Chapter 8 Remote Sensing & GIS Integration

Basics EM spectrum: fig p. 268 reflected emitted detection film sensor atmospheric attenuation

Recording type analog (film) must retrieve film resolution based on film type digital easier to retrieve data resolution based on sensors/unit area RASTER DATA

System classifications Passive systems use existing source of EM illumination Active systems provide source of EM illumination

Platforms airplane low & high altitude high resolution large scale satellite various altitudes low to high resolution small to large scale

Imaging characteristics spatial resolution most important characteristic basis lens film or sensor ground resolution spatial resolution scale

Imaging characteristics spectral resolution EM wavelengths to which a system is sensitive components number of bands (more is better) width of bands (narrow is better) radiometric differences between “steps” in exposure contrast temporal (daily, monthly, yearly, etc.)

Selecting image characteristics “appropriate” specifications ground resolution bands & widths spectral resolution determine what you need to observe what you might want in the future what you can afford

Photogrammetry obtaining reliable measurements from images science art scale - based on: focal length height of plane average terrain elevation

Photogrammetry sources of error relief displacement (due to central perspective) aircraft tilt orthophotographs/orthoimages correct for above errors use digital elevation model (DEM)

Photogrammetry thermal infrared (TIR) sense heat systems: TIMS & ATLAS panoramic distortion (fig p. 280)

Photogrammetry side-looking airborne radar (SLAR) oblique view (side view) feature foreshortening (compression of features tilted toward radar) incidence angle varies with distance from radar resolution varies with pulse length antenna size

Photogrammetry satellite all types of images advantages wider coverage tilt-free little relief displacement disadvantages low spatial resolution (Landsat TM is 30m, SPOT is 20m)

Extraction of Data steps (fig p. 290) detection identification analysis and deduction classification theorization (verify/nullify hypotheses)

Image elements tone/color – least complex size shape texture pattern height shadow association pattern – most complex

Computer-assisted classification classifying raster data automate of low complexity functions preprocessing: radiometric & geometric correction classification approaches supervised – classes assigned - fig p. 293 unsupervised - cluster analysis hybrid – unsupervised followed by supervised

Computer-assisted classification types of classifiers hard vs soft – fig p. 295 contextual – looks at neighboring pixels artificial neural networks complex determinations based on multiple inputs - fig p. 296 field checking

Change detection overlay map-to-map image-to-image output matrix map fig p. 297

Integration of GIS & Remote Sensing requirements same georeferencing system rectify or register resampling problem: raster-vector data styles three stages – fig p 299 separate but equal seamless integration (ArcView) total integration