Identifying coastline positions and types based on both the moisture content and feature knowledge of ground objects
文献类型：期刊论文
| 作者 | Chen, Chao3; Gong, Shaojun4; Xu, Nan5; Hou, Xiyong1; Yang, Zhaohui3; Sun, Weiwei2; Yang, Gang2 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 18期号:1页码:25 |
| 关键词 | Coastline moisture content of ground object feature knowledge spatial positioning type recognition |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2025.2521802 |
| 通讯作者 | Yang, Gang(yanggang@nbu.edu.cn) |
| 英文摘要 | The coastline is one of the most important basic geographical elements in the coastal zone. Traditional methods often fail to accurately identify coastline locations due to the instantaneity of remote sensing imaging and the dynamics of tidal waters. This study was conducted to develop a model for identifying coastline locations and types that considers both the moisture content and feature knowledge of ground objects based on a long-time series of satellite remote sensing images. The validation test showed that: (1) the model could accurately identify different coastline types with clear water-land boundaries and precise spatial positions; (2) the average distances between the coastlines identified by the proposed method and the true coastlines was 3.82 m with a root mean square error of 8.78 m, with 97.56% of the distance errors being less than one pixel width; (3) from 1985 to 2022, the total coastline length increased from 2,152.42 km to 2,264.79 km, with an average annual increase of 3.03 km per year. A clear trend of coastline artificialization was observed, with the proportion of natural coastlines decreasing from 87.31% to 63.07%. This study provides technical support that will enable accurate extraction of coastline remote sensing information and has significant implications for scientific management and sustainable development of coastal zone resources. |
| WOS关键词 | WATER INDEX NDWI ; CAP TRANSFORMATION ; SHORELINE CHANGE ; CHINA ; RECONSTRUCTION ; EXTRACTION ; IMPACT |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001514486700001 |
| 资助机构 | National Natural Science Foundation of China ; Project National Key R&D Program of China ; Zhejiang Province Pioneering Soldier and Leading Goose RD Project ; Key Technology Breakthrough Plan Project of Science and Innovation Yongjiang 2035 ; Youth Scientist Project National Key R&D Program of China |
| 源URL | [http://ir.yic.ac.cn/handle/133337/41191]  |
| 专题 | 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 通讯作者 | Yang, Gang |
| 作者单位 | 1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China 2.Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Peoples R China 3.Suzhou Univ Sci & Technol, Sch Geog Sci & Geomat Engn, Suzhou, Peoples R China 4.Zhejiang Ocean Univ, Marine Sci & Technol Coll, Zhoushan, Peoples R China 5.Hohai Univ, Coll Geog & Remote Sensing, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Chao,Gong, Shaojun,Xu, Nan,et al. Identifying coastline positions and types based on both the moisture content and feature knowledge of ground objects[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2025,18(1):25. |
| APA | Chen, Chao.,Gong, Shaojun.,Xu, Nan.,Hou, Xiyong.,Yang, Zhaohui.,...&Yang, Gang.(2025).Identifying coastline positions and types based on both the moisture content and feature knowledge of ground objects.INTERNATIONAL JOURNAL OF DIGITAL EARTH,18(1),25. |
| MLA | Chen, Chao,et al."Identifying coastline positions and types based on both the moisture content and feature knowledge of ground objects".INTERNATIONAL JOURNAL OF DIGITAL EARTH 18.1(2025):25. |
入库方式： OAI收割
来源：烟台海岸带研究所
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