title: Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000 and 2010.
abstract: Reducing emissions from deforestation and forest degradation (REDD+) is considered one of the most cost-effective strategies for mitigating climate change. However, historical deforestation and emission rates-critical inputs for setting reference emission levels for REDD+-are poorly understood. Here we use multi-source, time-series satellite data to quantify carbon emissions from deforestation in the Amazon basin on a year-to-year basis between 2000 and 2010. We first derive annual deforestation indicators by using the Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) product. MODIS indicators are calibrated by using a large sample of Landsat data to generate accurate deforestation rates, which are subsequently combined with a spatially explicit biomass dataset to calculate committed annual carbon emissions. Across the study area, the average deforestation and associated carbon emissions were estimated to be 1.59 ± 0.25 M ha•yr(-1) and 0.18 ± 0.07 Pg C•yr(-1) respectively, with substantially different trends and inter-annual variability in different regions. Deforestation in the Brazilian Amazon increased between 2001 and 2004 and declined substantially afterwards, whereas deforestation in the Bolivian Amazon, the Colombian Amazon, and the Peruvian Amazon increased over the study period. The average carbon density of lost forests after 2005 was 130 Mg C•ha(-1), ~11% lower than the average carbon density of remaining forests in year 2010 (144 Mg C•ha(-1)). Moreover, the average carbon density of cleared forests increased at a rate of 7 Mg C•ha(-1)•yr(-1) from 2005 to 2010, suggesting that deforestation has been progressively encroaching into high-biomass lands in the Amazon basin. Spatially explicit, annual deforestation and emission estimates like the ones derived in this study are useful for setting baselines for REDD+ and other emission mitigation programs, and for evaluating the performance of such efforts.
authors.affiliation: Department of Geographical Sciences, University of Maryland, College Park, Maryland, United States of America; Global Land Cover Facility, University of Maryland, College Park, Maryland, United States of America.
authors.forename: Xiao-Peng
authors.initials: XP
authors.lastname: Song
authors.affiliation: Department of Geographical Sciences, University of Maryland, College Park, Maryland, United States of America; Global Land Cover Facility, University of Maryland, College Park, Maryland, United States of America.
authors.forename: Chengquan
authors.initials: C
authors.lastname: Huang
authors.affiliation: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, United States of America.
authors.forename: Sassan S
authors.initials: SS
authors.lastname: Saatchi
authors.affiliation: Department of Geographical Sciences, University of Maryland, College Park, Maryland, United States of America.
authors.forename: Matthew C
authors.initials: MC
authors.lastname: Hansen
authors.affiliation: Department of Geographical Sciences, University of Maryland, College Park, Maryland, United States of America; Global Land Cover Facility, University of Maryland, College Park, Maryland, United States of America.
authors.forename: John R
authors.initials: JR
authors.lastname: Townshend
mesh_terms: D001272:Atmosphere / Q000032:analysis*; D018533:Biomass; D001838:Bolivia; D001938:Brazil; D002244:Carbon / Q000032:analysis*; D057231:Climate Change; D003105:Colombia; D003247:Conservation of Natural Resources / Q000379:methods; D004784:Environmental Monitoring / Q000379:methods; D065928:Forests; D010568:Peru; D014329:Tropical Climate
chemical_list: D002244:Carbon