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Urban Ecological Quality Assessment Based on Google Earth Engine and Driving Factors Analysis: A Case Study of Wuhan City, China

Author

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  • Weiwei Zhang

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215000, China
    School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215000, China
    Suzhou Key Laboratory of Spatial Information Intelligent Technology and Application, Suzhou 215000, China)

  • Wanqian Zhang

    (School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215000, China)

  • Jianwan Ji

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215000, China
    Suzhou Key Laboratory of Spatial Information Intelligent Technology and Application, Suzhou 215000, China)

  • Chao Chen

    (School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215000, China
    Suzhou Key Laboratory of Spatial Information Intelligent Technology and Application, Suzhou 215000, China)

Abstract

Ecological quality is a critical factor affecting the livability of urban areas. Remote sensing technology enables the rapid assessment of ecological quality (EQ), providing scientific theoretical support for the maintenance and management of urban ecology. This paper evaluates and analyzes the EQ and its driving factors in the city of Wuhan using remote sensing data from five periods: 2001, 2006, 2011, 2016, and 2021, supported by the Google Earth Engine (GEE) platform. By employing principal component analysis, a Remote Sensing Ecological Index (RSEI) was constructed to assess the spatiotemporal differences of EQ in Wuhan City. Furthermore, the study utilized the optimal parameter-based geographical detector model to analyze the influence of factors such as elevation, slope, aspect, population density, greenness, wetness, dryness, and heat on the RSEI value in 2021 and further explored the impact of changes in precipitation and temperature on the EQ in Wuhan. The results indicate that (1) principal component analysis shows that greenness and wetness positively affect Wuhan’s EQ, while dryness and heat have negative impacts; (2) spatiotemporal analysis reveals that from 2001 to 2021, the EQ in Wuhan showed a trend of initial decline followed by improvement, with the classification grades evolving from poor and average to good and better; (3) the analysis of driving factors shows that all nine indicators have a certain impact on the EQ in Wuhan, with the influence ranking as NDVI > NDBSI > LST > WET > elevation > population density > GDP > slope > aspect; (4) the annual average temperature and precipitation in Wuhan have a non-significant impact on the EQ. The EQ in Wuhan has improved in recent years, but comprehensive management still requires enhancement.

Suggested Citation

  • Weiwei Zhang & Wanqian Zhang & Jianwan Ji & Chao Chen, 2024. "Urban Ecological Quality Assessment Based on Google Earth Engine and Driving Factors Analysis: A Case Study of Wuhan City, China," Sustainability, MDPI, vol. 16(9), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3598-:d:1382633
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    References listed on IDEAS

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    2. Hai-Rui Huang & Jian Liang & Chae-Woo Ma, 2025. "Is the Sand Bubbler Crab ( Scopimera globosa ) an Effective Indicator for Assessing Sandy Beach Urbanization and Adjacent Terrestrial Ecological Quality?," Land, MDPI, vol. 14(4), pages 1-15, April.
    3. Ning Li & Haoyu Wang & Wen He & Bin Jia & Bolin Fu & Jianjun Chen & Xinyuan Meng & Ling Yu & Jinye Wang, 2025. "Spatiotemporal Detection of Ecological Environment Quality Changes in the Lijiang River Basin Using a New Dual Model," Sustainability, MDPI, vol. 17(2), pages 1-18, January.

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