
Identification and delineation of wetlands using EO technology represents an important task to perform wetland
inventories over large or inaccessible areas. The development of this specific product was motivated by a number of
national agencies interested in exploring the possibilities to reduce costs associated to national inventorying exercises.
Two main approaches were explored with different implications in terms of costs: 1) The first provides national agencies
                                                                     with a number of areas (polygons) with a high potential
                                                                     to include wetlands. This represents a unique support
                                                                     tool to plan field visits in a cost effective manner,
                                                                     focusing resources only on the areas of interest. Precise
                                                                     delineation can them be completed in the field or with
                                                                     support of manual interpretation of aerial photography
                                                                     or high-resolution EO data. 2) The second approach is
                                                                     more expensive but more accurate and provides wetland
                                                                     managers with a precise map of wetland areas. The
                                                                     production of such map involves a significant human
                                                                     intervention and hence increases the cost. However, the
                                                                     result comparable with that obtained with aerial
                                                                     photography is still cost effective for limited regions of
                                                                     interest, where the precise location of wetlands is
                                                                     required.




                                                                                                                                  6-3

                                  Use, duplication, or disclosure of this document or any of the information
                               contained herein is subject to the restrictions on the title page of this document.
SPECIFIC PRODUCTS: Topographic Dynamics in Coastal Wetlands
Coastal wetlands may be affected by currents, erosion and other factors that may impact the coastline. In addition, many
coastal wetlands represent main sources for sand extraction and other mining activities that may affect the topography of
the area. These changes are important indicators for the condition of the wetlands, as they may have significant impacts in
                                                                   the water cycle or may trigger salinisation processes
                                                                   affecting ground water. In this context, a specific geo-
                                                                   information product was generated that merge two
                                                                   different type of information in a synergic manner: 1)
                                                                   differences in the coastal line based on high-resolution
                                                                   imagery and 2) subsidence mapping, pointing out
                                                                   centimetre level movements of the ground. The former
                                                                   component requires the comparison of both the coast
                                                                   line extracted from an historical image and the present
                                                                   situation derived from a recent one. The latter
                                                                   component provides for each element in the image
                                                                   (with resolution of 25m in the ground) an estimate of
                                                                   the ground displacement at centimetre level between
                                                                   two different dates. This last product is based on the
                                                                   capabilities of Synthetic Aperture Radar (SAR) and
                                                                   Inteferometric processing.




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                            Use, duplication, or disclosure of this document or any of the information
                         contained herein is subject to the restrictions on the title page of this document.
SPECIFIC PRODUCTS: Digital Elevation Models
The ability of EO sensors to generate DEMs covering large areas
at relatively low cost is an important element for catchments
characterisation. DEMs may provide significant information for
a wetland site and can play a significant role in management
supporting: the delineation of the wetland catchment area,
visualization of wetlands information (i.e. 3D display),
determination of areas affected by toxic point discharges, the
location of recharge areas, and vulnerability to contamination,
flood modeling or estimation of runoff. DEM based on currently
available SAR sensor allows elevation information to be
extracted with vertical accuracies close 10m. This type of DEM
accuracy is not acceptable at site level, especially for
hydrological modeling, where errors of only few cm are
acceptable. However, it represents a cost effective manner to
map and characterize large catchment areas, especially with a
mountainous topography.




 CORE PRODUCTS: Peatland Fire Scar Mapping
 Peatland fires are becoming an increasing problem
 worldwide and have attracted in the last years the interest
 of the scientific community, not only in the context of
 wetlands conservation but also in the context of climate
 change. Due to the high carbon content of the peat
 biomass, large amounts of aerosols, different trace gases
 and carbon are released into the atmosphere with negative
 effects on human health and global warming. Multi-
 temporal optical imagery, such has Landsat ETM+ was
 used to detect fire scars, and identify areas which are
 recovering from such damaging peat fires occurring in
 Russia. This information allowed both to monitor specific
 peatland burning processes in time and to support the
 estimation of biomass burning and carbon dioxide
 emissions.




                                                                                                                    6-5

                                 Use, duplication, or disclosure of this document or any of the information
                              contained herein is subject to the restrictions on the title page of this document.
 SPECIFIC PRODUCTS: Biophysical Parameters
  The retrieval of water quality parameters over large lakes and wetland areas is one of the most interesting
 applications of EO technology to support wetlands management and conservation. Large water bodies and wetlands
 represent a main economic driver (e.g., fisheries industry) and source of fresh water for many populations in the
 surrounding areas. Therefore the continuous monitoring water quality represents not only a key aspect in wetlands
 conservation but a fundamental input for water management. Parameters such as turbidity, suspended solids and algae
 or chlorophyll concentration can be monitored from space with different resolutions (e.g., ranging from 30m using
 Landsat data to 300m using MERIS images). However, the extraction of such information in inland waters is still a
 complex technical process. For instance, it is worth noting that technical limitations associated to current available
 space-borne sensors, such as the spectral and spatial resolution, hinder the retrieval of such parameters over small
                                                                water bodies. Also, shallow water bodies further
                                                                complicate the information extraction process and may
                                                                render the measurements unreliable. In addition, the
                                                                extraction of absolute water quality information from
                                                                satellite imagery requires adequate in-situ
                                                                measurements for calibration purposes. The accuracy of
                                                                the information extracted depends strongly on the
                                                                different water constituents concentrations, available in
                                                                situ data and sensor used. In spite of these limits and
                                                                the still experimental stage of the retrieval methods
                                                                available, EO can provide today, under certain
                                                                conditions, accurate information on water quality. For
                                                                instance using MERIS images on clear open waters, it
                                                                is possible to reach accuracies of 73% in Chlorophyll-a
                                                                concentration within a range from 0.01 mg/m-3 a 10
                                                                mg/m-3.




6.2 Globwetland Product examples

The output products produced by the project team for the end users were packaged according to a
pre-defined protocol. Each product was presented to the end user with the same look and feel. The
information is presented as an ArcView “project”, which packages the information in an integrated
manner. The product is packaged and includes the following contents (varies per site).

The label that was created for all Globwetland products is shown below.




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                            Use, duplication, or disclosure of this document or any of the information
                         contained herein is subject to the restrictions on the title page of this document.
                                      Figure 4. CD Label for Globwetland products

Every CD that was delivered contained an insert which described the contents of the products
delivered to the end user. Below, we see an example of the complete products delivered to the
Creston Valley Wildlife Centre in Canada. Instructions for loading the products were included in
each product.




                                              Figure 5. Jewel case CD insert




                                                                                                           6-7

                        Use, duplication, or disclosure of this document or any of the information
                     contained herein is subject to the restrictions on the title page of this document.
The following describes in detail and sample product package, which was used for all Globwetland
products.

CD Contents
   “Sitename.exe” – executable file, or zip file which contains all integrated data
   Readme.txt file – contains instructions on how to extract the information onto a computer
     and the contents of the CD

      Un-zipped CD contents (on local disk drive):

         Base Map folder – includes suitable layers to compose the base map (basic features in vector
          format)
         Changes folder – includes all vector final products
         Classification folder – Includes all vector final products in shape file format
         Field Data folder – includes field data collected and used to support the analysis and
          validation
         Legend folder – includes standardized legend files for each product
         Satellite images folder – includes suitable SAR and Optical B/W and Colour images for
          reference
         Tables folder – includes results of analysis done by team, Ha per class, changes in classes,
          etc.
         Water Changes folder – includes all vector final products




                               Figure 6. Folder structure for product delivery to end users




A sample product which has been extracted from the delivered CD and loaded into ArcView is
presented below. Multiple “views” were created and delivered as part of the package using the
ArcView mapping layout functionality. The Land Use and Land Cover map view is shown in the
example below.



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                          Use, duplication, or disclosure of this document or any of the information
                       contained herein is subject to the restrictions on the title page of this document.
              Figure 7. Output Products – Land Use Land Cover




                                                                                      6-9

   Use, duplication, or disclosure of this document or any of the information
contained herein is subject to the restrictions on the title page of this document.
6.3 Globwetland Product List – all delivered products
                                                      Table 11. Complete list of all delivered Globwetland products
                                                                                                                                   Year of
 Country                      Wetland Site                                         Product Derived                    Scale                        EO Data used       Dissemination
                                                                                                                                   Product
Algeria    Réserve Naturelle du Lac de Béni Bélaïd             Land Use Land Cover                                      10,000           2006   Quickbird, Landsat   CD; WEB
Algeria    Réserve Naturelle du Lac de Béni Bélaïd             Long and Short Term Change Detection                     10,000           2000   Quickbird, Landsat   CD; WEB
Algeria    La Réserve Naturelle du Lac des oiseaux             Land Use Land Cover                                      10,000           2006   Quickbird, Landsat   CD; WEB
Algeria    La Réserve Naturelle du Lac des oiseaux             Long and Short Term Change Detection                     10,000           2000   Quickbird, Landsat   CD; WEB
Austria    Tamsweger Moore                                     Wetland Identification, Delimitation and attributes      25,000           2006   SPOT-5, ASTER        CD; WEB
Cameroon   Waza Logone                                         Land Use Land Cover                                      50,000           2000   LANDSAT              CD; WEB
Cameroon   Waza Logone                                         Long and Short Term Change Detection                     50,000           1986   LANDSAT              CD; WEB
Canada     Creston Valley                                      Land Use Land Cover                                      25,000           2004   SPOT                 CD; WEB
Canada     Creston Valley                                      Long and Short Term Change Detection                     25,000           1986   SPOT                 CD; WEB
Canada     Creston Valley                                      Water Cycle Regime                                       50,000           2004   RADARSAT-1           CD; WEB
Canada     McConnell River                                     Land Use Land Cover                                      50,000           2001   Landsat              CD; WEB
Canada     Shepody Bay                                         Water Cycle Regime                                       50,000           2004   RADARSAT-1           CD; WEB
Chad       Lake Lere                                           Land Use Land Cover                                      50,000           2000   LANDSAT              CD; WEB
Chad       Lake Lere                                           Long and Short Term Change Detection                     50,000           1986   LANDSAT              CD; WEB
Ecuador    Abras de Mantequilla                                Land Use Land Cover                                      50,000           2000   LANDSAT              CD; WEB
Ecuador    Abras de Mantequilla                                Water Cycle Regime                                       50,000           2004   RADARSAT-1           CD; WEB
Ecuador    La Segua                                            Land Use Land Cover                                      50,000           2000   LANDSAT              CD; WEB
Ecuador    La Segua                                            Water Cycle Regime                                       50,000           2005   RADARSAT-1, ASAR     CD; WEB
Ecuador    Reserva Biológica Limoncocha                        Land Use Land Cover                                      50,000           2002   LANDSAT              CD; WEB
Ecuador    Reserva Biológica Limoncocha                        Water Cycle Regime                                       50,000           2005   RADARSAT-1           CD; WEB
Egypt      Lake Burullus                                       Land Use Land Cover                                      50,000           2002   LANDSAT              CD; WEB
Egypt      Lake Burullus                                       Long and Short Term Change Detection                     50,000           1984   LANDSAT              CD; WEB
Egypt      Lake Burullus                                       Water Cycle Regime                                       50,000           2004   RADARSAT-1           CD; WEB
Egypt      Lake Burullus                                       Biophysical Parameters                                   50,000           2002   LANDSAT              CD; WEB
Finland    Keonsuo Mire                                        Land Use Land Cover                                      25,000           2004   SPOT                 CD; WEB
Finland    Keonsuo Mire                                        Long and Short Term Change Detection                     25,000           1999   LANDSAT              CD; WEB
Finland    Liminganlahti Bay Area                              Land Use Land Cover                                      25,000           2004   SPOT                 CD; WEB
Finland    Liminganlahti Bay Area                              Long and Short Term Change Detection                     25,000           1999   LANDSAT              CD; WEB
Finland    Siikalahti bay                                      Land Use Land Cover                                      25,000           2004   SPOT                 CD; WEB
Finland    Siikalahti bay                                      Long and Short Term Change Detection                     25,000           1999   LANDSAT              CD; WEB
Finland    Vanhankaupunginlahti and Laajalahti Bays            Land Use Land Cover                                      25,000           2004   SPOT                 CD; WEB
Finland    Vanhankaupunginlahti and Laajalahti Bays            Long and Short Term Change Detection                     25,000           1999   LANDSAT              CD; WEB




6-10
                                                Use, duplication, or disclosure of this document or any of the information
                                             contained herein is subject to the restrictions on the title page of this document.
                                                                                                                                             Year of
  Country                      Wetland Site                                         Product Derived                          Scale                           EO Data used    Dissemination
                                                                                                                                             Product
France      La Brenne                                           Land Use Land Cover                                            25,000              2005   SPOT              CD; WEB
France      La Brenne                                           Long and Short Term Change Detection                           50,000              2005   LANDSAT           CD; WEB
France      La Brenne                                           Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
France      Camargue                                            Land Use Land Cover                                            25,000              2005   SPOT              CD; WEB
France      Camargue                                            Long and Short Term Change Detection                           25,000              1994   SPOT              CD; WEB
France      Camargue                                            Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
France      Caps et Marais d'Opale                              Land Use Land Cover                                            25,000              2003   SPOT              CD; WEB
France      Caps et Marais d'Opale                              Long and Short Term Change Detection                           25,000              1994   SPOT              CD; WEB
France      Caps et Marais d'Opale                              Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
France      Forêt d'Orient                                      Land Use Land Cover                                            25,000              2004   SPOT              CD; WEB
France      Forêt d'Orient                                      Long and Short Term Change Detection                           25,000              1999   LANDSAT           CD; WEB
France      Forêt d'Orient                                      Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
France      Littoral audois                                     Land Use Land Cover                                            25,000              2003   SPOT              CD; WEB
France      Littoral audois                                     Long and Short Term Change Detection                           25,000              1999   SPOT              CD; WEB
France      Littoral audois                                     Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
France      Marais du Cotentin et du Bessin, Baie des Veys      Long and Short Term Change Detection                           50,000              2005   LANDSAT           CD; WEB
France      Marais du Cotentin et du Bessin, Baie des Veys      Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
France      Massif central                                      Land Use Land Cover                                            25,000              2006   SPOT              CD; WEB
France      Massif central                                      Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
Greece      Amvrakikos gulf                                     Land Use Land Cover                                            50,000              2004   SPOT              CD; WEB
Greece      Amvrakikos gulf                                     Long and Short Term Change Detection                           50,000              1999   SPOT              CD; WEB
Greece      Amvrakikos gulf                                     Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
Greece      Aokios, Loudias, Aliakmon Delta                     Land Use Land Cover                                            50,000              2004   SPOT              CD; WEB
Greece      Aokios, Loudias, Aliakmon Delta                     Long and Short Term Change Detection                           50,000              1994   SPOT              CD; WEB
Greece      Aokios, Loudias, Aliakmon Delta                     Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB
Greece      Aokios, Loudias, Aliakmon Delta                     Biophysical Parameters                                         50,000              1994   LANDSAT           CD; WEB
Greece      Kotychi lagoons                                     Land Use Land Cover                                            50,000              2004   SPOT              CD; WEB
Greece      Kotychi lagoons                                     Long and Short Term Change Detection                           50,000              1994   SPOT              CD; WEB
Greece      Kotychi lagoons                                     Water Cycle Regime                                             50,000              1994   RADARSAT-1        CD; WEB
Greece      Artificial Lake Kerkini                             Land Use Land Cover                                            50,000              2004   SPOT              CD; WEB
Greece      Artificial Lake Kerkini                             Long and Short Term Change Detection                           50,000              1994   SPOT              CD; WEB
Greece      Artificial Lake Kerkini                             Water Cycle Regime                                             50,000              2004   RADARSAT-1        CD; WEB




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                                                                                                                                           Year of
 Country                       Wetland Site                                       Product Derived                          Scale                           EO Data used    Dissemination
                                                                                                                                           Product
Italy       Lake Sabaudia (part of Laguna Pontine)             Land Use Land Cover                                           25,000              2004   SPOT              CD; WEB
Italy       Lake Sabaudia (part of Laguna Pontine)             Long and Short Term Change Detection                          25,000              2001   LANDSAT           CD; WEB
Italy       Laguna di Venezia: Valle Averto                    Land Use Land Cover                                           10,000              2004   IKONOS            CD; WEB
Italy       Laguna di Venezia: Valle Averto                    Long and Short Term Change Detection                          50,000              2000   LANDSAT           CD; WEB
Kenya       Lake Bogoria                                       Land Use Land Cover                                           50,000              2003   LANDSAT           CD; WEB
Kenya       Lake Bogoria                                       Long and Short Term Change Detection                          50,000              1986   LANDSAT           CD; WEB
Kenya       Lake Bogoria                                       Water Cycle Regime                                            50,000              2004   RADARSAT-1        CD; WEB
Kenya       Lake Bogoria                                       Digital Elevation Model                                       50,000              2004   ASAR              CD; WEB
Kenya       Lake Bogoria                                       Biophysical Parameters                                        50,000              2003   ASTER             CD; WEB
Kenya       Lake Naivasha                                      Land Use Land Cover                                           50,000              2003   LANDSAT           CD; WEB
Kenya       Lake Naivasha                                      Long and Short Term Change Detection                          50,000              1986   LANDSAT           CD; WEB
Kenya       Lake Naivasha                                      Water Cycle Regime                                            50,000              2004   RADARSAT-1        CD; WEB
Kenya       Lake Naivasha                                      Digital Elevation Model                                       50,000              2004   ASAR              CD; WEB
Kenya       Lake Naivasha                                      Biophysical Parameters                                        50,000              2003   ASTER             CD; WEB
Kenya       Lake Nakuru                                        Land Use Land Cover                                           50,000              2003   LANDSAT           CD; WEB
Kenya       Lake Nakuru                                        Long and Short Term Change Detection                          50,000              1986   LANDSAT           CD; WEB
Kenya       Lake Nakuru                                        Water Cycle Regime                                            50,000              2004   RADARSAT-1        CD; WEB
Kenya       Lake Nakuru                                        Digital Elevation Model                                       50,000              2004   ASAR              CD; WEB
Kenya       Lake Nakuru                                        Biophysical Parameters                                        50,000              2003   ASTER             CD; WEB
Mekong      Mekong River Basin                                 Chlorophyll                                                  500,000              2006   MERIS             WEB
Mekong      Mekong River Basin                                 Total Suspended Matter                                       500,000              2006   MERIS             WEB
Mekong      Mekong River Basin                                 Yellow Matter                                                500,000              2006   MERIS             WEB
Mekong      Mekong River Basin                                 Water Cycle Regime                                           500,000              2006   ASAR              WEB
Mekong      Mekong River Basin                                 Basin Wide Change Detection                                  500,000              2006   MERIS             WEB
Mekong      Mekong River Basin                                 Long and Short Term Change Detection                          50,000              1986   LANDSAT           WEB
Niger       Lac Tchad                                          Land Use Land Cover                                           50,000              2001   LANDSAT           CD; WEB
Niger       Lac Tchad                                          Long and Short Term Change Detection                          50,000              1986   LANDSAT           CD; WEB
Niger       Lac Tchad                                          Water Cycle Regime                                            50,000              2004   RADARSAT-1        CD; WEB
Niger       Lac Tchad                                          Chlorophyll                                                  300,000              2007   MERIS             WEB
Niger       Lac Tchad                                          Total Suspended Matter                                       300,000              2007   MERIS             WEB
Niger       Lac Tchad                                          Lake Surface Temperature                                   1,000,000              2007   ATSR              WEB
Niger       Lac Tchad                                          Yellow Matter                                                300,000              2007   MERIS             WEB
Niger       Lac Tchad                                          Z90 Signal Depth                                             300,000              2007   MERIS             WEB




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                                                                                                                                             Year of
  Country                        Wetland Site                                        Product Derived                          Scale                          EO Data used    Dissemination
                                                                                                                                             Product
Nigeria        Sambisa Game Reserve                              Land Use Land Cover                                            50,000             2001   LANDSAT           CD; WEB
Nigeria        Sambisa Game Reserve                              Long and Short Term Change Detection                           50,000             1987   LANDSAT           CD; WEB
Portugal       Estuário do Tejo                                  Land Use Land Cover                                            25,000             2004   SPOT              CD; WEB
Portugal       Estuário do Tejo                                  Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Portugal       Lagoa da Albufeira                                Land Use Land Cover                                            25,000             2004   SPOT              CD; WEB
Portugal       Lagoa da Albufeira                                Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Portugal       Lagoa de St. André et Lagoa de Sancha             Land Use Land Cover                                            25,000             2004   SPOT              CD; WEB
Portugal       Lagoa de St. André et Lagoa de Sancha             Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Russia         Central Meschera                                  Peatland Fire Mapping                                          50,000             2002   LANDSAT           CD; WEB
Russia         Dubna Lowland Wetlands                            Peatland Fire Mapping                                          50,000             2002   LANDSAT           CD; WEB
Sénégal        Bassin du Ndiaël                                  Land Use Land Cover                                            50,000             2002   LANDSAT           CD; WEB
Sénégal        Bassin du Ndiaël                                  Long and Short Term Change Detection                           50,000             1995   LANDSAT           CD; WEB
South Africa   Maloti-Drakensberg Transfrontier Park             Land Use Land Cover                                            50,000             2005   LANDSAT           CD; WEB
South Africa   Nyl floodplain                                    Land Use Land Cover                                            50,000             2005   LANDSAT           CD; WEB
South Africa   Nyl floodplain                                    Long and Short Term Change Detection                           50,000             1986   LANDSAT           CD; WEB
South Africa   Nyl floodplain                                    Wetland Delineation                                            50,000             2004   RADARSAT-1        CD; WEB
South Africa   P.E.I. Nature Reserve Park                        Land Use Land Cover                                            50,000             2005   LANDSAT           CD; WEB
South Africa   P.E.I. Nature Reserve Park                        Wetland Delineation                                            50,000             2004   RADARSAT-1        CD; WEB
South Africa   P.E.I. Nature Reserve Park                        Digital Elevation Model                                        50,000             2004   ASAR              CD; WEB
South Africa   St. Lucia System                                  Land Use Land Cover                                            50,000             2005   LANDSAT           CD; WEB
South Africa   St. Lucia System                                  Long and Short Term Change Detection                           50,000             1986   LANDSAT           CD; WEB
South Africa   St. Lucia System                                  Wetland Delineation                                            50,000             2004   RADARSAT-1        CD; WEB
Spain          Delta del Ebro                                    Land Use Land Cover                                            25,000             2005   SPOT              CD; WEB
Spain          Delta del Ebro                                    Long and Short Term Change Detection                           25,000             1999   LANDSAT           CD; WEB
Spain          Delta del Ebro                                    Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Spain          Delta del Ebro                                    Topographic Dynamics                                           50,000             1997   ERS-2             CD; WEB
Spain          Lagunas de Villafáfila                            Land Use Land Cover                                            10,000             2006   Quickbird         CD; WEB
Spain          Lagunas de Villafáfila                            Long and Short Term Change Detection                           10,000             1986   LANDSAT           CD; WEB
Spain          Lagunas de Villafáfila                            Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Spain          Las Tablas de Daimiel                             Land Use Land Cover                                            10,000             2005   Quickbird         CD; WEB
Spain          Las Tablas de Daimiel                             Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Spain          Mar Menor                                         Land Use Land Cover                                            25,000             2005   SPOT              CD; WEB
Spain          Mar Menor                                         Long and Short Term Change Detection                           25,000             1986   LANDSAT           CD; WEB
Spain          Mar Menor                                         Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Spain          Mar Menor                                         Biophysical Parameters                                         50,000             2005   ASTER             CD; WEB
Spain          Ria de Mundaka-Guernika                           Land Use Land Cover                                            25,000             2005   SPOT              CD; WEB
Spain          Ria de Mundaka-Guernika                           Long and Short Term Change Detection                           25,000             1986   LANDSAT           CD; WEB
Spain          Ria de Mundaka-Guernika                           Water Cycle Regime                                             50,000             2004   RADARSAT-1        CD; WEB
Spain          Ria de Mundaka-Guernika                           Biophysical Parameters                                         50,000             2005   ASTER             CD; WEB
Switzerland    Zürichsee                                         Wetland Identification, Delimitation and attributes            25,000             2006   SPOT-5, ASTER     CD; WEB




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                                                       contained herein is subject to the restrictions on the title page of this document.
7 TECHNICAL SOLUTION


7.1 Globwetland Information System

The Globwetland Information Service was implemented as a means to make the Globwetland
products easily accessible to the end users as well as International community, while linking them
into the Ramsar Sites Database which contains information on all designated Ramsar sites.

The following tools have been developed to foster electronic distribution of the derived information
through Globwetland; they are presented here in Chronological order. All applications developed
through the Globwetland project are accessible through the following address:

www.globwetland.org/maps




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                     contained herein is subject to the restrictions on the title page of this document.
7.1.1 Globwetland Web Mapper Application:

Implementation date                    March 2004 – present
Primary Function                       Direct links to Ramsar Sites Database
                                       Derived products can be viewed, layers turned on/off
Availability of Products               All Core and specific products derived under Phase I and II of the
                                       main Globwetland Project
Web Address                            www.globwetland.org/maps
                                       http://www.wetlands.org/_Globwetland/mapper.cfm
View of application




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                      contained herein is subject to the restrictions on the title page of this document.
7.1.2 Mekong River Basin Information Service:

Implementation     January 2007 - present
date
Primary Function   Ability to view derived products for Mekong River Basin
Availability of    Water Quality Parameters
Products           Basin Wide Change Detection
                   Basin Wide Water Cycle Regime
                   Long and Short Term Change Detection
Web Address        www.globwetland.org/maps

View of
application




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                   contained herein is subject to the restrictions on the title page of this document.
7.1.3 Lake Chad Basin Commission Information Service:


Implementation         January 2007 - present
date
Primary Function       Ability to view derived products for the Lake Chad Basin Commission
Availability of        Chlorophyll
Products               Total Suspended Matter
                       Lake Surface Temperature
                       Yellow Matter
                       Z90 Signal Depth
Web Address            www.globwetland.org/maps
                       http://delphinus.site4u.nl/lakechad/
View of application




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                      contained herein is subject to the restrictions on the title page of this document.
7.2 Globwetland Methodology

Field-surveys to produce/update data needed for wetland management as well as maintenance of
existing ground monitoring networks have economic costs and require technical equipment
unsustainable for many parts of the world. An ad hoc group of the International Association of
Hydrological Sciences (IAHS) reported a decline in the amount of infrastructure available at water
stations by roughly 20% in Canada, 30% in the former Soviet Union and 90% in African countries.
In this context, EO technology may represent a key complement to overcome the above problem.

In spite of its potential, EO technology is still not used operationally for wetlands’ management.
Main reasons for this limited use in the past are likely to be associated to the technical limits of
available sensors (e.g., in terms of resolution) for small wetlands, the limited communication
between the EO and the wetland communities, the lack of operational quantitative techniques for
retrieval of consistent hydrological and ecological parameters from EO data and the slow
introduction of information technology and digital spatial information within the day-to-day practices
of wetland managers in many parts of the world. The steps leading from EO development and
demonstration towards real applications in wetland management are taken in fact a long time and to
some extent are still far to be fully operational. However, there is an increasing interest for this
technology within the wetlands community. The Ramsar Convention includes already within its
technical guidelines dedicated chapters to EO and GIS. In addition, an increasing number of practical
examples of both the applicability and the cost-effectiveness of this technology to support wetlands’
management published by the wetland community show an increasing level of penetration of this
technology within the operational practices of wetland managers.

In general terms, recognized advantages of EO-based information services are as follows:

      Provide a homogeneous coverage over large areas at different scales, facilitating the
       collection of information at local, catchment and national levels.
      Enable changes of hydrological variables to be detected through periodical and frequent
       acquisitions.
      Allow the long-term analysis of changes in the status of water resources thanks to the
       availability of historical archives of satellites images, which in many areas of the world
       represent the only information source available.
      Enable the observation and monitoring of remote, inaccessible or insecure areas;
      Povide neutral information for integrated management of trans-boundary surface and ground
       water systems allowing the generation of common databases, inter-country comparable
       information and shared water management information systems;
      Observations may be used to move beyond the point-based readings provided by gauge
       networks to, for instance, basin wide measurements of discharge and storage.




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      Besides the above general benefits, it is worth reviewing the specific advantages and limitations
      of this technology for the main applications associated to wetland management. To this end, the
      following sections provide an analysis of the main methods used to derive the GlobWetland
      products. Even though a detailed explanation of the technical procedures is beyond the scope of
      this paper, a comprehensive summary of the different methodologies used, as well as their
      advantages and limits, is provided.


7.2.1 Land Use Land Cover

         The main layer of the GlobWetland information system is the land use and land cover map,
         since wetland managers need this type of information to complete or update wetland
         inventory databases and describe the current status of the wetlands and its surrounding areas.

         One of the earliest challenges to generate a standardized data set that could be used
         worldwide was to define a nomenclature which could be applied across such a broad
         sampling of wetland types and geographical areas. In this context, a practical approach was
         applied based on a hierarchical classification of all land cover types (including wetlands) that
         would enable mapping based on the full range of available EO images. The principal reason
         to do this was that there was no single global standard for classifying wetlands as part of
         whole landscape classification, which was needed to place wetlands in the context of land
         cover within a catchment context.




                             Figure 8. LULC over the Tejo Estuary, Portugal (1:25,000 scale)



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                       contained herein is subject to the restrictions on the title page of this document.
The Ramsar classification, widely adopted by Contracting Parties to the Convention, is one solution
to the need to subdivide wetlands on the basis of hydro-geomorphology and vegetation, but is not
useful for multi-scale EO application. After experiments based on the approach taken by the
European countries (which use Corine Land Cover, CLC), resulting in a hybrid of CLC and Ramsar
types at Levels 1, 2 and 3, user feedback strongly indicated that the project should adopt the standard
CLC typology using the standard CLC Levels 1, 2 and 3 but with extensions to describe Ramsar
wetland types at Level 4. This was found to be the most practical solution. In this context,
accordingly, the nomenclature adopted was based on CLC, while Ramsar Contracting Parties can
relate the legend back to Ramsar wetland types using a simple cross tabulation.

Concerning the classification process, in the last years, many different algorithms and techniques
have been tested over wetland areas successfully using both optical and SAR data. For the
GlobWetland products, an object based approach was selected because of both the capacity of
object-oriented methods to extract GIS-ready information and the high accuracies provided by these
methods by exploiting the spatial contextual information.




                                         Figure 9. LULC Processing Methodology




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                                              Table 12. LULC challenges and solutions

      Challenge                       Cause                                                   Solution
      Limited field work              Lack of resources from end users and project            Work with end users to interpret
                                                                                              features on imagery through
                                                                                              electronic communications
      Assignment of classes (how      Segmentation provides too many spectral clusters        Work with the end users, consult
      to determine what features      which cannot be logically grouped into classes          supporting material to verify
      to assign to the different                                                              assumptions about class assignment
      classes)                                                                                prior to processing data.

      Separation of natural and       Single date optical imagery is limited in its           Apply some classification rules
      agricultural vegetation with    ability to separate these features based on spectral    such as segmentation object’s
      very similar spectral           reflectance, in particular SPOT 1-4 and                 shape features to look for irregular
      properties                      Quickbird which do not have a mid-IR band               or regular properties to separate
                                                                                              these classes. Also masking areas
                                                                                              where different processing is
                                                                                              applied can help reduce confusion.
      Geometric precision and         In some cases, mountainous terrain caused               Establish a base layer at a similar
      ability to integrate with       distortions in SAR and Optical imagery; User            scale as the products which the user
      datasets provided by end        supplied data was not well documented,                  can work with, and ensure all
      users                           projection information was often missing                derived layers are geometrically
                                                                                              registered.
      Limited ability to capture      Some sites had only a few land cover classes            Work with available material and
      diversity of land classes in    (very homogeneous), lack of detailed site               interact with end users to improve
      product                         knowledge and supporting data                           representation of the area in the
                                                                                              resulting maps
      Separating pastures and         Single date optical imagery does not give               Work with users to identify some of
      cultivated land                 sufficient information to separate these areas – a      the parcels, provide them with the
                                      priori knowledge should be integrated                   option to manually re-assign with
                                                                                              the segmentation polygons
      Determining how to map the      The transition zone can be very close spectrally,       Use SAR to characterize the
      transition zone between         and is best mapped using multi-temporal imagery         transition zone to guide process for
      water and marsh, and marsh                                                              mapping marshes/water and land
      and land                                                                                classes



                                            Table 13. Change Detection methods applied

      Method                          Description                     Advantages                      Drawbacks
      Intersection of two LULC        Two LULC maps are               For large sites, this           Assumptions must be
      maps                            produced – the first based      approach yields good            made about LULC classes
                                      on the current year EO          results quickly.                for historical date based on
                                      image acquisition, the                                          knowledge of the site.
                                      second based on historical                                      Accuracy of historical
                                      data. The resulting LULC                                        LULC product can be
                                      maps are intersected in a                                       lower and affect the
                                      GIS to identify changes                                         changes identified
      Manual Inspection and           The       recent    LULC        Results are likely very         Application       of     this
      delineation of changes          polygons are overlaid over      accurate since the photo-       technique is best suited for
                                      a historical EO image and       interpretation approach is      smaller      sites     (time
                                      manually interpreted based      very precise.                   consuming).
                                      on local knowledge to
                                      identify         permanent
                                      changes



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                           contained herein is subject to the restrictions on the title page of this document.
                                        Table 14. Water Cycle Regime Classes and definitions

        WCR Class                     Definition
        Minimum water extent          Lowest observed location of water in the time series
        Maximum water extent          Highest observed state of water in the time series
        Seasonally Inundated          Difference between max and min water
        Inundated vegetation          Vegetation typically on the shores of water bodies or in rivers which exhibit the
                                      double bounce effect
        Floating Vegetation           Vegetation typically observed in open water areas on one or more of the imaging
                                      dates
        Permanent Land                All land areas which are not changing through the time series



Figure 9 summarizes the LULC creation process. The main steps can be summarized as follows. A
segmentation of the original images is carried out to divide the image into ‘‘objects’’ which are
groups of pixels that could have similar spectral or shape characteristics. Following a segmentation
process, specific features (e.g., band combinations, texture features characterizing the relationships
among the different values of neighbouring pixels in the image) were identified to characterize
objects and maximize separability of classes (the distance among classes in the feature space). These
features optimize information contained in the objects such as spectral values, shape, texture,
hierarchy or thematic attributes. Then an automatic supervised classification was applied using
different decision rules (e.g., Maximum Likelihood) that allows assigning land cover classes to
objects based on the ‘‘similarity’’ of the objects with the ground data information available. The
classification process was supported by the commercial software e-Cognition. Some manual editing
was done to improve classes such as ‘‘Port Areas’’, ‘‘Airports’’and ‘‘Industrial Units’’ since they are
difficult to identify using spectral information alone, therefore it was necessary to exploit the
ancillary information available and local knowledge.

It is worth noting that some wetland typologies are difficult to distinguish in satellite imagery
without the support of additional information (e.g. riparian forest vs. mixed deciduous forest, natural
grassland vs. pastures). In these cases, the manual post-processing of the classification results is
required in order to improve the final map. To this end, the resulting classification was exported to a
shape-file for further analysis in order to refine the final land cover information manually exploiting
the local knowledge provided by wetland managers. Although this manual process is potentially a
time-consuming exercise, the combination of semi-automated classification together with manual
cleaning of problem areas was found to be more efficient than a fully manual interpretation process.
This step can, in certain cases, increase the accuracies of the final results by 10%.

Concerning the selection of the EO data, optical imagery was used as a basis to perform
classification. Depending on the wetland size and the required mapping scales, selected images
ranged in resolution from less than 1 m e.g., (Ikonos of Quickbird) to 30 m (Landsat TM). In
particular, SPOT-5 10 m resolution multi-spectral data were well suited to derive LULC maps at the
1:25,000 scale, while Landsat data were used to achieve a 1:50,000 mapping scale. Spatial and


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                         contained herein is subject to the restrictions on the title page of this document.
spectral resolutions in addition to the quality of the supporting data have a direct impact on the
quality of the results achieved. After the data were acquired, it was geo-referenced to the national
standards of the corresponding countries, ensuring that the products were delivered in the end-user’s
specified projection. This process is required in order to ensure that end-users could integrate the
products with existing raster or vector information. Example of LULC map over Portugal integrated
in the GIS system is provided in Figure 8.

Finally, using a GIS, statistics were gathered on the resulting layers providing total areas (in hectares)
per class to create a standard visualization of class composition over a given area. Data were
packaged using the ESRI suite of products, into self-extracting archives.

The final overall accuracies obtained ranged from 85% to 95% depending on the type of wetland
and, especially, the in situ data available for calibration purposes. In order to summarize some of the
findings of the exercise carried out over the 50 GlobWetland sites, Table 12 provides an overview of
the main challenges faced as well as some strategies applied to solve the problems.


7.2.2 Long and short term change detection

Usually change detection involves the analysis of two remote sensing images acquired in a
geographical area of interest at two different times in order to derive relevant information concerning
the potential changes occurred in the earth surface between the two dates under consideration. In
recent years, the automatic detection and analysis of changes in multi-temporal remote sensing
images have assumed an ever increasing strategic role in several application domains. This is a direct
result of the wide range of real-world applications that benefit from these types of methodologies, as
proved by the impressive amount of literature published in this field. Examples of these applications
include environmental monitoring, agricultural surveys, global-change analysis, urban studies,
natural resources management, land degradation monitoring and natural hazards management,
among others. In the context of wetland monitoring, different approaches have been tested
successfully to identify changes in wetland areas. From a methodological viewpoint, the change
detection process entails a number of steps:

      Data collection: multi-temporal remote-sensing imagery (acquired at two different times over
       a geographical region of interest), suitable ground truth data, ancillary data, other prior
       information;
      Data preprocessing: radiometric correction and geometric co-registration of the images;
      Data analysis: extraction of the required information about changes occurred in the area of
       interest between the two considered times;


Each of these steps entails different technical problems that are beyond the scope of this paper. In
this context, we will focus the discussion on the approach selected in the GlobWetland project and
the main problems encountered.

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                      contained herein is subject to the restrictions on the title page of this document.
Although in the remote-sensing literature several change analysis methodologies have been
proposed, in many operational applications, change-analysis techniques are apparently applied
without much awareness about the current state of the art. In several cases, operators face the
change-analysis problem according to very simple and empirical techniques. In other cases, the
change analysis is carried out according to colour coded visualizations of multi-temporal images,
without the assistance of automatic approaches. However, even if colour compositions may be a
powerful tool for a human interpreter, nowadays it is mandatory to use computer-based semi-
automatic techniques to support the operator in understanding the multi-spectral and multi-temporal
data. This results in a reduction of the processing time and, in several cases, in an increment of the
accuracy of the analysis (thanks to a synergistic exploitation of both the objective quantitative
decision criteria and the operator post-processing).

The main criteria to properly identify changes in the GlobWetland project was to ensure that data
used for the analysis matched as close as possible in terms of seasonality, since permanent changes
(as opposed to seasonal) were of interest to the wetland managers. The selection of the historical
image used as a reference to make the comparison with the current situation was selected site by site
with the support of the users in order to allow a meaningful temporal range covering specific
historical events (e.g., floods, urban expansion, impacts of industrial areas) affecting the areas under
study.




     Figure 10. Change detection showing significant loss of forest areas in the Lake Bogoria catchment, Kenya: (a)
   Landsat TM data from 1986; (b) Landsat ETM+ acquired in 2003; (c) change analysis, blue polygons showing extent
                                                     of lost forest




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The team applied two different methodologies for the identification of changes: (i) a supervised
approach based on the generation and comparison of two different LULC maps (postclassification
comparison): one generated from recent data and other generated from historical imagery; and (ii) a
manual delineation of changes over the smaller sites based on photo interpretation. Table 3 provides
a summary of the main advantages and drawback identified in applying the above methodologies.

Concerning the first method, post-classification comparison is the simplest supervised technique,
which performs change detection by comparing the classification maps obtained by classifying
independently the two images considered. In this way, it is possible to detect changes and to
understand the kinds of transitions that have taken place over large areas in a cost-effective manner.
In spite of the obvious advantages and simplicity, the performances of this technique strongly depend
on the accuracies of the classification maps. In particular, it was found that the final map of changes
exhibits an accuracy close to the product of the accuracies yielded at the two times. This is a direct
result of the fact that this method does not take into account the dependence existing between two
images of the same area acquired at different times, i.e. the temporal correlation between images. In
addition to this drawback, when field data are possible to be gathered to calibrate and classify the
most recent image, several problems may exist in order to generate an accurate land cover map from
the reference historic date. This drawback can be overcome by exploiting existing information, old
maps, and even by extracting manually ground truth data over the old image by extrapolating recent
filed data to the pass with the support of a well-trained photo-interpreter with a good knowledge of
the area. As an example, Figure 10 shows a zoom over the Bogoria Lake in Kenya, where significant
areas of forest have been cut to increase the agricultural area.

The second method applied allows a high accuracy and geometric precision to be obtained but
requires the intervention of a well-trained operator, who may extract changes manually from the
images. This is a time-consuming task that involves overlaying the polygons of a recent land cover
map over a historical EO image with the support of a GIS and manually delineate changes and
interpret land cover transitions based on local knowledge. Due to the required effort, this technique is
suitable for small sites that require a high accuracy and a high resolution. It is worth noting that
similar to the above case, the lack of in situ field information gathered at the time of the historical
image renders the interpretation of the changes difficult and fully dependent on the operator
experience and knowledge of the area under analysis.


7.2.3 Water Cycle Regime Monitoring (WCR)

Inundated wetland dynamics’ monitoring using remote sensing has been widely treated in the
literature. This can be done through the estimation of the water levels using altimeter data or by
mapping the water table including the open water and the inundated vegetation. In the context of the
GlobWetland project, we focused on the second approach.

Monitoring the water table during a full hydrological year in order to characterize the water cycle of
wetlands requires the acquisition of images at different seasons ranging from the lowest water

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availability period to the season with the maximum water level and even during flood events. Such
image acquisition capacity, especially during flood events or during the rainy season in tropical
areas, characterized by abundant cloud coverage, hinders the use of optical sensor and requires the
use of SAR data, which is not affected by cloud coverage. Therefore this product relies on the
periodical collection of SAR images acquired at critical moments to capture the natural ebb and flow
of water in wetlands.

The extent of water in wetlands can be easily detected in SAR imagery due to the radar response to
both open water and inundated vegetation. On the one hand, under low wind conditions, radar
reflects on open water as in a mirror without returning any signal (‘‘backscatter’’) to the sensor. Such
lack of backscatter is characterized in the images by a very dark appearance. On the other hand, due
to the double bounce effect created by standing vegetation in water, inundated vegetation is easily
identifiable under certain conditions of vegetation types, height and density.

Through the collection of time series of SAR images, different states can be observed, providing
varying backscatter responses from the surfaces imaged. In order to produce the WCR map, a
consistent method to analyze the changes was devised. Initially, single pairs of images are considered
in isolation of the complete multi-temporal set, which proved to be a challenge since the logical
evolution of the water and vegetation classes was difficult to characterize. In order to overcome such
limitation, a more robust approach was developed based on a multi-temporal coincident analysis of
all SAR images available in a single stack.

RADARSAT-1 (C-band, HH polarization, fine beam – 10 m, 45 ° incidence angle) and ENVISAT
ASAR imagery (C-band, HH polarization, imaging mode – 25 m, 30° incidence angle) has been used
to create these products. Some Alternating Polarization (AP) data were also collected from Envisat
ASAR (C-band, HV polarization, imaging mode 30 m resolution). The information contained in a
single scene of AP data gave an indication of the structure of certain vegetation, particularly in
wetland areas due to the differences in backscatter caused by vegetation structure and orientation to
the sensor. Further investigation should be carried out to determine optimal acquisition and
processing strategies for AP data over wetlands.




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                           Figure 11. Water Cycle Regime map over Creston Valley, Canada

During the prototyping phase of the project, the classes in Table 14 were identified after different
testing and assessment as those that allow an accurate and reliable classification. In order to arrive to
these classes, the multi-temporal SAR data stack was combined to create three new images
(‘‘features’’): (i) a MIN image, which returns the minimum value of each pixel across the multiple
layers of the stack; (ii) the MAX image, which returns the maximum value of a given pixel in the
same stack; and (iii) a MEAN image, which returns the mean value of a given pixel across the stack.
Based on this approach, maximum separability can be achieved through the creation and subsequent
analysis of a Min–Mean–Max (MMM) image. For each SAR pixel, we considered maximum,
minimum and average values of backscattering coefficient among N multi-temporal images.

Segmentation algorithms were applied to the MMM image, and a supervised classification was used
to separate the classes. A key approach developed in the project was to apply a normalized difference
radar index (NDRI) (see equation below). The MIN and MAX portions of the MMM images were
used to derive an index which was useful for separating the objects based on their statistical
properties.

NDRI= (MIN – MAX) / (MIN+MAX)

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As a result, overall accuracies ranging from 80% to 90% were obtained over the different sites. It is
worth noting that, due to pixel size of the sensors used (around 25 m) and the segmentation process
carried out, the minimum mapping areas (the size of minimum water pond identifiable) range
between 0.50 ha and 1 ha. This provides a good compromise of thematic accuracy, temporal
coverage and geometric precision. As an example Figure 11 shows the Water Cycle Regime map
over Creston Valley in Canada.


7.2.4 Mapping of peatland fire scars

Mapping burned areas by means of EO technology has been extensively studied in the last few years.
The results demonstrated the capacity of optical data (over SAR images) to provide suitable
information to extract accurate and reliable burned scar maps at scales of 1:50,000 and better with
thematic accuracies over 85% in an automatic or semiautomatic manner. In the case of the peatland
fires map, the main target was to provide the local authorities with a single vector map identifying
burned areas over a large peatland region, without showing active fires visible in the imagery. In
order to determine the extent of fire scars, Landsat ETM+ data from a time period before and after
the fire event were used. The degree of forest re-growth has been assessed using a vegetation index
as calculated from the image data (NDVI). On the basis of such information, a comparison of the two
images was carried out in order to extract a vector layer depicting the extent of the burned areas. The
detection of fire scars is in fact a sophisticated change detection technique.

However, due to smoke and haze in the post-fire image (due to the presence of active fires at the
image acquisition time) and differing vegetation periods all attempts of automated classification
were not successful. In fact, for both dates the NDVI and the Tasseled Cap transformation were
calculated in order to better evidence changes in the vegetation cover that may be associated to a
burned area. Even with these artificial images burnt scars did not present a significant pattern. The
heterogeneity of the data and the complexity of the problem due to the presence of smoke in the
images led to the conclusion that visual interpretation by a trained expert was the only feasible
alternative in order to extract the required information with a high accuracy.

Visual interpretation was performed by examining the ETM data displayed on the screen at the scale
1:25,000. The burnt scars were identified through the comparison of the pre- and post-fire data.
Landsat ETM+ imagery before and after the fire event was analyzed by a team of experienced
experts. The hi-resolution PAN channel of the ETM system proved to be a very valuable source of
information: with a ground resolution of 15m and its recording spectra from blue to Near Infra-Red
(NIR) fire scars could be delineated at a high quality level. As well ETM’s thermal band, ranging
from 10.4 mm to 12.5mm at a spatial sampling interval of 60 m, gave important information on
smouldering fires. The results were compared with field data using a GIS in which all geo-referenced
remote-sensing data are integrated as well as available vector data from the end-user and GPS
recorded field observations.



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7.2.5 Water Quality

Wetland managers must rely on efficient means for identifying pollution sources, and assessing
water quality. Conventional methods used by water authorities and wetland managers to assess water
quality involve in situ measurements and collection of water samples for subsequent laboratory
analysis. Although these methods give accurate measurements for a point in time and space, they are
expensive, time-consuming, and, more important, do not give either the spatial or temporal view of
water quality that is needed for accurate assessment and monitoring of surface water quality. Remote
sensing may provide complementary means for obtaining relatively low-cost spatial and temporal
data about surface water conditions for large water bodies and wetland areas.

Chlorophyll, suspended sediments, turbidity, yellow matter and floating layers of algae are some of
the main parameters identified by water authorities which determine the water quality. Several
studies have been carried out focusing on the determination of these parameters. These studies have
shown that Landsat images at 30 m resolution as well as MODIS and MERIS at medium resolution
(300 m), among others, can be used to detect spatial and temporal variations in surface suspended
sediments, organic matter (yellow matter), chlorophyll and therefore green algae, and purple bacteria.
Regression analysis techniques are widely used by the remote-sensing community in establishing
mathematical expressions that, making use of water quality in situ measurements, link the spectral
behaviour of water bodies with the parameters which determine water quality. Another type of
technique, chromaticity analysis, has been also used in water quality assessment. Some relationships
have been found between both the chromatic and the biophysical characteristics of water bodies. In
particular, the relationships between the red chromatic component and the suspended sediment
concentration, the green chromatic component and the chlorophyll concentration, and finally, the
blue chromatic component and the turbidity of the water body were verified. However, these
techniques, which can be considered still experimental, have been extensively tested in large open
water bodies. However, the practical applicability of these techniques in small and shallow waters
such as most wetlands is still a technical challenge:

      Spectral resolution of most of current available sensors does not provide the sufficient degree
       of detail for accurately estimating water quality parameters at high resolutions.
      Estimates strongly depend on availability of in situ data acquisitions. In case, field data are
       not available for calibration, only relative measurements can be extracted.
      Sensors with adequate spectral information (e.g., MERIS) can only provide acceptable
       accuracies at medium resolutions over 300 m in the ground. This is insufficient for many
       wetlands.
      Most of the still experimental techniques developed for estimating inland water quality from
       EO technology are designed to operate in clear largewater bodies. The applicability of these
       techniques in wetlands, characterised by shallow waters, is still a research topic.
      The use of these techniques is limited to ‘‘visible’’ substances, such as chlorophyll, or
       suspended sediments. Micro-bacteria and other chemical discharges are not detectable by
       current sensors.

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Taking into account the above limitations, in the case of the GlobWetland project, special attention
was given to coastal wetlands and estuaries. In particular, using in situ data collected by the wetland
manager, a regression approach based on the Baruah et al. (2001) equation (see equation below) was
used to derive the constants and variables required to calculate suspended sediments and chlorophyll
concentrations (Figure 12). As an example, a Landsat TM image was collected from the archives
over the Axios site in Greece on a date which closely matched the collection of the in situ data.
Bands 1 and 3 were used to apply equation below to calculate suspended sediment at 30 m
resolution. Results are presented with a colour table showing the suspended sediment absolute values
over the delta region.

S = 67:3996+958:109 (Band 1) + 1624.33 (Band 3)

It is worth noting that besides the above case, where in situ data were available, the project also
focused on a number of wetlands, where the lack of in situ information hindered the possibility to
apply regression techniques. In those cases, especially in the African continent, a relative approach
was used in order to provide the user with relative but still useful information concerning the water
quality of their wetlands. In those cases, the information provided shows the level of concentration
from a minimum to a maximum without providing absolute concentration estimates. The value of
these products for wetland managers is twofold. On the one hand, maximum values may be used to
early identification of potential sources of pollution or algae blooms that may require the
development of special measurements and mitigation plans to be put in place. On the other hand, the
continuous provision of these relative maps and especially the variations with respect to specific
values taken as a reference (e.g., a specific moth characterized by clear waters) may allow the early
identification of variations with respect to normal conditions that may require a further inspection in
the field. Fig. 6b shows a case in South Africa, where only maximum and minimum values were
provided.




     Figure 12: Water Quality Parameters (a) absolute suspended sediments in Axios Delta, Greece (b) chlorophyll-a
                                 relative concentration values, Saint Lucia, South Africa



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7.2.6 Wetland identification and delineation

As mentioned previously, the main objective of this product is to support national agencies to
develop national inventories in a cost-effective manner. Therefore, the generation of this product
required the development of a methodology suitable to be applicable over large areas at regional and
even national scale. The particular nature of wetlands including shallow waters, inundated vegetation
and wet soils, render their identification from remote sensing technology a complex technical
problem that has been treated by the scientific community mainly from two main perspectives. On
the one hand, several studies have focused the attention in estimating soil moisture from SAR data.
On the other hand, many others have focused the attention of the automatic identification of water
bodies and inundated vegetation.

Concerning the former, SAR images offer the possibility to assessing soil moisture since they exploit
the relationship between the moisture content and dielectric constant of the soil. The radar signal is
not only sensitive to the soil dielectric constant but also to the geometric structure of the soil surface
and vegetation. Therefore, retrieval algorithms should be able to describe these complex parameters.
Many developed models have been found to be site-dependent hindering the development of more
widely applicable approaches. Alternative methods, such as the multi-temporal change detection
approach, have been proposed that allow monitoring changes in soil moisture conditions at regional
scales. This method is based on the use of a multi-temporal set of SAR images and subtracting each
radar image by a reference image, as an attempt to correct for the soil and vegetation effects specific
to each pixel of the image. Results suggested that, at the watershed scale, the mean effect induced by
different mixed roughness states is approximately constant during the year. However, in spite of the
research carried out, high-resolution SAR systems (25 m resolution) are only applicable today for
experimental applications and cannot be considered as operational.

It is worth noting that, in spite of the potential of SAR systems to identify water bodies and
inundated vegetation, the applicability of these methods over large areas is still a research issue. In
fact, most of the works carried out limit the analysis to a certain zone, where the location of wetlands
is already well known. It is worth noting that neither the first nor the second approach fulfils the
requirements for identifying and delineating wetlands over large areas at high resolutions. In fact,
one of the main problems affecting the development of the required product and related methodology
is the wide definition of wetlands provided by the Ramsar Convention: i.e., ‘‘wetlands are areas of
marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that
is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low
tide does not exceed six meters’’. In this contest, within the context of the GlobWetland project, an
alternative approach was developed that provides the national authorities with a map of potential
wetland sites as a support tool to facilitate the preparation of national inventories at large scale.




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    Figure 13: Identification and delineation of wetlands, La Brenne, France. The blue polypons depict the location of
                       identified wetlands extracted through the analysis of SPOT-5 and SAR data.

The proposed methodology is based on the combined use of different geo-information products
relevant to wetlands: land cover maps, slopes extracted from DEMs and distance to water classes
(and inundated vegetation). The first variable is created from the original shape-file of the
GlobWetland land use and land cover product. Land use and land cover classes have varying
‘‘potential’’ to be a wet area. For example, pastures have a higher probability than continuous urban
fabric to become wet if water is present. In this context, a corresponding ‘‘score’’ was assigned to
the various land use categories. The second variable is the slope created from a DEM. SRTM 90 m
data were used for this analysis. Once the wrong values were removed from the original SRTM data
set, slope maps were generated. A reclassification of the gridded slopes was done by assigning low
values to the steep slopes (greater than 10°), medium value to medium slopes (5–10°) and high
values for a low to flat slope (0–5°). The range of the values was from 0 to 4 being the same range as
that of the land use and land cover variable. Finally, the third variable used a proximity analysis to
water classes to map the potential spatial probability of areas becoming wet. The buffer analysis took
into account the relative size of areas to assign buffer distances. Buffer ‘‘scores’’ were assigned
based on their relative distance from the water classes. Highest score (4) was assigned to the areas
closest to the water classes, and lowest score (1) was assigned to areas farthest from the water
classes.

Following the calculation of these three variables, a gridded theme was produced, which is
essentially a resulting map for each of the three variables with potential values from 0 to 4 in each
category. Using GIS intersection methods, the three variables are added together to give the final
score. This result is then converted to a shape-file and the polygons with values from 9 to 12 are



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recoded to a ‘‘Potentially Humid Zone’’, values from 5 to 8 are recoded to a ‘‘Medium Probability
Area’’ and values from 0 to 4 are recoded to ‘‘Non-Humid Zone’’.

In addition to the above method and when more detailed delineation accuracy was required, a semi-
automatic classification based on segmentation followed by subsequent photo interpretation of the
satellite imagery was applied using a multisensory approach based on both optical SPOT-5 data and
multitemporal ASAR imagery. The combination of the two types of images in the interpretation
process yielded very good results, in particular, over La Brenne site in central France, where a
detailed validation was carried out. A comparison of the GlobWetland map was done vs. a very
detailed map produced by the French authorities in 1999 using photo-interpretation methods of very
high-resolution aerial imagery. The comparison showed that the total wetland area delineated by the
GlobWetland map using          satellite imagery was 7159 ha vs. the 6768 ha derived from aerial
photography. This represents an accuracy of 94% in the identification of wetlands. Figure 13 shows
an example of the product over La Brenne, where blue polygons show the wetlands identified.


7.2.7 Topography and its dynamics

A Digital Elevation Model (DEM) represents the shape of the earth’s relief in digital form. DEMs
offer many advantages over traditional maps because they provide significant analytical potential. In
addition to providing an estimated elevation value at virtually any point within the model, it is
possible to determine the direction (aspect) and degree of slope at any point.

SAR Interferometry has been developed in the last decades as a unique capacity of SAR systems to
derive topographic information. The principles of these techniques are based on the dual nature of
the SAR images. In particular, SAR systems measure both the magnitude and the phase of the
transmitted electromagnetic signal that is backscattered from the earth’s surface. Magnitude data
indicate the ‘‘brightness’’ of the surface as detected by the SAR. The phase represents the
combination of two factors; the first is a quantity related to the distance from the SAR sensor to the
surface, and the second is the surface scattering effect on the incident electromagnetic wave. If a
second SAR image is acquired from nearly the same location as the first after a certain period of time
then upon subtracting the phase of the second image from the first an ‘‘interferogram’’ is formed. If
the distance (called the baseline) between the two locations of the SAR platform is small then the
surface scattering effects will be the same for the two images and are thus cancelled in the formation
of the interferogram. Therefore the interferogram exhibits information related to the difference in
distances from the surface to the two SAR locations. Knowing the geometry involved, topography
can be derived from this information. It can be shown that the sensitivity of the phase to height is
proportional to the baseline. Heights can typically be measured to an accuracy of approx. +/-8–10 m
with systems such as ERS-Tandem mission (the accuracies reduce to +/-20 m in highly vegetated
areas).




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Optical satellites use stereo processing techniques to generate DEMs (e.g., SPOT-5). However, areas
with significant and/or continuous cloud coverage are difficult to map because it is difficult to find
matching image points on a stereo pair. In contrast, SAR satellites are not restricted by ambient light
or cloud conditions and can acquire global data at virtually anytime.

In the GlobWetland project, different examples of DEMs generation for wetlands’ management have
been generated over Africa (especially in Kenya and South Africa). The accuracies provided (in the
range of 10 m) do not allow wetland managers to characterize the topographic characteristics of the
wetland site at local scale. However, it is still very useful information to characterize the full
catchment areas at regional level.

Besides the capacity of SAR system to derive elevation information, the multi-temporal nature of
satellite data allows the observation of differences in the topography with accuracies in the range of
few centimeters. In particular, if surface motion occurs between the acquisition times of the two
imaging passes of the SAR platform then the interferogram phase represents a combination of
topographic information and surface change. If the topography for the area before the change is
known, it can be used to remove the topographic phase information from the interferogram leaving
simply the surface change phase. The sensitivity of the phase to surface change is proportional to the
wavelength of the transmitted electromagnetic wave. Thus changes in elevation can be measured
with accuracies at the centimeter level or better. This capacity was used in the GlobWetland project
to map the subsidence and ground displacements occurring in coastal areas due to erosion, water
extraction or manmade activities (such as mining).


7.3 Earth Observation Data Used to produce products

   In order to derive a list of suitable satellite imagery, the Project team first and foremost worked
   closely with the end users to refine their requirements, given the team’s a priori knowledge of not
   only EO capabilities and limitations, but also the availability of data over the areas of interest
   based on the commercial and scientific earth observation missions of the last 30 or so years.
   Knowing the sensor specific parameters, achievable scale given sensor spatial and spectral
   resolution, and the dates at which the various EO data are available based on mission durations
   we the main parameters kept in mind in order to make recommendations to the end users in
   finalizing the list of possible images to be collected.

   The following are some general comments on the suitability of sensors based on each of the Core
   Products:

   Core Product: Land Use and Land Cover Map, including wetland types
    Optical data is best suited for this application
    Appropriate sensor spatial resolution must be considered based on the end user requirements
         o Scales 1:2,500 – 1:10,000 (use very high resolution – sub metre)


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            o Scale 1:10,000 – 1:25,000 (use high resolution – 5 to 10 m)
            o Scale 1:25,000 – 1:50,000 (use medium resolution – 15-30 m)
            o Scale 1:50,000 – 1:200,000 (use coarse resolution – 30-300 m)
        A combination of panchromatic and multi-spectral information should be obtained (therefore
         pan-sharpening the multi-spectral information is recommended)
        Sensors that can achieve the stated resolutions need to be available for the year requested

The following table summarizes additional considerations for the selection of EO data for this
product:
                   Table 15. EO data selection considerations for the Land Use Land Cover product

 EO Data          Description                             10,000                      25,000                   50,000
 Sensor           EO Satellite and sensor to be           Quickbird, Ikonos           SPOT1-4,5                Landsat 5 TM, 7
                  used                                                                                         ETM+
 Format           The format of the data provided         Geotiff                     Geotiff, DIMAP           Geotiff
                  by the distributor. Preferably          Level 1                     Level 1                  Level 1
                  low levels of processing should         Path oriented               Path oriented            Path oriented
                  be applied to preserve the
                  radiometric information.
 Bands            The spectral bands included in         Panchromatic and             Multi-spectral bands     Multi-spectral
                  the data provided are of critical      multi-spectral is            for SPOT-5 include 2     bands for Landsat 7
                  importance. Visible and                preferable to allow          visible and 2 infrared   includes 3 visible
                  infrared bands should be               pan-sharpening.              bands. SPOT-1-4 also     SPOT-5 includes 2
                  included.                              Visible and Infra-red        include multispectral    visible and 2
                                                         bands (need multi-           bands, however the       infrared bands.
                                                         spectral).                   resolution is coarser
                                                                                      (20m)
 Resolution       This describes the size of the          < 5m for multi-spec.        10m for SPOT-5           30m for all Landsat
                  pixel for the sensor                    < 1m for pan                20m for SPOT 1-4         data is sufficient for
                                                                                                               this scale
 Timing (for                                             Peak vegetation              Peak vegetation          Peak vegetation
 collection)



Core Product: Change Detection Map
All the same points as above
     Crucially important is the availability of data that can generate a baseline map at the required
        scale for the baseline year selected (we encountered numerous cases where baseline data was
        not available for the year specified, therefore we recommended to move the baseline year up
        in order to maintain the scale)
     The project team was mindful of mixing different sensors which record data at different
        spectral ranges

The following table summarizes additional considerations for the selection of EO data for this
product:



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                   Table 16. EO data selection considerations for the Change Detection product

 EO Data        Description                             10,000                      25,000                   50,000
 Sensor         EO Satellite and sensor to be           Quickbird, Ikonos           SPOT1-4,5                Landsat 5 TM, 7
                used                                                                                         ETM+
 Format         The format of the data provided         Geotiff                     Geotiff, DIMAP           Geotiff
                by the distributor. Preferably          Level 1                     Level 1                  Level 1
                low levels of processing should         Path oriented               Path oriented            Path oriented
                be applied to preserve the
                radiometric information.
 Bands          The spectral bands included in         Panchromatic and             Multi-spectral bands     Multi-spectral
                the data provided are of critical      multi-spectral is            for SPOT-5 include 2     bands for Landsat 7
                importance. Visible and                preferable to allow          visible and 2 infrared   includes 3 visible
                infrared bands should be               pan-sharpening.              bands. SPOT-1-4 also     SPOT-5 includes 2
                included.                              Visible and Infra-red        include multispectral    visible and 2
                                                       bands (need multi-           bands, however the       infrared bands.
                                                       spectral).                   resolution is coarser
                                                                                    (20m)
 Resolution     This describes the size of the          < 5m                        10m for SPOT-5           30m for all Landsat
                pixel for the sensor                                                20m for SPOT 1-4         data is sufficient for
                                                                                                             this scale
 Timing (for                                           Peak vegetation of           Peak vegetation of       Peak vegetation of
 collection)                                           chosen historical            chosen historical year   chosen historical
                                                       year                                                  year

Core Product: Water Cycle Regime
    Since capturing the water extent at specific times of the year is critical, SAR data is most
      efficient for this purpose
    Many users requested this product at the same scale as the other products (typically 1:25,000
      or better), however, the best achievable scale using 10 m SAR data (finest resolution
      available with RADARSAT-1) is 1:50,000; therefore this mapping scale as well as the
      adjusted date according to the EO mission availability were applied.




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                     contained herein is subject to the restrictions on the title page of this document.
The following table summarizes additional considerations for the selection of EO data for this
product:
                   Table 17. EO data selection considerations for the Water Cycle Regime product

EO Data        Description                            10,000                      25,000                    50,000
Sensor         EO Satellite and sensor to be          N/A                         N/A                       RADARSAT-1
               used                                                                                         fine beam (large
                                                                                                            angle), ASAR IM
                                                                                                            mode (small angle)
Format         The format of the data provided        N/A                         N/A                       Geotiff
               by the distributor. Preferably                                                               Level 1
               low levels of processing should                                                              Path oriented
               be applied to preserve the
               radiometric information.
Bands /        Fine beam SAR data is only             N/A                         N/A                       C-HH
Polarization   available as a single                                                                        ASAR C-HH, VV,
               polarization (C band – HH).                                                                  HV, VH if
               ASAR can provide HH, VV,                                                                     available can be
               HV, and VH. The polarization                                                                 used to support the
               has great potential to help                                                                  fine beam
               discriminate inundated                                                                       RADARSAT
               vegetation, however the                                                                      imagery.
               resolution of ASAR is coarse.
Resolution     This describes the size of the         < 5m                        10m for SPOT-5            10m for all
               pixel for the sensor                                               20m for SPOT 1-4          Radarsat fine beam;
                                                                                                            20m for Envista
                                                                                                            ASAR
Timing (for                                                                                                 Wet/Dry periods.
collection)                                                                                                 Additional images
                                                                                                            can be useful,
                                                                                                            specially if
                                                                                                            historical flooding
                                                                                                            period can be
                                                                                                            captured .



Specific Product: Wetland Identification and delimitation
The information derived from SAR data for the Water Cycle regime can be used for this product;
however, additional multi-spectral information is required to map vegetated areas late in the growing
season. Appropriate sensors were selected in order to have multi-spectral information to augment the
analysis.

Specific Product: Topographic dynamics in coastal wetlands
Since the scale required is quite detailed (1:10,000) for the only country requesting this product
(Spain), high resolution imagery, as compared to air photos will be used.
The second aspect of this requirement is the long term monitoring of land subsidence on the cm or
mm scale. In order to measure this type of movement, only InSAR can be applied; since “traditional”
InSAR is likely not well suited to this type of environment (dynamic = low coherence), the

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                      contained herein is subject to the restrictions on the title page of this document.
alternative is to detect “hard” targets in successive SAR images, using “Coherent Target Monitoring”
technique. SAR sensors must be used for this purpose, and the resolution should be fine enough to
detect these targets from the same viewing geometry, with suitable baselines (preferably very small)

Specific Product: Digital Elevation Models
Repeat pass INSAR is the most appropriate method to derive elevation and create DEMs; therefore
the project team considered the availability of SAR data that is suitable for InSAR and DEM
generation.

Specific Product: Peatland Fire Scar Mapping
Since the fires are typically very small, we therefore need fine resolution Optical or SAR data before
and after the fire events. A change in reflectance or backscatter will be noticed due to the changes in
the vegetation, or surface roughness. As for the second part, peat fires need to be identified (hot
spots) as they occur during the season. This information can be extracted from the thermal band
coarse resolution optical sensors. Ionia, an ESA based service, includes the ATSR World Fire Atlas
service. ATSR-2 and AATSR are sensors which are onboard ERS-2 and Envisat (respectively) that
are capable of detecting hot spots from areas on the earth which are emiting high levels of thermal
radiation (heat). An online atlas is updated on a continual basis and can be accessed on the Ionia web
site (http://dup.esrin.esa.int/ionia/index.asp).

Specific Product: Biophysical parameters
The most suitable sensors to derive this information are MODIS and MERIS, however these sensors
are limited in terms of their resolution (500m and 300m, respectively). For the Globwetland end
users, the size of areas to be monitoried were quite small, and therefore not well suited for this type
of coarse imagery. Landsat and Aster data were used, however one is limited in terms of selecting
suitable EO data that can be matched to in situ data collection campaigns, which are typically still
required to extract quantitative information from the satellite imagery.

The following table summarizes the satellite imagery used to derive the products for the
Globwetland project.




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             Table 18. Satellite data collected and used to derive information products for Globwetland end users




                                                                                                                                                                                                                              ERS Tandem
                                                                                                                                                                                                 Rsat (SsarN)
                                                                                      Landsat 4-5




                                                                                                                                                                                   Rsat (Wide)
                                                                                                                                                        Rsat (Fine)

                                                                                                                                                                      Rsat (Std)
                              Quickbird




                                                                                                    Landsat 7
                                                       KVR-1000



                                                                           SPOT 1-4




                                                                                                                                                                                                                                                  TOTALS
                                                                  SPOT 5




                                                                                                                        Resurs
                     Ikonos




                                                                                                                                                 NOAA
                                                                                                                                         Modis
                                                EROS




                                                                                                                                                                                                                                           ASAR
                                                                                                                                 Meris




                                                                                                                                                                                                                ERS1

                                                                                                                                                                                                                       ERS2
                                                                                                                Aster
                                          IRS


   Countries
   Algeria              4                                                                                                                                                                                                                            4
   Egypt                                                                                    1           2         4                                           3                                                                              3    13
   Kenya                                                                                    2           2         3                                           9                                                                    4         6    26
   LCBC                                                                               11 11                                                                                              8                                                        30
   Senegal                                                                                  1           1                                                                                                                                            2
   South Africa                                                                8                                                                                                                                              14 17               39
   Canada                                                                      3                                                                              6                                                                              2    11
   Ecuador                                                                     3                                                                              9                                                                              9    21
   Austria                         2                                                                                                                                                                                                                 2
   Finland                                                                 10                                                                                                                                                                     10
   France                                                                  13                                                                           24                                                                                   9    46
   Greece                                                                      8                                  5                                     18                                                                                 12     43
   Italy                           2                                 1         1                                                                                                                                                                     4
   Portugal                        1                                           2                                                                        12                                                                                 12     27
   Russia                                                                      8                                                                                                                                                                     8
   Spain                5                                            5                                            5                                     20                                                                    20 20               75
   Switzerland                     2                                                                                                                                                                                                                 2
                        9          7       0      0        0         6 56 15 16 17                                         0       0       0       0 101                   0             8             0          0      0 38 90 363




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8 VALIDATION OF THE GLOBWETLAND PRODUCTS


8.1 Validation approach

     Each product generated by the Globwetland project team was accompanied by a
     Validation Report. The purpose is to give each stakeholder in the Globwetland project
     insight in the thematic and geometric accuracy of the product. Besides the quantitative
     assessment of the product, a qualitative analysis is given, largely based on the user’s
     experience with the product in their daily operations. Each report is produced by the
     Globwetland project team in cooperation with the end-users. The following questions
     are addressed and answered in these reports:
         How many points were recorded during the fieldwork campaign, by whom, at
            what time and according to which method?
         According to what methodology is the product validation carried out?
         What is the thematic accuracy of the product?
         What was the feedback of the user on the product provided?
         Were there any comments and/or suggestion from the users for improving the
            product?
         What has the Globwetland consortium done to address these comments and
            improve on the product?

    8.1.1 Fieldwork data

     The quantitative accuracy assessment of the product is done by the project team, using
     fieldwork data collected over the wetland site in most cases. Usually the fieldwork data
     is gathered end users with guidance provided by the project team.




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               contained herein is subject to the restrictions on the title page of this document.
      Dedicated fieldwork surveys were carried out in almost all Globwetland sites. The
      fieldwork has been planned carefully in cooperation with the end-users. The fieldwork
      is carried out close to the satellite image acquisition date when possible, to be able to
      compare the results of the classified satellite imagery with the data gathered in the field.

      For the Landuse/Landcover map, an approximate plan on which locations to visit
      throughout a site is made based on an archived satellite image (Landsat), on which the
      complexity of the landuse can be roughly estimated. A printout of the satellite image is
      then taken into the field together with staff members from the local site management. In
      the field, landuse classes are recorded and GPS location measured. The aim is to record
      a minimum of 25 samples per class to be distinguished on the satellite image. The
      points should also be spread equally throughout the management area as much as
      possible. The expert knowledge of the local management staff of the wetland site
      further steers the acquisition of points, based on specific classes of interest (e.g. floating
      vegetation or algae that can indicate pollution).

      For the Water Cycle regime, the fieldwork is done during the satellite overpass (when
      the image is recorded). The water extent and wetness of the soil can change quickly in
      some areas, influencing the backscatter properties of the land surface and hence the
      image. It is therefore essential to do the fieldwork parallel to the satellite image
      overpass, to be able to train and validate the water cycle regime map product.

      In general the fieldwork is carried out using a PDA with a GPS attached through a
      bluetooth connection. A georeferenced satellite image is loaded in the PDA and is
      superimposed with vector layers of roads, rivers, etc where available. The position of
      the observer in the field is displayed on the image on the PDA, allowing one to
      accurately determine the location where to record a point. The data is entered by
      choosing a class from a customized Globwetland pulldown menu on the PDA. The data
      is stored automatically with the GPS location. Back in the office, the data can easily be
      transferred to a PC and into ArcView, to be processed further towards training and
      validation data sets. Fieldwork for the Water cycle regime product is done using a
      simple GPS handheld receiver only, since only the water/land boundaries need to be
      recorded instead of complex landuse/landcover patterns. This fieldwork is usually
      carried out by the wetland staff themselves, after instructions from the project team
      through email and/or telephone contact.




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                 contained herein is subject to the restrictions on the title page of this document.
       8.1.2 Quantitative Validation Criteria

The Globwetland fieldwork points are converted to raster maps with a pixel size of 20m. Each
fieldwork point is thereby converted to a square area of 20x20m (4 pixels on a 10m resolution
LULC map based on SPOT-5 imagery). The fieldwork is set up in a way that each point is
recorded only in a homogeneous are with approximately a surrounding area of 100 m of similar
landuse.

The validation data is than cross-correlated with the LULC map, creating confusion matrix
between validation data and classified data. From the confusion matrix the “producers accuracy”
(or “accuracy”), “users accuracy” (or “reliability”) and the total accuracy are calculated. It is the
goal of the consortium to create maps with a total accuracy of 80-90%. When the 80-90% is not
achieved, efforts are undertaken to improve the classification and meet the target.

The validation results described here are the result of an iterative process of various
classifications, filtering techniques, data integration & manual recoding to be able to reach the
set target of 80-90%. The accuracy results presented here are established after improvements
based on user feedback (when provided).

The number codes in the confusion matrixes (x-, and y axis) refer to the Corine based class
codes on the LULC map. The complete list with classes corresponding to the number codes can
be found in the annex. The numbers in the table refer to the number of pixels classified. The
yellow boxes are the pixels well-classified. The accuracy figures are expressed as fraction
(between 0=0% and 1 =100%).

Not in all cases (enough) fieldwork data points are available for validating the maps. In this case
additional data is used as much as possible to create a validation set of points to be able to create
a confusion matrix and establish users and producers accuracy figures. In case a limited number
of field data point area available, the consortium have multiplied the number of validation
points by digitising additional points close to in-situ recorded points having the same class as
can be seen on the satellite image. This operation is done in cooperation with the end users, as
they are familiar with the area and can judge if the points are located well with respect to the
class attribute.


   8.1.3 Qualitative Validation Criteria

For each product a number of qualitative parameters are discussed, mainly through end user
interaction. The project team has provided the end users with the product, and documented the
overall qualitative evaluation according to the following criteria:

        Does the product integrate easily into existing operations?

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                   contained herein is subject to the restrictions on the title page of this document.
            Does the product serve an existing and/or future operational need?
            Does the quantitative accuracy meet the user’s expectation?
            Was the service from the Project team adequate?
            Was the documentation provided by the project team adequate?

      For each of the products, these issues will be addressed by the end users. The qualitative
      assessment is in this respect considered an important indication of the user’s satisfaction
      with the service delivered by the Globwetland project team. A high thematic accuracy alone
      is not (always) a guarantee for user satisfaction. Moreover, a lower accuracy figure than
      foreseen does not necessarily make the product useless, since it might still be a large
      improvement on existing maps. A short discussion summarizing the user impressions,
      identifying key points for improving and/or modifying the products is also included here.


                8.2        Sample Validation Report

      The following is a sample validation report which has been prepared for the Land Use and
      Land Cover as well as the Long and Short Term Change Detection products.

      ALGERIA

        Products                    LULC, LULCC

        Countries                   Algeria
        Wetland Sites               Réserve Naturelle du Lac des Oiseaux,
                                    Réserve Naturelle du Lac du Béni Bélaïd


      Quantitative Validation




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Product Summary

  Site                      Réserve Naturelle du Lac des                  Réserve Naturelle du Lac du
                            Oiseaux                                       Béni Bélaïd
  LULC                      2000 – 2006                                   2006
  Date of fieldwork         July 2005                                     July 2005
  Executing agency          Hamida Salhi,                                 Hamida Salhi,
                            Ministére de l'Agriculture et                 Ministére de l'Agriculture et de
                            de la Pêche Direction                         la Pêche Direction Genéral
                            Genéral des Forêts                            des Forêts
  Number of                 68                                            65
  validation points
  Date of satellite         Quickbird:                                    Quickbird:
  image                     10-04-2006                                    23-03-2006
                            Landsat:                                      Landsat:
                            24-04-2000                                    24-04-2000
  Overall accuracy          91.91%                                        87.34%




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              contained herein is subject to the restrictions on the title page of this document.
                              Table 19.Confusion Matrix – Land Use and Land Cover - Réserve Naturelle du Lac des Oiseaux, Algeria

                                                                                    311 -                           333 -
                                                   211 - Non-         223 -         Broad                           Spars.        411 -         512 -
                              112 - Disc.          Irri. Arable       Olives        leaved           331 -          Veg.          Inland        Water
Classification                Urban Fabric         Land               Groves        forest           Beaches        Areas         Marshes       bodies       Total       User Acc.
112 - Disc. Urban Fabric             42158                  1640                0               0               0             0             0            0       43798         96.26%
211 - Non-Irri. Arable Land                  0           847526                 0         10542            8459          8103               0            0     874630          96.90%
223 - Olives Groves                          0                    0     163928              4021                0             0             0            0     167949          97.61%
311 - Broad leaved forest              657                 26874           5021          186432                 0        1576            4128            0     224688          82.97%
331 - Beaches                         1472                        0             0           2197        117789          12758           13070            0     147286          79.97%
333 - Spars. Veg. Areas               2678                 74937           7367             9402           3776       328473             8135            0     434768          75.55%
411 - Inland Marshes                         6              2372                0              40          4322          6035          845254      1547        859576          98.33%
512 - Water bodies                           0                    0             0               0               0             0          2435      9502          11937         79.60%
Total                                46971               953349         176316           212634         134346        356945           873022     11049       2764632         2541062
Prod. Acc.                          89.75%               88.90%         92.97%           87.68%         87.68%        92.02%           96.82%    86.00%                       2764632

                                                                                                                                                             Overall
                                                                                                                                                             Accuracy          91.91%




        8-6
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                                                 contained herein is subject to the restrictions on the title page of this document.
                             Table 20.Confusion Matrix for – Long and Short Term Change Detection - Réserve Naturelle du Lac du Béni Bélaïd, Algeria




Classification           212 - Perm. Irri.   311 - Broad-          3112 - Wet      331 -            333 - Spars.      411 - Inland         511 - Water   523 - Sea                       User
                         Land                leaved forest         forest          Beaches          Veg. Areas        Marshes              courses       and ocean       Total           Acc.
212 - Perm. Irri. Land              13822                    652           149               262               227              767                 53               0           15932    86.76%
311 - Broad-leaved
forest                                 696             28717               635               112             1117              1003               970                0           33250    86.37%
3112 - Wet forest                      162                   522        10062                287                 0             3544                  0               0           14577    69.03%
331 - Beaches                            0                   701              7           31364              2170               151              1740         11094              47227    66.41%
333 - Spars. Veg.
Areas                                  342              1226               117             2788             27154                  0              223                0           31850    85.26%
411 - Inland Marshes                     0                   106          1512                34                 0           24354                234                0           26240    92.81%
511 - Water courses                      0                    0               0              324               636                89            24983           631              26663    93.70%
523 - Sea and ocean                      0                    0               0              331                 0                 0                 0        85176              85507    99.61%
Total                               15022              31924            12482             35502             31304            29908              28203         96901          281246       245632
Prod. Acc.                        92.01%              89.95%           80.61%           88.34%            86.74%            81.43%             88.58%       87.90%                        281246

                                                                                                                                                                         Overall
                                                                                                                                                                         Accuracy         87.34%




                                                                                                                                                                                 8-7
                                                        Use, duplication, or disclosure of this document or any of the information
                                                     contained herein is subject to the restrictions on the title page of this document.
8.2.1 Qualitative Assessment

The end-users (Hamida Salhi) provided the Globwetland team with suitable field data to assess the
accuracy of the product. Field work was superimposed with the land use and land cover information
to define points for accuracy validation. The confusion matrices produced above is based on the
classifications, fieldwork information and end users comments on all products given to us via a
shapefile (comments.shp) with comments about the eCognition classification. Each point’s
comments apply to the polygon that contains it. The following comments have been discussed with
the National Focal Point for the Ramsar Convention. Also, feedback from the end-user has been
collected at the Kenya Tiger Training, held in Naivasha on April 2006.

                                 Table 21.Qualitative assessment by Algerian end user

CRITERIA                                 EVALUATION                                    COMMENTS

 Does the product integrate easily       The product conforms to the                   The data overlay nicely with our
 into existing operations?              specifications.                                data.
 Does the product serve an existing      Yes, the road delineations will be            Really good precision
 and/or future operational need?        use in other projects
 Was the service from the Project        The project team was:
 team adequate?                          Responsive, kept us informed
                                         Communicated / clarified many
                                        points on several occasions.
                                         The project team visited our site and
                                        conducted field work with our staff
                                        in July 2005.
 Was the documentation provided by       Field guidelines were very useful.            Being at the Kenya Training helped
 the project team adequate?             Delivered products appeared                    us a lot to understand how the
                                        professional, and included basic               products are made and should be
                                        instructions to load the data.                 analyzed.




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    8.2.2 Improvements

    The Globwetland team has been in close contact with the end users in Algeria. They have
    provided us with valuable and extensive feedback on the prototype LULC map for their site.
    As described above they have carried out an elaborate validation according to their
    experiences with the area. As a result they have provided us with a shapefile with specific
    comments for individual polygons from the original LULC map send to them. Based on these
    comments and suggestions, the Globwetland team was able to improve on certain polygons
    and classes that were mis-classified in the first Prototype LULC map. The result is an
    improved version of the LULC map (the accuracy figures above are based on the improved
    version of the LULC map).

    Some examples of specific classes that were addressed during the improving of the LULC
    map:

          We are really happy to have access to a High resolution image.

          Good delineation of marshes area.

          The urban area could have been improved.

          A little bit of confusion between irrigated land class and forest.

    Nice overall project presentation with field pictures.




8.3 Expected accuracy of products

    Each product generated during the Globwetland project was validated using accuracy
    assessment methods. The purpose was to provide thematic and geometric accuracy
    information of the Globwetland products. Besides the quantitative assessment of the product,
    a qualitative analysis was also provided, largely based on the user’s experience with the
    product in their daily operations.

    The process for accuracy assessment is as follows. The Globwetland fieldwork points were
    converted to raster maps with a pixel size of 30m. Each fieldwork point was thereby
    converted to a square area of 30x30m (9 pixels on a 10m resolution LULC map based on
    SPOT-5 imagery). The fieldwork was thus set up in a way that each point is recorded only in
    a homogeneous area with at least a surrounding area of 30 m of similar landuse in each
    direction.

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       The validation data was then cross-correlated with the LULC map, creating a confusion
       matrix between validation data and classified data. From the confusion matrix the “producers
       accuracy” (or “accuracy), “users accuracy” (or “reliability”) and the total accuracy are
       calculated. The target accuracy for the production of wetland maps should be on the order to
       80-90%, which provides a very high level of confidence for the use of the maps. If the
       minimum stated accuracy requirements are not achieved, efforts should be undertaken to
       improve the classification and meet the target. Re-processing and refining of the
       classification approach, or possibly reducing the confusion between certain classes through
       recoding could improve the accuracy results.

       For each product a number of qualitative parameters are discussed, mainly through end user
       interaction. The consortium has provided the end users with the product, and documented the
       overall qualitative evaluation according to the following criteria:

             Does the product integrate easily into existing operations?
             Does the product serve an existing and/or future operational need?
             Does the quantitative accuracy meet the user’s expectation?
             Was the service from the Project team adequate?
             Was the documentation provided by the project team adequate?


       The process for validating the change detection maps relies upon historical knowledge of the
       study area, since the use of field work for historical data is typically not available, unless
       dedicated field work is collected on an annual basis. If this is the case, it would be
       worthwhile examining the historical information collected in the field to determine how it
       could be adapted to support the change analysis and also for validation assessment.

       For the Water Cycle Regime, the fieldwork should be carried out during the satellite
       overpass. The water extent and wetness of the soil can change quickly in some areas,
       influencing the backscatter properties of the land surface and hence the image. It is therefore
       essential to do the fieldwork parallel to the satellite image overpass, to train and validate the
       water cycle regime map product. The presence or absence of water in the various parts of the
       study area can be verified using spot checks, which can later be used for validation.




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9 USER ASSESSMENT

Users who participated in the Globwetland project varied in terms of their level of knowledge and
expectations. In addition, the project team was meant to interact mainly with the national focal
points, who would then work directly with the local wetland managers. In reality, the level of
knowledge at the local level compelled the project team to work with the national focal points and
the local wetland managers directly in order to collect information over the wetland sites which were
mapped.

In Portugal, wetland maps over the sites of interest had previously been completed using the
MedWet classification methods. The MedWet maps were based on field work and existing
topographic maps, as compared to the Globwetland maps which were derived using semi-automated
classification techniques from high resolution satellite imagery. The level of detail achieved using
the high resolution imagery in combination with manual interpretation provided more accurate
estimates of certain wetland classes.

The Greek end users were in the process of updating their NATURA2000 maps using conventional
airphoto interpretation techniques when the Globwetland project provided some initial products. The
Globwetland land use and land cover products were produced using 10m SPOT-5 data and semi-
automated classification methods. In this case the end users were very interested to adopt the image
processing methods (segmentation and image classification techniques used by the team), and
worked closely with the project team to derive a technique suitable for the production of the
NATURA2000 maps.

The Globwetland project was focused on ESA’s TIGER Programme, which focuses on the use of
remote sensing to improve water management in Africa. Globwetland was in fact a demonstrator
project for the TIGER programme. Many participating countries in Africa had limited resources to
support the project team both in terms of the existing maps and data as well as the local knowledge
for mapping the wetlands. One such user is the Lake Chad Basin Commission (LCBC) who
represented 4 countries through its national office in Chad. In the case of the LCBC sites (in Niger,


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                     contained herein is subject to the restrictions on the title page of this document.
Chad, Cameroon and Nigeria), the maps produced were the first of their kind and were immediately
useful to characterize the wetlands. The LCBC also participated in training sessions which were
offered to help improve capacity for the operational use of remote sensing for future wetland
mapping and monitoring activities.

        Specific examples of how the Globwetland products supported conservation activities for
different end users are presented below.


9.1 Greece – Nature Habitat mapping updating

Computer-assisted photo-interpretation of available datasets was used to update the existing habitats
map (Ministry of Environment, 2001). Enhanced images and location of field surveys were displayed
together with the existing Globwetland products in order to identify and accurately re-map the
extents of habitat types. The basic assumption was that over the last years, habitats did not change in
type, but only in extent and shape of polygons. This implies that natural and human processes acting
on the ecosystem resulted in smooth changes of transitional zones between neighbouring habitats,
and not in drastic changes such as appearance of new habitats or extinction of others. The land use /
land cover classification of 2004 displayed a very good result, especially in the wetland area. A
method based on the Globwetland LULC products was applied to update the existing habitat maps,
fulfilling the requirements under Natura 2000 as well as potentially the Ramsar Reporting
requirements.

                             Table 22. Support of conservation activities through Globwetland

                  Wetland       Product        Value of products and Conservation Actions
                  Site
                  Lake          Land Use       * regional coverage of large inaccessible sites
                  Bogoria       Land Cover     * production of Land Cover products which were not
                  (Kenya)       and Change     previously available
                                Detection      * detection of large upstream deforestation practices to be
                                               evaluated by the Kenya Widlife Service (KWS) in their
                                               planning process
                  Axios         Land Use       * the products derived through Globwetland helped to
                  (Greece)      and Land       derive a method for operational updating of habitat maps
                                Cover          in support of the Natura 2000 and Ramsar reporting
                                               requirements
                  La            Wetland        * a method using optical and radar data was used to map
                  Brenne        Delineation    the extents and nature of vegetation inside the shallow
                  (France)                     marshes in La Brenne
                                               * this method will support the national wetland inventory
                                               approach France will undertake in the coming years




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                     contained herein is subject to the restrictions on the title page of this document.
9.2 Portugal – finely detailed wetland maps

Medwet based products have been produced in Portugal based on the analysis of local field parcels
and detailed field work to stratify and classify the various Portuguese wetland sites. Under the
Globwetland project, three sites were mapped using detailed (0.60m spatial resolution) satellite
imagery. The Portuguese “Centro de Zonas Humidas” has compared the Globwetland products to
the Medwet derived products, and found that the increased level of detail which is possible to
achieve will provide them with much needed information on the specific nature of vegetation
composition in specific areas.


9.3 Lake Chad – Assessment of water quality parameters

The Lake Chad Basin Commission provided an assessment of the products delivered through the
Globwetland extension during a dedicated capacity building session held in the Netherlands in
October 2008. The following are some of the comments provided in this assessment based on the
different products delivered.




                    Figure 14. Lake Surface Temperature – derived from AATSR (10-10-2007)




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                    contained herein is subject to the restrictions on the title page of this document.
 Validation comments
   • Temperature in southern part of lake higher due to shallow waters, correctly measured by
       AATSR
   • Absolute temperature range (26-32) in accordance with real measurements
   • AATSR observed trend August to December is correct (cold  warm)
   • Current temperature monitoring done manually by extrapolation of single measurements
       (fieldwork)
   • AATSR observations are a great way to complement current manual measurements




                    Figure 15. Chlorophyll Concentration – Derived from MERIS (05-11-2007)


 Validation comments
   • Chlorophyll concentration is closely related to the re-filling of lake early November  fresh
       water is coming in from the south through the Chari river, reducing the Chlorophyll levels in
       the lake from south to north. This is nicely demonstrated by the MERIS time-series.




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                     Figure 16. Total Suspended Matter/Yellow Matter - MERIS (05-11-2007)


Validation Comments
   • The re-filling of the lake by the Chari river from the south, brings in turbulent waters,
       resulting in an increase in suspended and yellow matter, which is perfectly demonstrated by
       the MERIS measurements




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                    contained herein is subject to the restrictions on the title page of this document.
      •




                    Figure 17. (Z90) Signal Depth– MERIS (17-10-2007) – related to “Secchi Depth”

      Validation Comments
      • Towards November the fishing activities on the lake increase which might be related to the
         Increased turbidity and hence overall lower signal depth.
      • The lake is deeper in the middle, which could be a reason for the higher value for the signal
         depth here.




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10USER TRAINING AND CAPACITY BUILDING

Capacity building and awareness raising efforts required to achieve adoption of earth observation
varied across the project’s end users. Very detailed technical training sessions were held in Europe
and Canada, while more practical sessions were organized in Africa. Capacity building and
awareness raising must remain key foci for the adoption of earth observation in wetland conservation
activities to realize its potential.


10.1 Field work carried out with end users

In order to collect the best possible information for the production of the Globwetland products, the
Project team actively seeked to join the National Focal Points as well as the Wetland Managers in
their execution of local data collection efforts. The project team visited 16 countries and 38 sites,
with several sites having been visited on more than one occasion.




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                     contained herein is subject to the restrictions on the title page of this document.
                   Table 23. Wetland sites visited by Globwetland Team


       Country            Wetland name
       Algeria            Réserve Naturelle du Lac de Béni Bélaïd
       Algeria            La Réserve Naturelle du Lac des oiseauok
       Austria            Tamsweger Moore
       Cameroon           Waza Logone
       Chad               Lake Lere
       Finland            Keonsuo Mire
       Finland            Liminganlahti Bay Area
       Finland            Siikalahti bay
       Finland            Vanhankaupunginlahti and Laajalahti Bays
       France             La Brenne
       France             Camargue
       France             Caps et Marais d'Opale
       France             Forêt d'Orient
       France             Littoral audois
       France             Marais du Cotentin et du Bessin, Baie des Veys
       France             Massif central
       Greece             Amvrakikos gulf
       Greece             Aokios, Loudias, Aliakmon Delta
       Greece             Kotychi lagoons
       Greece             Artificial Lake Kerkini
       Italy              Lake Sabaudia (part of Laguna Pontine)
       Italy              Laguna di Venezia: Valle Averto
       Kenya              Lake Bogoria
       Kenya              Lake Naivasha
       Kenya              Lake Nakuru
       Niger              Lac Tchad
       Nigeria            Sambisa Game Reserve
       Portugal           Estuário do Tejo
       Portugal           Lagoa da Albufeira
       Portugal           Lagoa de St. André et Lagoa de Sancha
       Russia             Dubna Lowland Wetlands
       Sénégal            Bassin du Ndiaël
       South Africa       Maloti-Drakensberg Transfrontier Park
       Spain              Delta del Ebro
       Spain              Lagunas de Villafáfila
       Spain              Las Tablas de Daimiel
       Spain              Mar Menor
       Spain              Ria de Mundaka-Guernika




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       contained herein is subject to the restrictions on the title page of this document.
Figure 18. Vexcel Canada staff collect field data together with the Algerian National and Local Authorities.




                 Figure 19. Field data collection in Finland with the National Focal Point




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                contained herein is subject to the restrictions on the title page of this document.
                      Figure 20. Data Collection in Valle Averto, Italy.




                             Figure 21. Data Collection in Greece




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       contained herein is subject to the restrictions on the title page of this document.
       Figure 21. Data Collection in Caps et Marais d’Opale, France.




            Figure 22. Field data collection – Bogoria, Kenya




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contained herein is subject to the restrictions on the title page of this document.
                  Figure 23. Data collection planning - Lisbon, Portugal




                          Figure 24. Field data collection, Lesotho




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       contained herein is subject to the restrictions on the title page of this document.
                            Figure 25. Field data collection, Lagunas de Villafafilla, Spain




10.2 African Capacity Building

In support of all African countries participating in the project, GlobWetland hosted a five-day
training seminar, developed jointly by Wetlands International and Vexcel, from 24-28 April in Lake
Naivasha, Kenya, for users to understand and integrate the EO-derived products and services into
their work.

Participants from Algeria, Egypt, Lake Chad, Senegal, South Africa and Kenya attended the training
which featured lectures, ‘hands-on’ work with sample products and field work methods carried out in
an area adjacent to the training facility.

The seminar also sought to help African users critically assess their requirements, suggest new
products, identify inefficiencies and take over the process. All the training materials were
consolidated on a CD and given to all African end users at the close of the session. In addition, all
attendees received an ESA TIGER initiative training kit. GlobWetland makes up an integral part of
the TIGER initiative, which focuses on the use of EO data for improving water resource management
in Africa.




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                     contained herein is subject to the restrictions on the title page of this document.
   Figure 26. Participants from South Africa, Netherlands, Egypt, Chad, Senegal, Algeria, Kenya and Canada during the
          April 24-28, 2006 Globwetland workshop held at the Naivasha Training Institute in Naivasha, Kenya.




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                       contained herein is subject to the restrictions on the title page of this document.
Figure 27. African Training Kit provided by ESA to African Seminar participants




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  contained herein is subject to the restrictions on the title page of this document.
10.3 European Capacity Building

The Globwetland Final Meeting and Symposium took in Frascati (Italy) October 19-20, 2006. ESA
requsted that the project team provide a workshop focused on end users who wish to improve their
understanding of EO as a tool for wetland management in addition to some external stakeholders
such as members of the Scientific and Technical Review Panel (STRP). A 3 day workshop was thus
held mainly for the participation of the European end users of the Globwetland project. The
workshop featured lecture style presentation of materials, focusing on practical user cases from the
Globwetland project. In addition, hands on exercises were used to provide participants an
opportunity to manipulate EO data and discuss the practical aspects of implementing EO for wetland
management. This 3 day workshop took place from October 16-18, 2006 at the ESRIN training
facility in Frascati, Italy.

The workshop agenda is included below:

An outline of the workshop is provided below:

Day 1: October 16, 2006
(THEME: Data Collection)
09h00 – 09h30:       Introduction to workshop
09h30 – 10h30:       Presentation of Globwetland project, description of products
10h30 – 12h00:       Overview of EO data availability, characteristics of data, and modalities for
                     obtaining EO data
12h00 – 13h30:       Lunch
13h30 – 14h30:       Technical Review: Data Collection methods (Field, EO, Supporting) and
                     integration Case Study:
14h30 – 15h00:       Presentation of Exercise 1: Data Collection Planning
15h30 – 16h30:       Case Study 1: Creating a suitable Data Collection Plan (Emphasis on multi-
                     sensor approach, integration of field data, and timing for collection
                     Output: Documented Data Collection Plan per participant
16h30 – 17h00:       Plenary discussion on the theme of the day



Day 2: October 17, 2006
(THEME: Image Analysis and Interpretation)
09h00 – 10h30:       Introduction to image processing principles as they are applied to Globwetland
                     products (Pre-processing, analysis, finishing, as applied for core products:
                     LULC, CD, WCR)
10h30 – 11h45        Presentation of Exercise 2: Image pre-processing
11h45 – 12h30:       Case Study 2: Ingestion of data, pre-processing steps and integration


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                     contained herein is subject to the restrictions on the title page of this document.
                     Output: Data prepared for analysis in further work
12h30 – 14h00:       Lunch
14h00 – 15h00:       Case Study 3: Creation of LULC map
                     Output: LULC map over area of interest
15h00 – 15h30:       Presentation on key problem areas in generating LULC maps focusing on
                     wetland classes and capabilities/limitations of sensors and processing
                     algorithms for identification of the features of interest : importance of field
                     knowledge in interpretation
15h30 – 16h30:       Case Study 4: Creation of Change Detection map, Water Cycle Regime Map
                     Output: Detected change map, WCR map
16h30 – 17h00:       Plenary discussion on the theme of the day


Day 3: October 18, 2006
(THEME: Integration into wetland management practices)
09h00 – 09h30:       Presentation of wetland management challenge and the specific role of EO to
                     identify key parameters (to be presented by end user and/or Wetlands
                     International)
09h30 – 10h00:       Presentation of Case Study 5: Spatial analysis of EO derived information in
                     addressing wetland management issues
10h00 – 12h00:       Case Study 5: Integration of EO derived products with spatial information to
                     support wetland management approach
                     Output: Thematic maps showing impacts on wetlands
12h00 – 13h30:       Lunch
13h30 – 14h30:       Review of Specific Components developed in Globwetland, sensors to be used
                     in development of these products
14h30 – 15h00:       Presentation of Exercise 1: Generation of specific product
15h30 – 16h30:       Case Study 6: Creating biophysical parameters from CHRIS PROBA and
                     other EO data sources
                     Output: Chlorophyll, sediment, water temperature, etc products
16h30 – 17h00:       Plenary discussion on the theme of the day




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                    contained herein is subject to the restrictions on the title page of this document.
   Figure 28. Participants from Canada, The Netherlands, Italy, Portugal, Greece, Finland, France, and Spain during the
                       Oct 16-18, 2006 Globwetland workshop held at ESA ESRIN in Frascati, Italy.



10.4 User Handbook

In addition to the many hands on training sessions which were held both formally and through field
work/ data collection, a dedicated user manual was created to provide the international community
with a summary of the Globwetland project findings and best practices for successful
implementation. The “User Handbook” was produced with the intent for distribution to interested
parties, to be used as a reference document. It was professionally printed in full colour, and 200
copies were delivered to ESA, which has since reproduced it for broader distribution.

The User Handbook contains 80 pages, includes many illustrations and helpful guidance on the use
of Earth Observation to generate Globwetland products




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                        contained herein is subject to the restrictions on the title page of this document.
Figure 29. Globwetland User Handbook – professionally printed for broad distribution




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     contained herein is subject to the restrictions on the title page of this document.
11DISSEMINATION ACTIVITIES


11.1 Globwetland Website

The Globwetland website was established in 2004 and has continually been updated during the
execution of the project phases. Most information that has been produced through the execution of
the project can be accessed through the website, including:

      Project Participant information

      News, Links and Documents

      Information products (description of the various products)

      Contact information

      Globwetland Video (clips can be downloaded from the website)

      Fieldwork Pictures

      Globwetland Symposium Proceedings

      Web Mapping application (permits viewing of all GW derived products)




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                    contained herein is subject to the restrictions on the title page of this document.
                         Figure 30. Globwetland Website – Present on the web since 2004

The following are the summary statistics for the Globwetland site. The peak of activity occurred in
2006, when most of the products were completed in preparation for the Globwetland Sympoisum
which was held in October 2006.

                   Table 24. Summary statistics on www.globwetland.org web visits since 2004.

                                                      Page
                                     Year            Loads           Unique Visitors
                                     2004             1,766                506
                                     2005             9,779               2,530
                                     2006            13,237               3,798
                                     2007             9,123               2,566
                                     2008             5,537               2,005
                                    TOTAL            39,442              11,405




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                     contained herein is subject to the restrictions on the title page of this document.
11.2 Globwetland Symposium

A dedicated symposium was held at ESA ESRIN in Frascati, Italy Oct 19-20, 2006. The stated
purpose of the Globwetland Symposium was to bring together the Ramsar community and the
Remote Sensing Community to:

      Define the current state of the art on the use of Earth Observation for wetland management;
      Identify the key scientific, technical and policy-relevant challenges for the future;
      Explore future research, scientific and policy-relevant activities;
ESA and the Ramsar Secretariat formed a scientific committee to review and accept papers for
technical and poster sessions. In total, seven sessions were presented, which included 33 oral
presentations and 45 posters. Over 100 participants from Europe, Asia, North and South America as
well as Africa were present at the Symposium. The scientific committee was comprised of Dave
Pritchard, George Zalidis, Heather MacKey, Helmuth Grassl, Max Finlayson, Nick Davidson, and
Teresita Borges.




                                Figure 31. Globwetland Symposium call for abstracts.




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The following are the seven thematic sessions that formed the technical program of the Symposium.

      GlobWetland Final Presentation;
      Wetlands mapping: vegetation, land cover, land use and its dynamics;
      Inundated vegetation and floods;
      Wetland Hydrology and Modeling;
      Water quality and biophysical parameters;
      Wise use: inventorying, assessment and monitoring;
      Global and Regional mapping;
The proceedings from the Globwetland symposium can be accessed through the Globwetland
website at the following address:

http://www.globwetland.org/symposium06/symposium.html

In addition to the proceedings from the Symposium, a peer reviewed article which provides an
overview of the findings from the Globwetland project was published in a special issue of the
Journal of Environmental Management - dedicated to the Globwetland Symposium. This issue can
be accessed at http://www.sciencedirect.com.




            Figure 32. Lead paper published in special issue of the Journal of Environmental Management




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Figure 33. Globwetland Symposium Chair, Dr. Nick Davidson addresses questions during a Plenary Session.




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                contained herein is subject to the restrictions on the title page of this document.
11.3 Globwetland Video

The Globwetland Project team produced a professional promotional video which summarizes the
Globwetland project, and highlights some of the products as well as end user validation. The main
topics addressed in the video include:

      Introduction and background to the Globwetland Project

      Focus on Globwetland Products

      Highlights from project (Field work, Capacity Building, COP-9)

      Key benefits – focusing on user assessment

      Future use of Earth Observation for wetland management

The video was produced on DVD and featured in the opening of the Globwetland Symposium. It was
also disseminated to all Globwetland Symposium participants as part of the media kit which was
prepared. ESA has reproduced the video in its DVD form and used it to promote the Globwetland
project beyond the Symposium, in addition clips from the video are accessible through
www.globwetland.org .




                    Figure 34. Globwetland Video: Earth Observation for Wetland Management




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                    contained herein is subject to the restrictions on the title page of this document.
11.4 Globwetland per country / site reports

The Project team and ESA created a final report which provides summary information for every
country and wetland site which participated in the project. It has been professionally prepared in
preparation for the COP-10. Some examples from the reports are shown below.




                                           Figure 35. Globwetland Final Report




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                     contained herein is subject to the restrictions on the title page of this document.
                     Figure 36. National focus in Final Report – Algeria




                 Figure 37. Site specific focus in final Globwetland Report




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       contained herein is subject to the restrictions on the title page of this document.
11.5 COP-9

The Globwetland project team and ESA participated in the COP 9 meeting in Kampala, Uganda
from Nov 8-15, 2005. ESA hosted a booth at the meeting and distributed promotional material. In
addition ESA organized a side event which featured presentations by Kenyan and French end users
on the use of the prototype products which were delivered in the early stages of the project.




            Figure 38. COP-09 Side Event agenda and speakers, Nov 11, 2008 COP-09 Kampala, Uganda




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                    contained herein is subject to the restrictions on the title page of this document.
        Figure 39: French national Focal Point Marie Claude Ximenes presents France’s assessment of the Globwetland
                                                           products




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                         contained herein is subject to the restrictions on the title page of this document.
11.6 COP-10

ESA participated in the COP-10 meeting in Changwon, South Korea and organized a Side Event,
similar to COP-9.




             Figure 40. Side Event agenda and speakers, Nov 04, 2008 COP-10 Changwon, South Korea




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                    contained herein is subject to the restrictions on the title page of this document.
12CONCLUSION AND FUTURE PERSPECTIVES

One of the main strengths of the GlobWetland exercise was the strong participation of national
authorities and wetland managers in the project, supporting the definition of the system and fina
validation of the results. Such participation represented an asset as well as a challenge. In fact,
participant wetland managers represented a large range of technical, societal and economic
backgrounds, i.e., different countries, different previous knowledge on the use of EO and GIS
technologies and different level of resources for management purposes. In this context, a major
objective for the project was to define a suitable data set of geo-information that could match the
requirements of such a large range of end-users. In addition, the methodological approaches were
selected so that the processing chains were based on consolidated techniques with sound scientific
basis that could be portable and applicable worldwide.

The ultimate target of the final information system was to provide wetland managers and national
authorities with an efficient support tool for management and conservation activities. Obviously, the
type of uses of the GlobWetland information system varied significantly depending on the typology
and background of the participant organisation, the national legislation and their local priorities as
well as existing conditions (e.g., existing data sets).

For instance, in developed countries where habitat maps are usually already available for all
protected areas (e.g., in Greece), the GlobWetland system was used by national authorities to update
the existing information at local scales (1:25,000) fulfilling the requirements of the national
legislation (e.g., Natura 2000 in Europe) and the Ramsar Convention. This was carried out by using
enhanced images and data derived from field-surveys together with the GlobWetland geo-
information products (in particular, the LULC maps). The basic assumption behind this approach
was that over the last years, habitats did not change in type, but only in extent and shape of polygons.
This implies that natural and human processes acting on the ecosystem resulted in smooth changes of
transitional zones between neighbouring habitats, and not in drastic changes such as appearance of
new habitats or extinction of others. It is worth to mention a similar example in France, where the
derived products supported the definition of the national wetland inventory strategy.

The benefits of the project, and hence of the impact of EO technology in the wetland management
practices, were significantly higher in developing countries (e.g., Kenya, Chad and Senegal), where
in many cases, the GlobWetland data set represented the first and only updated digital geo-referenced
maps available over the areas of interest. For instance, the system and the derived products were

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installed at the Lake Chad Basin Commission (LCBC) GIS Laboratory in N’djamena and represent
now a basic data set for the management and conservation activities in the lake. In order to
consolidate the integration of the developed system into the LCBC management practices, and
ensuring its sustainability, dedicated training and capacity building activities were carried out. It is
worth noting the significant benefits derived from the land cover analysis carried out over the
African sites (especially in Kenya). The products covering a temporal range of 10 years showed the
significant impacts of agriculture around the protected areas of Lake Naivasha and Bogoria. In these
areas, the loss of forest and the increase of agricultural practices around the lakes represent
significant threats to the ecosystems and natural cycles of the wetlands due to erosion and increasing
water consumption. The products allowed for first time a synoptic estimation of the problem, the
identification of the affected areas and provided authorities with the basic data set to develop
contingency plans.

Finally, it is worth noting the intrinsic value of the whole GlobWetland data set, which may provide
the conservation and scientific community with a support to review and assess the status of wetlands
in many areas of the world, especially in Europe and Africa. In order to allow the scientific
community to consult the data, a dedicated web-GIS tool was developed. The tool allows the
visualization of all the results over the 50 sites under analyses and provides direct access to the
Ramsar database. The web-GIS tool can be the basis for a potential development of a worldwide
database of geo-information for wetlands in support of the Ramsar Convention.

The GlobWetland project demonstrated the capacity and limits of EO technology to support national
and local conservation authorities worldwide to undertake inventorying, monitoring and assessing of
wetlands and its ecological character. Clear benefits have been demonstrated, especially in
developing countries, and several users have already adopted this technology within their
management practices as a result of the project. However, the significant consultation process with
wetland managers, national authorities and scientists carried out during the project lifetime has
pointed out a significant gap between both the EO and the wetland communities. Exercises such as
the GlobWetland project have contributed to bridge that gap. However, more efforts are still required
in order to increase communication between the two communities in preparation of the next
generation of satellites. In 2012 the European Space Agency will launch the first of a new set of EO
missions, the sentinels, in the context of the European Global Monitoring for Environmental and
Security (GMES) program. This new generation of EO satellites will provide novel and advanced
capabilities to monitor the environment worldwide on a regular basis at different scales, providing a
unique capacity to monitor, and hence respond to, the environmental challenges affecting our planet.
The success of such new technology within the Ramsar community will depend on the capability of
both the space and the conservation sectors to work together in order to develop jointly cost-effective
applications that respond directly to the information needs and requirements of national and local
conservation authorities. ESA will continue supporting this effort with dedicated scientific and
application activities.




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13PROJECT TEAM

The GlobWetland project was funded through the European Space Agencys Data User Element
(DUE). The project team was composed of a Canadian lead consortium.




                              Figure 41. Globwetland project team members in 2004




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European Space Agency

The European Space Agency funded the GlobWetland project under the Data User Element. ESA
was instrumental in building the GlobWetland User Group, and obtaining commitment from
members to actively participate, given the potential benefits of the GlobWetland project to help them
implement their operational mandates. The Data User Element (DUE) is a programmatic component
of the Earth Observation Envelope Programme (EOEP), an optional programme of the European
Space Agency, currently subscribed by 14 ESA Member States. Likewise its predecessor the Data
User Programme (DUP), the DUE aims at bridging the gap that exists between scientific research
and pilot projects, and the sustainable provision of Earth Observation products at information level
that fully respond to the operational needs of the User Communities.

MDA Geospatial (formerly Vexcel Canada)

Located in Ottawa, Canada, Vexcel Canada was the Prime Contractor for the GlobWetland Project.
Vexcel has had previous experience in evaluating the feasibility of Earth Observation technology for
Wetland Monitoring, in its pre-cursor project entitled Treaty Enforcement Services using Earth
Observation for Wetlands, also funded through ESA. Since its incorporation in 1981, Vexcel Canada
has established itself as a world leader in the areas of signal processing, radar remote sensing, and
related software development. Today Vexcel develops and markets a variety of COTS software
packages for radar data processing. In addition, the company’s service department EOServ provides
value-added support to clients that choose not to invest in the software itself. Products available
through EOServ include digital elevation models, land subsidence maps, wetlands mapping and a
variety of other Remote Sensing derived information products.

Terrasphere (formerly Synoptics)

Synoptics, located in Wageningen, Netherlands, a sub-contractor to Vexcel Canada, worked closely
to define the information products to be delivered to the GlobWetland User Group. They assisted
with data analysis and processing during the latter stages of the project.

Wetlands International

Wetlands International, located in Wageningen, Netherlands, a sub-contractor to Vexcel Canada,
worked closely with the GlobWetland User Group to ensure the requirements are well understood
and documented with the Project team, and that the transfer of Earth Observation based methods for
monitoring wetlands was well understood by the User Group.

Remote Sensing Solutions GmbH

RSS GmbH, located in Munich, Germany, a sub-contractor to Vexcel Canada, worked primarily on
ensuing appropriate information is derived from Earth Observation for monitoring peatland fires in
Russia.


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                        Use, duplication, or disclosure of this document or any of the information
                     contained herein is subject to the restrictions on the title page of this document.
DISTRIBUTION LIST


          NAME                              COPY                               NAME                      COPY

Diego Prieto Fernandez, ESA                    X              Ramsar, STRP                                X

Kevin Jones, VCI                               X              Doug Taylor, Wetlands Int.                  X

Paul van der Voet, MS NL                       X              Florian Siegert, RSS GmbH                   X

Marc Paganini, ESA                             X              Master Documents: MDA GSI                   X




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                      Use, duplication, or disclosure of this document or any of the information
                   contained herein is subject to the restrictions on the title page of this document.

								
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