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Chinese Journal of Applied Ecology ›› 2022, Vol. 33 ›› Issue (9): 2521-2529.doi: 10.13287/j.1001-9332.202209.025

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Urbanization and ecological effect in mid-southern Liaoning urban agglomeration, China

ZHANG Dan-hua, WANG Yang*, YAO Ning   

  1. School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China
  • Received:2021-11-08 Accepted:2022-06-20 Online:2022-09-15 Published:2023-03-15

Abstract: China has entered the stage of rapid urbanization since the 1990s, resulting in a series of environmental problems. Based on the nighttime light remote sensing data and land use data from 1995 to 2020, we extracted the compound night light index (CNLI) to measure the urbanization level of mid-southern Liaoning urban agglomeration, evaluated habitat quality by InVEST model. We examined the relationship between urbanization level and habitat quality in mid-southern Liaoning urban agglomeration by using the correlation analysis method and the geographic weighted regression model. The results showed that CNLI increased by 0.14 from 1995 to 2020. The urbanization level increased continuously, with a pattern of “low in the east and high in the west”. The habitat quality decreased by 0.005, showing a pattern of “high in the east and low in the west”. The ecological environment became worse. There was a significant negative spatial correlation between urbanization level and habitat quality in mid-southern Liaoning urban agglomeration. The negative impact of urbanization level on habitat quality gradually decreased. In order to alleviate habitat degradation caused by urbanization and realize the coordinated and sustainable development of regional social economy, it was urgent to take a series of measures, such as delimiting the ecological protection red line, improving the intensive use of land, delimiting the urban boundary, promoting the coordinated development of regional integration.

Key words: urbanization, habitat quality, compound night light index, bivariate autocorrelation analysis, geographically weighted regression