High-resolution mapping of forest carbon stocks in the Colombian Amazon

[摘要] High-resolution mapping of tropical forest carbon stocks can assist forestmanagement and improve implementation of large-scale carbon retention andenhancement programs. Previous high-resolution approaches have relied onfield plot and/or light detection and ranging (LiDAR) samples of abovegroundcarbon density, which are typically upscaled to larger geographic areasusing stratification maps. Such efforts often rely on detailed vegetationmaps to stratify the region for sampling, but existing tropical forest mapsare often too coarse and field plots too sparse for high-resolution carbonassessments. We developed a top-down approach for high-resolution carbonmapping in a 16.5 million ha region (> 40%) of the Colombian Amazon –a remote landscape seldom documented. We report on three advances forlarge-scale carbon mapping: (i) employing a universal approach to airborneLiDAR-calibration with limited field data; (ii) quantifying environmentalcontrols over carbon densities; and (iii) developing stratification- andregression-based approaches for scaling up to regions outside of LiDARcoverage. We found that carbon stocks are predicted by a combination ofsatellite-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, andthe regional map based on stratification has 28% uncertainty in any givenhectare. High-resolution approaches with quantifiable pixel-scaleuncertainties will provide the most confidence for monitoring changes intropical forest carbon stocks. Improved confidence will allow resourcemanagers and decision makers to more rapidly and effectively implementactions that better conserve and utilize forests in tropical regions.

[学科分类] 地球化学与岩石