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Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. Regional analysis of pasture dynamic patterns was conducted using high-frequency temporal satellite data and ancillary data to better understand pasture degradation under varied soil, environmental, and pasture management conditions in the state of Rondônia, Brazil. The 10-day normalized difference vegetation index (NDVI) composite derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution was used to characterize different grass phenological patterns for 32 counties in Rondônia between 2001 and 2003. Six pasture greenness classes showed that high greenness pasture classes dominated in young pastures, while low greenness pasture classes were least common. As pastures aged, the proportion of high greenness pasture classes decreased and the proportion of low greenness pastures increased, indicating a decrease in forage productivity over time in Rondônia. The magnitude of productivity decline depended on environmental constraints and land use systems. To refine this analysis, trajectories of pasture change were determined using spectral mixture analysis applied to Landsat time series data from 1988 to 2001 with the focus on two counties that show contrasting patterns of potential of grass production: Pimenteira, representing the “degraded” pasture category, and Governador Jorge Teixeira, as the “productive” pasture category. The results revealed a clear pasture degradation pattern in Pimenteira, related to low soil fertility and dry climate conditions, while Governador Jorge Teixeira, with better soil fertility and intermediate precipitation, did not show signs of pasture degradation through time. [ABSTRACT FROM AUTHOR]

The Brazilian Amazon forest and cerrado savanna encompasses a region of enormous ecological, climatic, and land-use variation. Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout this region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The methodology of the CLAS approach is presented along with a study of its sensitivity to atmospheric correction errors. CLAS is then applied to a mosaic of Landsat images spanning the years 1999–2001 as a proof of concept and capability for large-scale, very high resolution mapping of the Amazon and bordering cerrado savanna. A total of 197 images were analyzed for fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate covers using a probabilistic spectral mixture model. Results from areas without significant land use, clouds, cloud shadows, and water bodies were compiled by the Brazilian state and vegetation class to understand the baseline structural typology of forests and savannas using this new system. Conversion of the satellite-derived PV data to woody canopy gap fraction was made to highlight major differences by vegetation and ecosystem classes. The results indicate important differences in fractional photosynthetic cover and canopy gap fraction that can now be accounted for in future studies of land-cover change, ecological variability, and biogeochemical processes across the Amazon and bordering cerrado regions of Brazil. [ABSTRACT FROM AUTHOR]