@Article{MouraHLGSASA:2015:SeDrEf,
               author = "Moura, Yhasmin Mendes de and Hilker, Thomas and Lyapustin, Alexei 
                         I. and Galv{\~a}o, L{\^e}nio Soares and Santos, Jo{\~a}o 
                         Roberto dos and Anderson, Liana O. and Sousa, C{\'e}lio Helder 
                         Resende de and Arai, Egidio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Oregon 
                         State University} and {NASA Goddard Space Flight Center} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of Oxford} 
                         and {Oregon State University} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Seasonality and drought effects of Amazonian forests observed from 
                         multi-angle satellite data",
              journal = "Remote Sensing of Environment",
                 year = "2015",
               volume = "171",
                pages = "278--290",
                month = "Dec.",
             keywords = "Amazon, Drought, Anisotropy, Greening, Browning, MAIAC, MODIS.",
             abstract = "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 (r2 = 
                         0.70) and LiDAR (r2 = 0.88) based estimates of LAI even in dense 
                         canopies (LAI \≤ 7 m2 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 ~ 7% of the basin), which was 
                         followed by a decline (browning) later during the dry season 
                         (across ~ 5% of the basin). Anomalies in vegetation browning were 
                         particularly strong during the 2005 and 2010 drought years (~ 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.",
                  doi = "10.1016/j.rse.2015.10.015",
                  url = "http://dx.doi.org/10.1016/j.rse.2015.10.015",
                 issn = "0034-4257",
             language = "en",
        urlaccessdate = "03 jun. 2024"
}