The first 10-m China's national-scale sandy beach map in 2022 derived from Sentinel-2 imagery
文献类型：期刊论文
| 作者 | Ni, Ming4; Xu, Nan4; Ou, Yifu3; Yao, Jiaqi6,7; Li, Zhichao1; Mo, Fan8; Huang, Conghong2,5; Xin, Huichao4; Xu, Hao4 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2024-12-31 |
| 卷号 | 17期号:1页码:21 |
| 关键词 | Coastal China remote sensing beach national-scale |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2024.2425163 |
| 产权排序 | 5 |
| 英文摘要 | Sandy beaches are at the frontline of resisting continuous sea level rise associated with anthropogenic climate change. However, accurate and comprehensive spatial information for monitoring, utilizing, and protecting sandy beaches is still lacking at the national or above scales. This study, for the first time, addresses this gap by collecting cloud-free, low-tide Sentinel-2 images in 2022 to map 10-m sandy beaches across China using the image classification method. We adopted the Support Vector Machine to derive the spatial distribution of sandy beaches, assess accuracy, and analyze spatial characteristics. Our results demonstrate the efficiency of the SVM model in mapping sandy beaches (User's accuracy: 96%, Kappa coefficient: 0.93). We identified 3,444 beaches in China, with a total length of 3,187.57 km, an average width of 69.93 meters, and a total area of 217.43 km(2), constituting 24.16% of the national coastline. Notably, Guangdong, Taiwan, and Hainan provinces are rich in beach resources, whereas Macao, Shanghai, Tianjin, and Jiangsu provinces have relatively fewer beach resources. Further, our results outperform the existing OpenStreetMap beach dataset. Our developed 10-m beach database is crucial for analyzing potential beach risks, uncovering socioeconomic values of beach resources, and promoting the sustainable coastal zone development in China. |
| WOS关键词 | SEA-LEVEL RISE ; COASTLINE CHANGES ; EROSION |
| 资助项目 | National Natural Science Foundation of China Grant[42301501] ; National Natural Science Foundation of China Grant[42101343] ; Natural Science Foundation of Jiangsu Province[BK20240258] ; Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China[KLSMNR-K202309] ; Jiangsu Marine Science and Technology Innovation Project[JSZRHYKJ202302] |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001353084200001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China Grant ; Natural Science Foundation of Jiangsu Province ; Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China ; Jiangsu Marine Science and Technology Innovation Project |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210967]  |
| 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, Nan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 2.Res Ctr Rural Land Resources Use & Consolidat, Natl & Local Joint Engn, Nanjing, Peoples R China 3.Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China 4.Hohai Univ, Coll Geog & Remote Sensing, Sch Earth Sci & Engn, Nanjing 210024, Peoples R China 5.Nanjing Agr Univ, Coll Land Management, Nanjing, Peoples R China 6.Tianjin Normal Univ, Acad Ecol Civilizat Dev JING JIN JI, Tianjin, Peoples R China 7.Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China 8.Minist Nat Resources, Land Satellite Remote Sensing Applicat Ctr, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ni, Ming,Xu, Nan,Ou, Yifu,et al. The first 10-m China's national-scale sandy beach map in 2022 derived from Sentinel-2 imagery[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2024,17(1):21. |
| APA | Ni, Ming.,Xu, Nan.,Ou, Yifu.,Yao, Jiaqi.,Li, Zhichao.,...&Xu, Hao.(2024).The first 10-m China's national-scale sandy beach map in 2022 derived from Sentinel-2 imagery.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1),21. |
| MLA | Ni, Ming,et al."The first 10-m China's national-scale sandy beach map in 2022 derived from Sentinel-2 imagery".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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