A remote sensing based vegetation classification logic for global land cover analysis
- Univ. of Montana, Missoula, MT (United States). School of Forestry
- Geological Survey, Sioux Falls, SD (United States). EROS Data Center
This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that (1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, (2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and (3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.
- OSTI ID:
- 31834
- Report Number(s):
- CONF-9206465-; ISSN 0034-4257; TRN: IM9517%%129
- Journal Information:
- Remote Sensing of Environment, Vol. 51, Issue 1; Conference: Workshop on the International Satellite Land Surface Climatology Project (ISLSCP), Columbia, MD (United States), 23-26 Jun 1992; Other Information: PBD: Jan 1995
- Country of Publication:
- United States
- Language:
- English
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