基于UAV-LiDAR的长白落叶松人工林单木断面积生长量估测模型研建OA
Development of a Basal Area Growth Estimation Model for Individual Trees of Larix olgensis Plantations based on UAV-LiDAR Data
[目的]基于高密度无人机激光雷达(UAV-LiDAR)数据构建长白落叶松人工林单木胸高断面积生长量(BAI)估测模型,评估 UAV-LiDAR技术在单木水平生长建模中的应用潜力,为森林精准经营与碳汇动态评估提供新的技术方案和决策依据.[方法]以黑龙江省孟家岗林场不同年龄、密度和立地条件下的长白落叶松人工林为对象,提取 UAV-LiDAR数据中的树冠结构和竞争指标作为主要变量,结合 147株解析木断面积生长数据,采用相关性分析、前向逐步回归、共线性诊断与混合效应模型建模方法,建立基于 UAV-LiDAR数据的单木生长量估测模型,同时构建基于实测数据的生长量估测模型,通过 10折交叉验证比较两模型的预测精度.[结果]UAV-LiDAR衍生的冠层竞争指标和树冠结构指标均与BAI显著相关,其中光竞争指数(LCI)和暴露冠表面积(ECA)分别作为相关性最高的竞争和树冠结构变量,优于传统实地测量的胸径和竞争指标.基于 UAV-LiDAR的单木断面积生长量估测模型拟合效果良好,调整后决定系数(R2a)达 0.846,均方根误差(RMSE)为 1.35 cm2·a-1,交叉验证结果显示,模型具有较好的稳健性和预测能力,其平均绝对误差(MAE)和平均绝对百分比误差(MAPE)分别为1.26 cm2·a-1 和 25.7%,均低于基于实测数据构建的模型.[结论]单时相 UAV-LiDAR数据在单木生长预测中展现出良好潜力,完全基于点云提取的变量能够有效量化驱动树木生长的树冠结构和冠层竞争机制,所构建的模型可为单木水平的生长预测和经营决策提供可靠的理论依据和技术支持.
[Objective]This study aims to construct a breast height basal area increment(BAI)estimation model for individual trees of Larix olgensis plantations by using high-density UAV-LiDAR data,in order to evaluate the application potential of UAV-LiDAR in individual tree growth modeling.[Method]The study was conducted in L.olgensis plantations with various ages,densities,and site conditions in Mengjiagang Forest Farm in Heilongjiang Province.The crown structure and competition indices derived from UAV-LiDAR data served as primary variables,integrated with BAI data obtained from 147 stem-analyzed trees.A modeling method based on correlation analysis,forward stepwise regression,collinearity diagnostics,and mixed-effects model was employed to develop an UAV-LiDAR-based individual tree growth model.A field-measurement-based model was also constructed for comparison.Predictive accuracy of the two models was evaluated and compared using 10-fold cross-validation.[Result]There were significant correlations between the UAV-LiDAR-derived canopy competition and crown structure variables and BAI.Among them,the light competition index(LCI)and exposed crown surface area(ECA)were the highest correlated variables of competition and canopy structure,respectively,outperforming traditional field-measured diameter at breast height and competition indices.The UAV-LiDAR-based individual BAI estimation model achieved a satisfactory goodness-of-fit,with adjusted coefficient of determination(R2a)of 0.846 and a root mean square error(RMSE)of 1.35 cm2·a-1.The results of cross validation showed that the UAV-LiDAR-based model possessed good robustness and predictive capability,yielding a mean absolute error(MAE)of 1.26 cm2·a-1 and a mean absolute percentage error(MAPE)of 25.7%,which were lower than those of the field-measurement-based model.[Conclusion]The uni-temporal UAV-LiDAR data has shown great potential in accurately predicting individual tree growth.Variables entirely derived from UAV-LiDAR can effectively quantify the crown structure and competition mechanisms driving tree growth.The developed model provides a theoretical foundation and technical support for individual tree-level growth prediction and forest management decision-making.
刘鑫;郝元朔;董利虎;赵颖慧;李凤日
东北林业大学林学院 哈尔滨 150040东北林业大学林学院 哈尔滨 150040东北林业大学林学院 哈尔滨 150040东北林业大学林学院 哈尔滨 150040东北林业大学林学院 哈尔滨 150040
农业科技
无人机激光雷达冠层竞争树冠结构单木生长量估测模型长白落叶松人工林
unmanned aerial vehicle light detection and ranging(UAV-LiDAR)canopy competitioncrown structureindividual tree growth modelLarix olgensis plantation
《林业科学》 2026 (8)
11-20,10
"十四五"国家重点研发计划课题(2023YFD2200802).
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