High-resolution mapping of forest carbon stocks in the Colombian Amazon
1Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, USA
2Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), Carrera 10 No. 20–30 Bogotá DC, Colombia
3Departamento de Ciencias Forestales, Universidad Nacional de Colombia Sede Medellín, Calle 59A No. 63–20, Medellín, Colombia
4Fundación Puerto Rastrojo, Carrera 10 No. 24–76 Oficina 1201, Bogotá DC, Colombia
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.