Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest
Forest growing stem volume (GSV) is regarded as one of the most important parameters for the quality evaluation and dynamic monitoring of forest resources. The accuracy of mapping forest GSV is highly related to the employed models and involved remote sensing features, and the criteria of feature ev...
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/2/402 |
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| author | Hui Lin Wanguo Zhao Jiangping Long Zhaohua Liu Peisong Yang Tingchen Zhang Zilin Ye Qingyang Wang Hamid Reza Matinfar |
| author_facet | Hui Lin Wanguo Zhao Jiangping Long Zhaohua Liu Peisong Yang Tingchen Zhang Zilin Ye Qingyang Wang Hamid Reza Matinfar |
| author_sort | Hui Lin |
| collection | DOAJ |
| description | Forest growing stem volume (GSV) is regarded as one of the most important parameters for the quality evaluation and dynamic monitoring of forest resources. The accuracy of mapping forest GSV is highly related to the employed models and involved remote sensing features, and the criteria of feature evaluation severely affect the performance of the employed models. However, due to the linear or nonlinear relationships between remote sensing features and GSV, widely used evaluation criteria inadequately express the complex sensitivity between forest GSV and spectral features, especially the saturation levels of features in a planted forest. In this study, novel feature evaluation criteria were constructed based on the Pearson correlations and optical saturation levels of the alternative remote sensing features extracted from two common optical remote sensing image sets (GF-1 and Sentinel-2). Initially, the spectral saturation level of each feature was quantified using the kriging spherical model and the quadratic model. Then, optimal feature sets were obtained with the proposed criteria and the linear stepwise regression model. Finally, four widely used machine learning models—support vector machine (SVM), multiple linear stepwise regression (MLR), random forest (RF) and K-neighborhood (KNN)—were employed to map forest GSV in a planted Chinese fir forest. The results showed that the proposed feature evaluation criteria could effectively improve the accuracy of estimating forest GSV and that the systematic distribution of errors between the predicted and ground measurements in the range of forest GSV was less than 300 m<sup>3</sup>/hm<sup>2</sup>. After using the proposed feature evaluation criteria, the highest accuracy of mapping GSV was obtained with the RF model for GF-1 images (R<sup>2</sup> = 0.49, rRMSE = 28.67%) and the SVM model for Sentinel-2 images (R<sup>2</sup> = 0.52, rRMSE = 26.65%), and the decreased rRMSE values ranged from 1.1 to 6.2 for GF-1 images (28.67% to 33.08%) and from 2.3 to 6.8 for Sentinel-2 images (26.85% to 33.28%). It was concluded that the sensitivity of the optimal feature set and the accuracy of the estimated GSV could be improved using the proposed evaluation criteria (less than 300 m<sup>3</sup>/hm<sup>2</sup>). However, these criteria were barely able to improve mapping accuracy for a forest with a high GSV (larger than 300 m<sup>3</sup>/hm<sup>2</sup>). |
| first_indexed | 2024-03-09T11:19:53Z |
| format | Article |
| id | doaj.art-0e5f132bbe6f4a22a4299e507d5a3288 |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-09T11:19:53Z |
| publishDate | 2023-01-01 |
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| series | Remote Sensing |
| spelling | doaj.art-0e5f132bbe6f4a22a4299e507d5a32882023-12-01T00:20:12ZengMDPI AGRemote Sensing2072-42922023-01-0115240210.3390/rs15020402Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir ForestHui Lin0Wanguo Zhao1Jiangping Long2Zhaohua Liu3Peisong Yang4Tingchen Zhang5Zilin Ye6Qingyang Wang7Hamid Reza Matinfar8Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, ChinaSoil Science Department, Lorestan University, Khoramabad 68151-44316, IranForest growing stem volume (GSV) is regarded as one of the most important parameters for the quality evaluation and dynamic monitoring of forest resources. The accuracy of mapping forest GSV is highly related to the employed models and involved remote sensing features, and the criteria of feature evaluation severely affect the performance of the employed models. However, due to the linear or nonlinear relationships between remote sensing features and GSV, widely used evaluation criteria inadequately express the complex sensitivity between forest GSV and spectral features, especially the saturation levels of features in a planted forest. In this study, novel feature evaluation criteria were constructed based on the Pearson correlations and optical saturation levels of the alternative remote sensing features extracted from two common optical remote sensing image sets (GF-1 and Sentinel-2). Initially, the spectral saturation level of each feature was quantified using the kriging spherical model and the quadratic model. Then, optimal feature sets were obtained with the proposed criteria and the linear stepwise regression model. Finally, four widely used machine learning models—support vector machine (SVM), multiple linear stepwise regression (MLR), random forest (RF) and K-neighborhood (KNN)—were employed to map forest GSV in a planted Chinese fir forest. The results showed that the proposed feature evaluation criteria could effectively improve the accuracy of estimating forest GSV and that the systematic distribution of errors between the predicted and ground measurements in the range of forest GSV was less than 300 m<sup>3</sup>/hm<sup>2</sup>. After using the proposed feature evaluation criteria, the highest accuracy of mapping GSV was obtained with the RF model for GF-1 images (R<sup>2</sup> = 0.49, rRMSE = 28.67%) and the SVM model for Sentinel-2 images (R<sup>2</sup> = 0.52, rRMSE = 26.65%), and the decreased rRMSE values ranged from 1.1 to 6.2 for GF-1 images (28.67% to 33.08%) and from 2.3 to 6.8 for Sentinel-2 images (26.85% to 33.28%). It was concluded that the sensitivity of the optimal feature set and the accuracy of the estimated GSV could be improved using the proposed evaluation criteria (less than 300 m<sup>3</sup>/hm<sup>2</sup>). However, these criteria were barely able to improve mapping accuracy for a forest with a high GSV (larger than 300 m<sup>3</sup>/hm<sup>2</sup>).https://www.mdpi.com/2072-4292/15/2/402forest growing stem volumespectral saturationfeature evaluation criterionkriging spherical modelquadratic model |
| spellingShingle | Hui Lin Wanguo Zhao Jiangping Long Zhaohua Liu Peisong Yang Tingchen Zhang Zilin Ye Qingyang Wang Hamid Reza Matinfar Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest Remote Sensing forest growing stem volume spectral saturation feature evaluation criterion kriging spherical model quadratic model |
| title | Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest |
| title_full | Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest |
| title_fullStr | Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest |
| title_full_unstemmed | Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest |
| title_short | Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest |
| title_sort | mapping forest growing stem volume using novel feature evaluation criteria based on spectral saturation in planted chinese fir forest |
| topic | forest growing stem volume spectral saturation feature evaluation criterion kriging spherical model quadratic model |
| url | https://www.mdpi.com/2072-4292/15/2/402 |
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