基于背包激光雷达的单木材积估测方法研究

Research on Individual Tree Volume Estimation Using Backpack LiDAR

背包式激光雷达(Backpack Laser Scanning,BLS)在森林资源调查中具有很大的应用潜力,但在复杂地表情景下,单木材积和林分蓄积量提取精度存在较大不确定性。以广西高峰林场为研究区,利用随机森林方法,基于BLS点云数据对单木材积和样地蓄积量进行估测。首先,对BLS点云进行单木分割,提取单木胸径(DBH)、树高(Htree)、冠幅直径(CD)、冠幅面积(CA)、冠幅体积(CV)、郁闭度(CC)、间隙率(GF)和叶面积指数(LAI)共8个特征参数,并计算56个分层高度指标(高度百分比、累积高度百分比、变异系数、冠层起伏率等)。然后,通过随机森林算法构建单木材积估测模型,并对比各种参数组合的预测精度。得到结果:(1)仅用8个单木结构特征参数进行建模,估测精度为:R^(2)=0.83、RMSE=0.097 m^(3);(2)加入分层高度指标的模型估测精度有所提升:R^(2)=0.87、RMSE=0.087 m^(3);(3)通过Boruta算法进行变量筛选,输入参数从64个减少至52个,估测精度差异不大:R^(2)=0.87、RMSE=0.087 m^(3);(4)样方蓄积量估测精度为:R^(2)=0.97,RMSE=0.703m^(3)·ha-1。结果表明,基于BLS点云建立随机森林单木材积估测模型可以较好地估测单木材积,样方蓄积量估测精度高。

Backpack Laser Scanning(BLS)is a potential tool in forest resource survey,but shows much uncertainty for the extraction accuracy of single-tree volume and forest stand volume in complex topographic circumstances.Using BLS point cloud data from the Gaofeng Forest Farm in Guangxi Province,this study implemented the estimation of single-tree volume and sample plot volume by random forest approach.First,individual tree segmentation was conducted using the BLS point cloud data,8 characteristic parameters were extracted including Diameter at Breast Height(DBH),Tree Height(Htree),Crown Diameter(CD),Crown Area(CA),Crown Volume(CV),Canopy Cover(CC),Gap Fraction(GF),and Leaf Area Index(LAI),and56 stratification height indicators were calculated(height percentage,cumulative height percentage,coefficient of variation,canopy undulation rate,etc.).Then,an individual treee volume estimation model was developed using the random forest technique,and the prediction accuracy of various parameter combinations was investigated.The results showed that:(1)modeling with only 8 characteristic parameters of an individual tree structure indicated an estimated accuracy of R^(2)=0.83、RMSE=0.097 m^(3);(2)modeling estimation accuracy was improved with the addition of the layered height index:R^(2)=0.87、RMSE=0.087 m^(3);(3)the Boruta algorithm for variable screening reduced the input parameters from 64 to 52,with little difference in estimation accuracy:R2=0.87,RMSE=0.087 m3;(4)the estimation accuracy of sample plot volume was R^(2)=0.97,RMSE=0.703 m^(3)·ha-1.The results suggested the application potential to use the BLS point cloud for individual tree volume estimation and the sample volume by random forest algorithm.