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Fraction images for monitoring intra-annual phenology of different vegetation physiognomies in Amazonia

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Abstract:

In this study we investigate the potential of fraction images derived from a linear spectral mixture model to detect vegetation phenology in Amazonia, and evaluate their relationships with the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices. Time series of MODIS 250-m data over three contrasting land cover types in the Amazon were used in conjunction with rainfall data, a land cover map and a forest inventory survey to support the interpretation of our findings. Each vegetation physiognomy was characterized by a particular intra-annual variability detected by a combination of the fraction images. Both vegetation and shade fractions were important to evaluate the seasonality of the open tropical forest (OTF). The association of these results with forest inventory data and the literature suggests that Enhanced Vegetation Index (EVI) and vegetation fraction images are sensitive to structural changes in the canopy of OTF. In cerrado grassland (CG) the phenology was better characterized by combined soil and vegetation fractions. Soybean (SB) areas were characterized by the highest ranges in the vegetation and soil fraction images. Vegetation fraction and vegetation indices for the OTF showed a significant positive relationship with EVI but not with Normalized Difference Vegetation Index (NDVI). Significant relationships for vegetation fraction and vegetation indices were also found for the CG and soybean areas. In contrast to vegetation index approaches to monitoring phenology, fraction images provide additional information that allows a more comprehensive exploration of the spectral and structural changes in vegetation formations.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160903474921

Affiliations: 1: School of Geography and the Environment, University of Oxford, Oxford, UK 2: School of Geography, University of Exeter, Devon, UK 3: National Institute for Space Research (INPE), Divisao de Sensoriamento Remoto, Av. dos Astronautas, Sao Jose dos Campos, Brazil 4: Museu Paraense Emilio Goeldi, Belem, Para, Brazil 5: Department of Soil, Water and Environmental Sciences, University of Arizona, Tucson, AZ, USA

Publication date: 2011-01-01

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