基于CA-Markov模型的艾比湖湿地自然保护区土地利用/覆被变化及预测

doi:10.13287/j.1001-9332.201611.027

应用生态学报 ›› 2016, Vol. 27 ›› Issue (11): 3649-3658.doi: 10.13287/j.1001-9332.201611.027

基于CA-Markov模型的艾比湖湿地自然保护区土地利用/覆被变化及预测

井云清^1,2, 张飞^1,2,3*, 张月^1,2

¹新疆大学资源与环境科学学院, 乌鲁木齐 830046;
²新疆大学绿洲生态教育部重点实验室, 乌鲁木齐 830046;
³新疆智慧城市与环境建模普通高校重点实验室, 乌鲁木齐 830046

收稿日期:2016-04-22 出版日期:2016-11-18 发布日期:2016-11-18
通讯作者: E-mail: zhangfei3s@163.com
作者简介:井云清,女,1993年生,硕士研究生.主要从事干旱区生态环境遥感应用研究.E-mail: jingyunqing1993@163.com
基金资助:
本文由新疆维吾尔自治区自然科学基金项目(2016D01C029)、国家自然科学基金项目(41361045,41130531)、国家自然科学基金项目(新疆联合基金本地优秀青年人才培养专项)(U1503302)、新疆维吾尔自治区青年科技创新人才培养工程项目(2013731002)和新疆大学新疆绿洲生态(教育部省部共建)重点实验室开放课题(XJDX0201-2012-01)资助

Change and prediction of the land use/cover in Ebinur Lake Wetland Nature Reserve based on CA-Markov model.

JING Yun-qing^1,2, ZHANG Fei^1,2,3*, ZHANG Yue^1,2

¹College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China;
² Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China;
³Common Univerisities Key Laboratory of Xinjiang Wisdom City and Environment Modeling, Urumqi 830046, China

Received:2016-04-22 Online:2016-11-18 Published:2016-11-18
Contact: E-mail: zhangfei3s@163.com
Supported by:
This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region, China (2016D01C029), the Natural Science Foundation of China (41361045, 41130531), the Xinjiang Local Outstanding Young Talent Cultivation Project of National Natural Science Foundation of China (U1503302), the Young Technology-Innovation Training Program Foundation for Talents from the Xinjiang Uygur Autonomous Region (2013731002) and the Key Laboratory of Oasis Ecology (Cooperated by Ministry of Education and Xinjiang) in Xinjiang University (XJDX0201-2012-01).

摘要/Abstract

摘要： 以艾比湖湿地自然保护区为研究区域,以1998、2006年Landsat TM影像和2014年Landsat OLI影像的土地利用/覆被分类结果为输入数据,采用CA-Markov模型,预测研究区未来的土地利用/覆被格局.在模型建立过程中,通过Markov模型求出转移概率矩阵和转移面积矩阵,确定CA模型转换规则,限制CA模型迭代次数.利用CA-Markov模型模拟预测研究区2014、2022和2030年土地利用/覆被格局,并采用2014年实际土地利用/覆被分类结果验证预测精度,得到2014年各土地利用/覆被类型面积预测误差均≤6.4%,空间位置预测精度达到76.0%.结果表明: 1998—2014年,艾比湖湿地自然保护区林草地、盐碱地、干涸湖床和沙漠增加,其中,盐碱地的增幅最突出,增加了37.4%;水体和其他地类减少,且水体的减少突出,减少了34.8%.2014—2030年,艾比湖湿地自然保护区林草地、盐碱地和沙漠将呈增加趋势,而干涸湖床、水体和其他地类将减少.该研究可以为艾比湖自然保护区的土地利用/覆被动态监测以及可持续发展提供依据.

Abstract: Taking Ebinur Lake Wetland Nature Reserve as the study area, and based on the classified result of Landsat TM images of 1998, 2006, and Landsat OLI images of 2014, this paper simulated and forecasted land use/cover types of Ebinur Lake Wetland Nature Reserve by using the CA-Markov model. In the model-building process, the transition probability matrix and the transition area matrix were obtained through the Markov model, which determined the conversion rules and iterative times of the CA model. The land use/cover pattern of the study area in 2014, 2022 and 2030 was simulated and forecasted with the CA-Markov model. Then the forecast result was compared with the actual classified data of 2014 to verify the forecast accuracy. The prediction error of land use/cover type area was not more than 6.4% in 2014, and the spatial location accuracy was 76.0%. The results showed that the forest and grassland, saline-alkali area, dry lake bed and desert had an increasing trend in 1998-2014. The saline-alkali area increased the most by 37.4% and the water body decreased by 34.8%. In 2014-2030, the areas of the forest and grassland, saline-alkali soil and desert would increase, while that of the dry lake bed, water body and other objects would decrease. This study could provide a basis for dynamic monitoring land use/cover as well as sustainable development of Ebinur Lake Wetland Nature Reserve.

井云清, 张飞, 张月. 基于CA-Markov模型的艾比湖湿地自然保护区土地利用/覆被变化及预测[J]. 应用生态学报, 2016, 27(11): 3649-3658.

JING Yun-qing, ZHANG Fei, ZHANG Yue. Change and prediction of the land use/cover in Ebinur Lake Wetland Nature Reserve based on CA-Markov model.[J]. Chinese Journal of Applied Ecology, 2016, 27(11): 3649-3658.

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基于CA-Markov模型的艾比湖湿地自然保护区土地利用/覆被变化及预测

Change and prediction of the land use/cover in Ebinur Lake Wetland Nature Reserve based on CA-Markov model.

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