Study on the Estimation of Forest Volume Based on Multi-Source Data.
Study on the Estimation of Forest Volume Based on Multi-Source Data.
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基于多源数据的森林蓄积量估算研究
DOI：
10.3390/s21237796
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发表时间：
2021-11-23
期刊：
影响因子：
--
通讯作者：
Zhang M
中科院分区：
文献类型：
--
作者：
Hu T;Sun Y;Jia W;Li D;Zou M;Zhang M
以多源遥感数据为基础，结合地面调查数据，对机器学习方法和普通克立格(OK)混合方法对森林蓄积模型的预测精度进行了对比分析。以孟家港林场的长白落叶松、红松、樟子松人工林为研究对象，基于中国林科院激光雷达、电荷耦合装置和高光谱(CAF-Litchy)综合系统，分别提取了可见植被指数、纹理特征、地形因子和点云特征变量。采用随机森林(RF)、支持向量回归(SVR)和人工神经网络(ANN)方法估计森林蓄积量。在小尺度空间中，样地蓄积量的估计不仅受周围环境的影响，还受邻近观测数据的影响。在这3种机器学习模型残差的基础上，应用OK插值法构建了新的混合森林蓄积量估计模型：随机森林克里格法(RFK)、回归支持向量机克里格法(SVRK)和人工神经网络克里格法(ANNK)。采用留一法(LOO)交叉验证法对6种森林蓄积量估计模型进行检验。这六个模型的预测精度都较好，RLoo2值均在0.6以上，混合模型的预测精度值都有不同程度的提高。在6个模型中，RFK混合模型的预测效果最好，RLoO2达到0.915。因此，基于多源遥感因子的机器学习方法对森林蓄积量的估计是有用的，特别是将机器学习和OK方法相结合构建的混合模型大大提高了森林蓄积量估计的精度，从而为森林蓄积量的遥感反演估计提供了一种快速有效的方法，方便了森林资源的管理。
We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.
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DOI：
10.3390/s21082782
发表时间：
2021-04-15
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Chen Z;Li J;Yang B
通讯作者：
Yang B
影响因子：
2.6
作者：
Harris, P.;Fotheringham, A. S.;Charlton, M.
通讯作者：
Charlton, M.
影响因子：
5
作者：
Iizuka, Kotaro;Yonehara, Taichiro;Kosugi, Yoshiko
通讯作者：
Kosugi, Yoshiko
影响因子：
2.2
作者：
Freeman, Elizabeth A.;Moisen, Gretchen G.;Wilson, Barry T.
通讯作者：
Wilson, Barry T.
影响因子：
5
作者：
Feng, Quanlong;Liu, Jiantao;Gong, Jianhua
通讯作者：
Gong, Jianhua