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Although seasonal and temporal variations in evapotranspiration (ET) in Amazonia have been studied based upon flux-tower data and coarse resolution satellite-based models, ET dynamics over human-impacted landscapes are highly uncertain in this region. In this study, we estimate ET rates from critical land cover types over highly fragmented landscapes in the southern Amazon and characterize the ET dynamics during the dry season using the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC, a Landsat-based ET model, that generates spatially continuous ET estimates at a 30 m spatial resolution widely used for agricultural applications, was adapted to the southern Amazon by using the NDVI indexed reference ET fraction (ETrF) approach. Compared to flux tower-based ET rates, this approach showed an improved performance on the forest ET estimation over the standard METRIC approach, with R2 = 0.73 from R2 = 0.70 and RMSE reduced from 0.77 mm/day to 0.35 mm/day. We used this approach integrated into the METRIC procedure to estimate ET rates from primary, regenerated, and degraded forests and pasture in Acre, Rondônia, and Mato Grosso, all located in the southern Amazon, during the dry season in 2009. The lowest ET rates occurred in Mato Grosso, the driest region. Acre and Rondônia, both located in the southwestern Amazon, had similar ET rates for all land cover types. Dry season ET rates between primary forest and regenerated forest were similar (p > 0.05) in all sites, ranging between 2.5 and 3.4 mm/day for both forest cover types in the three sites. ET rates from degraded forest in Mato Grosso were significantly lower (p < 0.05) compared to the other forest cover types, with a value of 2.03 mm/day on average. Pasture showed the lowest ET rates during the dry season at all study sites, with the dry season average ET varying from 1.7 mm/day in Mato Grosso to 2.8 mm/day in Acre. [ABSTRACT FROM AUTHOR]

While forest evapotranspiration (ET) dynamics in the Amazon have been studied both as point estimates using flux towers, as well as spatially coarse surfaces using satellite data, higher resolution (e.g., 30 m resolution) ET estimates are necessary to address finer spatial variability associated with forest biophysical characteristics and their changes by natural and human impacts. The objective of this study is to evaluate the potential of the Landsat-based METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model to estimate high-resolution (30 m) forest ET by comparing to flux tower ET (FT ET) data collected over seasonally dry tropical forests in Rondônia, the southwestern region of the Amazon. Analyses were conducted at daily, monthly and seasonal scales for the dry seasons (June-September for Rondônia) of 2000-2002. Overall daily ET comparison between FT ET and METRIC ET across the study site showed r² = 0.67 with RMSE = 0.81 mm. For seasonal ET comparison, METRIC-derived ET estimates showed an agreement with FT ET measurements during the dry season of r² >0.70 and %MAE <15%. We also discuss some challenges and potential applications of METRIC for Amazonian forests. [ABSTRACT FROM AUTHOR]

Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000-2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the accuracy to quantify forest (0.97), deforestation (0.85) and forest degradation (0.82) with an overall accuracy of 0.92. We show that 169,074 km2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010. In that same time frame, an additional 50,815 km2 of forest was directly altered by timber harvesting and/or fire, equivalent to 30% of the area converted by deforestation. While average annual outright deforestation declined by 46% between the first and second halves of the study period, annual forest degradation increased by 20%. Existing operational monitoring systems (PRODES: Monitoramento da Florestal Amazônica Brasileira por Satélite) report deforestation area to within 2% of our results, but do not account for the extensive forest degradation occurring throughout the region due to selective logging and forest fire. Annual monitoring of forest degradation across tropical forests is critical for developing land management policies as well as the monitoring of carbon stocks/emissions and protected areas. [ABSTRACT FROM AUTHOR]

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

Regional and global environmental modeling depend on soil data for input layers or parameterization. However, randomly located observations, such as provided by agricultural databases, are not always representative of trends identified in field studies conducted under carefully controlled conditions. Many researchers lament the paucity of soil profile data in Amazônia, and suggest that given more data, regional studies would more closely approximate field research results. We assess the ability of a well-populated regional database collected in the southwestern Brazilian Amazon to reproduce expected biogeochemical trends associated with forest clearing and pasture establishment, and explore the ramifications of relying on independently collected soil data for regional modeling. The Soteron database includes analyses of approximately 3000 soil cores collected for zoning purposes in the state of Rondônia. Pasture ages were determined from a time series of Landsat TM images classified using spectral mixture analysis. Although regional averages showed some of the temporal trends expected based on field study results (e.g. increase in pH following forest clearing), the trends were not statistically significant. Stratification by precipitation and other variables showed pasture age to be important but difficult to separate from other potential controls on soil conditions, mainly because of the reduced number of observations in each stratum. Using multiple regression, which permitted the inclusion of all potential explanatory factors and interactions, pasture age was shown to be a statistically significant predictor of soil conditions. However, the expected temporal sequence of changes documented by field chronosequence studies could not be reproduced. Properties dominated by large-scale environmental gradients – pH, sum of base cations, aluminum saturation, and exchangeable calcium – were moderately well modeled, while those more strongly linked to dynamic spatially heterogeneous processes such as biological cycling and land management, particularly organic carbon and nitrogen, could not be modeled. Management-induced soil changes occur at too fine a scale to be captured by most maps, and the relative changes are small compared with spatial heterogeneity caused by controls on soil development over large regions. Therefore, regardless of whether chronosequence-derived models of biogeochemical response to land-cover change are correct, the results of these models will not lead to spatially explicit maps that can be validated by regional reconnaissance, nor will they facilitate realistic predictions of the regional biogeochemical consequences of land-cover change. The change from local to regional scale entails a change in the relative importance of processes controlling soil property behavior. [ABSTRACT FROM AUTHOR]