Deforestation contributes 6-17% of global anthropogenic CO 2 emissions to the atmosphere 1 . Large uncertainties in emission estimates arise from inadequate data on the carbon density of forests 2 and the regional rates of deforestation. Consequently there is an urgent need for improved data sets that characterize the global distribution of aboveground biomass, especially in the tropics. Here we use multi-sensor satellite data to estimate aboveground live woody vegetation carbon density for pan-tropical ecosystems with unprecedented accuracy and spatial resolution. Results indicate that the total amount of carbon held in tropical woody vegetation is 228.7 Pg C, which is 21% higher than the amount reported in the Global Forest Resources Assessment 2010 (ref. 3). At the national level, Brazil and Indonesia contain 35% of the total carbon stored in tropical forests and produce the largest emissions from forest loss. Combining estimates of aboveground carbon stocks with regional deforestation rates 4 we estimate the total net emission of carbon from tropical deforestation and land use to be 1.0 Pg C yr −1 over the period 2000-2010-based on the carbon bookkeeping model. These new data sets of aboveground carbon stocks will enable tropical nations to meet their emissions reporting requirements (that is, United Nations Framework Convention on Climate Change Tier 3) with greater accuracy.When forests are cleared, carbon stored above and below ground in leaves, branches, stems and roots is released to the atmosphere. As a consequence, forest clearing, especially in the tropics, is a major source of CO 2 to the atmosphere. Although the proportion of carbon stored in forests comprises 70-80% of total terrestrial carbon 5 , the spatial and temporal variability in carbon storage is substantial 6 . This variability arises from natural and anthropogenic disturbances, as well as differences in stand age, topography, soils and climate. Globally, soils hold two to three times more carbon than that stored above ground in forest vegetation, but with the exception of cultivation, peatland fires and thawing permafrost, much of the carbon in soils is physically and chemically protected and not easily oxidized 7 . In contrast, carbon stored in aboveground biomass is readily mobilized by disturbance processes such as fire, wind throw, pest outbreaks and land conversion 8 .Efforts to quantify the amount of carbon stored in aboveground biomass over large areas of the tropics have been fraught with uncertainty. For example, estimates of aboveground carbon storage in tropical African forests vary by over ref. 9). In turn, the lack of reliable estimates of forest carbon storage introduces large uncertainties into estimates of terrestrial carbon emissions 10-14 . In Amazonia, recent studies have suggested

Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha −1 for a range of biomass between 0 and 454 Mg ha −1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate that the model successfully captured the regional distribution of AGB. The results showed a strong positive correlation (R 2 = 0.90) between the GLAS height metrics and predicted AGB.

Industrial logging has become the most extensive land use in Central Africa, with more than 600,000 square kilometers (30%) of forest currently under concession. With use of a time series of satellite imagery for the period from 1976 to 2003, we measured 51,916 kilometers of new logging roads. The density of roads across the forested region was 0.03 kilometer per square kilometer, but areas of Gabon and Equatorial Guinea had values over 0.09 kilometer per square kilometer. A new frontier of logging expansion was identified within the Democratic Republic of Congo, which contains 63% of the remaining forest of the region. Tree felling and skid trails increased disturbance in selectively logged areas.

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?