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

Maomao Zhang, Abdulla-Al Kafy, Bing Ren, Yanwei Zhang, Shukui Tan () and Jianxing Li
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Maomao Zhang: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China
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

Land, 2022, vol. 11, issue 8, 1-20

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.

Keywords: ecological quality; remote sensing ecological index (RSEI); influential factors; optimal parameter geographic detector (OPGD) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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