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Measuring Dynamic Changes in the Spatial Pattern and Connectivity of Surface Waters Based on Landscape and Graph Metrics: A Case Study of Henan Province in Central China

Author

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  • Bo Mu

    (College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China)

  • Guohang Tian

    (College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450002, China)

  • Gengyu Xin

    (College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China)

  • Miao Hu

    (College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China)

  • Panpan Yang

    (College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China)

  • Yiwen Wang

    (College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China)

  • Hao Xie

    (College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China)

  • Audrey L. Mayer

    (School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA)

  • Yali Zhang

    (College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China)

Abstract

An understanding of the scientific layout of surface water space is crucial for the sustainable development of human society and the ecological environment. The objective of this study was to use land-use/land-cover data to identify the spatiotemporal dynamic change processes and the influencing factors over the past three decades in Henan Province, central China. Multidisciplinary theories (landscape ecology and graph theory) and methods (GIS spatial analysis and SPSS correlation analysis) were used to quantify the dynamic changes in surface water pattern and connectivity. Our results revealed that the water area decreased significantly during the periods of 1990–2000 and 2010–2018 due to a decrease in tidal flats and linear waters, but increased significantly in 2000–2010 due to an increase in patchy waters. Human construction activities, socioeconomic development and topography were the key factors driving the dynamics of water pattern and connectivity. The use of graph metrics (node degree, betweenness centrality, and delta probability of connectivity) in combination with landscape metrics (Euclidean nearest-neighbor distance) can help establish the parameters of threshold distance between connected habitats, identify hubs and stepping stones, and determine the relatively important water patches that require priority protection or development.

Suggested Citation

  • Bo Mu & Guohang Tian & Gengyu Xin & Miao Hu & Panpan Yang & Yiwen Wang & Hao Xie & Audrey L. Mayer & Yali Zhang, 2021. "Measuring Dynamic Changes in the Spatial Pattern and Connectivity of Surface Waters Based on Landscape and Graph Metrics: A Case Study of Henan Province in Central China," Land, MDPI, vol. 10(5), pages 1-21, May.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:5:p:471-:d:547601
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    References listed on IDEAS

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    1. Bo Mu & Chang Liu & Guohang Tian & Yaqiong Xu & Yali Zhang & Audrey L. Mayer & Rui Lv & Ruizhen He & Gunwoo Kim, 2020. "Conceptual Planning of Urban–Rural Green Space from a Multidimensional Perspective: A Case Study of Zhengzhou, China," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
    2. Tian, Junfeng & Wang, Binyan & Zhang, Chuanrong & Li, Weidong & Wang, Shijun, 2020. "Mechanism of regional land use transition in underdeveloped areas of China: A case study of northeast China," Land Use Policy, Elsevier, vol. 94(C).
    3. Rahel Hamad & Heiko Balzter & Kamal Kolo, 2017. "Multi-Criteria Assessment of Land Cover Dynamic Changes in Halgurd Sakran National Park (HSNP), Kurdistan Region of Iraq, Using Remote Sensing and GIS," Land, MDPI, vol. 6(1), pages 1-17, March.
    4. Shanthala Devi, B.S. & Murthy, M.S.R. & Debnath, Bijan & Jha, C.S., 2013. "Forest patch connectivity diagnostics and prioritization using graph theory," Ecological Modelling, Elsevier, vol. 251(C), pages 279-287.
    5. Bodin, Örjan & Saura, Santiago, 2010. "Ranking individual habitat patches as connectivity providers: Integrating network analysis and patch removal experiments," Ecological Modelling, Elsevier, vol. 221(19), pages 2393-2405.
    6. Martin Herold & Joseph Scepan & Keith C Clarke, 2002. "The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses," Environment and Planning A, , vol. 34(8), pages 1443-1458, August.
    7. Wang, Yan, 2020. "Urban land and sustainable resource use: Unpacking the countervailing effects of urbanization on water use in China, 1990–2014," Land Use Policy, Elsevier, vol. 90(C).
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    Cited by:

    1. Min Zhao & Chenyang Li & Denielle M. Perry & Yuxiao Zhang & Yuwen He & Peng Li, 2022. "Connectivity Index-Based Identification of Priority Area of River Protected Areas in Sichuan Province, Southwest China," Land, MDPI, vol. 11(4), pages 1-21, March.
    2. Junhao Zhang & Xinjun Wang & Yujing Xie, 2021. "Implication of Buffer Zones Delineation Considering the Landscape Connectivity and Influencing Patch Structural Factors in Nature Reserves," Sustainability, MDPI, vol. 13(19), pages 1-18, September.

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