A remote sensing based vegetation classification logic for global land cover analysis
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.
Citation Information
Publication Year | 1995 |
---|---|
Title | A remote sensing based vegetation classification logic for global land cover analysis |
DOI | 10.1016/0034-4257(94)00063-S |
Authors | Steven W. Running, Thomas R. Loveland, Lars L. Pierce, R.R. Nemani, E. Raymond Hunt |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | Remote Sensing of Environment |
Index ID | 70186250 |
Record Source | USGS Publications Warehouse |
USGS Organization | Earth Resources Observation and Science (EROS) Center |