数字农科院2.0

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

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

作者机构：

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

期刊名称： Remote Sensing

ISSN： 2072-4292

年卷期： 2024 年 16 卷 14 期

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

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 ((Formula presented.) = 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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