Monitoring and Analysis of Coastal Salt Pans Using Multi-Feature Fusion of Satellite Imagery: A Case Study Along the Laizhou Bay
文献类型：期刊论文
| 作者 | Liu, Yilin1,2; Yan, Bing1; Zhi, Pengyao1; Gao, Zhiyou1,3; Zhao, Lihong1 |
| 刊名 | SUSTAINABILITY
 |
| 出版日期 | 2025-09-19 |
| 卷号 | 17期号:18页码:24 |
| 关键词 | coastal zone crystallization ponds deep learning evaporation ponds multi-feature fusion random forest |
| DOI | 10.3390/su17188436 |
| 通讯作者 | Zhi, Pengyao(zpy8474376@sdust.edu.cn) |
| 英文摘要 | Coastal ecosystems, located at the interface of terrestrial and marine environments, provide significant ecological functions and resource value. Coastal salt pans, as critical coastal resources with significant implications for coastal ecosystem health and resource management, have attracted extensive research attention. However, current studies on the extraction of spatiotemporal patterns of coastal salt pans remain relatively limited and superficial. This study takes coastal salt pans in Laizhou Bay as a case study, proposing a hierarchical classification method-Salt Pan Feature-Enhanced Fusion Image Random Forest (SPFEFI-RF)-based on multi-index synergy guidance and deep-shallow feature fusion, achieving high-precision extraction of coastal salt pans. First, a Modified Water Index (MWI) and Salt Pan Crystallization Index (SCI) were constructed from image spectral features, specifically targeting the extraction of evaporation ponds. Concurrently, a salt pan sample dataset was developed for the DeepLabv3+ (DL) method to extract deep semantic features and perform multi-scale feature fusion. Subsequently, a three-channel fusion strategy-R(MWI)-G(SCI)-B(DL)-was employed to produce the Salt Pan Feature-Enhanced Fusion Image (SPFEFI), enhancing distinctions between salt pans and background land cover. Finally, the Random Forest (RF) classifier using shallow spectral features was applied to extract salt pan information, further optimized by spatial domain denoising techniques. Results indicate that the SPFEFI-RF approach effectively extracts coastal salt pan features, achieving an overall accuracy of 92.29% and a spatial consistency of 85.14% with ground-truth data. The SPFEFI-RF method provides advanced technical support for high-precision extraction of global coastal salt pan spatiotemporal characteristics, optimizing coastal zone management decisions and promoting the sustainable development of coastal ecosystems and resources. |
| WOS关键词 | RANDOM FOREST ; CLASSIFICATION |
| 资助项目 | Shandong Provincial Natural Science Foundation ; Open Fund of the Key Laboratory of Marine Geology and Environment, Chinese Academy of Sciences[MGE2022KG1] ; National Natural Science Foundation of China[41706092] ; National Natural Science Foundation of China[42307255] ; National Natural Science Foundation of China[42206187] ; National Natural Science Foundation of China[42006148] ; [ZR2025MS527] |
| WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001581150600001 |
| 出版者 | MDPI |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/203470]  |
| 专题 | 海洋研究所_海洋地质与环境重点实验室 |
| 通讯作者 | Zhi, Pengyao |
| 作者单位 | 1.Shandong Univ Sci & Technol, Coll Earth Sci & Engn, Qingdao 266590, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Geol & Environm, Qingdao 266071, Peoples R China 3.Shandong Prov Geol & Mineral Engn Grp Co Ltd, Jinan 250014, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yilin,Yan, Bing,Zhi, Pengyao,et al. Monitoring and Analysis of Coastal Salt Pans Using Multi-Feature Fusion of Satellite Imagery: A Case Study Along the Laizhou Bay[J]. SUSTAINABILITY,2025,17(18):24. |
| APA | Liu, Yilin,Yan, Bing,Zhi, Pengyao,Gao, Zhiyou,&Zhao, Lihong.(2025).Monitoring and Analysis of Coastal Salt Pans Using Multi-Feature Fusion of Satellite Imagery: A Case Study Along the Laizhou Bay.SUSTAINABILITY,17(18),24. |
| MLA | Liu, Yilin,et al."Monitoring and Analysis of Coastal Salt Pans Using Multi-Feature Fusion of Satellite Imagery: A Case Study Along the Laizhou Bay".SUSTAINABILITY 17.18(2025):24. |
入库方式： OAI收割
来源：海洋研究所
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