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

de Moura, Yhasmin Mendes; Hilker, Thomas; Lyapustin, Alexei I.; Galvão, Lênio Soares; dos Santos, João Roberto; Anderson, Liana O.; Resende de Sousa, Célio Helder; Arai, Egidio

Citeable URL: https://ir.library.oregonstate.edu/concern/articles/tm70mx10q

To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work. This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/remote-sensing-of-environment/

MAIAC data for the Amazon Basin was obtained from NASA's Level 1 Atmosphere Archive and Distribution System (LAADS Web) ftp://ladsweb. nascom.nasa.gov/MAIAC. LiDAR data for this study were obtained from the "Sustainable Landscapes Brazil" project, operated as a cooperation between EMBRAPA and the U.S. Forest Service ( http://mapas.cnpm.embrapa.br/paisagenssustentaveis/).

Descriptions

Attribute Name	Values
Creator	de Moura, Yhasmin Mendes Hilker, Thomas Lyapustin, Alexei I. Galvão, Lênio Soares dos Santos, João Roberto Anderson, Liana O. Resende de Sousa, Célio Helder Arai, Egidio
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.
Resource Type	Article
DOI	https://doi.org/10.1016/j.rse.2015.10.015
Date Available	2016-01-21T18:19:45+00:00
Date Issued	2015-12-15
Citation	de Moura, Y. M., Hilker, T., Lyapustin, A. I., Galvão, L. S., dos Santos, J. R., Anderson, L. O., ... & Arai, E. (2015). Seasonality and drought effects of Amazonian forests observed from multi-angle satellite data. Remote Sensing of Environment, 171, 278-290. doi:10.1016/j.rse.2015.10.015
Journal Title	Remote Sensing of Environment
Journal Volume	171
Keyword	Anisotropy Browning Drought MODIS Greening Amazon MAIAC
Rights Statement	Copyright Not Evaluated
Funding Statement (additional comments about funding)	This study was partially funded by CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior), grant number 12881-13-9; and CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico), grant number PVE 401025/2014-4. L.O.A thanks UK NERC Amazonica grant NE/F005482/1 and FAPESP grant 2013/50533-5.
Publisher	Elsevier
Peer Reviewed	Yes
Language	English [eng]
Replaces	http://hdl.handle.net/1957/58113
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