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生态学杂志 ›› 2020, Vol. 39 ›› Issue (10): 3408-3420.doi: 10.13292/j.1000-4890.202010.009

• 研究报告 • 上一篇    下一篇

基于地理探测器的伊犁谷地生境质量时空演变及其影响因素

朱增云,阿里木江·卡斯木*   

  1. (新疆师范大学地理科学与旅游学院, 新疆师范大学丝绸之路经济带城市化发展研究中心, 乌鲁木齐 830054)
  • 出版日期:2020-10-10 发布日期:2021-04-09

Spatial-temporal evolution of habitat quality in Yili Valley based on geographical detector and its influencing factors.

ZHU Zeng-yun, Alimujiang Kasimu*   

  1. (School of Geography Science and Tourism, Research Center of Urbanization Development of Silk Road Economic Belt, Xinjiang Normal University, Urumqi 830054, China).
  • Online:2020-10-10 Published:2021-04-09

摘要: 利用1995—2015年土地利用分类成果,运用CAMarkov模型模拟预测2025年伊犁谷地土地利用的时空变化,结合InVEST模型评估1995—2025年伊犁谷地生境质量的时空格局,采用空间统计方法和地理探测器模型分析了生境质量的变化特征,并对生境质量的空间分布和变化趋势进行驱动因子探测、交互作用探测以及生态探测分析。结果表明:1995—2025年,耕地与建设用地面积呈逐渐增加趋势,林地与未利用地面积呈波动变化态势,草地与水域则出现减少态势,减少的趋势缓慢下降;生态退化度高值区主要在河谷平原耕作与城建区域且呈现条带状分布,应加强生态修复工程;低值区为高山林草地区域,总体上表现为低值区环绕高值区,呈现嵌套分布,应着重进行生态功能保护;生境质量与生态退化度的高、低值区域呈现相反的趋势;各县生态退化总体稳定,且退化程度较小,生境质量在1995—2015年处于下降趋势,2015—2025年有所上升;利用地理探测器识别出生境质量空间变化分布受自然和人为因子的综合作用,主导影响因子是土地利用,其次为坡度,在交互作用探测与生态探测中,各因子对生境质量空间分布的差异性表现显著,并呈现出双因子增强。

关键词: 生境质量, InVEST模型, CA-Markov模型, 地理探测器, 驱动因子, 伊犁谷地

Abstract: Based on land use classification from 1995 to 2015, we predicted temporal and spatial change of land use in Yili Valley in 2025 using the CA-Markov model. We assessed temporal and spatial patterns of habitat quality in Yili Valley from 1995 to 2025 using the InVEST model, and analyzed the characteristics of habitat quality changes using a spatial statistical method and geographical detector model. We further analyzed the spatial distribution and change trends of habitat quality by driving factor detection, interaction detection and ecological detection analyses. From 1995 to 2025, the area of cultivated land and construction land shows a gradually increasing trend, while that of forest land and unused land fluctuates. Water area shows a decreasing trend, with a slowly shrinking amplitude. The high-value areas of ecological degradation are mainly in the farmland and urban construction areas of the river valley and present as a stripe distribution, suggesting that ecological restoration projects should be strengthened. The low-value areas are alpine forests and grasslands, which are generally surrounded by high-value areas, with a nested distribution. Emphasis in those areas should be placed on the protection of ecological functions. The highand low-value areas of habitat quality show opposite trends to those of ecological degradation. The ecological degradation of each county is generally stable, with relative low degradation. The habitat quality shows a downward trend from 1995 to 2015, but increases from 2015 to 2025. The use of geographic detectors identifies that the spatial distribution of habitat quality is affected by a combination of natural and anthropogenic factors. The dominant influencing factor is land use, followed by slope. In the interaction detection and ecological detection, each factor significantly differs in affecting the spatial distribution of habitat quality, and shows a twofactor enhancement.

Key words: habitat quality, InVEST model, CA-Markov model, geographical detector, driving factor, Yili Valley.