Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions

Current markets and international agreements for reducing emissions from deforestation and forest degradation (REDD) rely on carbon (C) monitoring techniques. Combining field measurements, airborne light detection and ranging (LiDAR)‐based observations, and satellite‐based imagery, we developed a 30‐meter‐resolution map of aboveground C density spanning 40 vegetation types found on the million‐hectare Island of Hawaii. We estimate a total of 28.3 teragrams of C sequestered in aboveground woody vegetation on the island, which is 56% lower than Intergovernmental Panel on Climate Change estimates that do not resolve C variation at fine spatial scales. The approach reveals fundamental ecological controls over C storage, including climate, introduced species, and land‐use change, and provides a fourfold decrease in regional costs of C measurement over field sampling alone.

BackgroundAccurate, high-resolution mapping of aboveground carbon density (ACD, Mg C ha-1) could provide insight into human and environmental controls over ecosystem state and functioning, and could support conservation and climate policy development. However, mapping ACD has proven challenging, particularly in spatially complex regions harboring a mosaic of land use activities, or in remote montane areas that are difficult to access and poorly understood ecologically. Using a combination of field measurements, airborne Light Detection and Ranging (LiDAR) and satellite data, we present the first large-scale, high-resolution estimates of aboveground carbon stocks in Madagascar.ResultsWe found that elevation and the fraction of photosynthetic vegetation (PV) cover, analyzed throughout forests of widely varying structure and condition, account for 27-67% of the spatial variation in ACD. This finding facilitated spatial extrapolation of LiDAR-based carbon estimates to a total of 2,372,680 ha using satellite data. Remote, humid sub-montane forests harbored the highest carbon densities, while ACD was suppressed in dry spiny forests and in montane humid ecosystems, as well as in most lowland areas with heightened human activity. Independent of human activity, aboveground carbon stocks were subject to strong physiographic controls expressed through variation in tropical forest canopy structure measured using airborne LiDAR.ConclusionsHigh-resolution mapping of carbon stocks is possible in remote regions, with or without human activity, and thus carbon monitoring can be brought to highly endangered Malagasy forests as a climate-change mitigation and biological conservation strategy.

Skole et al.(1) claim that we do not make a case for highresolution carbon stock and emissions mapping in tropical forests. Specifically, they argue that (i) our Intergovernmental Panel on Climate Change (IPCC) Tier 1 estimates for the Peruvian Amazon study are biased, (ii) our plot-level carbon estimates used to calibrate airborne Light Detection and Ranging (LiDAR) are flawed, and (iii) our regional mapping of carbon stocks is low compared with a their estimate using field data from a previous local-scale study. However, their critique is based on misstatements about our methods and a misunderstanding of carbon stock variation throughout the region. (1) present a Tier 1 estimate, downscaled with forest fractional cover, of 538 teragrams (Tg) C. This reduces our purportedly biased estimate of 587 Tg by only 8%. However, far more importantly, we showed that these Tier 1 estimates have uncertainties exceeding 90%, rendering them extremely tenuous for use in monitoring carbon emissions.Skole et al.(1) also incorrectly state the allometries used in our field plots. As reported, 10 or more trees were measured for height in every plot distributed across the range of forest types found throughout the region. We measured the heights of the three largest trees in every plot, and these values were used in the model by Chave et al. (5). Because larger trees contribute far greater biomass to plot totals, our measurements accounted for the majority of biomass in all plots. Skole et al. (1) also suggest that an alternative allometric model from Winrock International (6) would be more appropriate for our area. They argue that the model of Chave et al. (5) was not calibrated to the height variation of locally measured trees, which is not true. Far from dismissing the Winrock International (6) model, we tested and rejected it, along with two other models, because each lacks parameters to account for height and/or wood density of individual trees.Skole et al. (1) next assume that the Winrock International (6) study was representative of our much larger study region. However, airborne LiDAR sampling revealed 20-35% lower carbon stocks in forests to the north and south of the local Winrock study, showing that the Winrock plots were placed in an area of locally high biomass. Extrapolating those local values to our entire study region misses critical carbon variation and produces an erroneous result. By substituting a locally derived carbon density value based on just 4 of 26 vegetation types found throughout the region, Skole et al.(1) overestimate regional carbon stocks by 25%-and ironically-underscore the importance of using a rigorous high-resolution method such as ours (2).The criticisms leveled by Skole et al. (1) reinforce the importance of our key findings: (i) forest carbon density varies significantly by factors operating at differing geological, ecological, and land-use scales, (ii) these variations in carbon density are invisible using IPCC Tier 1 mapping techniques but can be accounted for with a combination of airborne...