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

Steven W. Running, Thomas R. Loveland, Lars L. Pierce, R. R. Nemani, E. R. Hunt

Research output: Contribution to journalArticlepeer-review

228 Scopus citations


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.

Original languageEnglish
Pages (from-to)39-48
Number of pages10
JournalRemote Sensing of Environment
Issue number1
StatePublished - Jan 1995


Funding for this work wa:~ provided by NASA Contract NAS5-31368 to SWR, and a NASA Global Change fellowship to L. L. P. We thank I. Colin Prentice for useful discussions and support from the Departrm,,nt of Plant Ecology, Lund University.

FundersFunder number
National Aeronautics and Space AdministrationNAS5-31368


    Dive into the research topics of 'A remote sensing based vegetation classification logic for global land cover analysis'. Together they form a unique fingerprint.

    Cite this