SDGSAT-1 nighttime light data improve village-scale built-up delineation
文献类型：期刊论文
| 作者 | Li, Congrong2,3,4; Chen, Fang2,3,5; Wang, Ning6; Yu, Bo2,3; Wang, Lei1,2,3 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 297页码:17 |
| ISSN号 | 0034-4257 |
| 关键词 | SDGSAT-1 Nighttime light (NTL) data Trend method Quantile method Built-up area delineation |
| DOI | 10.1016/j.rse.2023.113764 |
| 通讯作者 | Chen, Fang(chenfang@radi.ac.cn) ; Wang, Lei(wanglei@radi.ac.cn) |
| 英文摘要 | While previous urban delineation studies have been based mainly on Defense Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) data at a 1-km or 500-m spatial resolution, the higher 10-m-spatial-resolution Sustainable Development Science Satellite-1 (SDGSAT-1) NTL data offer the possibility of sophisticated village-scale analysis. In this study, we developed the quantile method with downward heuristic rules for built-up delineation based on SDGSAT-1 NTL data and proposed a new threshold method, named the trend method framework, for urban dynamic delineation in combination with SDGSAT-1 NTL data and DMSP-VIIRS NTL data. Then, the methods developed in this study were applied to five urban agglomerations (the Beijing-Tianjin-Hebei Urban Agglomeration (BTH), Yangtze River Delta (YRD), Guangdong-Hong Kong-Macao Greater Bay Area (GHM), Chengdu-Chongqing Economic Circle (CCC), and Hainan Free Trade Port (HTP)) in China. The results showed that both the downward quantile method and the trend method can be used to delineate village-scale built-up areas. In the delineation of built-up areas based on SDGSAT-1 NTL data, the trend method provides clearer information on the built-up areas within lighted areas, leading to higher accuracy than the downward quantile method. In the built-up area delineation based on DMSP and VIIRS NTL data at 1-km resolution, the trend method combines SDGSAT-1 NTL data and accounts for information on time-series changes in urbanization, thus allowing for the more accurate extraction of built-up areas. In 2020, the trend method had higher delineation accuracies than the quantile method, with effectiveness accuracies (EAs) of 0.91, 0.77, 0.67, 0.94, and 0.65 for the YRD, BTH, CCC, GHM, and HTP, respectively, compared with the European Space Agency (ESA) land use and land cover (LULC) product. The trend method has very good application prospects for delineating built-up areas using the high-resolution SDGSAT-1 NTL dataset. |
| WOS关键词 | ANNUAL URBAN-DYNAMICS ; URBANIZATION ; CHINA ; EXPANSION ; CLIMATE ; RECORD ; IMPACTS ; HEALTH ; GROWTH ; TIME |
| 资助项目 | Director Fund of the International Research Center of Big Data for Sustainable Development Goals[CBAS2022DF002] ; China Postdoctoral Science Foundation[2022M713222] ; Youth Innovation Promotion Association, CAS[2022122] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:001106775500001 |
| 资助机构 | Director Fund of the International Research Center of Big Data for Sustainable Development Goals ; China Postdoctoral Science Foundation ; Youth Innovation Promotion Association, CAS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200315]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Fang; Wang, Lei |
| 作者单位 | 1.Guilin Univ Elect Technol, Sch Comp Sci & Informat Secur, Guilin 541004, Peoples R China 2.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 3.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China 4.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 6.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Congrong,Chen, Fang,Wang, Ning,et al. SDGSAT-1 nighttime light data improve village-scale built-up delineation[J]. REMOTE SENSING OF ENVIRONMENT,2023,297:17. |
| APA | Li, Congrong,Chen, Fang,Wang, Ning,Yu, Bo,&Wang, Lei.(2023).SDGSAT-1 nighttime light data improve village-scale built-up delineation.REMOTE SENSING OF ENVIRONMENT,297,17. |
| MLA | Li, Congrong,et al."SDGSAT-1 nighttime light data improve village-scale built-up delineation".REMOTE SENSING OF ENVIRONMENT 297(2023):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。