Seasonality and drought effects of Amazonian forests observed from multi-angle satellite data

Seasonality and drought in Amazon rainforests have been controversially discussed in the literature, partially due to a limited ability of current remote sensing techniques to detect its impacts on tropical vegetation. We use a multi-angle remote sensing approach to determine changes in vegetation structure from differences in directional scattering (anisotropy) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) with data atmospherically corrected by the Multi-Angle Implementation Atmospheric Correction Algorithm (MAIAC). Our results show a strong linear relationship between anisotropy and field (r(2) = 0.70) and LiDAR (r(2) = 0.88) based estimates of LAI even in dense canopies (LAI <= 7 m(2) m(-2)). This allowed us to obtain improved estimates of vegetation structure from optical remote sensing. We used anisotropy to analyze Amazon seasonality based on spatially explicit estimates of onset and length of dry season obtained from the Tropical Rainfall Measurement Mission (TRMM). An increase in vegetation greening was observed during the beginning of dry season (across similar to 7% of the basin), which was followed by a decline (browning) later during the dry season (across similar to 5% of the basin). Anomalies in vegetation browning were particularly strong during the 2005 and 2010 drought years (similar to 10% of the basin). We show that the magnitude of seasonal changes can be significantly affected by regional differences in onset and duration of the dry season. Seasonal changes were much less pronounced when assuming a fixed dry season from June through September across the Amazon Basin. Our findings reconcile remote sensing studies with field based observations and model results as they provide a sounder basis for the argument that tropical vegetation growth increases during the beginning of the dry season, but declines after extended drought periods. The multi-angle approach used in this work may help quantify drought tolerance and seasonality in the Amazonian forests. (C) 2015 Elsevier Inc. All rights reserved.