Improving pantropical forest carbon maps with airborne LiDAR sampling

Background: Countries interested in monitoring and quantifying the carbon stock of their tropical forests need cost-effective methodologies to map aboveground carbon density (ACD) at regional and national project levels, and with measurable precision and accuracy. This study reports on improvements made possible by the use of airborne high resolution LiDAR samples to regionally fine-tune freely available moderate resolution remote sensing data, and generate maps of ACD with greater detail and improved accuracy than existing pantropical data sets. Results: Regions in the Peruvian and Colombian Amazon indicate that, although existing pantropical data sets of ACD explain approximately 70% of the variance in ACD relative to high resolution LiDAR estimates, by fine calibration with airborne LiDAR samples, it is possible to reduce the relative root mean squared error from 25.2 and 31.4 MgC ha(-1), to 15.7 and 17.6 MgC ha(-1), respectively, in Colombia and Peru. Conclusion: Airborne LiDAR data can successfully be used for fine-tuning freely available moderate resolution remote sensing image data and significantly improving existing aboveground carbon density maps, to better meet the requirements for national and subnationl carbon density mapping.