Abstract
Abstract. 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.
Highlights
Tropical forests store roughly 475 billion tons of carbon (Pan et al, 2011), so retaining this carbon through conservation and increasing its stock through management activities that promote forest growth will play a major role in curbing a principal driver of climate change (Angelsen, 2008)
We address three questions pertinent to carbon mapping efforts in remote, inaccessible tropical forests: (i) Using available satellite imagery and airborne light detection and ranging (LiDAR) sampling, what are the principal determinants of aboveground carbon density detectable throughout the region? (ii) Despite limits to acquiring field inventory data on the ground, what are the estimated uncertainties associated with applying the universal LiDAR approach to the Colombian Amazon? (iii) What are the uncertainties associated with the stratification and regression approaches, and what are their advantages and disadvantages?
We demonstrate that high-resolution mapping of tropical forest carbon stocks assisted by airborne LiDAR can be accomplished with limited field calibration data and limited preexisting knowledge of the study region
Summary
Tropical forests store roughly 475 billion tons of carbon (Pan et al, 2011), so retaining this carbon through conservation and increasing its stock through management activities that promote forest growth will play a major role in curbing a principal driver of climate change (Angelsen, 2008). The logistical and cost burden of establishing an extensive plot network may limit the utility of LiDAR for carbon mapping, in forests that remain very remote, either by distance or by difficult terrain To address this problem, Asner et al (2012b) recently developed a “universal” equation to estimate tropical ACD from airborne LiDAR. Regression approaches may miss local or sub-regional controls over carbon stocks that can be resolved using stratification These two issues – LiDAR applicability with few field plots, and upscaling of LiDAR data to larger regions – remain critically important challenges to making high-resolution carbon stock and emissions monitoring possible.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
