Climate drivers of the Amazon forest greening

Our limited understanding of the climate controls on tropical forest seasonality is one of the biggest sources of uncertainty in modeling climate change impacts on terrestrial ecosystems. Combining leaf production, litterfall and climate observations from satellite and ground data in the Amazon forest, we show that seasonal variation in leaf production is largely triggered by climate signals, specifically, insolation increase (70.4% of the total area) and precipitation increase (29.6%). Increase of insolation drives leaf growth in the absence of water limitation. For these non-water-limited forests, the simultaneous leaf flush occurs in a sufficient proportion of the trees to be observed from space. While tropical cycles are generally defined in terms of dry or wet season, we show that for a large part of Amazonia the increase in insolation triggers the visible progress of leaf growth, just like during spring in temperate forests. The dependence of leaf growth initiation on climate seasonality may result in a higher sensitivity of these ecosystems to changes in climate than previously thought.

Soil, land use time, and sustainable intensification of agriculture in the Brazilian Cerrado region

The Brazilian Cerrado area is in rapid decline because of the expansion of modern agriculture. In this study, we used extensive field data and a 30-year chronosequence of Landsat images (1980-2010) to assess the effects of time since conversion of Cerrado into agriculture upon soil chemical attributes and soybean/corn yield in the Alto do Rio Verde watershed. We determined the rates of vegetation conversion into agriculture, the agricultural land use time since conversion, and the temporal changes in topsoil (0-20 cm soil depth) and subsurface (20-40 cm) chemical attributes of the soils. In addition, we investigated possible associations between fertilization/over-fertilization and land use history detected from the satellites. The results showed that 61.8% of the native vegetation in the Alto do Rio Verde watershed was already converted into agriculture with 31% of soils being used in agriculture for more than 30 years. While other fertilizers in cultivated soils (e.g., Ca⁺², Mg⁺², and P) have been compensated over time by soil management practices to keep crop yield high, large reductions in C _org (38%) and N _tot (29%) were observed in old cultivated areas. Furthermore, soybean and cornfields having more than 10 years of farming presented higher values of P and Mg⁺² than the ideal levels necessary for plant development. Therefore, increased risks of over-fertilization of the soils and environmental contamination with these macronutrients were associated with soybean and cornfields having more than 10 years of farming, especially those with more than 30 years of agricultural land use.

Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data

Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. The objective was to evaluate the variability of total suspended solids (TSS) and chlorophyll-a as well as the rainfall/temperature and land use impacts on these optically active constituents (OAC). The study area is the Passo Real reservoir in south Brazil. The methodology was divided in four steps. First, we used wavelet to detect anomalous periods of rainfall and temperature (2002-2014). Second, we carried out 12 field campaigns to obtain in situ measurements for limnological characterization (2009-2010). The third step was the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua satellites data corrected and non-corrected for bidirectional effects. Finally, we evaluated potential drivers of OAC changes over time using cross-correlation analysis. The results showed a decrease in the TSS and chlorophyll-a concentrations from the upper to the lower streams of the reservoir. The exponential regression between the MODIS red reflectance and TSS had an adjusted r² of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98mg/l and 2.33μg/l, respectively. MODIS estimates indicated a gradual transition in OAC from the upper to the lower streams in agreement with the patterns observed using field limnological data. The analysis of land use (greenness) showed two well-defined crop cycles per year. The highest seasonal concentrations of TSS and chlorophyll-a were observed in December and the lowest concentrations in April. Despite the interrelationships between both factors, our cross-correlation analysis indicated that the great concentrations of TSS and chlorophyll-a were primarily controlled by rainfall and secondarily by land use.

Scaling estimates of vegetation structure in Amazonian tropical forests using multi-angle MODIS observations

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r²= 0.54, RMSE=0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy (0.52≤ r²≤ 0.61; p<0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (σ⁰) from SeaWinds/QuikSCAT presented an r² of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon.