Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading

编号 030037803

推送时间 20230116

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading

来源期刊 INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

期 第378期

发表时间 20220624

关键词 topographic LiDAR data;  forest;  growth simulation;  shading;  crown asymmetry;  canopy height model; 

摘要 Reliable forest growth forecasting requires detailed tree data for forest simulation, while manual on-site collection of relevant data is work-intensive and unfeasible in larger forests. This paper proposes a complete methodology for fully automated forest growth simulation that relies primarily on airborne topographic Light Detection And Ranging (LiDAR) point clouds of individual trees. The proposed method estimates tree parameters and performs growth of individual trees based on an individual-based forest growth simulator, named BWINPro. In addition, competition and detailed asymmetric tree crown growth are modeled regarding the shading of tree crowns, which is estimated from the surrounding environment and neighbor trees. The result of the proposed approach is a new point cloud for subsequent analyses. The proposed method was validated by comparing canopy height models derived from the point clouds of the simulated trees with canopy height models derived from more recent ground truth point clouds. The results demonstrate the efficacy of the proposed method which achieves a 9.4% higher accuracy than the averaged linear regression model and, in the case of datasets with more distinct self-standing trees, where a tree crown boundary plays major role, a 4.1% higher accuracy than the directly fitted linear regression model.

服务人员 付贺龙

服务院士 唐守正

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