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Introduction to Remote Sensing Example Applications and Principles Exploring Images with MultiSpec User Interface and Band Combinations Questions…
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Definitions Lillesand and Kiefer (1994) “The science and art of obtaining information …acquired by a device that is not in contact with the object…” Lillesand and Kiefer (1994) – “The science and art of obtaining information …acquired by a device that is not in contact with the object…” CCRS Glossary A group of techniques for collecting image or other forms of data … from measurements made at a distance from the object, and the processing and analysis of the data.” CCRS Glossary – “A group of techniques for collecting image or other forms of data … from measurements made at a distance from the object, and the processing and analysis of the data.”
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Examples of Remotely Sensed Data Weather Weather Ocean Properties Ocean Properties Physical Geography Physical Geography Major Disturbance Events / Hazards Major Disturbance Events / Hazards Cultural Features – urban mapping Cultural Features Many other examples – thematic infothematic
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disturbance
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From Lillesand and Kiefer (1994) Decision
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From Lillesand and Kiefer (1994)
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From Lillesand Kiefer (1994) An Electromagnetic Wave l = wavelength n = frequency c = velocity of light
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O 3 absorption at 0.2 um
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H 2 O absorption at 1.4, 1.6, and 1.9 um
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CO 2 absorption at 2.0 um
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CH 4 absorption at 2.2 – 2.5 um
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Human eye
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Incident Energy Incident Energy Reflected Energy Reflected Energy E( l ) i – [ E( l ) a + E( l ) t ] = E r ( l)
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Rayleigh Scattering Atmospheric Interactions
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Types of Satellite Orbits Geostationary – 35,790 km above the Earth Sun-synchronous or “Polar”– between 700 and 800 km above the Earth
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Satellite Orbits http://science.nasa.gov/Realtime/jtrack/3d/JTrack3D.html
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“Resolution” in Remote Sensing Four types 1) Spatial –the ground coverage / pixel 2) Spectral – area and subdivision of the EM spectrum 3) Temporal – frequency of coverage 4) Radiometric – sensitivity to changes in energy intensity
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Issues of spatial & temporal scale of the sensor platform -SPATIAL- -SPATIAL- “grain” and “extent” ‘Pixel’ -TEMPORAL- Frequency of imaging Frequency of imaging
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September 15 September 30 October 15 October 30
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Color Theory and Band Combinations Additive colors vs. Subtractive colors Additive colors vs. Subtractive colors ‘Natural’ and ‘False’ color composites ‘Natural’ and ‘False’ color composites Displaying images, extracting information Displaying images, extracting information
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Additive Primaries Additive Primaries Subtractive Primaries Subtractive Primaries No single primary can be formed by mixture of other two All other colors can be formed by mixtures of additives Representations of color via pigments and dyes Yellow absorbs blue, cyan absorbs red, magenta absorbs green
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Blue (0.45 um) Green (0.55 um) Red (0.65 um)
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Green = 2.5 x Red = xBlue = x
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Feb. 14, 2006 MODIS Terra, Eastern US
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Snow Vs. Clouds Clouds scatter at all wavelengths Snow absorbs at >1.4 m
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Snow Vs. Clouds Clouds scatter at all wavelengths Snow absorbs at >1.4 m c
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Natural Color 3,2,1 Color IR 4,3,2 False Color 5,4,3
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MultiSpec RS Software Research and education Research and education Long period development and refinement Long period development and refinement Freely available Freely available Supported by tutorials Supported by tutorials http://cobweb.ecn.purdue.edu/~biehl/MultiSpec/ http://cobweb.ecn.purdue.edu/~biehl/MultiSpec/ http://cobweb.ecn.purdue.edu/~biehl/MultiSpec/
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