Remote Sensing Week 2
Remote Sensing Week 2 GEOG 2107
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This 2 page Class Notes was uploaded by Ivana Szwejkowski on Friday September 9, 2016. The Class Notes belongs to GEOG 2107 at George Washington University taught by Engstrom, R in Fall 2015. Since its upload, it has received 4 views. For similar materials see Intro to Remote Sensing in Geography at George Washington University.
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Date Created: 09/09/16
Remote Sensing Week 2 Data- RGB, infrared, radar, 4 resolutions 1. Spatial 2. Temporal 3. Spectral 4. Radiometric Remote sensing process State the problem, collect the data, analyze, conclusion; map. Spatial Resolution “germane to task” -resolution needed for effective detection/ analysis of features observed with sensors -for an object to be detected. Its size should be >= 2X the pixel size Examples -land cover mapping (buildings vs. urban areas, forest vs. trees) Spatial Resolution- Considerations -grain and extent Grain: smallest object distinguishable on image (detail)- spatial resolution, similar to pixel size Extent: area covered by an image Trade off: in general large extent=large grain size Temporal Resolution The ability to obtain repeat coverage for an area Timing is critical for some applications- crop cycles (plating, maximum greenness, harvest), catastrophic events Aircraft- potentially high Satellite- fixed orbit, systematic collection, printable sensors Spectral Resolution The number and dimension of the specific EMR wavelength regions to which sensor is sensitive Broadband- few relatively broad bands Hyper-spectral- many, relatively narrow bands Spectral Resolution Nanometers- 4-5 Blue, 5-6 Green, 6-7 Red; Broadband Hyperspectral- airborne visible infrared Imaging Spectrometer (AVIRIS) Radiometric Resolution Ability of a sensor to distinguish between objects of similar reflectance Measured in terms of the number of energy levels discriminated Affects ability to measure properties of objects Resolution: Trade-offs Impossible to maximize all four elements Meteorological satellites acquire image data daily, but at low spatial and spectral resolutions Landsat TM & ETM + satellite data Image interpretation Chapter 5 Act of examining images for the purpose of identifying and measuring objects and phenomena, and judging their significance. Art vs. Science Image interpretation tasks Detection Identification Measurement Problem-solving Not necessarily performed sequentially or in all cases Detection Lowest mode
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