EPSCoR's Xiao Leads International Team Studying Brazilian Amazon Carbon Loss

EPSCoR Update - June 2021

The Amazon rainforest covers approximately 50% of the world’s rainforests and nearly two-thirds of the Amazon is in Brazil. The Brazilian Amazon is important for global biodiversity, hydrology, climate, and carbon cycle. Accurate and timely data on spatial-temporal dynamics of the vegetation aboveground biomass (AGB) and forest area in the region are needed to understand the carbon balance, which is affected by land-use, logging and degradation, secondary forest regrowth, and climate. 

Dr. Xiangming Xiao, from the University of Oklahoma’s Department of Microbiology and Plant Biology, has led an international team of graduate students, post-doctoral researchers and research scientists (Yuanwei Qin, Jean-Pierre Wigneron, Philippe Ciais, Martin Brandt, Lei Fan, Xiaojun Li, Sean Crowell, Xiaocui Wu, Russell Doughty, Yao Zhang, Fang Liu, Stephen Sitch, and Berrien Moore III) to investigate the interannual changes in AGB and forest area by analyzing satellite-based annual AGB and forest area datasets. Specifically, the research team investigated the role of climate anomalies in the changes in forest area and AGB; whether recent changes in policies and human activities in 2019 have a detectable effect on forest area and AGB; and the relative contributions of deforestation and forest degradation (forest fragmentation, edge effects, logging, forest fire and drought) to interannual variation in AGB loss in the study period. 

“We used the annual L-band vegetation optical depth (L-VOD) from the Soil Moisture and Ocean Salinity (SMOS) passive microwave images that provide annual maps of AGB and annual forest area datasets to investigate the spatial–temporal dynamics of forest carbon in the Brazilian Amazon during 2010–2019,” Xiao said. 

“We found that the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies of the new Brazilian government. However, the net AGB loss was three times smaller in 2019 than in 2015,” Qin said. 

The team also found that the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in a net AGB loss of 0.67 Pg C during 2010–2019. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds that of deforestation. 

“We have attributed the AGB decreases in the Brazilian Amazon to direct human-induced deforestation, selective logging, forest fragmentation and edge effects, forest fires, as well as mortality from climatic disturbances including storms and drought,” Qin said. 

“This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority in the Brazilian Amazon to achieve the objectives of Reducing Emissions from Deforestation and Forest Degradation (REDD) program and the carbon emission reduction commitment of the 2015 Paris Agreement,” Xiao added. 

More information can be found in the following publication: https://doi.org/10.1038/s41558-021-01026-5


Funding for this project was provided in part by the NASA Land Use and Land Cover Change programme (grant no. NNX14AD78G); the Inter-American Institute for Global Change Research (IAI) (grant no. CRN3076) under the US National Science Foundation (grant no. GEO-1128040); NSF EPSCoR project (grant no. IIA-1301789); NASA’s GeoCarb Mission (GeoCarb Contract no. 80LARC17C0001); SMOS project of the TOSCA Programme, CNES, France (Centre National d’Etudes Spatiales); RECCAP2 project (ESA Climate Change Initiative-contract no. 4000123002/18/I-NB) and the H2020 European Institute of Innovation and Technology (4C; grant no. 821003); Newton Fund (Met Office Climate Science for Service Partnership Brazil); European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 947757 TOFDRY) and DFF Sapere Aude grant (no. 9064–00049B); National Natural Science Foundation of China (grant nos. 41801247 and 41830648); Natural Science Foundation of Jiangsu Province (grant no. BK20180806); China Scholarship Council (grant no. 201804910838); and Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA20010202).