HUBEI AGRICULTURAL SCIENCES ›› 2024, Vol. 63 ›› Issue (11): 35-42.doi: 10.14088/j.cnki.issn0439-8114.2024.11.007

• Resource & Environment • Previous Articles     Next Articles

Evaluation and prediction of land ecological security in Hubei Province based on PSR model

CHEN Zhi-chun, PENG Yu-ling, LIANG Jia-yi   

  1. College of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
  • Received:2024-01-11 Online:2024-11-25 Published:2024-12-03

Abstract: Taking Hubei Province as an example, the PSR model, entropy weight method, comprehensive index method, obstacle model, GM (1,1) prediction model and other methods were used to evaluate and analyze the land ecological security and main influencing factors in Hubei Province from 2015 to 2021. Based on GIS, the land use change in the study area was analyzed, and the development trend of land ecological security from 2022 to 2027 was predicted. The results showed that the land ecological security index in Hubei Province increased rapidly during the research period, and only decreased in 2016 and 2019 due to the economic environment, natural disasters and sudden infectious diseases. It had reached a relatively safe range in 2021. During the research period, the land structure in Hubei Province was relatively reasonable and did not undergo significant changes. There was still room for land structure improvement, and the red lines for natural resources and ecological protection had been strictly adhered to. However, the maintenance of arable land area could not be ignored. The obstacle degree had shifted from the response layer to the pressure and state layer, and the main limiting factors had shifted to population density, urbanization rate and the proportion of cultivated land, which were the key points of future regulation. The land ecological security status in Hubei Province would remain relatively safe from 2022 to 2027, and might break through to safety in future, with a good development trend.

Key words: land ecological security, evaluation, prediction, PSR model, entropy weight method, obstacle model, GM(1, 1) model, Hubei Province

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