Authors
Xiao-Yan Yu, Ya-Xian Gao, Guang-Pu Wei, Tong Zhou
Published in
Huan jing ke xue= Huanjing kexue. Volume 47. Issue 6. Pages 3795-3803. Jun 08, 2026.
Abstract
The aim of this study was to analyze the spatial and temporal characteristics of vegetation coverage and resilience in Inner Mongolia, to explore the correlation between the two time series, and to predict the evolutionary trend of vegetation resilience from 2024 to 2026. Based on the kernel normalized vegetation index (kNDVI) dataset constructed by satellite (MOD13Q1V6.1) and early warning indicators, the spatial and temporal changes of vegetation coverage and vegetation resilience are assessed from 2004 to 2023 in Inner Mongolia. The correlation between the spatial changes of the vegetation coverage and vegetation resilience is analyzed using Pearson's correlation analysis, and the evolutionary trend of vegetation resilience in the next three years is predicted by the BP neural network. The results show that: ① From 2004 to 2023, approximately 85.63% of the vegetation coverage in Inner Mongolia showed an increasing trend, and the changes in vegetation resilience showed a spatial distribution pattern of increasing in the east and central part of Inner Mongolia and decreasing in the western part. ② The trends of vegetation coverage and vegetation resilience in spatial and temporal changes were not completely consistent. In the ecological restoration project, only pursuing the increase of cover may not be able to enhance the stability of the system, and more attention should be paid to the dynamic response mechanism of vegetation resilience. ③ In the next three years, the overall trend of vegetation resilience in Inner Mongolia will be upward, mainly focusing on the ecological restoration projects in the Yinshan Mountains, Horqin Sands, and Daxing'anling Mountains, but the problem of declining vegetation resilience in the ecological restoration projects in the western parts of the country and other local areas still requires further attention.
PMID:
42336424
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.
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