Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data
Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the inf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4674 |
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| author | Heyi Guo Sornkitja Boonprong Shaohua Wang Zhidong Zhang Wei Liang Min Xu Xinwei Yang Kaimin Wang Jingbo Li Xiaotong Gao Yujie Yang Ruichen Hu Yu Zhang Chunxiang Cao |
| author_facet | Heyi Guo Sornkitja Boonprong Shaohua Wang Zhidong Zhang Wei Liang Min Xu Xinwei Yang Kaimin Wang Jingbo Li Xiaotong Gao Yujie Yang Ruichen Hu Yu Zhang Chunxiang Cao |
| author_sort | Heyi Guo |
| collection | DOAJ |
| description | Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management. |
| format | Article |
| id | doaj-art-9e854f032f134694be62c62c5e0ed3b3 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9e854f032f134694be62c62c5e0ed3b32025-08-20T02:50:43ZengMDPI AGRemote Sensing2072-42922024-12-011624467410.3390/rs16244674Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source DataHeyi Guo0Sornkitja Boonprong1Shaohua Wang2Zhidong Zhang3Wei Liang4Min Xu5Xinwei Yang6Kaimin Wang7Jingbo Li8Xiaotong Gao9Yujie Yang10Ruichen Hu11Yu Zhang12Chunxiang Cao13Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaFaculty of Social Sciences, Kasetsart University, Bangkok 10900, ThailandKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAcademy of Forestry Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaTimely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management.https://www.mdpi.com/2072-4292/16/24/4674tree speciessentinel-1/2random forest, XGBoostdeep learninglarge scale mapping |
| spellingShingle | Heyi Guo Sornkitja Boonprong Shaohua Wang Zhidong Zhang Wei Liang Min Xu Xinwei Yang Kaimin Wang Jingbo Li Xiaotong Gao Yujie Yang Ruichen Hu Yu Zhang Chunxiang Cao Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data Remote Sensing tree species sentinel-1/2 random forest, XGBoost deep learning large scale mapping |
| title | Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data |
| title_full | Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data |
| title_fullStr | Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data |
| title_full_unstemmed | Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data |
| title_short | Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data |
| title_sort | dominant tree species mapping using machine learning based on multi temporal and multi source data |
| topic | tree species sentinel-1/2 random forest, XGBoost deep learning large scale mapping |
| url | https://www.mdpi.com/2072-4292/16/24/4674 |
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