Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data
文献类型：期刊论文
| 作者 | Shao, Mengmeng3; Xie, Xiao2; E, Chao1; Li, Jingzhong2; Ren, Wanxia2 |
| 刊名 | ENVIRONMENTAL RESEARCH LETTERS
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 19期号:6页码:9 |
| 关键词 | multi-modal earth observation data very-high-resolution UAV image deep learning surface water pollution susceptibility water quality |
| ISSN号 | 1748-9326 |
| DOI | 10.1088/1748-9326/ad4a92 |
| 通讯作者 | Xie, Xiao(xiexiao@iae.ac.cn) |
| 英文摘要 | The sustainability and suitability of water resources are of great importance for maintaining urban populations. The landscapes and environment around urban waters have always been the main focus of maintaining water quality for sustainable water supplies. Early-stage field investigations recognized the influence of land use/land cover (LULC) on water quality. To extend the research scope in spatial and temporal dimensions, remote sensing techniques have been utilized to discover the relationships between LULC and water quality. However, these remote sensing datasets generally had a medium spatial resolution, making them unable to support the fine-detailed land classifications that are critical to explore the water quality in an urban area. Moreover, although more details regarding the land surface are available from the currently-generated high-resolution and very-high-resolution remote sensing images, this land surface information is too complex for the state-of-the-art deep learning approaches and benchmark datasets. This manuscript reports our efforts on developing a framework to explore the fine-resolution relationship between surface water pollution and LULC. To address the cost of computing time and limitations of well-labelled datasets, we employ a foundation model-enhanced approach for water extraction and water-surrounded LULC classification. We propose an estimator of surface water pollution susceptibility to main pollutants based on the surrounding LULCs. Selecting the Future City of Beijing as the study area, based on very-high-resolution remote sensing images, the experiment proved that our proposed approach could effectively map the susceptibility of surface water pollution caused by its surrounding land use and land cover. To our knowledge, the relationship of LULCs and water quality have not been investigated using 0.5 m spatial resolution data. We hope our work can provide a prospective fine-detailed water quality analysis in the community of water environment of remote sensing. |
| WOS关键词 | LAND-COVER ; QUALITY ; PATTERNS |
| 资助项目 | Fundamental Applied Research Foundation of Liaoning Province |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001230499800001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | Fundamental Applied Research Foundation of Liaoning Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205535]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xie, Xiao |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Appl Ecol, Shenyang, Peoples R China 3.Xuzhou Univ Technol, Sch Food & Biol Engn, Xuzhou, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shao, Mengmeng,Xie, Xiao,E, Chao,et al. Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data[J]. ENVIRONMENTAL RESEARCH LETTERS,2024,19(6):9. |
| APA | Shao, Mengmeng,Xie, Xiao,E, Chao,Li, Jingzhong,&Ren, Wanxia.(2024).Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data.ENVIRONMENTAL RESEARCH LETTERS,19(6),9. |
| MLA | Shao, Mengmeng,et al."Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data".ENVIRONMENTAL RESEARCH LETTERS 19.6(2024):9. |
入库方式： OAI收割
来源：地理科学与资源研究所
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