森林蓄积量的精确估算是提升森林可持续经营水平的基础。基于机载激光雷达点云数据,生成49个激光雷达点云特征变量,结合地面调查样地数据,采用固定参数、Pearson相关分析和逐步回归3种方法,筛选出建模自变量,并通过线性回归和非线性回归拟合建立海南热带雨林国家公园3种主要树种(相思、橡胶、桉树)的蓄积量模型。结果表明:1)在3种树种的线性与非线性模型中,最优模型精度均在0.83以上,相思和桉树的非线性模型较优,橡胶的线性模型较优。2)高度类变量对蓄积量模型的影响最大,激光点云的强度、密度对蓄积量模型的影响较大,郁闭度也有一定影响。机载激光雷达点云数据获得的结构参数在建模中起着重要作用,未来可在相关工作中推广使用。

The accurate estimation of forest volume is the basis for improving the level of sustainable forest management.Based on the airborne laser point cloud data,49 laser point cloud feature variables were generated.Combined with the ground survey sample data,three methods of fixed parameter,Pearson screening,and stepwise regression screening were used to screen out the independent variables used for modeling,and then linear and nonlinear regression fitting was used to establish the accumulation models of the three main tree species in Hainan Tropical Rainforest National Park.The results show that:1)Among the linear and nonlinear models of the three tree species(Acacia confusa,Hevea brasiliensis, Eucalyptus robusta),the accuracy of the optimal models was above 0.83 with A.confusa and E.robusta having better nonlinear models,and H.brasiliensis having a better linear model.2)The height class variable has the greatest influence on the accumulation model.The intensity and density of the laser point cloud have a greater influence on the accumulation model,and the coverage class variable also has some influence.The structural parameters obtained from airborne LiDAR data play an important role in modeling,and can be popularized and used in related operational work in the future.