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青岛市土地利用演化驱动力因素分析
引用本文:凌涛,李鹏,左小清.青岛市土地利用演化驱动力因素分析[J].安徽农业科学,2016(13):257-262.
作者姓名:凌涛  李鹏  左小清
作者单位:1. 昆明理工大学国土资源工程学院,云南昆明,650093;2. 大连理工大学城市学院,辽宁大连,116600
摘    要:在RS和GIS技术支持下,首先,以青岛市1986、2000和2006年3期遥感影像为基础,以谷歌地图为参考,对3期遥感影像进行分类处理,提取出了同时期的土地利用数据。其次,利用数学模型对1986~2006年青岛市土地利用数量变化、土地利用速度变化、土地利用程度变化等方面进行了分析。再次,结合同期青岛市社会经济统计资料,应用主成分分析法得到了影响青岛市土地利用发生变化的主要社会经济驱动力因子,计算得出青岛市社会经济驱动力指数,指出社会经济因素对土地利用的驱动力呈不断上升的趋势;应用多元线性回归分析法建立了青岛市水域、林地、建筑用地和耕地的社会经济驱动力模型,得到了影响每种土地利用类型的社会经济因子。最后,运用Markov模型预测了2012~2030年青岛市的土地利用变化趋势。

关 键 词:遥感  土地利用变化  驱动力  主成分分析  Markov模型

Analysis of Evolution Driving Force Factor of Land Use in Qingdao City
LING Tao,LI Peng,ZUO Xiao-qing.Analysis of Evolution Driving Force Factor of Land Use in Qingdao City[J].Journal of Anhui Agricultural Sciences,2016(13):257-262.
Authors:LING Tao  LI Peng  ZUO Xiao-qing
Abstract:Based on RS and GIS technology, the remote sensing images in 1986, 2000 and 2006 of Qingdao City were chosen as the base da-ta. Firstly, with Google map as the reference, the remote sensing images of the three phases were classified;and the land use data in the same period was extracted. Secondly, corresponding mathematical model was used to analyze the land use quantity change, land use speed change and land use degree change in the 20 years in Qingdao City. Thirdly, based on the social and economic statistical data in Qingdao in the 20 years, main social economic driving force factors of land use change in Qingdao City was obtained by principal component analysis;the social and economic driving force index in Qingdao was calculated in the 20 years. It was proved that the driving force of social and economic driving force to land use showed an increasing rising trend in the 20 years. The social economic driving force model of waters, forest land, construction land and cultivated land in Qingdao was established by the multivariate regression analysis method. And the social and economic factor affect-ing each and use type was obtained. Finally, land use change trend of Qingdao City was forecasted in future 24 years under the support of Markov model.
Keywords:Remote sensing  Land use change  Driving force  Principal component analysis  Markov model
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