Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning

文献类型： 外文期刊

第一作者： Zhang, Wenbo

作者： Zhang, Wenbo;Liu, Xiaohuang;Liu, Jiufen;Li, Hongyu;Zhao, Xiaofeng;Luo, Xinping;Wang, Ran;Xing, Liyuan;Wang, Chao;Zhao, Honghui;Zhang, Wenbo;Liu, Jiufen;Li, Hongyu;Zhao, Xiaofeng;Wang, Ran;Zhang, Wenbo;Liu, Xiaohuang;Liu, Jiufen;Li, Hongyu;Zhao, Xiaofeng;Luo, Xinping;Wang, Ran;Xing, Liyuan;Wang, Chao;Zhao, Honghui;Xu, Bin

作者机构：

关键词： forest ecosystem; forest dominant tree species; machine learning; migration learning; change detection

期刊名称：REMOTE SENSING （ 影响因子：4.2； 五年影响因子：4.9 ）

ISSN：

年卷期： 2024 年 16 卷 14 期

页码：

收录情况： SCI

摘要： The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018-2022, achieving high migration accuracy (R2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine.

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