Land cover characterization using multitemporal red, near-ir, and thermal-ir data from NOAA/AVHRR

Ramakrishna Nemani, Steve Running

Research output: Contribution to journalReview articlepeer-review

189 Scopus citations

Abstract

A simple land cover classification scheme is proposed based on energy absorption and exchange properties of various land cover types, observable from remote sensing. Seasonal trajectories of the Normalized Difference Vegetation Index (NDVI) and surface temperature (T(s)), routinely available from NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer), are used to characterize different land cover types into four groups: water limited (shrub, grass), energy limited (wetlands, boreal forests, snow, ice, and water), atmospherically coupled (aerodynamically rough canopies, forests), and atmospherically decoupled (aerodynamically smooth canopies, crops). Further separation is achieved using growing-season average NDVI for shrub and grass, seasonal NDVI amplitude for deciduous vs. evergreen, and near-infrared (NIR) reflectance for broadleaf vs. needleleaf vegetation. The methodology using threshold-based rules is completely remote sensing based; classification rules are simple and easily modifiable. A first test of this logic over the continental United States, when compared with existing maps, showed that the methodology adequately captures the spatial distribution of various land cover types. The logic is also useful for monitoring seasonal dynamics of land cover, evapotranspiration, and disturbances due to fire, floods, insects/disease, and other anthropogenic processes. Future improvements needed to deal with mixed landscapes and global implementation details are discussed.

Original languageEnglish
Pages (from-to)79-90
Number of pages12
JournalEcological Applications
Volume7
Issue number1
DOIs
StatePublished - Feb 1997

Keywords

  • Canopy structure
  • Land cover
  • Remote sensing
  • Surface temperature
  • Vegetation index

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