|Published in||Remote Sens|
Ana Carolina M. Pessôa, Liana O. Anderson, Nathália S. Carvalho, Wesley A. Campanharo, Celso H. L. Silva Junior, Thais M. Rosan, João B. C. Reis, Francisca R. S. Pereira, Mauro Assis, Aline D. Jacon, Jean P. Ometto, Yosio E. Shimabukuro, Camila V. J. Silva, Aline Pontes-Lopes,Thiago F. Morello. and Luiz E. O. C. Aragão
National Institute for Space Research, Remote Sensing Division, Av. dos Astronautas, n1758, São José dos Campos SP 12227-010, Brazil
National Center for Monitoring and Early Warning of Natural Disasters&mdashCemaden, Technological Park of São José dos Campos, Dr. Altino Bondensan Road, n500, São José dos Campos SP 12247-016, Brazil
College of Life and Environmental Sciences Amory Building, University of Exeter, Rennes Drive, Exeter EX4 4RJ, UK
Atrium Forest Consulting, R. Tiradentes, n435, Piracicaba SP 13400-760, Brazil
National Institute for Space Research, Earth System Science Center, Av. dos Astronautas, n1758, São José dos Campos SP 12227-010, Brazil
Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC, Alameda da Universidade, S/N, São Bernardo do Campo SP 09606-045, Brazil
Author to whom correspondence should be addressed.
Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.