21 April 2016 Spatiotemporal NDVI, LAI, albedo, and surface temperature dynamics in the southwest of the Brazilian Amazon forest
Carlos Alexandre Santos Querin, Cristina Aparecida Beneditti, Nadja Gomes Machado, Marcelo José Gama da Silva, Juliane Kayse Albuquerque da silva Querino, Luiz Alves dos Santos Neto, Marcelo Sacardi Biudes
Author Affiliations +
Abstract
During the last decades, the Amazon rainforest underwent uncontrolled exploitation that modified its environmental variables. The current paper analyzes the spatiotemporal dynamics of the normalized difference vegetation index (NDVI), leaf area index (LAI), and surface albedo, and temperature in two different vegetation covers, preserved and deforested areas. We calculated the remote-sensing products using Landsat 5 TM images obtained during the dry season 1984, 1991, 2000, and 2011 of the central region of the State of Rondônia, Brazil. The results showed a reduction of vegetation indexes NDVI (∼0.70 in 1984 to ∼0.27 in 2011) and LAI (∼1.8 in 1984 to ∼0.3 in 2011), with an increase of surface albedo (0.12 in 1984 to 0.20 in 2011) and temperature (∼24°C in 1984 to 30°C in 2011) as the effect of the rainforest converted in grassland during the study period. No changes in any variables were observed in the protected area. Forest conversion into grassland resulted in a decrease of 69% in NDVI and 110% in LAI and a rise of 59% and 24% in albedo and surface temperature, respectively.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2016/$25.00 © 2016 SPIE
Carlos Alexandre Santos Querin, Cristina Aparecida Beneditti, Nadja Gomes Machado, Marcelo José Gama da Silva, Juliane Kayse Albuquerque da silva Querino, Luiz Alves dos Santos Neto, and Marcelo Sacardi Biudes "Spatiotemporal NDVI, LAI, albedo, and surface temperature dynamics in the southwest of the Brazilian Amazon forest," Journal of Applied Remote Sensing 10(2), 026007 (21 April 2016). https://doi.org/10.1117/1.JRS.10.026007
Published: 21 April 2016
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Cited by 22 scholarly publications.
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KEYWORDS
Vegetation

Temperature metrology

Climatology

Remote sensing

Earth observing sensors

Humidity

Climate change

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