Journal of Natural Resources, Volume. 35, Issue 12, 2980(2020)

Research on driving mechanism of ecological land loss based on Bayesian network

Tao ZHENG1,2,3, Shuang CHEN1、*, Tong ZHANG1,2, Li-ting XU1,2, and Li-ya MA1,2
Author Affiliations
  • 1Nanjing Institute of Geography & Limnology, CAS, Nanjing 210008, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3The Center for Modern Chinese City Studies, East China Normal University, Shanghai 200062, China
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    From the micro perspective, the driving factors of ecological land loss in urban riverside area are analyzed, especially based on the quantitative analysis of the factors with crucial influences such as policy and planning, which has important reference significance for the formulation of ecological land protection policies. In this paper, the Bayesian network model is used to integrate the neighborhood factors, natural factors, policy and planning factors that lead to ecological land loss. And the relationship between ecological land change and its driving factors is clearly illustrated by a good graphical description method. The research results show that from 2005 to 2018, about 11.0% of the ecological land in Nanjing riverside area is lost, and the stable proportion of ecological land is 89.0%. The sensitivity of ecological land protection intensity to ecological land loss is as high as 9.37%, the sensitivity of construction potential factor is 2.53%, and the sensitivity of development difficulty is only 0.21%. The effect of shoreline planning is better than that of land use planning, which indicates that the protection policy based on the function goal of ecological land should be made to ensure the long-term existence of ecological space.

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    Tao ZHENG, Shuang CHEN, Tong ZHANG, Li-ting XU, Li-ya MA. Research on driving mechanism of ecological land loss based on Bayesian network[J]. Journal of Natural Resources, 2020, 35(12): 2980

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    Paper Information

    Received: May. 14, 2019

    Accepted: --

    Published Online: May. 8, 2021

    The Author Email: CHEN Shuang (schens@niglas.ac.cn)

    DOI:10.31497/zrzyxb.20201213

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