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Chapter 3 Land Use Data

3 LAND USE DATA

H. George and F.O. Nachtergaele

Abstract This paper reviews the present status of land use data of global coverage, with particular relevance to their availability and content. The paper highlights that global land use information is of significant value for a range of regional and global studies, e.g. land degradation, desertification, food security, climatic change (greenhouse gas inventory, carbon sequestration, climate modeling, etc.). However, there is a paucity of global data sets that contain land use information. Moreover, the quality of available information is quite variable and often presents a confused mixture of land use and land cover categories. This is partly due to the limitations in the methods used for deriving and inventorying land use classes. The very broadly defined land use categories in legends of available maps (applications of land use classification systems) are inadequate for studies that focus on the collection of biophysical and economic aspects of land use and on context related socio-economic data. Several efforts are being made to improve the current situation at the regional level. Efforts are also being made at the national level by several countries to improve the quality, availability and applicability of locally produced land use data. This is largely in response to the growing need for countries to make land use decisions that are not only consistent with their international commitments but that are also responsive to the widening scope of their local decision-making needs. Such needs usually aim at better management of natural resources and the environment whilst taking a wide range of stakeholder interests into account. National efforts are being complemented by FAO in a recently launched initiative to assist developing countries to enhance local collection and processing of agricultural land use data. The task of building up 'homogenized' regional to global land use databases from the data collected by different countries faces technical hurdles that are linked to several factors, including, inconsistent definitions of land use, lack of harmonization and differences in the methods used for inventorying. Among the likely first steps needed for overcoming these

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hurdles is (a) effective coordination among potential data users of global land use data to define and harmonize their data needs, and (b) the development of an attribute based land use correlation system through which internally consistent national land use classification systems could be linked and queried. 1. WHAT IS LAND USE? The term "land use" (LU) is often used improperly to describe some regional to global datasets1 which contain a mixture of both "land use" and "land cover" information. "Land use" is in reality quite distinct from "land cover". de Bie (2000) defines LU as "A series of operations on land, carried out by humans, with the intention to obtain products and/or benefits2 through using land resources3". In contrast, land cover is defined as "the observed bio-physical cover on the Earth's surface" (FAO, 2000)4. According to various sources quoted in Briassoulis (2000) 5, land cover may be described as the physical, chemical, ecological or biological categorization of the terrestrial surface, e.g. grassland, forest, or concrete, while land use refers to the human purposes that are associated with that cover, e.g. raising cattle, recreation, or urban living. A specific LU often corresponds to a single land cover, e.g. pastoralism to unimproved grassland. However, a given land cover class may support several distinct land uses (e.g. a forest may be used simultaneously for timbering, slash-and-burn agriculture, hunting/gathering, fuelwood collection, recreation, wildlife preserve, and watershed and soil protection; such a land use typically has multiple-purposes). Various land uses (e.g. as carried out within a given farm system) may involve the maintenance of several distinct covers (e.g. cultivated land (fields), woodlots, grassland, and built-up areas). A significant change in LU (e.g. a land use ‘conversion’) is likely to cause a change in land cover, but land cover may change even if the LU remains unaltered6. The relationship between land cover and LU (i.e. various distinct land uses occurring within a given land cover unit) is therefore complex. A pilot study in Lebanon revealed that the derivation of LU from land cover at a scale of 1:50,000 required substantial
1

Selected eco-system terms (denoting climate, soil/terrain, cover, and land use aspects) are frequently mixed to derive labels that implicitly suggest certain land use(s) (e.g. tropical rain forest or alpine meadow). 2 Products are material or tangible outputs (e.g. grains) whereas benefits are immaterial or intangible ones (e.g. soil protection by cover crops). An operation is a distinct and intended management action by humans to modify land aspects (and having one or more impacts on the soil/terrain, flora/fauna, infrastructure or air). Examples of operation classifiers include cropping pattern, cultivation factor (the number of years that a plot is under cultivation expressed as a percentage of the total number of years in the cultivation/ non-cultivation cycle), and inputs. 3 "Land resources" concern all aspects of land that enable, support, constrain or influence present as well as potential land use (de Bie, 2000). “Land” refers to a delineable area of the earth's terrestrial surface, encompassing all attributes of the biosphere immediately above or below this surface, including those of the near-surface climate, the soil and terrain forms, the surface of hydrology (including shallow lakes, rivers, marshes and swamps), the near surface sedimentary layers and associated groundwater and geo-hydrological reserve, the plant and animal populations, the human settlements pattern and physical results of past and present human activities (terracing, water storage or drainage structure, roads, buildings, etc.,) (FAO, 1999). 4 ftp://ftp.fao.org/agl/agll/docs/landglos.pdf 5 http://www.rri.wvu.edu/WebBook/Briassoulis/contents.htm 6 http://www.rri.wvu.edu/WebBook/Briassoulis/contents.htm

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Chapter 3 Land Use Data

ancillary data and field information. Moreover, the decision rules developed for this derivation were not applicable elsewhere7. Thus, proceeding on the basis that LU is concerned primarily with the purpose(s) of operations (to obtain desired products and benefits) that are carried out by humans on land and with the specifics of the operations themselves, there is a wide range of parameters that could potentially be used to "describe" the LU operations at a given location. Taking agricultural land use as an example, a "full" description could include answers to questions such as Where? (i.e. the geographic location and extent of the spatial unit under consideration); What? (the associated bio-physical setting, including land cover), Why or For What Purpose? (the specific benefits, i.e. products and services, that are sought), and How? (the required technological inputs/ materials such as fertiliser, irrigation, labour, and the sequence of carried out operations like planting, weeding, etc) 8 . In addition, one could conceivably also include in such a description additional "contextual" information on socioeconomic conditions (e.g. land tenure, labour costs, market conditions, etc.) which are often important drivers in determining LU. It has been suggested, however, that a more limited set of parameters should be used for classifying LU. de Bie (2000, p.69-73) proposes that an hierarchic, parametric approach based on refining "land use purpose(s)" with combinations of standardized descriptions of land management actions (which he terms "operations sequence" classifiers) could be used to adequately describe multi-purpose land use classes. He specifically cautions against the use of context information for LU classification purposes. A proposal by Young (1998) for an international system of land use classification also uses "function" (i.e. purpose) as a primary means of differentiating land use classes (See Annex I)9. Subsequent discussion / review of global land use data sets will therefore be limited to those that contain information related predominantly to the "purposes" of the operations carried out (i.e. the "Why/For What purpose?"). Furthermore, this presentation will be restricted to datasets that are currently of global coverage. 2. USER NEEDS FOR LAND USE INFORMATION LU information is needed for a wide variety of decision-making purposes. LU directly affects land and triggers processes such as land degradation, desertification and loss of biodiversity. Knowledge of LU is therefore needed in formulating measures to combat these processes. Current land use is an important criterion for better targeting of areas for implementing projects by local or international entities. Knowledge of regions where major crops are grown is of importance for early warning for food security10. LU information is needed for various
7 8

http://www.fao.org/WAICENT/FAOINFO/SUSTDEV/EIdirect/EIre0058.htm Stomph et al (1992) propose five attributes for describing the biophysical components of land use practices - land, the biophysical attributes of inputs, the interventions of humans relative to pests & diseases, interventions relative to soil properties, and material exports. 9 The Young (1998) proposal does not specifically cater for multi-purpose LU-classes. 10 See GIEWS: http://www.fao.org/waicent/faoinfo/economic/giews/english/giewse.htm

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perspective studies (models) related to study food security at national, regional and global levels. LU information is also needed for policy formulation and planning, agrarian reform and rural development -- especially in situations where there is a scarcity of land and there is competition for different forms of potential LUs. Changes in land use are also of importance factor in estimating long term climate change and in carbon sequestration studies. A wide range of decision-making requirements implies the need for users to gain access to an equally wide range of LU data that fit their needs. Generally, user needs may be differentiated using criteria such as spatial extent (e.g. local to global), temporal frequency (e.g. interannually to every 10 years), quantity (volume of data) and analytical complexity (range of parameters to be measured, quality). 3. APPROACHES FOR COLLECTING LAND USE DATA While the range of LU data that would be of interest to various end-users is large, the available means for collecting / capturing such data remain rather limited. Currently LU data are collected principally through (1) observation and direct interviews (i.e. specific surveys) (2) through inference from land cover or (3) indirectly through use of ancillary data (e.g. population census). There are shortcomings and advantages associated with each of these options. Direct observation and/or interviews with the users in the field represent the most accurate means of collecting detailed information on land use11. However, this approach when used for exhaustive enumeration (i.e. survey of all uses over all geographic areas) suffers the obvious drawback of being rather costly when the region of interest is large. For large regions, survey costs can be reduced considerably through the use of statistical-based sampling approaches, such as multiple frame or grid surveys (FAO, 1998). The potential of replacing air-photos with increasingly low-cost high-resolution satellite imagery (e.g. EROS) will make the adoption of area-frame sampling methodologies even more affordable to many developing countries. The derivation of LU from land cover can be problematic (see section on "What is land use?"). Nonetheless, what makes this approach appealing is that the required land cover information can readily be mapped with the help of satellite remote sensing imagery. Remote sensing satellites offer multiple advantages of (1) a high frequency of information acquisition, (2) synoptic (global) coverage, (3) a relatively long time series of data acquisition which facilitates retrospective monitoring studies, and (4) relatively low cost per square kilometre compared to other means of acquiring data over large regions. These advantages have made satellite remote sensing the preferred choice (for some applications it is the only feasible choice) for the timely production of geospatial datasets at regional to global scales that contain land use information. In general, such datasets will be particularly useful for
11

A database software tool for capturing a wide range of detailed agricultural land-use information during field surveys was developed by FAO/ITC.See http://www.itc.nl/education/larus/landuse/landuse.html

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responding to questions such as "Where are the fields?" and "What is cultivated?", but not to questions related to “How are field operations carried?” and "What are their impacts?". Indications of land use may also be inferred indirectly from certain geo-referenced data sets, such as statistical data on crops, irrigation, use of fertilizers/pesticides, animal populations, labour, etc. Many such datasets are regularly collected by countries and are readily available at little to no cost to end-users. Such datasets are, however, often poorly compatible, and they pose limits to which one may reliably study land use. Such "indirect" datasets are not a focus of this paper. 4. GLOBAL LAND USE DATASETS A literature search for land use data sets of global coverage yielded few results. The general paucity of global land use data has previously been noted by others (e.g. Nachtergaele, 2000; Young, 1998; Wood et al., 2000). Brief descriptions of each major data set encountered, grouped by data producer, are presented below. Food and Agricultural Organisation (FAO) Many countries carry out annual and periodic national agricultural surveys (including decennial agricultural census) 12 . FAO, as part of its mandate, collects agricultural data, including land use data, from all countries13. The data are collected by a variety of means, e.g. (a) through annual questionnaires (b) through electronic data transfers (c) national/ international publications (d) reports made by FAO statisticians during country visits and/or reports by the local FAO representatives. These data are subsequently disseminated to the public through various publications and on-line via the FAOSTAT database 14 . FAO acknowledges several shortcomings of the data it receives from many developing countries which ultimately bear responsibility for the quality of the data they provide. Notable shortcomings include: incomplete or limited range of variables, incomplete coverage of all regions, questionable reliability and inconsistent definitions of land use terms. Data in FAOSTAT are aggregated at the country level. Specific global data sets related to land use include those on primary crops, agricultural area, arable and permanent crops, arable land, permanent crops, permanent pasture, forest and fuelwood, non-arable and nonpermanent, irrigated areas, agricultural machinery, fertilizers and pesticides, production and agricultural machinery. Subsets of data from FAOSTAT are available at other sites, notably that of the World Resources Institute15.

12

The variety of data collected varies according to specific information needs and priorities of the various countries. For example, nutrient budgets at farm level in The Netherlands, crop and production statistics for subsidy monitoring within the European Community, and, for Ethiopia, areas of improved seed application, irrigation, fertiliser and pesticide application. 13 http://apps.fao.org/notes/datasources-e.htm 14 http://apps.fao.org/ 15 http://earthtrends.wri.org/

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Global land use related datasets from the latest (1990) agricultural census are available online16 from FAO. Of note is the fact that these datasets contain country-level information on several variables, including agricultural land, cropland, as well as areas of temporary and permanent crops. The variables reported vary according to the country. The shortcomings of the agricultural data mentioned earlier give rise to difficulties in compiling harmonised regional / global datasets or in reliably inferring trends from the data countries submitted to FAO (Young, 1998). These difficulties point to the need for greater standardization of terminology 17 and/or development of a parametric land use correlation system through which land use classes from existing national systems could be correlated. FAO is currently working on the development of such a correlation system. A major appeal of such a correlation system is that it does not require countries to change their existing national classification systems, many of which have been developed in response to local decisionmaking needs (See Annex II: Factors driving the revision of existing LU classification systems) and in which countries have already made significant investments in development and local adoption (Examples of countries or provinces which have recently updated their land use classification systems include Australia 18 , United States 19 , British Columbia Canada20). Recently, FAO and the World Bank produced maps of Farming Systems for six regions of the world: Africa, East Asic and the Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa and South Asia. These maps show populations of farms that have broadly similar resource bases, enterprise patterns, household livelihoods and constraints21. The map for Africa include the following categories: 1. Irrigated 2. Tree crop 3. Forest based 4. Rice-tree crop 5. Highland perennial 6. Highland temperate mixed 7. Root crop 8. Cereal-root crop mixed 9. Maize mixed 10. Large commercial & smallholder 11. Agro-pastoral millet/sorghum 12. Pastoral 13. Sparse (arid), and 14. Coastal artisanal fishing. FAO through its Forestry Department regularly collects and disseminates information on the state of the world's forests. Global databases which can be accessed through the Department's web site 22 contain statistics on, among others, plantations, total area of forests and other wooded lands as well as maps showing protected areas and forests. A recently prepared global map of forest "cover"23 prepared using AVHRR satellite remote sensing data for the Global Forest Resource Assessment 2000 contains 4 classes, namely, closed forest, open and fragmented forest, other wooded land, and other land cover. Both of the first 2 categories include forest plantations; such plantations are not shown as separate sub-classes.
16 17

http://www.fao.org/waicent/faoinfo/economic/ess/census/wcares/wcaresfr.htm Similar standardization was a prerequisite to the development of the Land Cover Classification System (LCCS). The LCCS is now operational: see http://www.fao.org/sd/2001/en0101_en.htm 18 http://www.brs.gov.au/land%26water/landuse/landuse.html 19 http://www.planning.org/lbcs/ 20 http://www.for.gov.bc.ca/ric/Pubs/LandUse/CORPORATELANDUSE/index.htm 21 http://wbln0018.worldbank.org/essd/rdv/vta.nsf/Gweb/FAORegions 22 http://www.fao.org/forestry/fo/country/nav_world.jsp 23 http://www.fao.org/forestry/fo/fra/main/index.jsp

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The AQUASTAT24 information system of FAO includes data of global coverage on, among others, areas of main irrigated crops by country as well as maps of water-managed areas (as a percentage of cultivated areas). NASA The Matthews Global Cultivation Intensity map is based on existing maps of vegetation and satellite imagery, and it shows the percentage of each one-degree square latitude/ longitude grid cell that is under cultivation, versus the percentage of natural vegetation, including five classes (Matthews, 1983). The five classes are defined according to the following relative percentages of cultivated to natural vegetated areas: 0-100, 20-80, 50-50, 75-25, 100-0. The dataset can be downloaded free of cost from a number of sites including the NASA Goddard Institute for Space Studies (GISS)25 and the United Nations Environment Programme, GRID, Geneva26. USGS A number of maps containing one or more land use classes are available from the EROS data centre. These include a Global Ecosystems map and a Land use/ land cover system map27. Both maps contain a mixture of land cover and land use classes. The legend for the Global Ecosystems map which is reproduced in Annex III shows that out of a total of 97 classes, only a small number of these, 14, could be considered directly related to land use. IFPRI and WRI recently used these datasets to derive a series of maps (including agricultural extent and land use intensity) as part of the PAGE (Pilot Analysis of Global Ecosystems) project28. UNEP-WCMC The UNEP World Conservation Monitoring Centre (UNEP-WCMC) collects data on protected areas of the world. By definition, a protected area is "an area of land and/or sea especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means"29. The following six protected-area management categories are recognized by UNEP-WCMC (1) strict nature reserve/ wildnerness area (2) national park (3) natural monument (4) habitat/species management area (5) protected landscape/seascape, and (6) managed resource protected area. The UNEP-WCMC site provides access to a global database on national designated protected areas30. University of Kassel
24 25

http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/aquastat.htm http://www.giss.nasa.gov/data/landuse/vegeem.html 26 http://www.grid.unep.ch/data/grid/landcover.html 27 http://edcdaac.usgs.gov/glcc/tabgeo_globe.htmla 28 http://www.ifpri.cgiar.org/pubs/books/page/maps.htm 29 http://www.unep-wcmc.org/ 30 http://www.unep-wcmc.org/protected_areas/data/nat2.htm

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A digital global map of irrigated areas31 is available through the University of Kassel. The map, which was developed with contributions from FAO (AQUASTAT) is in raster format. It has a resolution of 0.5 degree by 0.5 degree and provides the percentage of each 0.5*0.5 degree cell that was equipped for irrigation in 1995. 5. GENERAL ASSESSMENT OF EXISTING GLOBAL DATASETS Datasets that deal primarily (i.e. directly) with land use purposes are rare. In general, land use content constitutes a small (often minor) part of datasets devoted to other themes (e.g. land cover, ecosystems, etc.) and land use aspects must often be implied. In many cases, the data sets are inferred from satellite imagery. In such instances, the categories of LU information in the datasets reflect the difficulties of inferring LU from land cover as well as limitations that are linked to the spatial resolution of the satellite sensors used for mapping. Global data sets can be built up using data from individual country submissions, however, some of these data sets may be of questionable accuracy due to the variable quality of the data contributed by different countries. 6. INITIATIVES FOR IMPROVING THE CURRENT SITUATION Despite the important contribution that LU information could make in improving decision making, it is generally recognised that 2 major factors impede greater use of existing LU data, especially in developing countries, namely (1) inadequate availability (at various scales), otherwise stated - the data simply does not exist, and (2) inadequate quality (due to a lack of standards to guide data collection - which make country-to-country comparisons as well as data aggregation difficult). Several regional to global initiatives are aimed at overcoming these impediments. Food and Agricultural Organisation (FAO) FAO is currently compiling a geo-referenced global database on sub-national agricultural statistics based on the data collected by countries during agricultural censuses. These datasets will allow users to obtain information aggregated by second or third level administrative district in countries on a global basis. Similar efforts, but on a regional scale are being undertaken by other workers (e.g. Texas A&M's Blackland Research and Extension Centre32).

International Food Policy Research Institute (IFPRI)
31 32

http://www.usf.uni-kassel.de/english/personal/petrasub/irrigation.htm http://www.brc.tamus.edu/char/ACT.html

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IFPRI33 in collaboration with the University of Wisconsin are developing methodologies to estimate global agricultural lands by interpreting areas of cropland and pasture from 1km AVHRR data. Land Use and Land Cover Change (LUCC) The Science plan of LUCC (a programme element of IGBP and IHDP) includes a proposal for the development of global datasets on land use34. To date some progress has been made on development of land use plans that focus on specific regions (e.g. South-East Asia and the Miombo Basin in southern Africa). The dataset for Miombo contain 'land use' data in 4 classes at a 5 km resolution grid for southern Africa35 as well as cropland use intensity for 4 countries36 as derived from satellite imagery. Millennium Ecosystem Assessment Ecosystem assessments ranging from local to global scales are planned as part of this recently launched UN programme37. It aims at analysing the capacity of the world's ecosystems to provide goods and services to humanity. It is anticipated that the ecological datasets that will be produced within the framework of this programme will contain elements related to land use. Southern Africa and South-east Asia have been selected as priority focal regions. 7. CONFRONTING THE INADEQUACY OF GLOBAL LAND USE DATA: ROLE OF THE GEO-SPATIAL COMMUNITY The availability and quality of LU information produced by some countries is generally considered poor for a variety of reasons (e.g. the information is not up to date, there is incomplete coverage, the definition of given land use category can vary even within the same country, or the methods used for inventorying LU are unsuitable). These deficiencies constitute a barrier in compiling consistent, harmonized regional to global data sets from national data. Poor quality and availability of national LU data are often linked to factors such as insufficient financial resources as well as limited administrative awareness of the potential uses of LU information. In general, adequate administrative awareness is considered a prerequisite for developing a demand for adequate land use data collection. As noted earlier, there is already a notable decreasing trend in the cost of satellite imagery of sufficient spatial resolution that could be used in applying area-based sampling surveys. The remote sensing community could therefore help to improve the current situation even further by addressing problems at the national level, in particular, by (1) supporting studies which aim at developing/refining the methodologies for inventorying LU using satellite imagery (2)

33 34

www.ifpri.org http://www.geo.ucl.ac.be/LUCC/scienceplan/sp8.html 35 http://miombo.gecp.virginia.edu/cd/Miombocd/Docs/Database.html 36 http://miombo.gecp.virginia.edu/cd/Miombocd/Docs/database/cropland/index.htm#Four 37 http://www.millenniumassessment.org/en/

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supporting events which aim at building awareness among decision makers of the utility of land use data collection, and (3) supporting the development of a parametric land use correlation system which is fundamental for building harmonized regional datasets from national ones. In this latter regard, the geo-spatial community may also need to build awareness among certain developing countries in order to encourage them to eliminate restrictions to information sharing and/or local access to remote sensing data that are based on out-dated national-security considerations (George, 2000) 8. SUMMARY AND RECOMMENDATIONS The main points and recommendations are summarized below,  There is a general paucity of global data sets on land use. Several initiatives exist to develop some specific datasets. Some of these efforts could be better coordinated to avoid duplication. Building consistent/ harmonized global datasets by compiling separate national datasets requires prior development of a land use correlation system. International organisations and other entities should support the development and validation of such a system. General improvement of the quality of land use data requires greater awareness by decision-makers of its importance for planning purposes and adoption of enhanced, more cost-effective approaches for land use inventorying. Evolving remote sensing and other spatial technologies are crucial for the development of many land use datasets. The geospatial community could therefore play an important role in making LU data collection more affordable, especially for developing countries, while recognizing that the creation of certain agricultural land use related datasets will always rely on ground observations (e.g. data on technological inputs and the timing of agricultural operations).





References de Bie, C.A.J.M. 2000. Comparative performance analysis of agro-systems. ITC dissertation no. 75, 232p; Available at http://www.itc.nl/education/larus/landuse/ Duhamel, C. 1998. First approximation of a reference land use classification. Final Report to FAO, 31p. FAO, 1998. Multiple frame agricultural surveys. Volume 2. FAO Statistical Development Series, p.242.

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FAO/UNEP, 1999. Terminology for integrated resources planning and management. FAO, 2000. Land cover classification system (LCCS), 179p. George, H. 2000. Developing countries and remote sensing: how intergovernmental factors impede progress. Space Policy, 16, 267-273. Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies, Journal of Climate and Applied Meteorology, volume 22, pp. 474-487. Nachtergaele, F.O.F. 2000. Soil Resources Information; in "Global environmental databases Present situation; future directions". Tateishi, R and D. Hastings (editors). ISPRS Working Group IV/6 (1996-2000). Geocarta International Centre, Hong Kong, 157-177 Stomph, T.J., L.O. Fresco, L.O. & H. van Keulen, 1994. Land use system evaluation: Concepts and methodology. Agricultural System, 44. Wood, S., Sebastian, K. and Scherr, S.J. 2000. Pilot analysis of global ecosystems Agroecosystems. World Resources Institute, 110p. Young, A. 1998. Land resources. Now and for the future. Cambridge University Press, 319p.

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ANNEX I

A PROPOSAL FOR AN INTERNATIONAL SYSTEM OF LAND USE CLASSIFICATION (from Young, 1998)

Land use based on natural ecosystems:    not used; partial or total conservation; collection (of plant products, of animal products, of plant and animal products)

Land use based on managed ecosystems:     production and multipurpose forestry (management of natural forests, forest plantations) livestock production (extensive grazing, intensive grazing, confined) crop production (shifting, temporary cropping, permanent cropping, wetland cultivation, confined) fisheries production (fishing, aquaculture)

Settlement and related land use:     recreation (many sub-classes) mineral extraction (mining, quarrying) settlement (residential, commercial, industrial, infrastructure) use restricted by security

ANNEX II      

SOME ISSUES DRIVING THE REVISIONS OF EXISTING LAND USE CLASSIFICATION SYSTEMS

lack of complete inventories of existing land uses lack of definitions of land uses, land use activities and functions lack of consistency in categorizing land uses and in a manner which is consistent with legislation broader user expectations (an increasing range of LU applications e.g. site selection, taxation, environmental impact assessment, ..) lack of standards for data collection (at times related to lack of inter-agency cooperation) insufficient accounting of certain activities (e.g. aboriginal activities); declared interests in the land (where land use may be affected by such interests) or legal entities (e.g. legal boundaries, tenures, regulations that affect land use) lack of reliability of land use data; timeliness lack of standards for sharing land use information lack of software tools that exploit GIS, databases and other modern information technologies

  

65 ANNEX III

Chapter 3 Land Use Data

Olson Global Ecosystem Legend (land use related classes are underlined)
49 VOLCANIC ROCK 50 SAND DESERT 51 SEMI DESERT SHRUBS 52 SEMI DESERT SAGE 53 BARREN TUNDRA 54 COOL SOUTHERN HEMISPHERE MIXED FORESTS 55 COOL FIELDS AND WOODS 56 FOREST AND FIELD 57 COOL FOREST AND FIELD 58 FIELDS AND WOODY SAVANNA 59 SUCCULENT AND THORN SCRUB 60 SMALL LEAF MIXED WOODS 61 DECIDUOUS AND MIXED BOREAL FOREST 62 NARROW CONIFERS 63 WOODED TUNDRA 64 HEATH SCRUB 65 COASTAL WETLAND - NW 66 COASTAL WETLAND - NE 67 COASTAL WETLAND - SE 68 COASTAL WETLAND - SW 69 POLAR AND ALPINE DESERT 70 GLACIER ROCK 71 SALT PLAYAS 72 MANGROVE 73 WATER AND ISLAND FRINGE 74 LAND, WATER, AND SHORE 75 LAND AND WATER, RIVERS 76 CROP AND WATER MIXTURES 77 SOUTHERN HEMISPHERE CONIFERS 78 SOUTHERN HEMISPHERE MIXED FOREST 79 WET SCLEROPHYLIC FOREST 80 COASTLINE FRINGE 81 BEACHES AND DUNES 82 SPARSE DUNES AND RIDGES 83 BARE COASTAL DUNES 84 RESIDUAL DUNES AND BEACHES 85 COMPOUND COASTLINES 86 ROCKY CLIFFS AND SLOPES 87 SANDY GRASSLAND AND SHRUBS 88 BAMBOO 89 MOIST EUCALYPTUS 90 RAIN GREEN TROPICAL FOREST 91 WOODY SAVANNA 92 BROADLEAF CROPS 93 GRASS CROPS 94 CROPS, GRASS, SHRUBS 95 EVERGREEN TREE CROP 96 DECIDUOUS TREE CROP 100 NO DATA

0 INTERRUPTED AREAS 1 URBAN 2 LOW SPARSE GRASSLAND 3 CONIFEROUS FOREST 4 DECIDUOUS CONIFER FOREST 5 DECIDUOUS BROADLEAF FOREST 6 EVERGREEN BROADLEAF FORESTS 7 TALL GRASSES AND SHRUBS 8 BARE DESERT 9 UPLAND TUNDRA 10 IRRIGATED GRASSLAND 11 SEMI DESERT 12 GLACIER ICE 13 WOODED WET SWAMP 14 INLAND WATER 15 SEA WATER 16 SHRUB EVERGREEN 17 SHRUB DECIDUOUS 18 MIXED FOREST AND FIELD 19 EVERGREEN FOREST AND FIELDS 20 COOL RAIN FOREST 21 CONIFER BOREAL FOREST 22 COOL CONIFER FOREST 23 COOL MIXED FOREST 24 MIXED FOREST 25 COOL BROADLEAF FOREST 26 DECIDUOUS BROADLEAF FOREST 27 CONIFER FOREST 28 MONTANE TROPICAL FORESTS 29 SEASONAL TROPICAL FOREST 30 COOL CROPS AND TOWNS 31 CROPS AND TOWN 32 DRY TROPICAL WOODS 33 TROPICAL RAINFOREST 34 TROPICAL DEGRADED FOREST 35 CORN AND BEANS CROPLAND 36 RICE PADDY AND FIELD 37 HOT IRRIGATED CROPLAND 38 COOL IRRIGATED CROPLAND 39 COLD IRRIGATED CROPLAND 40 COOL GRASSES AND SHRUBS 41 HOT AND MILD GRASSES AND SHRUBS 42 COLD GRASSLAND 43 SAVANNA (WOODS) 44 MIRE, BOG, FEN 45 MARSH WETLAND 46 MEDITERRANEAN SCRUB 47 DRY WOODY SCRUB 48 DRY EVERGREEN WOODS


				
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