The explicit model of forest carbon storage based on remote sensing

doi:10.11947/j.AGCS.2024.20230089

Abstract

Abstract: Facing the national carbon peaking and carbon neutrality goals, and the demand of international carbon trading market, the carbon sinks status and future carbon potential of terrestrial ecosystems are in urgent need of research. Forest is the important carbon sink in the terrestrial ecosystem, the method based on ground observation has a large workload and the sampling statistical results are difficult to evaluate, the method based on satellite remote sensing inversion lacks theoretical explanation and has poor universality. Based on the carbon storage model of single tree, this paper proposes an explicit forest carbon storage model. The forest carbon storage is expressed by remote sensing image resolution, vegetation coverage, and canopy height, the parameters are theoretically calculated by the characteristics of single trees. In order to verify the accuracy, robustness and applicability, forest simulation data under different conditions is constructed, the experimental results show the superiority of the model in various aspects, which can overcome the theoretical explanation in machine/deep learning inversion of forest carbon reserves, and realize high-resolution mapping of global forest carbon.

Key words: forest carbon storage, remote sensing model, forest canopy density, forest height, simulated forest

CLC Number:

P258

ZHU Ningning, YANG Bisheng, DONG Zhen. The explicit model of forest carbon storage based on remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(1): 36-49.

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