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生态学杂志 ›› 2023, Vol. 42 ›› Issue (10): 2514-2525.doi: 10.13292/j.1000-4890.202310.007

• 研究报告 • 上一篇    下一篇

近20年陕北黄土丘陵区景观生态风险时空变化及其冷热点格局

何钊全1,2*,尚雪1,2,张铜会3,云建英3   

  1. (1延安大学生命科学学院, 陕西延安 716000; 2陕西省红枣重点实验室, 延安大学, 陕西延安 716000; 3中国科学院西北生态环境资源研究院, 兰州 730000)

  • 出版日期:2023-10-10 发布日期:2023-10-08

Spatiotemporal variations of landscape ecological risk and its cold-hot spot pattern in the loess hills of northern Shaanxi over the past 20 years.

HE Zhaoquan1,2*, SHANG Xue1,2, ZHANG Tonghui3, YUN Jianying3   

  1. (1School of Life Sciences, Yan’an University, Yan’an 716000, Shaanxi, China; 2Shaanxi Key Laboratory of Chinese Jujube, Yan’an University, Yan’an  716000, Shaaxi, China; 3Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China).

  • Online:2023-10-10 Published:2023-10-08

摘要:

合理评价陕北黄土丘陵生态恢复区景观生态风险状况,对优化区域景观格局和维护生态系统功能具有现实意义。本文利用延安市2000—2020年土地利用类型数据,基于景观格局指数与GIS空间分析,揭示景观生态风险水平及其时空演变格局。结果表明:(1)景观生态风险指数(ERI)处于0~0.449,风险等级主要表现为低、较低及中风险区,以片状形式广泛分布于西南、黄河沿岸及西北等市域边际区县;高风险区集中于宝塔区。相比2000年,2020年的高、较高风险区面积增多1.27×104和25.25×104 hm2,景观生态风险增大趋势明显。(2) 2000—2020年,景观生态风险的Moran’s I为正值,空间相关性显著,“高-高”和“低-低”值为其主要空间集聚模式,但空间集聚性先增强后减弱,局部差异性逐步突显。(3) 景观生态风险热点分布于宝塔区,其土地利用以建筑用地和耕地为主,系统干扰度高,景观破碎分离,呈现出高值集聚模式;景观生态风险冷点主要分布在黄龙县、黄陵县及富县,区域海拔较高,人为干扰强度低,林地、草地斑块面积大,景观分离度和破碎度小,呈现出低值集聚模式。近20年,冷热点区域面积变化明显,空间格局波动频繁。因此,延安市景观生态风险整体上较小,但不同风险等级及其空间集聚模式和冷热点区域持续转化,需规划土地利用结构,提升景观安全性和稳定性。


关键词:

Abstract: A rational assessment of landscape ecological risk (LER) in the ecological restoration region of loess hills in northern Shaanxi is crucial for optimizing regional landscape pattern and maintaining ecosystem function. Landscape pattern index and GIS spatial analysis were used to demonstrate the level and spatial-temporal evolution pattern of LER using data from land use of Yan’an from 2000 to 2020. The results showed that: (1) The landscape ecological risk index (ERI) was in the range of 0-0.449, with low, medium-low, and medium risk areas dominating risk grades that were patchily dispersed in the southwest, along the Yellow River, and northwest. The high-risk area was centered in the Baota district. In 2020, the area of high and medium-high risk regions increased by 1.27×104 hm2 and 25.25×104 hm2 compared to 2000, respectively, with a notable expanding tendency of LER. (2) From 2000 to 2020, Moran’s I of LER was positive, with a significant geographic correlation. The main spatial aggregation modes of LER were “high-high” and “low-low”, but the spatial aggregation initially strengthened and then declined, and the local difference gradually became prominent. (3) The LER hotspots were located in the Baota region, where built-up construction land and farmland predominated, with substantial system disturbance and fractured and split landscape, indicating a high-value agglomeration pattern. The LER cold spots were primarily located in Huanglong, Huangling, and Fuxian counties, where the high regional altitude and large patch area of forest and grassland were responsible for the low intensity of human disturbance and the low degree of landscape separation and fragmentation, resulting in a low-value agglomeration pattern. In the past 20 years, the area of the cold-hot spot regions changed dramatically, and its distribution pattern shifted frequently. Consequently, LER in Yan’an was relatively low, with variations of risk levels, spatial agglomeration patterns, and cold-hot zones. Land use structure should be planned to promote the security and stability of landscape.


Key words: landscape pattern, ecological risk, spatial autocorrelation, local hotspot analysis, Yan’an.