An explicit forest carbon stock model and applications

Abstract

How to achieve reliable monitoring of global forest carbon sinks is of great urgency, and the combination of remote sensing and ground observation has become a hot topic. The relationship between remote sensing features (vegetation indices, spectral, textural, backscattering coefficients) and forest carbon stock is still unclear, hence this paper proposes a pixel-level, multi-scale, high-precision Explicit Forest carbon stock Model (EFM) that is universal and adaptive. First, the pixel size, forest canopy density, terrain slope, and forest height were used in the construction of EFM; Second, the EFM parameters were solved by simulated forest scene; Third, the EFM was used in simulated and real forest scenes to verify the accuracy, robustness, and applicability, the experiments show that the relative error is about 15%; Finally, the first time mapping forest carbon stock over 200,000 km2 area at 2 m scale was completed by the EFM. The EFM convert the calculation unit from individual tree to pixel compared with allometric growth equation, and overcome the poor universality of regression inversion methods, which can be used to monitor forest carbon dynamics at global scale.