Journal of Ecology and Rural Environment ›› 2021, Vol. 37 ›› Issue (8): 972-982.doi: 10.19741/j.issn.1673-4831.2020.0821

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Research on Ecological Environment Quality in Central Yunnan Based on MRSEI Model

NONG Lan-ping1,2,3, WANG Jin-liang1,2,3, YU Yuan-he1,2,3   

  1. 1. Faculty of Geography, Yunnan Normal University, Kunming 650500, China;
    2. Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China;
    3. Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China
  • Received:2020-10-02 Online:2021-08-25 Published:2021-08-27

Abstract: This study adopted Central Yunnan as the study area. Data sources used include the Moderate Resolution Imaging Spectroradiometer (MODIS), a digital elevation model (DEM), and socio-economic data. A modified remote sensing ecological model was used to monitor the eco-environmental quality of Central Yunnan. In addition, the spatiotemporal variation in eco-environmental quality was analyzed by means of the variation coefficient, Theil-Sen median degree, and spatial autocorrelation. Finally, a geographical detector was used to isolate influencing factors. The results show that:(1) The eco-environmental quality of Central Yunnan improved insignificantly from 2000 to 2018 and stabilized. Among the four cities in Central Yunnan, Chuxiong Prefecture showed the most stable eco-environmental quality. (2) Spatial aggregation in eco-environmental quality occurred in counties in Central Yunnan. A longitudinal zonal spatial pattern of "High-High aggregation"-"Low-Low aggregation"-"No significance" occurred from the western to eastern parts of Central Yunnan and the eco-environmental quality in the west exceeded that in the east. (3) Improved eco-environmental quality was observed at an altitude exceeding 3 000 m and at a slope ranging from 20° to 30°. The distribution of the areas with poor eco-environmental quality was consistent with that of high population density. (4) The normalized difference vegetation index (NDVI) and WET were positively correlated with eco-environmental quality, whereas the normalized difference built-up and bare-soil index (NDBSI), Land surface temperature (LST), population, and gross domestic product (GDP) were negatively correlated. The interactions between multiple factors showed a greater impact on eco-environmental quality in comparison to that of any single factor. Among these interactions, that between natural factors and socioeconomic factors, such as the NDVI which was affected by anthropogenic activities to a larger extent, had a greater impact on eco-environmental quality.

Key words: remote sensing ecological index, eco-environmental quality, spatial and temporal variation, coefficient of variation, spatial autocorrelation, geographic detector

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