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基于改进型遥感生态指数的城市生态系统质量评估

Urban ecosystem quality assessment based on the improved remote sensing ecological index.

作者信息

Zhang Guolin, Kuang Honghai

机构信息

School of Geographical Sciences, Southwest University, Chongqing, Asia, China.

出版信息

PeerJ. 2025 Apr 29;13:e19297. doi: 10.7717/peerj.19297. eCollection 2025.

DOI:10.7717/peerj.19297
PMID:40321817
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12047219/
Abstract

The remote sensing ecological index (RSEI) is an important tool for assessing ecosystem quality. However, its land surface temperature (LST) component poses challenges due to complex calculations and mismatched spatial resolution with other indicators. This study proposed an improved remote sensing ecological index (DRSEI). By replacing the LST component in RSEI with the difference index (DI) (representing PM concentration), the new index better reflects air pollution's impact on ecosystem quality. The results demonstrated that DRSEI outperformed the RSEI in assessing ecosystem quality in Chongqing's urban area. It exhibited three advantages: stronger correlation with the ecological index (EI), standard deviation values closer to EI's baseline, and lower root mean square error. The applicability of the DRSEI and RSEI varied across different regions: the DRSEI proved to be more suitable for highly urbanized areas, whereas the RSEI performed better in suburban regions. Further analysis revealed that the spatial variability of indicators influenced their loadings in principal component analysis, thereby affecting ecosystem quality assessment results. This study emphasizes the importance of considering the spatial distribution of indicators when constructing ecological indices. The findings suggest DRSEI could effectively assess ecosystem quality in urbanized areas. This approach provides new insights for urban ecological monitoring and environmental management.

摘要

遥感生态指数（RSEI）是评估生态系统质量的重要工具。然而，由于其地表温度（LST）成分计算复杂且与其他指标的空间分辨率不匹配，带来了挑战。本研究提出了一种改进的遥感生态指数（DRSEI）。通过用差异指数（DI）（代表PM浓度）取代RSEI中的LST成分，新指数能更好地反映空气污染对生态系统质量的影响。结果表明，在评估重庆市区的生态系统质量方面，DRSEI优于RSEI。它具有三个优点：与生态指数（EI）的相关性更强、标准差数值更接近EI的基线、均方根误差更低。DRSEI和RSEI在不同地区的适用性有所不同：DRSEI被证明更适合高度城市化地区，而RSEI在郊区表现更好。进一步分析表明，指标的空间变异性影响了它们在主成分分析中的载荷，从而影响了生态系统质量评估结果。本研究强调了在构建生态指数时考虑指标空间分布的重要性。研究结果表明DRSEI能够有效评估城市化地区的生态系统质量。该方法为城市生态监测和环境管理提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/a662390d62c5/peerj-13-19297-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/5d0974831109/peerj-13-19297-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/b64669708703/peerj-13-19297-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/ad87308808df/peerj-13-19297-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/70f8028c51d9/peerj-13-19297-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/204d79e1cec5/peerj-13-19297-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/480ca2c578ef/peerj-13-19297-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/a662390d62c5/peerj-13-19297-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/5d0974831109/peerj-13-19297-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/b64669708703/peerj-13-19297-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/ad87308808df/peerj-13-19297-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/70f8028c51d9/peerj-13-19297-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/204d79e1cec5/peerj-13-19297-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/480ca2c578ef/peerj-13-19297-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/249b/12047219/a662390d62c5/peerj-13-19297-g007.jpg

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