Guidelines for the Use of Digital Imagery for Vegetation MappingA basic reference for those considering digital imagery, particularly satellite imagery for vegetation mapping. Contents: using remote sensing and GIS for mapping vegetation; remote sensors and remotely sensed data; determining appropriate uses for satellite imagery; defining the classification scheme; collecting reference data; assessing accuracy; creating polygons; project management; the basic tour; and case studies. Important terms and ideas are introduced while showing the progression of key activities in the classification and mapping process. |
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accuracy assessment sites aerial photographs airborne video analysis ancillary data AVHRR bands change detection chapter classification scheme classified image Congalton Conifer created crown closure data base data sources deciduous delineated digital image digital imagery ecological unit electromagnetic spectrum error matrix example field data collection Figure filtering ground identified image analyst image classification image processing interpretation inventory land cover Landsat TM layer Lillesand LTA unit mapping project monitoring multispectral needed panchromatic photointerpretations pixels polygons premap preprocessing project manager radar raster reference data remotely sensed data resource managers sampling satellite data satellite image satellite imagery Scenario I Task sensor shrub shrubland spatial resolution species generally contribute spectral classes SPOT SPOT satellite Step storage study area techniques tion TM image TM imagery training sites unsupervised classification USDA Forest Service users vegetation classification vegetation mapping vegetation types WOIC
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Page 165 - IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Society, Institute of Electrical and Electronics Engineers, 445 Hoes Lane, Piscataway, NJ 08854.
Page 171 - ... used as a starting point for a series of descriptive and analytical statistical techniques for accuracy assessment.
Page 123 - Using Cluster Analysis to Improve the Selection of Training Statistics in Classifying Remotely Sensed Data: Photogrammetric Engineering and Remote Sensing Vol.
Page 156 - Classification of Wetlands and Deepwater Habitats of the United States" by Cowardin, Carter, Golet, and LaRoe (US Fish and Wildlife Service Report FWS/OBS-79/31, December 1979).
Page 16 - IO 1 10° 10 rl io- 2 lo- 1 10° lo 1 io 2 io 3 io 4 io 5 io 6 io 7 io 8 io 9 f[Hz] Figure 4.
Page 18 - ... measure of the smallest object that can be resolved by the sensor or the area on the ground represented by each pixel. The finer the resolution, the lower the number
Page 175 - Sensor: Any device that gathers energy, EMR or other, converts it into a signal and presents it in a form suitable for obtaining information about the environment.
Page 165 - Engineering and Remote Sensing, American Society for Photogrammetry and Remote Sensing, 5410 Grosvenor Lane, Suite 210, Bethesda, MD 20814-2160. Remote Sensing of Environment, Elsevier Publishing Company, 52 Vanderbilt Avenue, New York, NY 10017. Remote Sensing Reviews, Harwood Academic Publishers, 50 West 23rd Street, New York, NY 10010. This list was compiled with the assistance of Dr. James W. Merchant. For additional copies or an updated list, please contact: James W. Merchant, Center for Advanced...
Page 36 - Level n Rangeland Herbaceous rangeland Shrub and brush rangeland Mixed rangeland Forest land Deciduous forest land Evergreen forest land Mixed forest land Water Streams and canals Lakes Reservoirs Bays and estuaries Figure 4.3 displays a classification scheme used by Region 6 of the FS. It was specifically designed to accommodate Landsat TM classifications, and approximates the highest level of detail obtainable from TM data when significant effort is devoted to fieldwork and analysis.
Page 56 - Producer's accuracy, which is based on omission error, is the probability of a reference site being correctly classified. It is calculated by dividing the total number of correct accuracy sites for a class (diagonal elements) by the total number of reference sites for that class, found in the bottom cell in each column. • User's accuracy, which is based on commission error, is the probability that a pixel on the map actually represents that category on the ground. User's accuracy is calculated...