Study on the Estimation of Forest Volume Based on Multi-Source Data
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 <i>Larix olgensis</i>, <i>Pinus koraiensis...
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/23/7796 |
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| author | Tao Hu Yuman Sun Weiwei Jia Dandan Li Maosheng Zou Mengku Zhang |
| author_facet | Tao Hu Yuman Sun Weiwei Jia Dandan Li Maosheng Zou Mengku Zhang |
| author_sort | Tao Hu |
| collection | DOAJ |
| description | 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 <i>Larix olgensis</i>, <i>Pinus koraiensis</i>, and <i>Pinus sylvestris</i> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>L</mi><mi>o</mi><mi>o</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>L</mi><mi>o</mi><mi>o</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T04:45:12Z |
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| language | English |
| last_indexed | 2024-03-10T04:45:12Z |
| publishDate | 2021-11-01 |
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| spelling | doaj.art-2b635981d4c149dbba71ae226e3d02672023-11-23T02:59:16ZengMDPI AGSensors1424-82202021-11-012123779610.3390/s21237796Study on the Estimation of Forest Volume Based on Multi-Source DataTao Hu0Yuman Sun1Weiwei Jia2Dandan Li3Maosheng Zou4Mengku Zhang5School of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaWe 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 <i>Larix olgensis</i>, <i>Pinus koraiensis</i>, and <i>Pinus sylvestris</i> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>L</mi><mi>o</mi><mi>o</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>L</mi><mi>o</mi><mi>o</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/1424-8220/21/23/7796forest volumemulti-source remote sensing factorordinary Kriging (OK)random forest (RF)support vector regression (SVR)artificial neural network (ANN) |
| spellingShingle | Tao Hu Yuman Sun Weiwei Jia Dandan Li Maosheng Zou Mengku Zhang Study on the Estimation of Forest Volume Based on Multi-Source Data Sensors forest volume multi-source remote sensing factor ordinary Kriging (OK) random forest (RF) support vector regression (SVR) artificial neural network (ANN) |
| title | Study on the Estimation of Forest Volume Based on Multi-Source Data |
| title_full | Study on the Estimation of Forest Volume Based on Multi-Source Data |
| title_fullStr | Study on the Estimation of Forest Volume Based on Multi-Source Data |
| title_full_unstemmed | Study on the Estimation of Forest Volume Based on Multi-Source Data |
| title_short | Study on the Estimation of Forest Volume Based on Multi-Source Data |
| title_sort | study on the estimation of forest volume based on multi source data |
| topic | forest volume multi-source remote sensing factor ordinary Kriging (OK) random forest (RF) support vector regression (SVR) artificial neural network (ANN) |
| url | https://www.mdpi.com/1424-8220/21/23/7796 |
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