基于MCR-ANN-CA模型的包头市生态用地演变模拟
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摘要
以内蒙古自治区包头市为研究区,耦合最小累积阻力(MCR)模型、人工神经网络(ANN)和元胞自动机(CA)构建MCR-ANN-CA模型。利用MCR模型量化包头市各用地类型演变为生态用地时的阻力,构建CA适宜性规则;利用ANN模型提取CA邻域转换规则,基于包头市2006、2011年土地利用数据及归一化植被指数(NDVI)、高程、坡度、水体距离、人口密度多项数据,对2016年生态用地演变情景进行模拟,以2016年实际生态用地分布为参照,将该模型模拟结果与CA-Markov模型的模拟结果进行对比(以2016年实际景观分布为参照),结果显示,两种模型模拟结果的卡帕一致性指数(Kappa index of agreement,KIA)分别为0. 89和0. 87,相对误差分别为3. 10%和5. 31%,MCR-ANN-CA模型显示了较高的模拟精度。
To simulate the ecological land transition of Baotou City,Inner Mongolia,the MCR-ANN-CA model was built. This model was composed of minimum cumulative resistance model( MCR),artificial neural network( ANN) and cellular automata model( CA). The MCR model was used to simulate the resistance faced by ecological land during its transition. To measure the resistance,the data of normalized difference vegetation index( NDVI),digital elevation model( DEM),slope,distance from water,population density and industrial park distribution were normalized and superimposed to produce the resistance surface of MCR model. Then a cumulative consumption resistance surface was generated by using the cost distance tool of ArcGIS software. The generated surface was taken as the suitability map of CA model. The ANN model was used to extract CA neighborhood transfer rules and consider the neighborhood land use structure. The CA model combined the cumulative consumption resistance surface and the neighborhood transfer rules extracted by using the CNN model. And the MCR-CNN-CA model was finally formed. Based on the land use data of 2006 and 2011,the transition of construction land of Baotou City in 2016 was simulated by using the MCR-ANN-CA model. The simulation result of the model was compared with the CA-Markov model. The Kappa index of agreement( KIA) of the two models were0. 89 and 0. 87,and the relative errors were 3. 10% and 5. 31%,respectively. The MCR-ANN-CA model showed high simulation precision.
To simulate the ecological land transition of Baotou City,Inner Mongolia,the MCR-ANN-CA model was built. This model was composed of minimum cumulative resistance model( MCR),artificial neural network( ANN) and cellular automata model( CA). The MCR model was used to simulate the resistance faced by ecological land during its transition. To measure the resistance,the data of normalized difference vegetation index( NDVI),digital elevation model( DEM),slope,distance from water,population density and industrial park distribution were normalized and superimposed to produce the resistance surface of MCR model. Then a cumulative consumption resistance surface was generated by using the cost distance tool of ArcGIS software. The generated surface was taken as the suitability map of CA model. The ANN model was used to extract CA neighborhood transfer rules and consider the neighborhood land use structure. The CA model combined the cumulative consumption resistance surface and the neighborhood transfer rules extracted by using the CNN model. And the MCR-CNN-CA model was finally formed. Based on the land use data of 2006 and 2011,the transition of construction land of Baotou City in 2016 was simulated by using the MCR-ANN-CA model. The simulation result of the model was compared with the CA-Markov model. The Kappa index of agreement( KIA) of the two models were0. 89 and 0. 87,and the relative errors were 3. 10% and 5. 31%,respectively. The MCR-ANN-CA model showed high simulation precision.
引文
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[3]刘枝军,王宏卫,杨胜天,等.极端干旱区绿洲生态用地规划[J].生态学报,2018,38(22):7950-7959.LIU Zhijun,WANG Hongwei,YANG Shengtian,et al. Ecological land use planning of an oasis in an extremely arid area[J].Acta Ecologica Sinica,2018,38(22):7950-7959.(in Chinese)
[4]王鹏,王亚娟,刘小鹏,等.干旱区生态移民土地利用景观格局变化分析———以宁夏红寺堡区为例[J].干旱区资源与环境,2018,32(12):69-74.WANG Peng,WANG Yajuan,LIU Xiaopeng,et al. Analysis of landscape pattern change of ecological emigration land in aridarea—taking the red temple area in Ningxia as an example[J]. Journal of Arid Land Resources and Environment,2018,32(12):69-74.(in Chinese)
[5] YU Q,YUE D,WANG Y,et al. Optimization of ecological node layout and stability analysis of ecological network in desertoasis:a typical case study of ecological fragile zone located at Deng Kou County(Inner Mongolia)[J]. Ecological Indicators,2018,84:304-318.
[6]于强,岳德鹏,YANG Di,等.基于En KF-MCRP模型的生态用地扩张模拟研究[J/OL].农业机械学报,2016,47(9):285-293.YU Qiang,YUE Depeng,YANG Di,et al. Simulation on ecological land use expansion based on En KF-MCRP model[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):285-293. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20160939&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2016. 09. 039.(in Chinese)
[7]龚健,陈耀霖,张志.基于多分类Logistic回归模型的区域土地利用变化及驱动因素研究[J].湖北农业科学,2016,55(17):4545-4550.GONG Jian,CHEN Yaolin,ZHANG Zhi. Regional land use change and its driving forces analysis based on multinomial Logisticregression[J]. Hubei Agricultural Sciences,2016,55(17):4545-4550.(in Chinese)
[8]刘孝国,郄瑞卿,董军.基于马尔可夫模型的吉林市土地利用变化预测[J].中国农学通报,2012,28(29):226-229.LIU Xiaoguo,QIE Ruiqing,DONG Jun. Based on Markov model in the prediction of Jilin City land use change prediction[J].Chinese Agricultural Science Bulletin,2012,28(29):226-229.(in Chinese)
[9]孙旭丹,张立亭,罗亦泳,等.基于SD和CA模型的土地资源配置策略研究———以鄱阳湖生态经济区为例[J].东华理工大学学报(社会科学版),2018,37(1):22-26.SUN Xudan,ZHANG Liting,LUO Yiyong,et al. Research on land resource allocation based on SD and CA model—takingPoyang Lake eco-economic zone for example[J]. Journal of East China University of Technology(Social Science Edition),2018,37(1):22-26.(in Chinese)
[10] WIEKRAMASURIYA R C,BREGT A K,DELDEN H V,et al. The dynamics of shifting cultivation captured in an extendedconstrained cellular automata land use model[J]. Ecological Modelling,2009,220(18):2302-2309.
[11]刘敬杰,夏敏,刘友兆,等.基于多智能体与CA结合模型分析的农村土地利用变化驱动机制[J].农业工程学报,2018,34(6):242-252.LIU Jingjie,XIA Min,LIU Youzhao,et al. Driving mechanism of rural land use change based on multi-agent system andcellular automata[J]. Transactions of the CSAE,2018,34(6):242-252.(in Chinese)
[12]何丹,周璟,高伟,等.基于CA-Markov模型的滇池流域土地利用变化动态模拟研究[J].北京大学学报(自然科学版),2014,50(6):1095-1105.HE Dan,ZHOU Jing,GAO Wei,et al. An integrated CA-Markov model for dynamic simulation of land use change in LakeDianchi Watershed[J]. Acta Scientiarum Naturalium Universitatis Pekinensis,2014,50(6):1095-1105.(in Chinese)
[13]宋磊,陈笑扬,李小丽,等.基于CA-Markov模型的长沙市望城区土地利用/覆盖变化预测[J].国土资源导刊,2018,15(2):17-23.SONG Lei,CHEN Xiaoyang,LI Xiaoli,et al. The CA-Markov model based prediction of land use/cover changes inWangcheng District,Changsha City,Hunan Province[J]. Land&Resources Herald,2018,15(2):17-23.(in Chinese)
[14]胡碧松,张涵玥.基于CA-Markov模型的鄱阳湖区土地利用变化模拟研究[J].长江流域资源与环境,2018,27(6):1207-1219.HU Bisong,ZHANG Hanyue. Simulation of land-use change in Poyang Lake Region based on CA-Markov model[J].Resources and Environment in the Yangtze Basin,2018,27(6):1207-1219.(in Chinese)
[15]张洁,蔡逸涛,杨强,等.基于CA-SVM模型的莆田市城市扩张特征研究[J].南京林业大学学报(自然科学版),2018,42(4):148-152.ZHANG Jie,CAI Yitao,YANG Qiang,et al. Study on characteristic of urban expansion based on CA-SVM model:takingPutian City in Fujian Province as an example[J]. Journal of Nanjing Forestry University(Natural Science Edition),2018,42(4):148-152.(in Chinese)
[16]陈晓敏,解智峰.城市土地集约利用评价及驱动力分析———以内蒙古包头市为例[J].国土资源情报,2011,2(6):46-52.CHEN Xiaomin,XIE Zhifeng. Evaluation and driving force analysis of intensive use of urban land—taking Baotou City of InnerMongolia as an example[J]. Land and Resources Information,2011,2(6):46-52.(in Chinese)
[17]于佳生.包头市生态城市评价及对策研究[D].包头:内蒙古科技大学,2015.YU Jiasheng. The evaluation and countermeasure research of ecological city in Baotou City[D]. Baotou:Inner MongoliaUniversity of Science and Technology,2015.(in Chinese)
[18]王鑫.包头市工业企业环境污染管理问题与对策研究[D].呼和浩特:内蒙古大学,2015.WANG Xin. Baotou industrial pollution of the environment management problems and counter measures[D]. Huhhot:InnerMongolia University,2015.(in Chinese)
[19]张敏.包头市城市建设用地扩展研究[D].呼和浩特:内蒙古师范大学,2011.ZHANG Min. Research on the expansion of urban construction land in Baotou[D]. Huhhot:Inner Mongolia NormalUniversity,2011.(in Chinese)
[20]魏伟.基于CLUE-S和MCR模型的石羊河流域土地利用空间优化配置研究[D].兰州:兰州大学,2018.WEI Wei. Land use operation allocation based on CLUES-S and MCR model in Shiyang River Basin[D]. Lanzhou:LanzhouUniversity,2018.(in Chinese)
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