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Integrating Remote Sensing and GIS for Effective Spatial Analysis

Learn about remote sensing basics, imaging characteristics, photogrammetry, data extraction, computer classification, change detection, and GIS integration. Explore platforms, image characteristics, and data extraction techniques. Enhance your spatial analysis skills.

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Integrating Remote Sensing and GIS for Effective Spatial Analysis

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  1. Chapter 8 Remote Sensing & GIS Integration

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

  3. 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

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

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

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

  7. 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.)

  8. 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

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

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

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

  12. 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

  13. 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)

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

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

  16. 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

  17. 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

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

  19. 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

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