SUPERVISED CLASSIFICATION OF LANDSAT THEMATIC MAPPER IMAGERY IN A SEMI-ARID RANGELAND BY NONPARAMETRIC DISCRIMINANT ANALYSIS
In this article the authors used a nonparametric discriminant function in a supervised classification of Landsat Thematic Mapper satellite imagery of a ~240,000-ha semi-arid region in the Snake River Plains, southwestern Idaho. First, agriculture pixels were classified by distance from the soil baseline and water pixels by the thermal band value. Next, successive nonparametric discriminant functions were used to separate grassland and shrubland categories with subsequent classifications of vegetation within major classes. Accuracy in separating grasslands and shrublands was 80 percent and remained consistent relative to different thresholds in minimum percent ground cover defining shrublands. Within major grassland shrubland groups, researchers achieved 64 percent accuracy in separating dominant vegetation classes.
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Availability:
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Corporate Authors:
American Society for Photogrammetry and Remote Sensing
5410 Grosvenor Lane, Suite 210
Bethesda, MD United States 20814-2160 -
Authors:
- Knick, S T
- Rotenberry, J T
- Zarriello, T J
- Publication Date: 1997-1
Language
- English
Media Info
- Features: Figures; References; Tables;
- Pagination: p. 79-86
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Serial:
- Photogrammetric Engineering and Remote Sensing
- Volume: 63
- Issue Number: 1
- Publisher: American Society for Photogrammetry and Remote Sensing
- ISSN: 0099-1112
Subject/Index Terms
- TRT Terms: Arid land; Artificial satellites; Discriminant analysis; Ground cover; Imagery; Landsat satellites; Shrubs
- Uncontrolled Terms: Satellite imagery
- Old TRIS Terms: Discriminate analysis; Landsat; Semiarid land
- Subject Areas: Design; Highways; Planning and Forecasting; I21: Planning of Transport Infrastructure;
Filing Info
- Accession Number: 00732113
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 10 1997 12:00AM