干旱区城市增长边界划定方法与实证——以内蒙古呼和浩特市为例
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摘要
以呼和浩特市区为研究区,构建耦合模型对研究区UGB进行划定。一方面,从城市扩展外部约束的角度,划分不同等级"源",构建生态阻力综合评价体系,结合生态禁建区的分布,通过UEER模型构建研究区综合生态安全格局,提取城市用地刚性增长边界;另一方面,从城市扩展内生机制的角度,选取适当驱动因子,通过CA-Markov模型对研究区2025年城市用地扩展进行模拟,提取城市用地增长边界;通过耦合阻力与驱动力对城市用地扩展的模拟结果,并参照研究区土地利用总体规划,最终划定研究区2025年城市用地增长边界。结果表明,2025年城市增长边界内的面积共514.65 km~2,剩余可建设用地面积57.38 km~2。该耦合模型既可以体现出城市用地克服种种生态阻力进行扩展的结果,又可以反映出城市自身发展运动的趋势。此方法可为同类生态环境脆弱的城市发展规划提供参考,亦可为其他城市UGB的划定提供借鉴。
Taking Hohhot City as the study area, this paper established a coupling model to delimit UGB in the study area. On the one hand, in terms of external constraints of urban expansion, the authors built an ecological resistance comprehensive evaluation system by dividing the sources into different grades. Combining with the distribution of forbidden development areas, this paper constructs an integrated ecological security pattern by UEER model and extracts its rigid growth boundary. On the other hand, from the perspective of urban expansion endogenous mechanism, selecting appropriate driving factors, the authors simulated the land use change of study area in 2025 by Ca-Markov model and extracted its rigid growth boundary. In this paper, the coupling resistance and driving force were used to simulate the urban land expansion. The predicted results of the two models were intersected, thus the boundary of urban land expansion in 2025 in the research area was finally delimited. The results illustrate that the area within the urban growth boundary in 2025 will cover 514.65 km~2, and the remaining construction land area will cover 57.38 km~2. The coupling model can not only demonstrate the expansion of urban land against various ecological obstacles, but also reflect the trend of urban development. The coupling model is able to provide references for the development plan in the same cities which bear fragile ecological environment arid regions, it also facilitates the delimitation of UGB in other cities.
Taking Hohhot City as the study area, this paper established a coupling model to delimit UGB in the study area. On the one hand, in terms of external constraints of urban expansion, the authors built an ecological resistance comprehensive evaluation system by dividing the sources into different grades. Combining with the distribution of forbidden development areas, this paper constructs an integrated ecological security pattern by UEER model and extracts its rigid growth boundary. On the other hand, from the perspective of urban expansion endogenous mechanism, selecting appropriate driving factors, the authors simulated the land use change of study area in 2025 by Ca-Markov model and extracted its rigid growth boundary. In this paper, the coupling resistance and driving force were used to simulate the urban land expansion. The predicted results of the two models were intersected, thus the boundary of urban land expansion in 2025 in the research area was finally delimited. The results illustrate that the area within the urban growth boundary in 2025 will cover 514.65 km~2, and the remaining construction land area will cover 57.38 km~2. The coupling model can not only demonstrate the expansion of urban land against various ecological obstacles, but also reflect the trend of urban development. The coupling model is able to provide references for the development plan in the same cities which bear fragile ecological environment arid regions, it also facilitates the delimitation of UGB in other cities.
引文
[1]方创琳,鲍超,黄金川,等.中国城镇化发展的地理学贡献与责任使命[J].地理科学,2018,38(3):321-331.
[2]叶玉瑶,苏泳娴,张虹鸥,等.生态阻力面模型构建及其在城市扩展模拟中的应用[J].地理学报,2014,69(4):485-496.
[3]周艳,黄贤金,徐国良,等.长三角城市土地扩张与人口增长耦合态势及其驱动机制[J].地理研究,2016,35(2):313-324.
[4] Yu K. Security patterns and surface model in landscape ecological planning[J]. Landscape&Urban Planning,1996,36(1):1-17.
[5] Tayyebi A,Pijanowski B C,Pekin B. Two rule-based Urban Growth Boundary Models applied to the Tehran Metropolitan Area,Iran[J]. Applied Geography,2011,31(3):908-918.
[6] Nelson A C,Moore T. Assessing urban growth management:The case of Portland,Oregon,the USA's largest urban growth boundary[J]. Land Use Policy,2015,10(4):293-302.
[7] Tobler W R. Cellular Geography. Philosophy in Geography[J].Springer Netherlands,1979:379-386.
[8]张继平,乔青,刘春兰,等.基于最小累积阻力模型的北京市生态用地规划研究[J].生态学报,2017,37(19):6 313-6 321.
[9]张宸睿,符海月,张鑫,等.基于生态约束——发展潜力的城镇建设用地开发适宜性分区[J].水土保持通报,2017,37(3):174-181,187.
[10]陶卓霖,喻忠磊,王砾,等.基于空间区位条件的城市扩展生态阻力面模型及应用[J].地理研究,2018,37(1):199-208.
[11]杨丽,傅春.赣南生态屏障区林地时空变化分情景模拟[J].地理科学,2018,38(3):457-463.
[12]李志明,宋戈,鲁帅,等.基于CA-Markov模型的哈尔滨市土地利用变化预测研究[J].中国农业资源与区划,2017,38(12):41-48.
[13]罗双晓,何政伟,高箐,等.基于CA-Markov模型的天府新区土地时空变化预测[J].水土保持研究,2018,25(3):157-163.
[14]吴晶晶,田永中,许文轩,等.基于CA-Markov模型的乌江下游地区土地利用变化情景分析[J].水土保持研究,2017,24(4):133-139.
[15]雷花.基于BP神经网络和SVM模型的西安市高陵区UGB划定研究[D].西安:长安大学,2017.
[16]丛佃敏,赵书河,于涛,等.综合生态安全格局构建与城市扩张模拟的城市增长边界划定——以天水市规划区(2015—2030年)为例[J].自然资源学报,2018,33(1):14-26.
[17]冯琰玮,甄江红.基于径向基神经网络的呼和浩特市生态安全预警研究[J].干旱区资源与环境,2018,32(11):87-92.
[18]王金亮,谢德体,邵景安,等.基于最小累积阻力模型的三峡库区耕地面源污染源—汇风险识别[J].农业工程学报,2016,32(16):206-215.
[19]王琦,付梦娣,魏来,等.基于源—汇理论和最小累积阻力模型的城市生态安全格局构建——以安徽省宁国市为例[J].环境科学学报,2016,36(12):4 546-4 554.
[20]王钊,杨山,王玉娟,等.基于最小阻力模型的城市空间扩展冷热点格局分析——以苏锡常地区为例[J].经济地理,2016,36(3):57-64.
[21]潘竟虎,刘晓.基于空间主成分和最小累积阻力模型的内陆河景观生态安全评价与格局优化——以张掖市甘州区为例[J].应用生态学报,2015,26(10):3126-3136.
[22]刘世梁,刘琦,张兆苓,等.云南省红河流域景观生态风险及驱动力分析[J].生态学报,2014,34(13):3 728-3 734.
[23]王倩倩,覃志豪,王斐.基于多源遥感数据反演地表温度的单窗算法[J].地理与地理信息科学,2012,28(3):24-26,62,113.
[2]叶玉瑶,苏泳娴,张虹鸥,等.生态阻力面模型构建及其在城市扩展模拟中的应用[J].地理学报,2014,69(4):485-496.
[3]周艳,黄贤金,徐国良,等.长三角城市土地扩张与人口增长耦合态势及其驱动机制[J].地理研究,2016,35(2):313-324.
[4] Yu K. Security patterns and surface model in landscape ecological planning[J]. Landscape&Urban Planning,1996,36(1):1-17.
[5] Tayyebi A,Pijanowski B C,Pekin B. Two rule-based Urban Growth Boundary Models applied to the Tehran Metropolitan Area,Iran[J]. Applied Geography,2011,31(3):908-918.
[6] Nelson A C,Moore T. Assessing urban growth management:The case of Portland,Oregon,the USA's largest urban growth boundary[J]. Land Use Policy,2015,10(4):293-302.
[7] Tobler W R. Cellular Geography. Philosophy in Geography[J].Springer Netherlands,1979:379-386.
[8]张继平,乔青,刘春兰,等.基于最小累积阻力模型的北京市生态用地规划研究[J].生态学报,2017,37(19):6 313-6 321.
[9]张宸睿,符海月,张鑫,等.基于生态约束——发展潜力的城镇建设用地开发适宜性分区[J].水土保持通报,2017,37(3):174-181,187.
[10]陶卓霖,喻忠磊,王砾,等.基于空间区位条件的城市扩展生态阻力面模型及应用[J].地理研究,2018,37(1):199-208.
[11]杨丽,傅春.赣南生态屏障区林地时空变化分情景模拟[J].地理科学,2018,38(3):457-463.
[12]李志明,宋戈,鲁帅,等.基于CA-Markov模型的哈尔滨市土地利用变化预测研究[J].中国农业资源与区划,2017,38(12):41-48.
[13]罗双晓,何政伟,高箐,等.基于CA-Markov模型的天府新区土地时空变化预测[J].水土保持研究,2018,25(3):157-163.
[14]吴晶晶,田永中,许文轩,等.基于CA-Markov模型的乌江下游地区土地利用变化情景分析[J].水土保持研究,2017,24(4):133-139.
[15]雷花.基于BP神经网络和SVM模型的西安市高陵区UGB划定研究[D].西安:长安大学,2017.
[16]丛佃敏,赵书河,于涛,等.综合生态安全格局构建与城市扩张模拟的城市增长边界划定——以天水市规划区(2015—2030年)为例[J].自然资源学报,2018,33(1):14-26.
[17]冯琰玮,甄江红.基于径向基神经网络的呼和浩特市生态安全预警研究[J].干旱区资源与环境,2018,32(11):87-92.
[18]王金亮,谢德体,邵景安,等.基于最小累积阻力模型的三峡库区耕地面源污染源—汇风险识别[J].农业工程学报,2016,32(16):206-215.
[19]王琦,付梦娣,魏来,等.基于源—汇理论和最小累积阻力模型的城市生态安全格局构建——以安徽省宁国市为例[J].环境科学学报,2016,36(12):4 546-4 554.
[20]王钊,杨山,王玉娟,等.基于最小阻力模型的城市空间扩展冷热点格局分析——以苏锡常地区为例[J].经济地理,2016,36(3):57-64.
[21]潘竟虎,刘晓.基于空间主成分和最小累积阻力模型的内陆河景观生态安全评价与格局优化——以张掖市甘州区为例[J].应用生态学报,2015,26(10):3126-3136.
[22]刘世梁,刘琦,张兆苓,等.云南省红河流域景观生态风险及驱动力分析[J].生态学报,2014,34(13):3 728-3 734.
[23]王倩倩,覃志豪,王斐.基于多源遥感数据反演地表温度的单窗算法[J].地理与地理信息科学,2012,28(3):24-26,62,113.