基于机载LiDAR数据估测林分平均高

Estimation of Forest Stand Mean Height Based on Airborne LiDAR Point Cloud Data

[目的]以2016年9月广西壮族自治区高峰林场实验区获取的机载LiDAR点云数据为基础,通过提取30 m×30 m空间林分尺度下的LiDAR点云特征变量实现对林分平均高的估测。[方法]首先将105块实测林分平均高度的样地数据按照3:1的比例随机划分为训练样本(79)和检验样本(26),采用随机森林回归(RFR)和支持向量回归(SVR)两种机器学习算法对79个训练样本与对应的林分LiDAR点云特征变量回归建模。建模方案包括随机森林模型、支持向量机模型及随机森林+支持向量机组合模型。其次利用26个检验样本数据评价模型预测精度。最后统计3个模型中训练样本和检验样本对应的精度评价指标,以一个预测精度高、泛化能力强的模型作为最终模型进行林分平均高制图。[结果]表明:随机森林模型的训练样本和检验样本的决定系数(R2)分别为0.8861和0.8375,均方根误差(RMSE)分别为1.22和1.56;支持向量机模型的训练样本和检验样本的决定系数(R2)分别为0.8864和0.8409,均方根误差(RMSE)分别为1.21和1.54;组合模型的训练样本和检验样本的决定系数(R2)分别为0.8598和0.8532,均方根误差(RMSE)分别为1.35和1.48;[结论]组合模型的泛化能力及预测精度最好,支持向量机次之,最后为随机森林。利用组合模型可有效完成研究区林分平均高制图。

[Objective] Based on the airborne LiDAR point cloud data obtained in the experimental area of the Gaofeng Forest Farm in Guangxi Zhuang Autonomous Region in September 2016 to estimate the stand mean height by extracting the LiDAR point cloud characteristic variables at the spatial stand scale of 30 m×30 m. [Method] The data of mean height obtained from 105 stands were randomly divided into training samples(79) and test samples(26)according to a ratio of 3:1. Two machine learning algorithms, namely random forest regression and support vector regression, were adopted to conduct regression modeling of 79 training samples and corresponding LiDAR point cloud feature variables. The modeling schemes include random forest model, support vector machine model and random forest + support vector combination model. Secondly, the 26 test sample data were used to evaluate the prediction accuracy of the model. Finally, the accuracy evaluation indexes corresponding to the training samples and the test samples in the three models were counted, and a model with high prediction accuracy and strong generalization ability was taken as the final model to map the stand mean height of forest. [Result] The result showed that the determination coefficient(R2) of the training samples and the test samples of the random forest model were 0.8861 and0.8375, respectively, and the root mean square errors(RMSE) were 1.22 and 1.56, respectively. The R2 of the training samples and the test samples of support vector machine model were 0.8864 and 0.8409, respectively, and the RMSE were 1.21 and 1.54, respectively. The R2 of the training samples and the test samples of the combined model were0.8598 and 0.8532 respectively, and the RMSE were 1.35 and 1.48 respectively. [Conclusion] The combined model has the best generalization ability and prediction accuracy, followed by support vector machine and the random forest. The combined model can effectively complete the mapping of stand mean height in the study area.