Sandy Beach Extraction Method Based on Multi-Source Data and Feature Optimization: A Case in Fujian Province, China
文献类型：期刊论文
| 作者 | Meng, Jie1,2; Xu, Duanyang2; Tao, Zexing2; Ge, Quansheng2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-08-08 |
| 卷号 | 17期号:16页码:2754 |
| 关键词 | sandy beach multi-source data recursive feature elimination ensemble learning Fujian |
| DOI | 10.3390/rs17162754 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Sandy beaches are vital geomorphic units with ecological, social, and economic significance, playing a key role in coastal protection and ecosystem regulation. However, they are increasingly threatened by climate change and human activities, highlighting the need for large-scale, high-precision monitoring to support sustainable management. Existing remote-sensing-based sandy beach extraction methods face challenges such as suboptimal feature selection and reliance on single data sources, limiting their generalization and accuracy. This study proposes a novel sandy beach extraction framework that integrates multi-source data, feature optimization, and collaborative modeling, with Fujian Province, China, as the study area. The framework combines Sentinel-1/2 imagery, nighttime light data, and terrain data to construct a comprehensive feature set containing 44 spectrum, index, polarization, texture, and terrain variables. The optimal feature subsets are selected using the Recursive Feature Elimination (RFE) algorithm. Six machine learning models-Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost)-along with an ensemble learning model, are employed for comparative analysis and performance optimization. The results indicate the following. (1) All models achieved the best performance when integrating all five types of features, with the average overall F1-score and accuracy reaching 0.9714 and 0.9733, respectively. (2) The number of optimal features selected by RFE varied by model, ranging from 19 to 36. The ten most important features across models were Band 2 (B2), Elevation, Band 3 (B3), VVVH_SUM, Spatial Average (SAVG), VH, Enhanced Water Index (EWI), Slope, Variance (VAR), and Normalized Difference Vegetation Index (NDVI). (3) The ensemble learning model outperformed all others, achieving an average overall accuracy, precision, recall, and F1-score of 0.9750, 0.9733, 0.9725, and 0.9734, respectively, under the optimal feature subset. A total of 555 sandy beaches were extracted in Fujian Province, covering an area of 43.60 km2 with a total perimeter of 1263.59 km. This framework demonstrates strong adaptability and robustness in complex coastal environments, providing a scalable solution for intelligent sandy beach monitoring and refined resource management. |
| URL标识 | 查看原文 |
| WOS关键词 | INDEX ; MORPHOLOGY ; VEGETATION ; IMAGES |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001558549400001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216092]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Xu, Duanyang |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Meng, Jie,Xu, Duanyang,Tao, Zexing,et al. Sandy Beach Extraction Method Based on Multi-Source Data and Feature Optimization: A Case in Fujian Province, China[J]. REMOTE SENSING,2025,17(16):2754. |
| APA | Meng, Jie,Xu, Duanyang,Tao, Zexing,&Ge, Quansheng.(2025).Sandy Beach Extraction Method Based on Multi-Source Data and Feature Optimization: A Case in Fujian Province, China.REMOTE SENSING,17(16),2754. |
| MLA | Meng, Jie,et al."Sandy Beach Extraction Method Based on Multi-Source Data and Feature Optimization: A Case in Fujian Province, China".REMOTE SENSING 17.16(2025):2754. |
入库方式： OAI收割
来源：地理科学与资源研究所
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