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<p>In the hilly regions of southern China, complex terrain significantly affects UAV-LiDAR point cloud processing, leading to discrepancies in canopy structural parameters before and after ground point fitting—a phenomenon termed “canopy distortion.” This distortion poses a challenge for the high-precision extraction of three-dimensional (3D) canopy structures. Using <i>Cunninghamia lanceolata</i> as the study species, this research systematically analyzed how terrain factors influence single-tree canopy parameters. Ground points were fitted using the RANSAC algorithm to derive 3D slopes (X, Y, Z) and intercepts (X, Y, Z). These derived variables, along with conventional terrain attributes (slope, aspect, curvature), were employed to quantify terrain-induced effects on canopy parameters. A Bayesian-optimized random forest model, featuring automatic parameter tuning to enhance predictive performance, was applied to predict changes in canopy parameters and assess the relative contributions of each terrain factor. Following normalization, terrain effects were mitigated, though residual distortions persisted: mean treetop offsets in X and Y directions remained below 0.1 m; changes in crown width and projected area were negligible; mean tree height decreased by 0.77 m; crown length increased by 0.05 m; while crown surface area and volume decreased by 0.54 m<sup>2</sup> and 0.62 m<sup>3</sup>, respectively. Tree height was primarily affected by intercept Y, crown volume and surface area by slope X, and crown length by curvature and slope. Model accuracy was highest for treetop X offset (<i>R</i><sup>2</sup> = 0.730) and lowest for crown surface area (<i>R</i><sup>2</sup> = 0.143). These findings elucidate how terrain induces distortions in canopy parameters and underscore the necessity of systematically quantifying terrain effects using enriched topographic variables and predictive modeling. This study provides a theoretical foundation for accurately extracting 3D canopy structures in complex terrains and supports intelligent forest monitoring and site quality assessment.</p>

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Terrain effects on 3D structural parameters of individual Chinese fir (Cunninghamia lanceolata) trees derived from UAV point clouds

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Fan Wang,
  • Xin Tan,
  • Jianluo Cui,
  • Yueyuan Yang,
  • Zhijie Xue,
  • Kunyong Yu,
  • Jian Liu

摘要

In the hilly regions of southern China, complex terrain significantly affects UAV-LiDAR point cloud processing, leading to discrepancies in canopy structural parameters before and after ground point fitting—a phenomenon termed “canopy distortion.” This distortion poses a challenge for the high-precision extraction of three-dimensional (3D) canopy structures. Using Cunninghamia lanceolata as the study species, this research systematically analyzed how terrain factors influence single-tree canopy parameters. Ground points were fitted using the RANSAC algorithm to derive 3D slopes (X, Y, Z) and intercepts (X, Y, Z). These derived variables, along with conventional terrain attributes (slope, aspect, curvature), were employed to quantify terrain-induced effects on canopy parameters. A Bayesian-optimized random forest model, featuring automatic parameter tuning to enhance predictive performance, was applied to predict changes in canopy parameters and assess the relative contributions of each terrain factor. Following normalization, terrain effects were mitigated, though residual distortions persisted: mean treetop offsets in X and Y directions remained below 0.1 m; changes in crown width and projected area were negligible; mean tree height decreased by 0.77 m; crown length increased by 0.05 m; while crown surface area and volume decreased by 0.54 m2 and 0.62 m3, respectively. Tree height was primarily affected by intercept Y, crown volume and surface area by slope X, and crown length by curvature and slope. Model accuracy was highest for treetop X offset (R2 = 0.730) and lowest for crown surface area (R2 = 0.143). These findings elucidate how terrain induces distortions in canopy parameters and underscore the necessity of systematically quantifying terrain effects using enriched topographic variables and predictive modeling. This study provides a theoretical foundation for accurately extracting 3D canopy structures in complex terrains and supports intelligent forest monitoring and site quality assessment.