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应用生态学报 ›› 2018, Vol. 29 ›› Issue (2): 626-634.doi: 10.13287/j.1001-9332.201802.018

• 研究报告 • 上一篇    下一篇

南四湖湿地景观格局脆弱度的时空分异特征

梁佳欣, 李新举*   

  1. 山东农业大学资源与环境学院, 山东泰安 271018
  • 收稿日期:2017-07-17 出版日期:2018-02-18 发布日期:2018-02-18
  • 通讯作者: E-mail: lxj0911@126.com
  • 作者简介:梁佳欣, 女, 1992年生, 硕士研究生. 主要从事资源环境遥感、土地利用规划研究. E-mail: jxliang201609@126.com
  • 基金资助:

    本文由国家自然科学基金项目(41171425)资助

Characteristics of temporal-spatial differentiation in landscape pattern vulnerability in Nansihu Lake wetland, China.

LIANG Jia-xin, LI Xin-ju*   

  1. College of Resources and Environment, Shandong Agriculture University, Tai’an 271018, Shandong, China
  • Received:2017-07-17 Online:2018-02-18 Published:2018-02-18
  • Contact: E-mail: lxj0911@126.com
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (41171425).

摘要: 选取南四湖湿地1985、2000年Landsat 5 TM及2015年Landsat 8 OLI遥感影像为数据源,利用敏感度指数和适应度指数构建景观脆弱度指数,结合半变异函数和空间自相关等空间统计学方法,探究南四湖湿地景观格局脆弱度的适宜研究尺度及该尺度下脆弱度的时空分异特征.结果表明: 1 km×1 km等间距网格能消除随机因素对各景观指数值的影响,为适宜研究尺度.1985—2015年,南四湖湿地整体景观格局脆弱度呈恶化趋势,较高、高脆弱度范围随时间显著扩张.脆弱度空间异质性上升,受非结构性因素的影响增强,由空间自相关引起的空间变异略有减弱.脆弱度全局空间正相关性较强,呈空间集聚模式,且集聚现象日趋明显;局域自相关以空间局部聚集为主,高-高区的显著性水平最强,低-低区的显著性随时间增强.气温、降水等自然因素影响湖区景观格局脆弱度的空间分布,社会经济活动、政策体制等人为因素是脆弱度恶化的主要原因.

Abstract: With remote sensing images from 1985, 2000 Lantsat 5 TM and 2015 Lantsat 8 OLI as data sources, we tried to select the suitable research scale and examine the temporal-spatial diffe-rentiation with such scale in the Nansihu Lake wetland by using landscape pattern vulnerability index constructed by sensitivity index and adaptability index, and combined with space statistics such as semivariogram and spatial autocorrelation. The results showed that 1 km × 1 km equidistant grid was the suitable research scale, which could eliminate the influence of spatial heterogeneity induced by random factors. From 1985 to 2015, the landscape pattern vulnerability in the Nansihu Lake wetland deteriorated gradually. The high-risk area of landscape pattern vulnerability dramatically expanded with time. The spatial heterogeneity of landscape pattern vulnerability increased, and the influence of non-structural factors on landscape pattern vulnerability strengthened. Spatial variability affected by spatial autocorrelation slightly weakened. Landscape pattern vulnerability had strong general spatial positive correlation, with the significant form of spatial agglomeration. The positive spatial autocorrelation continued to increase and the phenomenon of spatial concentration was more and more obvious over time. The local autocorrelation mainly based on high-high accumulation zone and low-low accumulation zone had stronger spatial autocorrelation among neighboring space units. The high-high accumulation areas showed the strongest level of significance, and the significant level of low-low accumulation zone increased with time. Natural factors, such as temperature and precipitation, affected water-level and landscape distribution, and thus changed the landscape patterns vulnerability of Nansihu Lake wetland. The dominant driver for the deterioration of landscape patterns vulnerability was human activities, including social economy activity and policy system.