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High-resolution forest carbon stocks and emissions inthe AmazonGregory P. Asnera,1, George V. N. Powellb, Joseph Mascaroa, David E. Knappa, John K. Clarka, James Jacobsona,Ty Kennedy-Bowdoina, Aravindh Balajia, Guayana Paez-Acostaa, Eloy Victoriac, Laura Secadad, Michael Valquid,and R. Flint HugheseaDepartment of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305;bWorld Wildlife Fund, Washington , DC 20090;cPeruvian Ministry ofEnvironment, Lima 27, Peru;dWorld Wildlife Fund, Lima 14, Peru; andeInstitute of Pacific Islands Forestry, United States Forest Service, Hilo, HI 96720Edited by B. L. Turner, Arizona State University, Tempe, AZ, and approved August 10, 2010 (received for review April 9, 2010)Efforts to mitigate climate change through the Reduced Emissionsfrom Deforestation and Degradation (REDD) depend on mappingand monitoring of tropical forest carbon stocks and emissions overlarge geographic areas. With a new integrated use of satelliteimaging, airborne light detection and ranging, and field plots, wemapped aboveground carbon stocks and emissions at 0.1-ha re-solution over 4.3 million ha of the Peruvian Amazon, an area twicethat of all forests in Costa Rica, to reveal the determinants offorest carbon density and to demonstrate the feasibility of mappingcarbon emissions for REDD. We discovered previously unknownvariation in carbon storage at multiple scales based on geologicsubstrate and forest type. From 1999 to 2009, emissions from landuse totaled 1.1% of the standing carbon throughout the region.Forest degradation, such as from selective logging, increased re-gional carbon emissions by 47% over deforestation alone, andsecondary regrowth provided an 18% offset against total grossemissions. Very high-resolution monitoring reduces uncertainty incarbon emissions for REDD programs while uncovering fundamen-tal environmental controls on forest carbon storage and theirinteractions with land-use change.deforestation|forest degradation|Peru|Reduced Emissions fromDeforestation and Degradation|United Nations Framework Convention onClimate ChangeBetween 10% and 15% of global carbon dioxide emissionsoriginate from deforestation and degradation of tropical for-ests (1, 2). Emblematic of these emissions, the southwestern Per-uvian Amazon is undergoing carbon changes via road building,mining, timber extraction, and farming. Meanwhile, the UnitedNations Framework Convention on Climate Change is workingto develop a program to curb carbon emissions via the pro-gram for Reduced Emissions from Deforestation and Degrada-tion (REDD) (3, 4). REDD has the potential to connect carbonemitters with governments positioned to reduce forest carbonlosses through monetary compensation. In addition to offsettingemissions, REDD could provide indirect support for biodiversityconservation through reduced habitat loss, thus providing a uniquesolution to the longstanding tension between conservation inter-ests and other land-use needs in tropical forest regions such as thePeruvian Amazon.There are many challenges to making REDD work, and map-ping forest carbon stocks and emissions at the high resolutiondemanded by investors and monitoring agencies remains a tech-nical barrier. Satellite remote sensing offers a practical means tomonitor forest cover (5, 6), but has not provided high-resolutionestimates of carbon emissions (7). In contrast, field plots pro-vide effective localized estimates of forest carbon stocks, butnatural variation in forest carbon density may render plot-basedapproaches ineffective for estimating carbon over large areas.Furthermore, although plot-based studies are needed for long-term monitoring of forest dynamics, they are time-consuming andare usually placed to avoid land-use change, which is the mainanthropogenic factor responsible for carbon flux to the atmo-sphere in tropical forests. New approaches are critically needed toextend the role of field plots to capture regional variation and tobridge a major gap between field and satellite observations.One new approach is airborne light detection and ranging(LiDAR), which, when used with field calibration plots, is ca-pable of estimating aboveground forest carbon densities (in unitsof Mg C ha−1) (8). However, airborne LiDAR has not beenproven for carbon mapping of high diversity Amazon forests, anda key obstacle to large-scale use of LiDAR for REDD moni-toring is its relatively high cost of operation and small geographiccoverage. However, combined with a strategic use of satellitedata, airborne LiDAR may yield cost-effective, high-resolutionmaps of forest carbon stocks and emissions (9). This potentialhas never been realized at large geographic scales that would bepertinent to an international REDD program.Here we report on a study to apply a new multiscale, multi-temporal method to analyze carbon stocks and emissions through-out 4.3 million ha of lowland Amazon forest in the Department ofMadre de Dios, Peru, as a procedure for achieving national-scaleREDD mapping while assessing determinants of biomass stocks ata large geographic scale. Although subnational within Peru, thestudy area is equivalent to twice that of Costa Rica’s forests, andour study was designed with a survey size that is logistically easy toimplement multiple times to achieve necessary coverage for largernations. The Madre de Dios region has undergone relativelymoderate land-use change throughout the past century. However,paving of the Interoceanic Highway since 2006, along with newtimber concessions and an influx of artisanal gold miners duringthe past 5 y, has rapidly increased land-use pressure. In this con-text, we sought to understand the sources of spatial and temporalvariability in carbon stocks and emissions throughout this largeand rapidly changing region of the Amazon basin. Our approachinvolves multiscale steps ranging from automated satellite map-ping of deforestation and degradation to airborne LiDAR map-ping to local-scale plot calibration measurements. The approachprovides high-resolution maps of aboveground carbon densitiesand a retrospective mapping of carbon emissions based on currentcarbon densities and past forest cover changes (SI Materialsand Methods).Results and DiscussionAirborne LiDAR data yielded forest canopy height, underlyingterrain, and canopy vertical profile, providing a comprehensive,Author contributions: G.P.A. and G.V.P. designed research; G.P.A., G.V.P., J.M., D.E.K.,J.K.C., J.J., T.K.-B., G.P.-A.,


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