USING FRACTION IMAGES TO STUDY NATURAL LAND COVER CHANGES IN THE AMAZON

Autor(es)

Shimabukuro, Yosio E. ; Anderson, Liana O. ; Aragao, Luiz E. O. C. ; Huete, Alfredo ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8; v. N/A, p. 2-pg., 2006-01-01.

Resumo

Satellite data such as the vegetation indices are a crucial tool for studying vegetation phenology patterns from regional to global scales. In this study, we investigated the relationship of the fraction images, derived from the linear spectral mixture model, with the NDVI and EVI, the most used indices to evaluate the phenological response using remote sensing data from the MODIS sensor. Our objectives were to understand how the vegetation indices are related with the vegetation fraction and to evaluate if the information provided by the shade and soil fraction images can be used to explain the vegetation indices behavior. We used a temporal series data of the MOD13A1 product for the 2002 year, the precipitation data from 125 meteorological stations, and a land cover map generated based on the 2002 images. We studied two different vegetation physiognomies to analyse if the fraction images were landscape dependent. Our results showed that for the Open Tropical Forest, the vegetation fraction image presented a significant correlation with the EVI (r(2)=0.84) but not with the NDVI. For the Cerrado grassland landscape, the vegetation fraction image presented high correlation with the NDVI (r(2)=0.93) and EVI (r(2)=0.98). Significant correlations were also found for the shade and soil fraction images for the land cover studied, showing that these additional information are a useful source of data to understand the vegetation canopy structural changes and to analyze the responses provided by the vegetation indices correctly. (AU)

• Processo FAPESP: 03/01727-0 - Classificação e monitoramento da cobertura vegetal e uso da terra utilizando dados do sensor MODIS
• Beneficiário: Yosio Edemir Shimabukuro
• Modalidade de apoio: Auxílio à Pesquisa - Regular