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A methodological framework for prioritizing wetland restoration from cropland: A case study Jianghan Plain, China

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  • Yang, Ran
  • Li, Xiaoyan
  • Mao, Dehua
  • Wang, Zongming
  • Cheng, Lisha
  • Dong, Yulin
  • Sun, Hongchao

Abstract

Restoring wetlands from croplands is a promising approach to respond to the frequent floods, natural habitat loss, and biodiversity decline associated with wetland loss. However, wetland restoration policy and planning remains scare, especially in the Jianghan Pain (JHP), a relatively economically developed floodplain region of China. In this study, by establishing a comprehensive remote sensing indicator system including cropland productivity, water occurrence frequency, soil wetness, topographic features, anthropogenic disturbance, and hydrological connectivity, we developed a novel methodological framework to identify potential wetland restoration areas from cropland with varied priorities for the JHP. Our results revealed that 0.77% of croplands (234.58 km2) in JHP were suitable for wetland restoration, and were mainly identified in the eastern JHP. Among these, 56.66 km2 was found to have a high restoration priority, and Hanchuan, Shayang and Tianmen were the top three counties with areas of high-priority for wetland restoration. In terms of the challenges from extreme rain events in the future, wetland restoration in JHP should be highlighted for improving regional ecosystem services. This study is expected to support the policy improvement and decision making and to guide the implementation of wetland restoration in JHP.

Suggested Citation

  • Yang, Ran & Li, Xiaoyan & Mao, Dehua & Wang, Zongming & Cheng, Lisha & Dong, Yulin & Sun, Hongchao, 2024. "A methodological framework for prioritizing wetland restoration from cropland: A case study Jianghan Plain, China," Land Use Policy, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:lauspo:v:137:y:2024:i:c:s026483772300491x
    DOI: 10.1016/j.landusepol.2023.107025
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    References listed on IDEAS

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    1. Wenke Qin & Wenpeng Li & Zhuohao Zhang & Weiya Chen & Min Wan, 2024. "Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain," Land, MDPI, vol. 13(12), pages 1-20, November.

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