Journal article
Coniferous tree species mapping using Landsat data
Remote Sensing of Environment, Vol.9(1), pp.11-26
1980
Abstract
The identification and mapping of 12 surface-cover types within Crater Lake National Park, Oregon, including seven classes of coniferous tree species, has been accomplished through the use of LANDSAT digital data. The 12 surface-cover types were mapped with an average accuracy of 88.8%, as compared with detailed ground truth. The classification of the LANDSAT data was accomplished through use of the Interactive Digital Image Manipulation System (IDIMS) available at the EROS Data Center in Sioux Falls, South Dakota. The combined effects of a quantity and quality of ground truth, the use of the controlled clustering classification technique, and the prudent placement of Intensive Study Areas (ISA) on the image-processing CRT screen for training statistic collection provided very subtle spectral reflectance differences between coniferous tree species. Slope angle, slope aspect, and surface-cover type variation, and to a lesser degree, crown size, and crown density were the main environmental factors that accounted for spectral reflectance variation of surface-cover types within the park. Through an appreciation of the influence of environmental factors on the reflectance value of surface-cover types, and an appreciation for the placement of training areas to sample the environmental effects on reflectance, one can reduce misclassification or nonclassification possibilities.
Details
- Title
- Coniferous tree species mapping using Landsat data
- Authors
- Stephen J Walsh (Author) - Oklahoma State University, United States
- Publication details
- Remote Sensing of Environment, Vol.9(1), pp.11-26
- Publisher
- Elsevier Inc.
- Date published
- 1980
- DOI
- 10.1016/0034-4257(80)90044-9
- ISSN
- 0034-4257
- Organisation Unit
- University of the Sunshine Coast, Queensland
- Language
- English
- Record Identifier
- 99449926002621
- Output Type
- Journal article
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- Environmental Sciences
- Imaging Science & Photographic Technology
- Remote Sensing
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Source: InCites