A remote sensing based vegetation classification logic for global land cover analysis

Remote Sensing of Environment
By: , and 

Links

Abstract

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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title A remote sensing based vegetation classification logic for global land cover analysis
Series title Remote Sensing of Environment
DOI 10.1016/0034-4257(94)00063-S
Volume 51
Issue 1
Year Published 1995
Language English
Publisher Elsevier
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 10 p.
First page 39
Last page 48