Evaluation of Urban Ecological Environment Quality Based on Google Earth Engine: A Case Study in Xi’an, China
Hao Su 1
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School of Public Administration, Xi’an University of Architecture and Technology, Xi’an, China
Submission date: 2022-02-10
Final revision date: 2022-07-26
Acceptance date: 2022-07-27
Online publication date: 2022-12-19
Publication date: 2023-01-12
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Hao Su   

School of Public Administration, Xi'an University of Architecture and Tecnology, 710055, Xi'an, China
Pol. J. Environ. Stud. 2023;32(1):927-942
The rapid expansion of cities has accelerated the impact of human activities on the ecological environment. Existing studies conducted quantitative evaluations of ecological environment quality through complex remote sensing image screening and processing. Dynamic monitoring and modeling based on cloud platform programming are still lacking at present. This paper evaluated the quality of the ecological environment in Xi’an in 2000, 2005, 2010, 2015, and 2020 using the remote sensing ecological index (RSEI) model. We selected high-quality remote sensing images based on the Google Earth Engine platform (GEE). The calculation of four indicators and the Principal Component Analysis (PCA) were performed on the platform using JavaScript. The geodetector model was used to detect factors affecting the spatial differentiation of RSEI. The results showed that: (1) The ecological environment quality of Xi’an city during 2000-2020 showed a temporal trend of decreasing and then increasing, and a spatial trend of north-low and south-high. The RSEI value for Xi’an was 0.665 in 2000, 0.653 in 2005, 0.623 in 2010, 0.644 in 2015, and 0.651 in 2020. (2) The Bad level and Fair level of RSEI were mainly distributed in the city’s built-up area, and RSEI values were better in the Qinling Mountains. From 2000 to 2020, we found the deterioration areas of RSEI mainly distributed in the Weiyang, Baqiao, Yanliang, Chang’an, and Huyi districts. The improvement areas were mainly distributed in the southeastern mountains. (3) From the geodetector results, elevation (DEM), slope (SLO), precipitation (PRE), temperature (TEM), distance to main roads (ROA), distance to settlements (SET), land use/land cover (LUC), GDP, and population (POP) significantly influenced the regional RSEI spatial differentiation. The rankings of the explanatory power of the single factors were mainly: PRE>TEM>DEM> ROA>LUC>SLO>GDP> SET>POP. PRE has the strongest explanatory power of the nine factors. (4) Positive spatial auto correlation existed for the RSEI values in Xi’an. The Moran’s I was 0.513 in 2000, 0.659 in 2005, 0.749 in 2010, 0.716 in 2015, and 0.631 in 2020, respectively. The Local Moran's I of RSEI values showwd H-H and L-L clustering. This study provides significant information to identify ecologically fragile urban areas and support ecological environment policymaking.
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