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基于CA-Markov模型的甘州区土地利用变化预测分析 被引量:16

Prediction and Analysis of Land Use Change Based on CA-Markov Model in Ganzhou District
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摘要 研究以张掖市甘州区为例,以黑河流域2000年和张掖市2007年土地利用/土地覆盖(LUCC)数据集的矢量数据、2014年Landsat 8 OLI_TIRS 8月份遥感影像和Google Earth影像为数据源,以Arc GIS10.1、ENVI 4.8和IDRISI 17.00为计算平台,利用CA-Markov模型对2014年土地利用变化进行模拟和预测,并将预测结果与2014年甘州区LUCC解译数据作对比分析,其Kappa系数为87.87%,表明预测与现实一致性较佳。最后对2021年甘州区土地利用分类进行模拟和预测。从预测结果可以看出:2000—2021年间甘州区耕地面积和建设用地面积呈现较快的增长趋势,未利用地面积呈现较快的减少趋势;林地和水域呈现缓慢地增长趋势;草地面积变化较小。简要分析了其变化原因主要归因于政策、社会经济、人口和科学技术几方面。对2021年甘州区土地利用变化的预测研究结果可为该区土地利用合理规划管理及经济发展政策的制定提供科学的决策依据。 Ganzhou District in Zhangye City was used as an example in this study,the data were land use/land cover (LUCC) data of the Heihe river basin in 2000 and Zhangye City in 2007,the Landsat 8OLI_TIRS remote sensing images in August,2014 and Google Earth images.The authors used ArcGIS 10.1,ENVI 4.8 and IDRISI 17.00 as the computational platform to simulate and predict land use change in 2014 by CA-Markov model.By contrastive analysis between the predict result and the LUCC data of Ganzhou in 2014,the kappa coefficient was 87.87%,it showed that the prediction and the reality were well consistent.Finally,the land use classes of Ganzhou in 2021 was simulated and predicted.The predicted results showed that from 2000 to 2021 in Ganzhou,the area of cultivated land and construction land presented a rapid growth trend,the unused land area decreased rapidly;forestland and water area had a slowly increasing trend;grassland area changed a little.The reasons were mainly due to several aspects including policy,social economy,population and science technology.The prediction results of land use change of 2021 in Ganzhou could provide a decision basis for rational planning and management of land use and the formulation of economic development policy.
作者 张俊平 李净
出处 《中国农学通报》 2017年第4期105-110,共6页 Chinese Agricultural Science Bulletin
基金 甘肃省科技计划/西北师范大学青年教师科研能力提升计划项目"基于遥感技术的山区辐射平衡"(NWNU-LKQN-14-4)
关键词 CA-Markov模型 Kappa系数 土地利用变化 土地利用预测 CA-Markov model Kappa coefficient land use change land use prediction
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