| 基于机载激光雷达点云的桉树林分蓄积量估算模型构建 |
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| 引用本文： | 邓焯,李斌,范光鹏,赵天忠,于永辉.基于机载激光雷达点云的桉树林分蓄积量估算模型构建[J].浙江农林大学学报,2022,39(6):1330-1339. |
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| 作者姓名： | 邓焯 李斌 范光鹏 赵天忠 于永辉 |
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| 作者单位： | 1.北京林业大学 信息学院，北京 1000832.北京林业大学 国家林业草原林业智能信息处理工程技术研究中心，北京 1000833.北京林业大学 林业信息化研究所，北京 1000834.广西壮族自治区国有高峰林场，广西 南宁 530001 |
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| 基金项目： | 国家重点研发计划项目(2017YFD0600906) |
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| 摘 要： | 目的 通过2018年1—2月广西国有高峰林场机载激光雷达数据及地面调查数据，采用参数方法和非参数方法建立回归模型，反演桉Eucalyptus树人工林森林蓄积量。 方法 通过点云提取点云高度参数、点云密度参数、林分郁闭度等点云特征变量，采用参数方法(逐步回归、偏最小二乘回归)和非参数方法(随机森林回归、支持向量机回归)进行林分蓄积量构建，通过与样地实测数据对比，进行模型回归预测性能评估，进而选择出表现最优蓄积量反演模型。 结果 采用留一法对以上4种模型进行验证，结果显示:逐步回归模型R2为0.85、均方根误差(RMSE)为23.93 m3·hm?2、平均绝对误差(MAE)为18.18 m3·hm?2；偏最小二乘回归模型R2为0.81、RMSE为26.52 m3·hm?2、MAE为19.94 m3·hm?2；核函数为RBF的支持向量回归模型R2为0.88、RMSE为21.35 m3·hm?2、MAE为16.62 m3·hm?2；随机森林回归模型R2为0.84、RMSE为24.53 m3·hm?2、MAE为17.41 m3·hm?2。 结论 采用随机森林进行变量筛选后，RBF-SVR模型拟合优度及泛化能力最优；通过逐步筛选法结合方差膨胀因子(VIF)方法优选变量的逐步回归模型次之；最后为随机森林回归模型与偏最小二乘回归模型。可见，在解决林业激光雷达领域中的回归预测问题时，采用非参数方法构建RBF-SVR模型更有优势。本研究建立的4种森林类型蓄积量模型，各模型均有较高精度且符合森林资源调查相关技术规定要求。图6表6参25
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| 关 键 词： | 机载激光雷达 林分蓄积量 参数回归 非参数回归 变量筛选 |
| 收稿时间： | 2022-01-10 |
Estimation model of Eucalyptus stand volume based on airborne LiDAR Point Cloud |
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| DENG Zhuo,LI Bin,FAN Guangpeng,ZHAO Tianzhong,YU Yonghui.Estimation model of Eucalyptus stand volume based on airborne LiDAR Point Cloud[J].Journal of Zhejiang A&F University,2022,39(6):1330-1339. |
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| Authors: | DENG Zhuo LI Bin FAN Guangpeng ZHAO Tianzhong YU Yonghui |
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| Affiliation: | 1.College of Information, Beijing Forestry University, Beijing 100083, China2.Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing Forestry College, Beijing 100083, China3.Institute of Forestry Information, Beijing Forestry University, Beijing 100083, China4.State Owned Gaofeng Forest Farm of Guangxi Zhuang Autonomous Region, Nanning 530001, Guangxi, China |
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| Abstract: | Objective LiDAR, as an active remote sensing technology, has proven to be an effective and efficient means for large-scale dynamic monitoring and investigation for forest resources. With an analysis of the airborne LiDAR data and ground survey data collected of Guangxi state-owned Gaofeng forest farm from January to February 2018, this paper is aimed to establish a regression model by using parametric and nonparametric methods to inverse the forest volume of Eucalyptus plantation. Method First, point cloud characteristic variables such as point cloud height parameters, point cloud density parameters and stand canopy density were extracted from point cloud after which parametric methods (stepwise regression, partial least squares regression) and nonparametric methods (random forest regression and support vector machine regression) were used to construct stand volume. Then the prediction performance of model regression is evaluated by comparing the estimated data of sample plot with the measured ones so that the optimal volume inversion model could be selected. Result After being verified by leaving one method, results are as follows: for the stepwise regression model, R2=0.85, RMSE=23.93 m3·hm?2 and MAE=18.18 m3·hm?2; for the partial least squares regression model, R2=0.81, RMSE=26.52 m3·hm?2 and MAE=19.94 m3·hm?2; for the support vector regression model with RBF kernel function, R2=0.88, RMSE=21.35 m3·hm?2, MAE=16.62 m3·hm?2 whereas for the random forest regression model, R2=0.84, RMSE=24.53 m3·hm?2 and MAE=17.41 m3·hm?2. Conclusion After variable screening with random forest; the RBF-SVR model has demonstrated the best fitness and generalization ability, followed by the stepwise regression model of optimizing variables by stepwise screening method combined with variance inflation factor (VIF) method whereas the random forest regression model and partial least squares regression model came last. It was also shown that nonparametric method is a better choice in the construction of RBF-SVR model in solving the problem of regression prediction in the field of forestry LiDAR and that the volume models of four forest types established in this study have high accuracy and met the requirements of relevant technical regulations of forest resources investigation. Ch, 6 fig. 6 tab. 25 ref.] |
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