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Previous attempts to map land cover at broad scales were based on single year time series and usually on the Normalized Difference Vegetation Index (NDVI). Single years of data lack statistical representativity and the NDVI is partially driven by short term climatic characteristics. 2 approaches to produce land cover classifications are investigated that are not excessively influenced by short term climatic variability: averaging a climate driven variable over several years, and measuring a more climate independent variable. The 2 approaches are tested, compared and combined for the African continent using 8 years of advanced very high resolution radiometer (AVHRR) Global Area Coverage (GAC) data. The results demonstrate that times series of the ratio between surface temperature and NDVI are less influenced by interannual variations in climatic conditions than NDVI time series and thus produce more stable land cover classifications. This finding is consistent with the biophysical interpretations of these two variables.

Additional information

Authors: EHRLICH D, JRC Ispra (IT);LAMBIN E F, JRC Ispra (IT)
Bibliographic Reference: Article: International Journal of Remote Sensing (1996)
Record Number: 199610124 / Last updated on: 1996-03-01
Original language: en
Available languages: en
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