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Application of the Optimal Parameter Geographic Detector Model in the Identification of Influencing Factors of Ecological Quality in Guangzhou, China

Author

Listed:
  • Maomao Zhang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Abdulla-Al Kafy

    (Department of Geography & the Environment, The University of Texas at Austin, Austin, TX 78712, USA)

  • Bing Ren

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China)

  • Yanwei Zhang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China)

  • Shukui Tan

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China)

  • Jianxing Li

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430079, China
    These authors contributed equally to this work.)

Abstract

The ecological environment is important for the survival and development of human beings, and objective and accurate monitoring of changes in the ecological environment has received extensive attention. Based on the normalized difference vegetation index (NDVI), wetness (WET), normalized differential build-up and bare soil index (NDBSI), and land surface temperature (LST), the principal component analysis method is used to construct a comprehensive index to evaluate the ecological environment’s quality. The R package “Relainpo” is used to estimate the relative importance and contribution rate of NDVI, WET, NDBSI, and LST to the remote sensing ecological index (RSEI). The optimal parameter geographic detector (OPGD) model is used to quantitatively analyze the influencing factors, degree of influence, and interaction of the RSEI. The results show that from 2001 to 2020, the area with a poor grade quality of the RSEI in Guangzhou decreased from 719.2413 km 2 to 660.4146 km 2 , while the area with an excellent quality grade of the RSEI increased from 1778.8311 km 2 to 1978.9390 km 2 . The NDVI (40%) and WET (35%) contributed significantly to the RSEI, while LST and NDBSI contributed less to the RSEI. The results of single factor analysis revealed that soil type have the greatest impact on the RSEI with a coefficient (Q) of 0.1360, followed by a temperature with a coefficient (Q) of 0.1341. The interaction effect of two factors is greater than that of a single factor on the RSEI, and the interaction effect of different factors on the RSEI is significant, but the degree of influence is not consistent. This research may provide new clues for the stabilization and improvement of ecological environmental quality.

Suggested Citation

  • Maomao Zhang & Abdulla-Al Kafy & Bing Ren & Yanwei Zhang & Shukui Tan & Jianxing Li, 2022. "Application of the Optimal Parameter Geographic Detector Model in the Identification of Influencing Factors of Ecological Quality in Guangzhou, China," Land, MDPI, vol. 11(8), pages 1-20, August.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:8:p:1303-:d:886768
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    References listed on IDEAS

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    1. Maomao Zhang & Weigang Chen & Kui Cai & Xin Gao & Xuesong Zhang & Jinxiang Liu & Zhiyuan Wang & Deshou Li, 2019. "Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China," IJERPH, MDPI, vol. 16(22), pages 1-22, November.
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