文献类型：期刊文献

英文题名：Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping

作者：Cheng, Zhaohui[1];Li, Yongze[1];Sun, Xiong[1];Yuan, Jiajun[1];Liu, Dazhao[1,3];Xiang, Qinyuan[2]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Coll Math & Comp Sci, Zhanjiang 524088, Peoples R China;[3]Guangdong Engn Technol Res Ctr Ocean Remote Sensin, Zhanjiang 524088, Peoples R China

年份：2026

外文期刊名：JOURNAL OF OCEANOLOGY AND LIMNOLOGY

收录：SCI-EXPANDED(收录号：WOS:001730616600001)、、WOS

语种：英文

外文关键词：mangrove fine-scale classification; improved Extreme Gradient Boosting (XGBoost); multi-source unmanned aerial vehicle (UAV) data; feature selection

外文摘要：Unmanned aerial vehicle (UAV) datasets can derive diverse features, providing crucial support for fine-scale mangrove species classification. However, achieving high classification accuracy remains challenging due to complex feature interactions. This study utilized multi-source UAV data, including multispectral imagery, light detection and ranging (LiDAR) point clouds, and high-resolution RGB images, from the Gaoqiao Mangrove Nature Reserve, Zhanjiang, Guangdong, South China. Three hybrid feature groups were made by integrating shared multispectral features, vegetation indices, and structural features with texture features derived from principal component analysis (PCA), independent component analysis (ICA), or minimum noise fraction (MNF) dimensionality reduction. An improved Extreme Gradient Boosting (XGBoost) algorithm was developed for dominant feature selection, and random forest (RF) and XGBoost models were built for performance evaluation. The optimal results were obtained using PCA features selected by the improved XGBoost algorithm combined with the XGBoost classifier, achieving an overall accuracy of 98.48% with the user accuracy variance of only 0.000 05 among species. These findings indicate that the modified XGBoost algorithm can enhance classification accuracy and robustness, offering technical support for precise mangrove monitoring, protection, and restoration.