Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing
文献类型：期刊论文
| 作者 | Chen, Ya; Zhen, Weina2,3; Li, Yu2,3; Zhang, Ninghui2,3; Shi, Yishao4; Shi, Donghui2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 15期号:21页码:5182 |
| 关键词 | urban blue space spatiotemporal analysis mechanism simulation landscape analysis |
| DOI | 10.3390/rs15215182 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | With rapid urban development in Beijing, there is a critical need to explore urban natural resources and understand their underlying mechanisms. Urban blue space (UBS) has gained increasing attention due to its potential to drive microcirculation, mitigate heat islands, and enhance residents' well-being. In this study, we used remote sensing data to extract UBS in Beijing and employed exploratory spatial data analysis (ESDA) methods to examine its spatial and temporal development over the past two decades. We adopted a mesoscopic perspective to uncover the full spectrum of landscape patterns and quantitatively simulate the mechanisms influencing the area of UBS and landscape patterns. Our findings are as follows: (1) The UBS area in Beijing exhibited fluctuating growth from 2000 to 2020. (2) Spatial clustering of UBS was stable with subtle changes. (3) The ecological conditions in Beijing improved over the last 21 years, indicated by increased habitat diversity and richness, while notable landscape fragmentation posed significant challenges. (4) Science and technology management-related factors, such as UEM, EDUI, and STI, emerged as the most influential mechanisms for the UBS area. The coefficients for these factors were 0.798, 0.759, and 0.758, respectively. Following closely were vegetation conditions (NDVI) with a coefficient of 0.697 and an annual average temperature (T) with a coefficient of 0.692. (5) Precipitation was identified as the most vital influencing factor for the UBS landscape, with a significant correlation coefficient of 0.732. It was followed by residential population (POP), with a coefficient of 0.692, and economic conditions represented by gross domestic product (GDP), with a coefficient of 0.691. |
| WOS关键词 | PRINCIPAL COMPONENTS REGRESSION ; LAND ; SYSTEM ; HEALTH ; GREEN ; URBANIZATION ; SUPPORT ; IMAGERY ; IMPACT ; FUTURE |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版者 | MDPI |
| WOS记录号 | WOS:001121074000001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201019]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 作者单位 | 1.Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China 2.Beijing Forestry Univ, Sch Marxism, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Ya,Zhen, Weina,Li, Yu,et al. Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing[J]. REMOTE SENSING,2023,15(21):5182. |
| APA | Chen, Ya,Zhen, Weina,Li, Yu,Zhang, Ninghui,Shi, Yishao,&Shi, Donghui.(2023).Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing.REMOTE SENSING,15(21),5182. |
| MLA | Chen, Ya,et al."Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing".REMOTE SENSING 15.21(2023):5182. |
入库方式： OAI收割
来源：地理科学与资源研究所
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