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Remote sensing has the potential of improving our ability to map and monitor pasture degradation. Pasture degradation is one of the mostimportant problems in the Amazon, yet the manner in which grazing intensity, edaphic conditions and land]use age impact pasture biophysicalproperties, and our ability to monitor them using remote sensing is poorly known. We evaluate the connection between field grass biophysicalmeasures and remote sensing, and investigate the impact of grazing intensity on pasture biophysical measures in Rondônia, in the BrazilianAmazon. Above ground biomass, canopy water content and height were measured in different pasture sites during the dry season. Using LandsatThematic Mapper (TM) data, four spectral vegetation indices and fractions derived from spectral mixture analysis, i.e., Non]PhotosyntheticVegetation (NPV), Green Vegetation (GV), Soil, Shade, and NPV+Soil, were calculated and compared to field grass measures. For grazedpastures under dry conditions, the Normalized Difference Infrared Index (NDII5 and NDII7), had higher correlations with the biophysicalmeasures than the Normalized Difference Vegetation Index (NDVI) and the Soil]Adjusted Vegetation Index (SAVI). NPV had the highestcorrelations with all field measures, suggesting this fraction is a good indicator of pasture characteristics in Rondônia. Pasture height wascorrelated to the Shade fraction. A conceptual model was built for pasture biophysical change using three fractions, i.e., NPV, Shade and GV tocharacterize possible pasture degradation processes in Rondônia. Based upon field measures, grazing intensity had the most significant impact onpasture biophysical properties compared to soil order and land]use age. The impact of grazing on pastures in the dry season could be potentiallymeasured by using remotely sensed measures such as NPV.

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