A vegetation carbon sink prediction method based on fine characterization of crown layer three-dimensional structure
By constructing a three-dimensional canopy structure and employing ray tracing technology and Monte Carlo integration based on multiple importance sampling, the problem of low accuracy of leaf-scale information in canopy photosynthesis models was solved, enabling high-precision prediction of carbon sinks in large-area complex vegetation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING NORMAL UNIVERSITY
- Filing Date
- 2026-03-31
- Publication Date
- 2026-07-24
AI Technical Summary
Existing canopy photosynthesis models suffer from low accuracy in simulating canopy photosynthesis information at the leaf scale, especially in carbon sink estimation of large-area complex vegetation, where undersampling leads to insufficient accuracy.
A method based on the fine characterization of the canopy three-dimensional structure was adopted. The three-dimensional structure of the canopy was constructed by lidar point cloud data and surface hyperspectral reflectance orthophotos. The photosynthetic photon flux absorbed point by point in the turbid medium was calculated by using ray tracing technology combined with Monte Carlo integration of multiple importance sampling. The leaves were divided into shaded leaves and sun-sun leaves for vertical layering, and the photosynthetic rate of each layer was calculated. Finally, the total carbon sink was obtained by summing them up.
It improves the accuracy of carbon sink prediction for large-area complex vegetation, realizes the precise quantification of leaf distribution density and key photosynthetic parameters within the canopy, enhances the sampling density and uniformity of photosynthetic photon absorption calculation, and improves the accuracy of carbon sink estimation.
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