Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping

Zhaohui CHENG; Yongze LI; Xiong SUN; Jiajun YUAN; Dazhao LIU; Qinyuan XIANG

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Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping

Updated：2026-03-30

- Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping
- Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping
- 海洋湖沼学报(英文) 2026年页码：1-19
- Affiliations：
  
  1.College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
  2.College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China
  3.Guangdong Engineering Technology Research Center for Ocean Remote Sensing and Information Technology, Zhanjiang 524088, China
- Author bio：
  
  llddz@163.com
  545974941@qq.com
- Funds：
- DOI：
  中图分类号：
- 收稿：2025-08-18，
  
  网络首发：2026-03-30，
- Accepted：
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Zhaohui CHENG, Yongze LI, Xiong SUN, 等. Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping[J/OL]. 海洋湖沼学报(英文), 2026,1-19.

CHENG Zhaohui,LI Yongze,SUN Xiong,et al.Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping[J].Journal of Oceanology and Limnology,
Zhaohui CHENG, Yongze LI, Xiong SUN, 等. Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping[J/OL]. 海洋湖沼学报(英文), 2026,1-19. DOI：

CHENG Zhaohui,LI Yongze,SUN Xiong,et al.Improved XGBoost with multi-source UAV data for high-accuracy fine-scale mangrove mapping[J].Journal of Oceanology and Limnology, DOI：.

摘要

Abstract

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.

关键词

Keywords

references

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