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Chinese Journal of Ecology ›› 2025, Vol. 44 ›› Issue (11): 3735-3745.doi: 10.13292/j.1000-4890.202511.013

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Spatiotemporal variations and driving forces of vegetation coverage in the Liaohe Delta from 2000 to 2020.

SUN Haozhong1,2, WANG Ming1,3,4, WANG Guodong2*, ZHANG Tao1,2, YUAN Yusong2, ZHAO Meiling2, MENG Jingci2   

  1. (1Institute for Peat & Mire Research, Northeast Normal University, Changchun 130024, China; 2Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; 3Key Laboratory of Geographical Processes and Ecological Security of Changbai Mountains, Ministry of Education, Changchun 130024, China; 4State Environmental Protection Key Laboratory for Wetland Conservation and Vegetation Restoration, Northeast Normal University, Changchun 130024, China).

  • Online:2025-11-10 Published:2025-11-12

Abstract: Based on Landsat images, annual average temperature, and GDP in the Liaohe River Delta from 2000 to 2020, we analyzed the characteristics of spatiotemporal variation and driving factors of vegetation NDVI using trend analysis, geographic detector model, and land use transfer matrix. The results showed that Liaohe River Delta had an overall high vegetation coverage. From 2000 to 2020, the NDVI showed a fluctuating upward trend with a rate of 0.028 per decade. The area of high vegetation coverage had increased, while the areas of low and medium low vegetation coverage had decreased. The NDVI of most areas was greater than 0.6, and the overall vegetation coverage was good. Spatially, the NDVI was high in the east and south, and low in the central and western regions, with high and medium-high vegetation coverage areas being the main ones. The NDVI of the Liaohe River Delta from 2000 to 2020 was jointly influenced by natural factors and human activities. Land use type, population density, GDP, and annual precipitation were the main influencing factors, while slope, aspect, and vegetation type were indirect ones. The explanatory power of land use types on NDVI changes was over 40%, followed by population density and GDP (over 10%). In the process of influencing vegetation NDVI, various factors were not independent of each other but had interactive effects. The interaction enhanced the impact of single factors such as annual average temperature and elevation, which had relatively small effects, on vegetation NDVI.


Key words: NDVI, driving factor, geographical detector, land use type