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    刘维, 于强, 裴燕如, 武英达, 牛腾, 王逸菲. 黄河流域生态空间网络特征[J]. 北京林业大学学报, 2022, 44(12): 142-152. DOI: 10.12171/j.1000-1522.20210159
    引用本文: 刘维, 于强, 裴燕如, 武英达, 牛腾, 王逸菲. 黄河流域生态空间网络特征[J]. 北京林业大学学报, 2022, 44(12): 142-152. DOI: 10.12171/j.1000-1522.20210159
    Liu Wei, Yu Qiang, Pei Yanru, Wu Yingda, Niu Teng, Wang Yifei. Characteristics of spatial ecological network in the Yellow River Basin of northern China[J]. Journal of Beijing Forestry University, 2022, 44(12): 142-152. DOI: 10.12171/j.1000-1522.20210159
    Citation: Liu Wei, Yu Qiang, Pei Yanru, Wu Yingda, Niu Teng, Wang Yifei. Characteristics of spatial ecological network in the Yellow River Basin of northern China[J]. Journal of Beijing Forestry University, 2022, 44(12): 142-152. DOI: 10.12171/j.1000-1522.20210159

    黄河流域生态空间网络特征

    Characteristics of spatial ecological network in the Yellow River Basin of northern China

    • 摘要:
        目的  黄河流域在生态环境保护中有着举足轻重的地位,为了有效地应对城市快速发展所带来的生态环境问题,本文针对黄河流域构建了生态空间网络,以保护生物多样性、减缓景观格局破碎化。
        方法  以黄河流域为研究对象,基于土地利用数据、生态斑块面积周长、归一化植被指数(NDVI)、改进的归一化水体指数(MNDWI)和平均斑块分维数,选取了生态源地;利用GIS空间分析和MCR模型算法构建最小累积生态阻力面,提取出潜在生态廊道和生态节点;最终得到由生态源地和潜在生态廊道组成的黄河流域潜在生态空间网络并对其进行鲁棒性分析。
        结果  (1)黄河流域潜在生态空间网络由294块生态源地和369条潜在生态廊道组成。(2)生态源地主要土地利用类型为林地、草地和水域,主要分布在东北部和西南部,东南部也有零星分布。(3)生态廊道在东北部和西南部密度较大,生态空间网络结构复杂,在小块生态源地之间长度较长,对研究区生态环境发挥关键作用。(4)网络中共提取125个生态节点并使用matlab进行模拟攻击测试其鲁棒性,总体上随机攻击下生态空间网络的恢复鲁棒性优于恶意攻击,连接鲁棒性在攻击一定数量的节点后呈现大幅度上涨,网络结构崩溃。
        结论  本文基于MCR模型构建黄河流域的潜在生态空间网络并对其进行了拓扑结构分析,为生态空间网络优化提供重要借鉴意义,为促进黄河流域高质量发展、提高黄河流域的生态文明建设水平提供理论指导。

       

      Abstract:
        Objective  The Yellow River Basin has a pivotal role in ecological protection. In order to effectively cope with the ecological problems caused by rapid urban development, this paper constructs an ecological spatial network for the Yellow River Basin to protect biodiversity and mitigate landscape pattern fragmentation.
        Method  This paper takes the Yellow River Basin as the research object, finding an approach to identify its spatial ecological network. First, the ecological source sites were selected by comprehensively considering land-use data, perimeter of the ecological patch area, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and mean patch fractal dimension; then, the minimum cumulative ecological resistance surface was constructed and potential ecological corridors and ecological nodes were extracted by GIS spatial analysis and MCR model algorithm. Finally, the spatial ecological network of the Yellow River Basin consisting of ecological source sites and potential ecological corridors was obtained. The robustness of the proposed network was analyzed.
        Result  (1) The potential ecological network in the Yellow River Basin consisted of 294 ecological source sites and 369 potential ecological corridors. (2) The main types of land utilization were woodland, grassland and water, which were chiefly distributed in the northeast and southwest part, with some scattered distribution in the southeast part. (3) Ecological corridors were denser in the northeast and southwest part, with complex ecological spatial network structure and longer lengths between small ecological source sites, which played a key role in the ecological environment of the study area. (4) A total of 125 ecological nodes in the network were extracted and tested for recovery and connection robustness using simulated attacks. On the whole, the recovery robustness of ecological space network under random attack was better than that of malicious attack, the connection robustness increased greatly after attacking a certain number of nodes. And then the network structure collapsed.
        Conclusion  Based on the MCR model, this paper constructs a potential ecological spatial network in the Yellow River Basin and analyzes its topology, which provides an important reference for optimizing the ecological spatial network and provides theoretical guidance for promoting the high-quality development and improving the ecological civilization in the Yellow River Basin of northern China.

       

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