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An Introduction to Remote Sensing & GIS

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An Introduction to Remote Sensing & GIS The Electromagnetic SpectrumSatellite SensorsAVHRRLandsat MSS Gleason, Art, Scott Kaiser, and Tamara Smith1994 Center for Earth Observation Users Guide. 8th Revision August 2004 by Larry Bonneau, Yale University.Yale University Genocide Studies Project, Remote Sensing & GIS Research An Introduction to Remote Sensing & GIS Introduction Remote sensing is the measurement of object properties on Earth’s surface using data acquired from aircraft and satellites. It attempts to measure something at a distance, rather than in situ, and, for this research’s purposes, displays those measurements over a two-dimensional spatial grid, i.e. images. Remote-sensing systems, particularly those deployed on satellites, provide a repetitive and consistent view of Earth facilitating the ability to monitor the earth system and the effects of human activities on Earth. There are many electromagnetic (EM) band-length ranges Earth’s atmosphere absorbs. The EM band ranges transmittable through Earth’s atmosphere are sometimes referred to as atmospheric windows. The human eye only detects, viz. the reflective solar radiance humans actually see, that part of the EM scale in the band length range 0.4 – 0.7 µm. But remote sensing technology allows for the detection of other reflective and radiant (e.g. thermal) energy band-length ranges that reach or are emitted by Earth’s surface, and even some Earth’s atmosphere reflects, e.g. the EM reflective qualities of clouds. Hence, for viewing purposes red, green, and blue (RGB) false color assignments are used to express the reflective qualities of objects in these EM band-length groups, and the combination and mixing of these false color assignments express the true physical reflective qualities of all objects present in an image. The primary benefit of Global Information Systems (GIS) is the ability to interrelate spatially multiple types of information assembled from a range of sources. These data do not necessarily have to be visual. Shape files are helpful for interpolating and visualizing many other types of data, e.g. demographic data. Many study and research models rely on the ability to analyze and extract information from images by using a variety of computer available research tools and then express these findings as part of a project with images in a variety of layers and scenes. When utilizing satellite images to assess most types of land cover change, primarily those involving change in vegetation coverage, variations in climate must be considered. For better control and accuracy in these analyses, comparing images acquired during the same month or season is advisable. But due to the limited availability of satellite images, obtaining materials corresponding both spatially and temporally to the location and period under research are not always possible. Furthermore, annual and seasonal climate data are not always available for the region or temporal period being researched. Sometimes, changes in average rainfall, temperature, etc. must be inferred using more macro regional or global data. One standard remote sensing application for detecting temporal change in land cover, especially vegetation, is the Normalized Difference Vegetation Index (NDVI). The NDVI application involves a ratio formula between the visual red and NIR EM bands. This ratio application helps to distinguish healthy and stronger vegetation reflection from other materials with similar reflective qualities in those EM band wavelength groups. NDVI applications are useful because two images can be processed into a false color composite, which allows for visual temporal change detection in vegetation coverage. Moreover, by applying standardized thresholds to multiple NDVI manipulated images, one can create classification training regions and execute supervised computer-generated classifications of multiple images. From these resulting images, area summary reports are calculated. These empirical data enable a more accurate assessment of change in area of the corresponding land-cover classes. Information pertaining to some of the above topics, as well as a more comprehensive description on some remote sensing technologies including a glossary of terms, is given in the sections below. 1Yale University Genocide Studies Project, Remote Sensing & GIS Research Visible Light Near IR Thermal IR green red blue Gamma- X-Rays UV-Rays Infrared Microwave & TV & Rays Radar Radio 10-7 10-6 10-5 10-4 10-3 10-2 10-1 1 µm 10 102 103 104 105 106 107 λµm (1 nm) (1 m) Reflected Energy Radiant Energy Peak, 0.5 µm Peak, 9.7 µm The Electromagnetic SpectrumCosmic Rays 0.4 –0.7 µm 2Yale University Genocide Studies Project, Remote Sensing & GIS Research The Electromagnetic Spectrum Gamma rays <0.30 nm This range is completely absorbed by the upper atmosphere and not available for remote sensing. X-rays 0.03—30.0 nm This range is completely absorbed by the atmosphere and not employed in remote sensing. UV-rays 0.03—0.40 µm This range is completely absorbed by the atmosphere and not employed in remote sensing. Photographic UV 0.30—0.40 µm This range is not absorbed by the atmosphere and detectable with film and photo detectors but with severe atmospheric scattering. Visual Blue 0.45—0.52 µm Because water increasingly absorbs electromagnetic (EM) radiation at longer wavelengths, band 1 provides the best data for mapping depth-detail of water-covered areas. It is also used for soil-vegetation discrimination, forest mapping, and distinguishing cultural features. Visual Green 0.50—0.60 µm The blue-green region of the spectrum corresponds to the chlorophyll absorption of healthy vegetation and is useful for mapping detail such as depth or sediment in water bodies. Cultural features such as roads and buildings also show up well in this band. Visual Red 0.60—0.70 µm Chlorophyll absorbs these wavelengths in healthy vegetation. Hence, this band is useful for distinguishing plant


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