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The role of remote sensing

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					              Remote Sensing



An increasingly important source of data
                  for GIS


Chapter 9 GIS Data Collection
Roadmap
  Data collection
  Introduction to remote sensing
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Data Collection
   One of most expensive GIS activities
   Many diverse sources
   Two broad types of collection
     Data capture (direct collection)
     Data transfer
   Two broad capture methods
     Primary (direct measurement)
     Secondary (indirect derivation)
Data Collection Techniques
                   Raster           Vector

 Primary     Digital remote   GPS measurements
             sensing images
             Digital aerial   Survey
             photographs      measurements,
                              photogrammetry
 Secondary   Scanned maps     Topographic surveys

             DEMs from maps   Toponymy data sets
                              from atlases
Primary Data Capture
   Capture specifically for GIS use
   Raster – remote sensing
     e.g. Landsat, SPOT and IKONOS satellites and
     aerial photography (orthophotos)
   Resolution is key consideration
     Spatial
     Spectral
     Temporal
     (Radiometric)
Roadmap
  Data collection
  Introduction to remote sensing
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Remote Sensing
  “is the measurement or acquisition of
  information of some property of an
  object or phenomena by a recording
  device that is not in physical or intimate
  contact with the object or phenomena
  under study”
Remote Sensing
Remote Sensing
includes ground-based, aircraft, spacecraft
and satellite-based systems
products can be analog (e.g., photos) or
digital images
remotely sensed images need to be
interpreted to yield thematic information
(roads, crop lands, etc.)
Remote sensing applications
           • MAPPING
           • MONITORING
           • MODELLING

     (Global, continental, landscape, local)
Aerial photography
 important for updating large scale
 topographic maps (e.g., new roads,
 urban areas)
 stereo-effect: pairs of images that are
 displaced produce 3-D effect
 allows for measuring elevation
SATELLITE REMOTE SENSING
ADVANTAGES

SYNOPTIC COVERAGE (STUDY OF
INTER – RELATIONSHIPS)
REPETITIVITY (ENABLES MONITORING
OF CHANGES)
MULTISPECTRAL IMAGING (BEYOND
VISIBLE REGION)
SURVEY OF INACCESSIBLE TERRAIN
Satellite-based systems
  data recorded for pixels = picture
  elements
  size on-ground of a pixel varies from 1m
  to 60m or more for commercial systems
  images are sent back from satellite as
  very large raster data sets


   Spatial resolution
Roadmap
  Data collection
  Introduction to remote sensing
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Concepts
 Active or passive sensors
   Passive: sensors measure the amount of
   energy reflected from the earth’s surface
   Active: sensor emits radiation in the direction
   of the target, it then detects and measures
   the radiation that is reflected or backscattered
   from the target.
Concepts
  energy sources and radiation principles
  (e.g., Stefan-Boltzmann law, Wien’s
  displacement law)
  different sensors measure different
  parts of the electromagnetic spectrum
 Electromagnetic spectrum
                                     0.4          0.5     0.6          0.7 mm




                                           blue
                                                                         near




                                                                 red
                                UV
                                                                         infrared

wavelength (mm)                                                                                                   wavelength (mm)

    10-6   10-5   10-4   10-3    10-2       10-1             1           10                 102   103   104   105         106   107


                                                        near infrared
                                                        mid infrared
                                                                         thermal infrared
                                                        Visible Light




                                                                                                              microwave




                                                                                                                                 TV and radio
The remote sensing process
Sources
of Energy
                                 Sensing
                                 Systems




                 Earth Surface
Atmospheric windows




     Primary blockers: Ozone, water vapour, CO2
  Object-EMR
  Interactions


EMR can be:

1: scattered
2: reflected
3: absorbed / emitted
4: transmitted

by the object
Roadmap
  Data collection
  Introduction
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Spectral signatures:
the integration of the above factors
Typical Reflectance Signatures
     However, in reality
        there is never
a ‘typical’ spectral signature
Satellite-based systems
 Landsat , SPOT, IKONOS, etc.
 US, Russian, Indian, Japanese,
 European, and Canadian satellites
 panchromatic versus multispectral
 Landsat: 7-8 spectral bands,
 some in the visible spectrum
 newer systems have many more bands
 (hyperspectral images)

                                      Movie
 Spectral
resolution
              Spectral resolution




Radiometric
 resolution
Landsat TM image of
Hongkong


(bands 7,4,3 - 60m
resolution) shows
vegetation in green,
urban areas in purple/
white, water in
blue/black




Source: Eosat
         Panchromatic image of the Jefferson memorial

              from IKONOS-1
              1 m resolution




       Spatial
     resolution

               Source: http://gis.washington.edu/cfr250/lessons/remote_sensing/

http://geospatial.amnh.org/remote_sensing/widgets/zoom3/index.html   http://geospatial.amnh.org/remote_sensing/widgets/sensor_integration/index.html   swaths
Image fusion
(Pan sharpening)




   Combining higher resolution panchromatic images with lower resolution multispectral images.
Roadmap
  Data collection
  Introduction
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Digital image processing
   digital satellite data usually need
   considerable processing
   registration and atmospheric correction
   analysis:
   - measurement
   - classification
   - estimation
Image display


True-color composite image                      Near Infrared Composite                  Shortwave Infrared Composite
         (3, 2, 1)                                       (4,3,2)                                (7,4,3 or 7,4,2)

                  Vegetation in the NIR band is highly reflective due to chlorophyll,
              and an NIR composite vividly shows vegetation in various shades of red.
  Water appears dark, almost black, due to the absorption of energy in the visible red and NIR bands.


                                                Reflectance in the SWIR region is due primarily to moisture content.
                                            SWIR bands are especially suited for camouflage detection, change detection,
                                                          disturbed soils, soil type, and vegetation stress.




                                                           http://geospatial.amnh.org/remote_sensing/widgets/band_combo/index.html
Measurement
  temperature
  vegetation biomass
  - Normalized Difference Vegetation
  Index (NDVI)
  elevation
  crop condition
  urbanized area
Roadmap
  Data collection
  Introduction
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Classification
   identify and map areas with similar
   characteristics
   assign meaningful categories such as
   landuse or landcover classes to pixel
   values
Classification
   Two main types:
     Supervised
     • need “training areas” (ground-truth) (e.g.,
       Maximum likelihood, parallelepiped)
     Unsupervised
     • statistical approaches (e.g., ISOData, K-
       means)
Classification
                                   Unsupervised
                            (Classes determined a posteriori)




      Supervised
  (Classes predetermined)
Supervised classification
Determine Species Types
                                                                LODGEPOLE PINE
                                                                   LEADING
                                                                DOUGLAS FIR
                                                                  LEADING

                                                                MIXED / OTHERS

                        Yellow = Pine leading
                        Orange = Fir leading
                        Light Green = Pine and Fd type
                        Dark Green = Fd, Spruce, Pine type
                        Purple = Fd, Spruce, Aspen minor pine




         http://geospatial.amnh.org/remote_sensing/guides/image_interp/land_cover_class.html#which_class
Classification
   reflectance varies with time of day
   often large uncertainty in classification
   - pixels may contain several classes
   (mixed pixel problem; one solution is
   spectral unmixing)
   despite good image processing
   systems: requires lots of experience
   (part art, part science)
Estimation
  objective is to estimate total amounts of
  a quantity, or areas under cultivation for
  an administrative or management area
  examples: crop areas, forest resources,
  drought monitoring
DROUGHT MONITORING
(CANADIAN PRAIRIES)
        (SEVERITY, EXTENT, LONG TERM IMPACT)
         JULY 2001                JULY 2002




             LANDSAT THEMATIC MAPPER
                (SOURCE: CCRS 2002)
Roadmap
  Data collection
  Introduction
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Other systems
  meteorological satellites
    e.g., Advanced Very High Resolution
    Radiometer
    coarser resolution but higher frequency
    and larger areas covered
    designed for meteorology but used for
    many other purposes (e.g., NDVI)
Satellite orbits




 Geostationary Orbit: The satellite appears stationary
 with respect to the Earth's surface.


                                                    Earth observation satellites usually follow the sun synchronous orbits.
                                                    A sun synchronous orbit is a near polar orbit whose altitude is such
                                                    that the satellite will always pass over a location at a given latitude
                                                    at the same local solar time. In this way, the same solar illumination
    Temporal                                        Condition (except for seasonal variation) can be achieved for the images
                                                     of a given location taken by the satellite.
    resolution
                                                                                                                        Movie
Other systems
  radar remote sensing (e.g., microwave)
    advantages in areas where cloud cover is
    frequent (e.g., tropical areas close to the
    equator)
    difficult to interpret
SAR image of Washington, DC
Other systems
  aerial video
    visible light
    using off-the-shelf video cameras and post-
    processing systems
    cheap, rapid data collection for monitoring
    and data capture
    Light Detection and Ranging
                                Using many rapid small bursts of laser light,
                               an aircraft-borne apparatus records reflection
                                            from multiple sources.




Forest canopy (1st return)




Ground surface (last return)
Survey costs
     Field              Type            Scale     Imagery     Cost
                                                             (/km2)
Agriculture      Phenol. change       1:1,000,000 Landsat        80$
Forestry         Forest mapping        1:250,000 Landsat         6$
Regional         Feasibility Study      1:50,000 Spot SX        40$
Planning
Environment      Risk zone mapping      1:50,000 KFA 1000       150$
                                                 (5-12m)
Topography       Base map               1:10,000 aerial       2,000$
                                                 photos
Cadastre         Survey map              1:2,000 aer. photos 10,000$
Urban Planning Cadastre, utilities,        1:500 aer. photos 40,000$
               topography
Socioeconomic applications
  delineation of newly urbanized areas
  (e.g., Quito, Manila)
  mapping of villages for population
  estimation (e.g., Sudan, W-Africa) with
  Landsat (rooftop surveys)
  Defense Meteorological Satellite
  Program’s (DMSP) nighttime visible
  light emissions
DMSP data




  Japan                      South-East Asia
          (water areas masked)
   Population Distribution 1980




Source: U.S. Bureau of the Census
Roadmap
  Data collection
  Introduction
  Concepts
  Spectral signatures
  Digital image processing
  Classification
  Other systems / applications
  Remote sensing and GIS
Remote sensing and GIS
 remotely sensed data is an important
 data source (currency, frequency)
 large scale: e.g., “cities revealed”,
 subpolygon information.
 medium scale: framework data,
 urban/non-urban, crop conditions, etc.
 small scale: NDVI, global land cover
 data sets


                                          Datasets
Remote sensing and GIS
  requires considerable processing to
  achieve high accuracy products
  image rectification and registration with
  GIS data sets difficult with raster
  systems (resampling)
  image interpretation guided by GIS data
  raster to vector conversion usually
  required (polygon generation)
The remote sensing process
Reference                       Maps
                                              User
data              Visual
                                Statistics
                                              Decision
Air photos        Digital
                                GIS data      Maker
Digital data                    sets


Data           Interpretation   Information   Target
products                        products      audience
An overview of spectral and spatial resolutions
    A summary of
 spatial and temporal
resolutions associated
 with remote sensing
       systems.

				
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posted:10/29/2011
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