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Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis

Author

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  • Na Chen

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Gang Cheng

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Jie Yang

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Huan Ding

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Shi He

    (College Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

Abstract

Monitoring the quality of the urban ecological environment has become one of the important elements of promoting a sustainable urban development. The remote sensing ecological index (RSEI) provides a new research direction in urban ecological environment monitoring, combined with remote sensing. However, by using the principal component analysis method in RSEI, the calculation results are complicated and the workload is huge. To effectively assess the urban ecological environment, an improved remote sensing ecological index (IRSEI) was created to improve the ease of data use by using the entropy weighting method with spatiotemporal characteristics and seasonal variations. Furthermore, a geographically weighted regression model was used to quantify the impact of human activities on the urban ecological environment quality. The results showed that the IRSEI could provide a new method for monitoring the urban ecological environment quality, which makes the work easier while ensuring the validity of the results. It was concluded that (1) seasonal differences in the ecological quality of the study area were evident in the IRSEI model and the overall ecological environment quality of Jining City had been on an upward trend in the past 20 years; (2) the ecological quality in the study area was unevenly distributed spatially, with the southwestern part being better than the northeastern part, and the ecological grade being predominantly between moderate and good; and (3) the spatial aggregation effect of the IRSEI was increasing with time. The geographically weighted regression (GWR) revealed the influence of human activities on the ecological environment quality, among which economic level was positively related to ecological improvement, but the population density and night light index were negatively related to improvements in the ecological environment; road network density only showed a negative correlation in 2020. As Jining urbanizes, attention should be paid to protecting the built environment and population distribution.

Suggested Citation

  • Na Chen & Gang Cheng & Jie Yang & Huan Ding & Shi He, 2023. "Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8464-:d:1153749
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    References listed on IDEAS

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    1. Weilong Yang & Yi Zhou & Chaozhu Li, 2023. "Assessment of Ecological Environment Quality in Rare Earth Mining Areas Based on Improved RSEI," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    2. Pengwen Gao & Alimujiang Kasimu & Yongyu Zhao & Bing Lin & Jinpeng Chai & Tuersunayi Ruzi & Hemiao Zhao, 2020. "Evaluation of the Temporal and Spatial Changes of Ecological Quality in the Hami Oasis Based on RSEI," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    3. Yao, ZHOU & Jiang, CHANG & Shan-shan, FENG, 2022. "Effects of urban growth boundaries on urban spatial structural and ecological functional optimization in the Jining Metropolitan Area, China," Land Use Policy, Elsevier, vol. 117(C).
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    2. Ming Chang & Shuying Meng & Zifan Zhang & Ruiguo Wang & Chao Yin & Yuxia Zhao & Yi Zhou, 2023. "Analysis of Eco-Environmental Quality and Driving Forces in Opencast Coal Mining Area Based on GWANN Model: A Case Study in Shengli Coalfield, China," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    3. Hui Li & Haitao Jing & Geding Yan & Huanchao Guo & Wenfei Luan, 2023. "Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City," Sustainability, MDPI, vol. 15(15), pages 1-19, August.
    4. Ming Shi & Fei Lin & Xia Jing & Bingyu Li & Yang Shi & Yimin Hu, 2023. "Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau," Sustainability, MDPI, vol. 15(18), pages 1-25, September.

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