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论文信息
题目：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
关键词：forest ecosystem; forest dominant tree species; machine learning; migration learning; change detection
作者：WenboZhang, Xiaohuang Liu, Bin Xu, Jiufen Liu，Hongyu Li, Xiaofeng Zhao，Xinping Luo, Ran Wang, Liyuan Xing, Chao Wang and Honghui Zhao
DOI：https://doi.org/10.3390/rs16142547
论文摘要
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.
重要图表
— 研究区域的概况图 —
— 研究区域中树种的样本数据分布 —
— 研究流程图—
— 研究区域森林恢复时间的计算及其一些示例—
— 森林恢复时间的准确性验证 —
— 2018-2023 年每个分类模型的分类准确性—
— 基于 XGB 算法的优势树种分布 2018-2023—
— 基于 XGB 算法的 2018-2023 年每种树种的生产者准确性和用户准确性—
—2018-2023年每个分类特征的特征重要性—
本文创新
针对森林优势树种遥感监测中可能存在的年度地面样本数据缺失的问题，采用CCDC算法对样本数据进行年际迁移，实现了较高的迁移精度。
心得收获
分类特征的持续重要性。在本研究中，发现大多数特征在基于 MDG 指标的特征重要性评估中表现出持续的重要性。
CCDC 算法在样品迁移中表现出优异的性能。获得的最终结果具有很高的准确性，其中𝑅2为 0.8303，RMSE 为 4.64。XGB 算法具有绝对的优势。
XGB 算法的绝对优势。基于 XGB 算法的分类模型每年都表现出显著的分类优势，分类准确率几乎在所有年份都在 80% 以上，Kappa 系数高于 0.75。
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