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基于CA-Markov和InVEST模型的城市景观格局与生境质量时空演变及预测
引用本文:褚琳,张欣然,王天巍,李朝霞,蔡崇法.基于CA-Markov和InVEST模型的城市景观格局与生境质量时空演变及预测[J].生态学杂志,2018,29(12):4106-4118.
作者姓名:褚琳  张欣然  王天巍  李朝霞  蔡崇法
作者单位:1.华中农业大学农业部长江中下游耕地保育重点实验室, 武汉 430070;;2.华中农业大学资源与环境学院, 武汉 430070
基金项目:本文由国家重点研发计划项目(2017YFC0505401)、国家自然科学基金项目(41877071)和中央高校基本科研业务费专项(2662016QD030)资助
摘    要:由人类活动引起的城市景观格局变化是影响陆地生态系统的重要因素之一,城市景观格局分布对区域生物多样性服务功能的影响极大.为研究城市景观格局和生境质量演化及其变化原因,以武汉市作为典型区,基于武汉市2005、2010和2015年3期景观类型信息,使用马尔柯夫模型分析其景观格局时空变化特征;采用CA-Markov模型模拟预测武汉市2020年自然增长情景下的景观格局发展方向;使用Logistic回归模型分析景观变化的驱动力;结合InVEST模型评价3期景观格局分布下的生境质量分布和变化,以及2020年模拟情景下的生境质量分布特征,探究景观格局变化与人类活动之间的关系.结果表明: 2005—2015年间,研究区景观类型变化以耕地和人工表面为主.耕地面积呈现逐年减少的趋势,主要转变为人工表面.人工表面面积不断增多,主要由水田和旱地转变而来.研究区整体景观生境质量级别下降,大量高生境质量级别的景观向低生境质量级别转化.整体景观生境质量指数减少,生物多样性服务功能降低,生境质量发生退化.2015—2020年间,武汉市景观格局和生境质量继续保持2005—2015年的变化趋势,表现为人工表面增加、景观生境质量指数减少、生物多样性服务功能降低、生境质量退化.研究区景观格局变化的主要驱动因子是国内生产总值变化和地区财政收入变化.人类社会经济活动是影响研究区景观格局变化和生境质量下降的重要驱动力,城镇化扩张和填湖造地是武汉市生态景观变化的主要原因.

关 键 词:InVEST模型  CA-Markov模型  景观格局  生境质量  Logistic回归模型  驱动力
收稿时间:2018-05-31

Spatial-temporal evolution and prediction of urban landscape pattern and habitat quality based on CA-Markov and InVEST model
CHU Lin,ZHANG Xin-ran,WANG Tian-wei,LI Zhao-xia,CAI Chong-fa.Spatial-temporal evolution and prediction of urban landscape pattern and habitat quality based on CA-Markov and InVEST model[J].Chinese Journal of Ecology,2018,29(12):4106-4118.
Authors:CHU Lin  ZHANG Xin-ran  WANG Tian-wei  LI Zhao-xia  CAI Chong-fa
Institution:1.Ministry of Agriculture Key Laboratory of Arable Land Conservation in Middle and Lower Reaches of Yangtze River, Huazhong Agricultural University, Wuhan 430070, China; ;2.College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
Abstract:The change of urban landscape caused by human activities is one of the most important factors affecting terrestrial ecosystem. The distribution of urban landscape pattern has great impacts on the service function of regional biodiversity. To reveal the variation of landscape pattern and habi-tat quality in cities and its driving factors, we extracted landscape type information of Wuhan in 2005, 2010, 2015, and analyzed spatial-temporal evolution of landscape pattern using Markov transition model. The CA-Markov model was used to simulate the landscape pattern in 2020 under the natural growth scenario. The driving factor for landscape variation was analyzed using Logistic regression model. Combined with InVEST model, spatial pattern of habitat quality and its variation in three phases were calculated and evaluated. The simulated habitat quality in 2020 was obtained and its distribution characteristics were analyzed. The relationship between variation of landscape pattern and human activities was explored. The results showed that cultivated land and manufactured surface were the landscape types with highest variations between 2005 and 2015. The area of cultivated land continued to decline, with most of the area being transferred into manufactured surface. The area of manufactured surface continued to increase, most of which was transferred from paddy field and dry land. From 2005 to 2015, the habitat quality declined, with a large number of landscapes with high habitat quality level being changed to low habitat quality level. The overall index of habitat quality decreased and the biodiversity service function declined, indicating the degeneration of habitat quality. In 2015-2020, the evolutionary trend of landscape pattern and habitat quality would keep consistent with the past decade, with an increasing area of artificial surface, decreasing index of habitat quality, weakening biodiversity service function, and degenerating habitat quality. The most important factor accounted for the landscape pattern change in the study area was the changes in Gross Domestic Product (GPD) and regional fiscal revenue. Human socio-economic activities were the key driving force for the spatial variation of landscape and degeneration of habitat quality. Urbanization and land reclamation by filling lakes were the main reasons for landscape pattern variation in Wuhan.
Keywords:InVEST model  CA-Markov model  landscape pattern  habitat quality  Logistic regression model  driving force  
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