[Spatio-temporal Evolution Patterns and Spatial Differentiation Attribution of Ecological Quality in Guiyang from...

Exploring the trend of long time-series ecological quality evolution and spatial differentiation of influencing factors in Guiyang is of great significance for realizing regional ecological protection and high-quality development strategies. Based on the 7-period Landsat remote sensing images from 1991 to 2020, the remote sensing ecological index （RSEI） of Guiyang from 1991 to 2020 was calculated using the GEE remote sensing big data platform, and a geodetector, Hurst index, and coefficient of variation with different random sampling quantities and classification strategies were used to analyze the evolutionary pattern of ecological quality, change trends, and spatial differentiation influencing factors. The results showed that ① The ecological quality of Guiyang in the past 30 years was mainly medium and good, showing a wave-like pattern of change, with the highest and lowest values occurring in 2020 （mean value 0.58） and 2010 （mean value 0.47）, respectively, and the overall trend of the RSEI in the past ten years was good, with a spatial trend of decreasing distribution from the northeast to the southwest. ② The domain of values of the coefficient of variation for the RSEI in Guiyang was in the range of （0-2）, of which 46.61% of the area was in high fluctuation change, and the overall volatility of the RSEI was large； the mean value of the Hurst index was 0.59, and the area greater than 0.5 accounted for 73.98%, most of which showed weak persistence, and the future trend of change was the same as that of the past 30 years. ③ Different classification methods and random samples affect the q-value results of the geodetector, but the trend of the size ordering of the explanatory power of different factors was generally consistent. Land use, nighttime lighting index, slope, and population density indicators had stronger explanatory power for RSEI spatial differentiation； factor interaction detection was two-factor enhancement and nonlinear enhancement； and the interaction between land use and other factors was most favorable for explaining RSEI spatial differentiation.