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地理研究 >

2017 , Vol. 36 >Issue 3: 529 - 540

DOI: https://doi.org/10.11821/dlyj201703011

研究论文

基于潜力—约束和SLEUTH模型松散耦合的南京城市扩展模拟

  • 徐海龙 , 1 ,
  • 尹海伟 , 2 ,
  • 孔繁花 1 ,
  • 许峰 1
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  • 1. 南京大学国际地球系统科学研究所,南京 210023
  • 2. 南京大学城市规划与设计系,南京 210093
尹海伟（1978- ）,男,山东青州人,副教授,主要从事城市与区域规划和城市生态研究。E-mail:

作者简介：徐海龙（1991- ）,男,山东枣庄人,硕士,主要从事城市景观生态研究。E-mail:

收稿日期: 2016-08-16

  要求修回日期: 2016-12-23

  网络出版日期: 2017-03-20

基金资助

国家自然科学基金项目（51478217,41440006,31170444）

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Urban sprawl simulation based on the loose coupling between potential-limitation and SLEUTH model in Nanjing city

  • XU Hailong , 1 ,
  • YIN Haiwei , 2 ,
  • KONG Fanhua 1 ,
  • XU Feng 1
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  • 1. International Institute for Earth System Science (ESSI), Nanjing University, Nanjing 210023, China
  • 2. Department of Urban Planning and Design, Nanjing University, Nanjing 210093, China

Received date: 2016-08-16

  Request revised date: 2016-12-23

  Online published: 2017-03-20

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《地理研究》编辑部
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摘要

将潜力—约束与SLEUTH模型进行松散耦合,在建设用地适宜性评价的基础上,将不同生态安全格局情景融入SLEUTH模型的排除图层,对研究区2013-2040年的城市用地空间扩展进行多情景模拟。研究表明：① 两个模型的松散耦合能够有效提高像元尺度上模型校正的精度。② 用地扩展在以“边缘增长”和“填充增长”为主的同时,在城市外围形成了较为明显的“跳跃式”发展,表明模型的松散耦合能够更好地捕捉城市发展政策所导致的新城市增长中心。③ 三种生态安全格局情景方案的城市用地模拟结果均呈增长趋势,但高生态安全格局情景的新增城市用地面积和增长率均最小,表明将生态敏感性作为城市发展的约束图层,能够有效保护研究区的自然生态空间,大幅降低生态安全风险。

关键词： 城市扩展; 潜力—; 约束模型; 生态安全格局; SLEUTH模型; 情景分析

本文引用格式

徐海龙 , 尹海伟 , 孔繁花 , 许峰 . 基于潜力—约束和SLEUTH模型松散耦合的南京城市扩展模拟[J]. 地理研究, 2017 , 36(3) : 529 -540 . DOI: 10.11821/dlyj201703011

Abstract

Urban land growth is driven by many factors entailing spatial complexity and regional specificity. Because of difficulties in bridging the divide between transformation rules derived from bottom-up processes and top-down urban development policies using the SLEUTH model, accurate assessment of the impacts of top-down urban development policies on urban expansion is a challenging task. In contexts of top-down growth-oriented urban land management, urban land growth mainly manifests as the growth of new regions, especially as clumping growth. It is, therefore, necessary to incorporate other urban land growth models to improve the predictive ability of the SLEUTH model relating to urban development policies. We used a loose coupling of the potential-limitation and SLEUTH models to investigate urban change in Nanjing and its surroundings. Applying the potential-limitation model, we first integrated potential factors associated with urban development and ecological constraints into city development strategies, quantitatively evaluating the suitability of construction land in relation to different ecological security patterns in the study area. We subsequently, blended different scenarios for these ecological security patterns into the excluded layers, and used the SLEUTH model to simulate the urban space sprawl of this area from 2013 to 2040. We obtained the following results. First, the loose coupling of the two models improved the accuracy of calibration at the pixel scale. Moreover, reconstruction results obtained with the SLEUTH model were consistent with the actual situation. Second, "marginal growth" and "fill-in growth" patterns dominated the urban land sprawl. Simultaneously, a number of new city development centers were emerging outside the city, presenting rapid and conspicuous development. These findings demonstrate that loosely coupled models are better than the SLEUTH model, used alone, to capture the movement of the city center induced by urban development policies and the locations for the emergence of new urban growth centers. Consequently, the SLEUTH model performs better in relation to urban development policies, and is able to bridge the divide, to some extent, between conversion rules derived through bottom-up processes and top-down urban development policies. Third, while the simulation results for the three ecological security pattern scenarios indicated an increasing trend for urban sprawl, differences in their growth rates were significant. For the scenario with a high ecological security pattern, the newly added urban land area and the growth rate were both minimal, indicating that ecological sensitivity analysis applied as a layer constraining urban development could effectively protect the natural ecological space in the research area, and dramatically reduce the ecological security risk. These results can be used to guide and support future urban space sprawl management, urban planning, and land use planning decisions in the study area.

Key words： urban sprawl; potential- limitation model; ecological security pattern; SLEUTH model; scenario analysis

1 引言

随着国内城市化进程的不断推进,城市人口不断增长,城市用地规模不断扩大,必将导致自然生态空间向城市空间的快速转换,给中国的资源与环境带来巨大压力与挑战,对区域生态安全也将产生深远影响[1]。土地利用动态变化模型能够分析预测土地利用动态变化过程,更好地理解和解释土地利用动态变化的原因,帮助城市土地管理者分析不同情境下土地利用的变化特征及其影响,为制定切实有效的土地开发利用政策提供科学支撑[2,3]。元胞自动机（cellular automata,CA）因其具有灵活性、开放性、非线性、自适应性等多重特征和功能,能够通过简单的局部转换规则来模拟复杂的城市空间格局变化,且与遥感数据与GIS联系紧密,成为最具影响力的城市增长模型[3-6]。在CA模型中最经典的当数Clarke等[5]开发的SLEUTH（Slope,Land use,Exclusion,Urban,Transportation,Hillshade）模型。该模型可以结合大型空间数据库和各种分辨率的遥感数据,从中观到宏观、十年到百年的时空尺度上模拟预测城市土地利用的变化,已被广泛应用于城市增长模拟及长期预测研究中[5-9]。近年来,在中国许多城市的增长模拟中也被广泛采用[10-15]。
尽管国内外有大量通过将不同政策情景融入SLEUTH模型的排除图层中来间接表征不同政策对城市扩展影响的研究案例,但很多研究均发现“将规划政策融入模型转换规则中去非常困难”[16],模拟结果尚无法很好地捕捉这些自上而下发展政策的影响[6,17]。城市用地增长受诸多因子驱动,具有空间复杂性和区域特质[7]。由于SLEUTH模型还很难在自下而上思路获取的转换规则与自上而下的城市发展政策之间构建联系桥梁[18],因而容易造成模拟结果总体精度较高而新增城市像元匹配精度偏低的问题[16]。这与模型中元胞状态高度依赖于其邻域元胞状态有关,已有城市向外扩张容易,而新形成的城市扩散中心增长则不易发生[8,16,17]。在中国以自上而下为主的城市用地增长管理体制下,城市用地增长多以新城新区增长为主,团块增长特征明显。因而,融合其他城市用地增长模型、综合考虑城市用地的增长机制就显得尤为重要[19,20]。
借鉴损益分析方法（cost-benefit analysis）,在多因素加权叠置分析的基础上,宗跃光等[21]提出了综合考虑自然生态限制性因子和社会经济发展潜力因子的潜力—约束模型,比较符合中国以自上而下为主的城市用地增长管理体制,能够较好地识别规划研究区潜在的战略性增长空间。然而,该模型基于行政区划单元的发展潜力评价尚不能很好地与基于像元尺度的发展约束评价在空间上进行很好的镶嵌。鉴于此,尹海伟等[22]改进了此模型,通过综合实力评价与空间可达性分析较好地实现了区域综合发展潜力的空间栅格化,并通过不同发展理念下的相互作用判别矩阵,得到不同生态安全格局情景下的建设用地适宜性方案,能够较科学地刻画研究区未来用地的发展趋势和空间布局。然而,潜力—约束模型只能获取不同生态安全格局情景下的城市用地增长方案,不能有效预测不同时期建设用地空间扩展的动态变化过程。因而,通过潜力—约束模型与SLEUTH模型的松散耦合,实现两个模型的优势互补,将潜力—约束模型的用地适宜性评价结果融入SLEUTH模型的排除图层,能够将城市不同发展理念融入SLEUTH模型中,弥补SLEUTH模型在自上而下城市发展政策表现能力方面的不足,使其更好地捕捉城市发展政策的影响。
本文以南京及其附近区域为研究区,将城市发展的潜力因素和生态约束因素融入到城市发展战略中,采用潜力—约束模型,对研究区不同生态安全格局情景下的建设用地适宜性进行了定量评价,并采用SLEUTH模型模拟了研究区2013-2040年的城市用地空间扩展,揭示了未来城市空间增长的格局特征。研究结果对南京城市用地空间增长管理具有重要的指导意义。

2 研究区概况与研究方法

2.1 研究区概况

南京市位于31°14′N~32°36′N、118°22′E~119°14′E,属北亚热带季风气候,四季分明,历年平均气温16 ℃,年平均降雨量为1106.5 mm。1980年代以来,南京市的社会经济发展迅速,GDP总量由1985年的109亿元增长为2015年的9721亿元,常住城镇人口由1985年的197万增长为2014年的649万,城镇化率也由45%增至81%。随着南京市社会经济的快速发展和城镇化的快速推进,城市建设用地空间围绕主城区快速向西、南和东部扩展,形成了南京市一主（主城区）三副（江北、仙林、江宁）的城市格局。2015年,随着南京市江北新区入选国家级新区,南京市将步入跨江和拥江发展的新阶段。本文以南京市中心城区及其周边区域为研究区,面积约为4006 km2,包括南京市一主三副的大部分区域（图1）。
Fig. 1 Location of the study area

图1 研究区概况

2.2 数据来源与预处理

本文所使用的主要数据有：南京市1988年7月5日、2000年6月12日、2009年10月3日、2012年7月31日的TM遥感影像,30 m分辨率的ASTER GDEM数据（资料来源：地理空间数据云网站）;1 ∶ 5万地形图。
首先,在ERDAS中将1988-2012年4期TM遥感影像数据进行波段融合,并根据研究区边界进行裁剪。然后,在ArcGIS中基于地形图对影像进行几何精校正（RMS小于1个像元）,并设定坐标系统为WGS-1984,投影设定为UTM。再次,在ERDAS中采用监督分类方法,得到研究区4个时期的土地利用类型图（图2）,分类结果的Kappa系数均在0.80以上,分类精度较好。最后,通过对研究区四个时期的道路交通图进行扫描数字化,得到研究区的道路交通图（图2）。
Fig. 2 Land use of Nanjing

图2 南京市土地利用分类图

2.3 研究方法

2.3.1 基于潜力—约束模型的建设用地适宜性评价 首先,基于GIS软件平台,采用空间可达性分析方法对研究区发展潜力进行空间定量分析[22-24]。主要分析过程为：① 使用Fragstats 3.3软件,采用半径为300 m的移动窗口,分别计算研究区1988年和2012年建设用地的PLAND指数,并将PLAND值大于70%且面积大于3 km2的城市斑块提取出来,作为研究区未来城市发展的增长极;② 根据增长极的几何中心所在行政区的未来增长潜力进行综合实力的赋值（主城区赋值100,其他区域80）;③ 根据研究区1988年和2012年的道路交通图,并综合考虑坡度、河流等阻碍因子,采用GIS中的费用距离（cost distance）方法,分别计算距离增长极的空间可达性水平（min）;④ 空间某一像元发展潜力主要受最邻近增长极强弱和距离增长极时间距离的影响,采用式（1）计算研究区每一个栅格单元的发展潜力,并按照自然断裂点法将其划分为极低发展潜力、低发展潜力、中发展潜力、高发展潜力、极高发展潜力5个等级。
P i = O S j × 1 1 + 0.01 × e 0.15 × T ij （1）
式中：Pi是第i个栅格的发展潜力;OSj是第j个城市发展增长极的综合实力得分值;Tij是指第i个栅格到第j个城市发展增长极的可达时间（min）。
然后,采用生态环境敏感性方法对研究区发展的生态约束进行定量评价[25]。主要分析过程如下：① 根据研究区实际与数据的可获得性,选取植被、水域、地形、农田作为生态敏感性分析的主要影响因子,并构建生态因子敏感性等级体系并赋值（表1）;② 采用多因子叠置分析方法,得到研究区生态敏感性总图,并按敏感性程度划分为5个等级：极高敏感性、高敏感性、中敏感性、低敏感性、非敏感性。
最后,根据发展潜力和生态敏感性的分析结果,采用潜力—约束模型,使用不同城市发展理念下的相互作用判别矩阵,得到高、中、低生态安全格局下的建设用地适宜性评价结果（图3）。
Fig. 3 Land suitability based on high, medium and low ecological security patterns

图3 高、中、低生态安全格局下的用地适宜性评价结果

Tab. 1 Sensitivity grades and values ofecological sensitivity factors

表1 生态因子敏感性等级与赋值

生态因子 分类 赋值 生态敏感性等级
植被 公园 9 极高敏感性
缓冲区 100 m 7 高敏感性
密林（NDVI>0.6） 7 高敏感性
疏林（0<NDVI≤0.6） 5 中敏感性
草地 3 低敏感性
坡度 [0, 5%) 1 极低敏感性
[5%, 10%) 3 低敏感性
[10%, 15%) 5 中敏感性
[15%, 25%) 7 高敏感性
[25%,100%] 9 极高敏感性
水域 长江 9 极高敏感性
缓冲区200 m 7 高敏感性
大型水库和河流 7 高敏感性
缓冲区100 m 5 中敏感性
小型水库、河流、水塘 5 中敏感性
农田 5 中敏感性
2.3.2 SLEUTH模型数据准备 根据研究目的,需要输入5个GIF格式的灰度栅格数据图层（城市范围、交通、坡度、山体阴影与排除图层）。城市范围与交通图层分别由4个时期的土地利用类型和道路交通数据生成（图4）。坡度与山体阴影图层分别由研究区的DEM数据生成（图4）。SLEUTH模型校正阶段使用的排除图层根据1988年中生态安全格局下的建设用地适宜性评价结果来设置,预测阶段使用的排除图层则分别根据2012年不同生态安全格局情景下的建设用地适宜性评价结果来进行设置。将极高适宜性、高适宜性、中适宜性、低适宜性、极低适宜性不被城市化的概率分别赋值为0、25、50、75、100。最后,所有数据均转换为模型需要的GIF格式栅格数据,栅格大小60 m×60 m,且所有数据图层的范围保持一致。
2.3.3 SLEUTH模型校正与情景模拟 模型校正的目的是获取一套增长的参数集（即五个模拟系数的值）,从而对研究区的城市增长进行有效模拟。模型采用强制蒙特卡罗迭代计算法（Brute-force Monte Carlo method）进行参数的校正,参数校正分为粗校正、精校正、终校正和模拟参数获取4个阶段进行,每个步骤得到的一套增长的参数集都用于下一个步骤的参数校准,并不断缩小各系数的取值范围,利用实验结果与真实数据进行对比,可以生成一系列统计量,用以评估模拟结果的精度[6,26,27]。
Fig. 4 Input layer data used for the SLEUTH model

图4 SLEUTH模型需要的输入图层数据

将研究区1988年的数据图层作为模型校正的初始图层,2000年、2009年、2012年3个时期的数据图层作为校正图层,按照预设的中生态安全格局情景进行参数校正。模型校正的粗校正与精校正阶段,数据分别重采样为240 m×240 m和120 m×120 m;每一个模型校正阶段,均采用Compare、Pop、Edges、Cluster、Slope、Xmean、Ymean 7个指数的乘积即OSM（the optimal SLEUTH metric）作为模型校正和参数区间缩小的主要判据,并缩小5个系数的取值范围,产生5个新的系数区段。模型校正最后阶段（Derive阶段）取步长为1,采用100次蒙特卡罗迭代,生成该情景下的5个最终系数值（表2）。利用终校正最后得到的系数初始化模型的预测模块,模拟生成2012年的研究区城市开发概率图（图5）,并根据研究区1988-2012年城市用地实际增长情况,选取城市化概率阈值（50%）,重建中生态安全格局情景下的2012城市扩展范围,并在像元尺度上与2012年的现状城市范围进行对比分析,以定量评估模型模拟的准确性（表3、图5）。
Tab. 2 Results of coefficients for each of the SLEUTH Model's calibration stages

表2 SLEUTH模型校正结果

增 长 参 数 粗校准 精校准 终校准 最优系数值
迭代次数 5 7 9
总模拟次数 3125 5400 7776
OSM 0.386 0.482 0.532
范围 步长 范围 步长 范围 步长
散布系数 1~100 25 1~25 5 6~21 3 23
繁殖系数 1~100 25 1~50 10 11~41 6 52
扩展系数 1~100 25 50~75 5 50~65 3 79
坡度系数 1~100 25 50~100 10 60~100 8 46
道路重力系数 1~100 25 50~100 10 50~90 8 77
Tab. 3 Evaluation of the accuracy of the SLEUTH model at the pixel scale

表3 像元尺度上的模型精度评价

非城市像元 城市像元 新增城市像元 总精度（%）
总像元数 839852 274241 218144
重建像元 836602 277491 221394
正确像元 791042 216366 160269
生产者精度（%） 94.19 78.90 73.47
用户精度（%） 94.55 77.97 72.39

Fig. 5 Comparison of calibration results and theactual situation in 2012

图5 SLEUTH模型2012年校正结果与实际情况的对比

SLEUTH模型通过调整自修改模块阈值系数、修改模型增长系数和调整排除图层三种方式来实现对未来城市发展的模拟和预测[28-31]。本文使用2012年的坡度、山体阴影图层、交通、城市范围和三种情景方案下的排除图层作为模型预测的初始化输入数据,在预测模式下运行100次蒙特卡罗迭代运算。考虑到南京市城市化步入新的阶段和宏观经济形势步入新常态,未来的城市用地增长主要以存量用地为主,因而适当提高了2013-2040年的城市开发概率阈值,将2040年度城市开发概率图（图6）上大于70%的栅格作为未来的城市化区域,得到不同情景下的2040年城市用地扩展模拟结果（表4、图6）。
Tab. 4 Predicted statistics on land uses in 2040 for the three scenarios (km2)

表4 2040年不同情景下的城市土地利用情况（km2）

用地类型
建设用地 绿地 农田 水体 其他
2012年 987.27 519.44 2145.44 301.24 57.35
情景1 1401.43（0.92%） 463.81 1802.96 289.89 52.65
情景2 1513.58（1.12%） 449.39 1710.12 287.97 49.68
情景3 1640.32（1.34%） 438.31 1609.41 283.14 39.56

注：括号内为2013-2040年的年均增长率（%）。

Fig. 6 Simulation of urban sprawl in 2040 under different scenarios

图6 不同情景下2040年城市空间扩展

3 结果分析

3.1 基于潜力—约束模型的用地适宜性评价结果

由图3可见,在生态优先、兼顾发展的理念下（高生态安全格局情景）,生态敏感性等级对研究区的用地适宜性具有重要影响,只有敏感性等级较低且发展潜力较高的区域才最适宜发展;而在发展为主、生态底线的理念下（低生态安全格局情景）,发展潜力等级对研究区的用地适宜性具有重要影响,生态作为发展的底线加以控制,此时只要敏感性等级不是很高且发展潜力较高的区域都适宜发展,因而该情景下的高与极高适宜性区域明显多于高生态安全格局情景。在生态与经济发展并重的理念下（中生态安全格局情景）,敏感性和发展潜力对研究区的用地适宜性均具有重要影响,其高与极高适宜性的区域面积也介于高、低生态安全格局情景之间。

3.2 SLEUTH模型的校准结果

由表2可见,模型校正得到的最终系数值中,扩展系数最大（79）,表明其对城市用地增长具有重要影响,研究区城市用地增长主要以城市边缘增长为主;道路重力系数次之（77）,仅略小于扩展系数,表明道路交通对研究区城市发展具有重要影响,TOD发展模式也是研究区城市用地增长的重要模式;散布系数最小（23）,说明研究区城市自发增长的现象不明显,非城市像元被随机选定成为新的城市像元的可能性较小;繁殖系数为52,说明自发增长形成新城市中心增长的可能性较高,“新扩展中心增长”的扩张方式比较显著;坡度系数为46,说明研究区地形条件对城市用地增长产生了一定的抑制作用,这与南京市的地形地貌特征有关。
通过不同校正阶段OSM指数的变化可以看出（表2）,OSM指数由0.386增长为0.532,表明模型校准精度不断提高,拟合优度较好。在像元尺度上,2012年预测结果与实际情况的数量特征与空间分布具有较好的一致性（表3、图5）,表明融入建设用地适宜性的SLEUTH模型具备了捕捉城市发展政策所导致的城市发展中心转移和新的城市增长中心出现的能力,能够较好地捕捉研究区河西、仙林、江宁和江北地区的新城开发。模型模拟的总体精度为90.42%,模拟正确的城市像元数的用户精度和生产者精度分别为78.90%和77.97%,新增城市像元的模拟精度也超过了70%。与不考虑建设用地适宜性评价结果的传统SLEUTH模拟结果相比（如与相关文献[1]的模型校正结果相比）,该模拟精度有了较大的提高。

3.3 城市空间扩展的多情景分析

由表4可见,三种生态安全格局情景下的模型模拟结果均表明研究区未来仍将面临一定的城市用地增长,但年均增长率均低于1.4%,远小于1988-2012年的年均增长率（6.81%）。高生态安全格局情景（情景1）的城市用地增长数量最小（城市用地新增408.82 km2）、增长速率最低（年均增长率为0.92%）,绿地、农田等自然生态空间被侵占的数量也低于其他情景方案;城市用地增长数量最大和速度最快的是低生态安全格局情景（情景3）,该方案新增城市用地653.05 km2,年均增长率1.34%,对绿地、农田的侵占也最多;中生态安全格局情景（情景2）的城市用地增长了526.31 km2,年均增长率为1.12%,均介于情景1和情景3之间。由此可见,融合不同生态安全格局的情景方案能够有效控制城市用地增长的数量与速度,且避免对绿地、农田等自然生态空间的大量侵占与蚕食。
由图6可见,三种情景下的研究区城市用地增长主要以城市外围的边缘增长与城市内部的填充式增长为主,城市交通走廊TOD增长和新的城市中心增长（主要位于江北新城、江宁地区和东部滨江的龙潭新城）也较为明显。由此可见,潜力—约束与SLEUTH模型的松散耦合可以将研究区不同生态安全格局情景融入SLEUTH模型的排除图层,使得模型更好地捕捉了城市用地增长的块状区域（新城新区等城市政策划定的区域）,具备了较好的政策表现能力。与此同时,三种生态安全格局情景下的绿地和水域等自然生态空间的减少幅度均不高,表明将生态敏感性分析作为研究区城市发展的约束图层,并将其作为限制发展区域融入SLEUTH模型的排除图层,能够有效保护自然生态空间不被侵占,很好地贯彻了生态优先的城市发展理念。

4 结论与讨论

SLEUTH模型在基于历史数据的基础上,通过修改预测参数或设置排除图层,可以较好地预测未来的城市用地增长和土地利用变化,已经成为城市规划的有力工具[5,6,14,26,28],但SLEUTH模型目前还很难很好地模拟政府决策对城市土地利用的潜在影响[5,6]。然而,在快速城市化的中国,自上而下的城市发展政策特别是发展方向调整和新城新区的开发建设等,往往会使城市产生团块状、跳跃式发展。本文结合RS与GIS技术,尝试将潜力—约束模型与SLEUTH模型进行松散耦合,模拟了2013-2040年不同发展情景下的城市用地增长趋势。
研究结果表明：两个模型的松散耦合能够有效提高像元尺度上模型校正与模拟的精度,能够更好地捕捉城市发展政策所导致的城市发展中心转移和新的城市增长中心出现的区域,使SLEUTH模型具备了较好的城市发展政策表现能力,在一定程度上搭建了基于自下而上获取的转换规则与自上而下的城市发展政策之间联系的桥梁。与此同时,将生态敏感性分析作为研究区城市发展的约束图层,构建了不同生态安全格局的三种发展情景,能够有效保护研究区的自然生态空间,大幅降低生态安全风险。本文的研究结果可为研究区未来城市用地空间增长管理、城市规划和土地利用规划提供决策支持与参考依据。
模型校正阶段会产生一系列的模型准确性判定指数,且在选择哪些指数能够更好表征模型的精度问题上目前还存在较大争论[5-7,26,27]。本文研究采用了OSM指数作为模型校正和参数区间缩小的主要判据,因为很多研究均表明该指数计算时选取的7个指数的相关性均较小,且能够很好地反映模型模拟的准确性[14,27,30]。
尽管本文在潜力—约束模型与SLEUTH模型耦合分析方面进行了有益尝试,但仍存在一些亟须解决的问题。首先,在分析城市不同增长极的发展潜力过程中,由于较难获取增长极的综合实力数据,本文做了简化处理,采用主观赋值法将增长极的综合实力值分别赋值为100和80。其次,在SLEUTH模型的校正过程中,应该分别使用不同情景下对应的排除图层,而本文仅以中生态安全格局情景进行了模型校正。最后,在利用SLEUTH模型模拟未来城市发展趋势时,如能考虑未来规划道路路网,并对不同等级的道路网赋以不同的权重,将会进一步提高模型捕捉自上而下城市用地增长空间的能力。

The authors have declared that no competing interests exist.

参考文献

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[Li Xia, Yang Qingsheng, Liu Xiaoping. Science in China: Earth Sciences, 2007, 37(9): 1242-1251.]

[11]
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DOI

[Liu Xiaping, Li Xia, Peng Xiaojuan.Niche-based cellular automata for sustainable land use planning. Acta Ecologica Sinica, 2007, 27(6): 2392-2402.]

[12]
刘勇, 吴次芳, 岳文泽, 等. 基于SLEUTH模型的杭州市城市扩展研究. 自然资源学报, 2008, 23(5): 797-807.以杭州市中心城区为研究区，以1991、1996、2000、2005年Landsat TM／ETM^＋为数据源，通过缓冲区分析、SLEUTH模型等方法，分析杭州市城市扩展情况和预测4种可能方案。SLEUTH模型校正结果最终的 Compare和Lee—Sallee值分别为0．95和0．59，与近期城市用地数量和形态较为吻合，但在反映城市波动增长和新组团开发时存在不足。 SLEUTH模型生成了现有发展趋势、交通引导、农田适度保护、紧凑城市等4种预测方案。与历史趋势相比，预测方案的面积呈线性增加，扩展热点继续向外围 组团转移，扩展阶段为多核心发展。扩展强度空间分布上符合幂函数形式，距CBD12km和距城市边缘500m处为转折点，未来扩展强度均低于历史水平。若 延续现有趋势，城市开发量为189km^2，耕地年均消耗量为9km^2。从规划角度来看，适当的农田保护措施可抑制城市扩散，交通可引导城市新区开发和 减缓主城区压力，紧凑城市可使城市分布集中，从而可节约用地和调控城市形态。

DOI

[Liu Yong, Wu Cifang, Yue Wenze, et al.Applying SLEUTH for simulating urban expansion of Hangzhou. Journal of Natural Resources, 2008, 23(5): 797-807.]

[13]
吴晓青, 胡远满, 贺红士, 等. 沈阳市城市扩展与土地利用变化多情景模拟. 地理研究, 2009, 28(5): 1264-1275.利用基于遥感手段获取的沈阳市城市扩展与土地利用变化历史数据, 对SLEUTH城市扩展模型进行校正,对未来(2005～2030年)不同管理情景下的城市扩展与土地利用变化过程进行模拟,并对其发展变化趋势和生态环 境影响进行分析与比较.结果显示,在三种管理情景下,未来的沈阳市城市建设用地都将持续增加,大量的耕地资源被侵占;但不同管理情景下,城市景观格局和区 域面临的景观生态风险却表现出明显差异.SLEUTH模型的模拟结果较好地反映了沈阳市不同土地利用政策、规划方案等对未来城市扩展和土地利用变化以及区 域景观生态风险的潜在影响,同时也指出了当前城市增长管理政策中存在的不足之处.

DOI

[Wu Xiaoqing, Hu Yuanman, He Hongshi, et al.Research for scenarios simulation of future urban growth and land use change in Shenyang city. Geographical Research, 2009, 28(5): 1264-1275.]

[14]
李明杰, 钱乐祥, 吴志峰, 等. 广州市海珠区高密度城区扩展SLEUTH模型模拟. 地理学报, 2010, 65(10): 1163-1172.高密度城区是城市的核心区,也是城区扩展的源,对该区域的精确识别以及扩展模拟研究,具有重要的意义与价值.以广州市海珠区1979、1990、2000、2008年4期Landsat影像为数据源,运用非渗透表面端元选取模型(V-I-S)与归一化混合光谱分析模型(NSMA)相结合的方法,辅以单窗算法反演地表温度数据(LST),高精度提取非渗透表面丰度,进而设置合适阈值表征研究区高密度城区范围.在此基础上基于SLEUTH模型设置4种场景模拟和预测海珠区高密度城区扩展,并用景观指数分析方法对研究区1979-2050年长达70年的空间扩展状况进行分析.主要结论为:①SLEUTH模型同样适用于小尺度区域的扩展模拟.②SLEUTH模型模拟中基于自然、人文以及城市扩展内在动力机制等条件参数的设置,促使模拟结果精度更高.③SLEUTH模型模拟结果表明,自1979至今,海珠区高密度城区以较快扩展速率扩张,尤以1990-2004年间变动增长最快;未来的20年其增长速率减缓,并于2030年前后趋于稳定.这种扩张格局与变化状况与研究区产业结构、经济政策、土地规划决策等因素密不可分.

DOI

[Li Mingjie, Qian Lexiang, Wu Zhifeng, et al.The SLEUTH model simulation of high density urban sprawl in Haizhu district of Guangzhou city. Acta Geographica Sinica, 2010, 65(10): 1163-1172.]

[15]
朱飞鸽, 胡瀚文, 沈兴华, 等. 基于SLEUTH模型的上海城市增长预测. 生态学杂志, 2011, 22(9): 2107-2114.基于SLEUTH模型,利用1989-2005年高分辨率土地利用数据, 模拟4种土地资源保护情景下,2006-2035年上海的城市增长,以及城市化对城市景观格局的影响.结果表明:在各种情景下,城市土地利用类型的面积均 呈增长趋势,增量随着保护等级的提高而逐渐减少；城市增长方式主要是边缘增长,扩散增长和道路影响增长出现在郊区,增长主要发生在西北方向,西南和东南方 向次之；城市化显著影响城市景观格局,并存在阶段性；早期阶段城市化导致景观破碎化,斑块密度增加,景观多样性和异质性提高；后期阶段,城市化将导致景观 多样性和异质性降低,城市景观趋于均质化,加大农田和绿地景观的保护等级将减缓城市化降低景观多样性和增加均质化的趋势.

[Zhu Feige, Hu Hanwen, Shen Xinghua, et al.SLEUTH model-based prediction of urban growth of Shanghai. Chinese Journal of Ecology, 2011, 22(9): 2107-2114.]

[16]
Jantz C A, Goetz S J, Shelley M K.Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B, 2004, 31(2): 251-272.Declining water quality in the Chesapeake Bay estuary is in part the result of disruptions in the hydrological system caused by urban and suburban development throughout its 167 000 km2 watershed. A modeling system that could provide regional assessments of future development and explore the potential impacts of different regional management scenarios would be useful for a wide range of applications relevant to the future health of the Bay and its tributaries. We describe and test a regional predictive modeling system that could be used to meet these needs. An existing cellular automaton model, SLEUTH, was applied to a 23 700 km2 area centered on the Washington - Baltimore metropolitan region, which has experienced rapid land-use change in recent years. The model was calibrated using a historic time series of developed areas derived from remote sensing imagery, and future growth was projected out to 2030 assuming three different policy scenarios: (1) current trends, (2) managed growth, and (3) ecologically sustainable growth. The current trends scenario allowed areas on the urban fringe that are currently rural or forested to be developed, which would have implications for water quality in the Chesapeake Bay and its tributaries. The managed growth and ecologically sustainable scenarios produced growth patterns that were more constrained and which consumed less natural resource land. This application of the SLEUTH model demonstrates an ability to address a range of regional planning issues, but spatial accuracy and scale sensitivity are among the factors that must be further considered for practical application. (A)

DOI

[17]
Akιn A, Clarke K C, Berberoglu S.The impact of historical exclusion on the calibration of the SLEUTH urban growth model. International Journal of Applied Earth Observation and Geoinformation, 2014, 27: 156-168.This paper aims to emphasize the importance of the calibration process in urban growth modeling studies. The application of cellular automata (CA) in urban modeling can give insights into a wide variety of urban phenomena. The SLEUTH model, being as a well-tested CA, was utilized. Calibration data for the model were acquired from different sources of remotely sensed data recorded in 1967, 1977, 1987, 1998 and 2007. In this context three different excluded maps representing different scenarios were utilized during the calibration process in order to analyze the effects of different policies on urban growth. Each calibration scenario yielded its own parameter values. Thirteen calibration metrics for each scenario were derived. Integrating different exclusion layers to the beginning of the calibration process has reduced the number of possible growth patterns. The overall growth characteristics of Adana were similar for all calibration results and defined as organic growth except for the fact that the spatial allocation and the amount of potential urban pixels were different.

DOI

[18]
Lahti J.Modelling urban growth using cellular automata: A case study of Sydney, Australia. Enschede. The Netherlands: International Institute for Geo-Information Science and Earth Observation, 2008.

[19]
Tian L, Shen T.Evaluation of plan implementation in the transitional China: A case of Guangzhou city master plan. Cities, 2011, 28: 11-27.ABSTRACT Evaluation of plan implementation is very complex, and empirical study is scarce due to the methodological difficulties. Over the last two decades, there has been a great deal of urban planning activities and rapid city development in China, but there is a lack of evaluation of plan implementation. This research aims to help bridge this gap, and it explores to what extent a plan has been implemented and what factors have affected plan implementation, taking the Guangzhou city master plan as a case study. It adopts the grid overlay method and compares the land use plan and actual land use to obtain the result of accordance, deviation and unfulfilment. The discrepancy between the land use plan and actual land development is examined based on both land use type and the spatial planning management unit. By analyzing several cases at the site development control plan level, this paper explores why the land development is not consistent with the land use plan.

DOI

[20]
Long Y, Gu Y, Han H.Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of Beijing. Landscape and Urban Planning, 2012, 108: 103-111.Evaluation of urban planning implementation (UPI) has attracted extensive attention from urban planners and researchers in recent years. Existing literature, however, mainly focuses on the conformity approach and does not take the spatial and temporal heterogeneity of UPI into account. In addition, previous research failed to distinguish the effects of urban planning from other institutional forces, as well as market incentives on urban expansion. To bridge this gap, we proposed a spatiotemporal approach to evaluate the effectiveness of UPI based on logistic regression and geographical information system (GIS) by identifying the spatiotemporal heterogeneous effects of urban planning on urban expansion. An empirical research was conducted in the Beijing Metropolitan Area (BMA) by analyzing five urban master plans drafted in 1958, 1973, 1982,1992 and 2004. Five periods from 1947 to 2008 were examined to evaluate the dynamic effects of each master plan and other related factors. The effects of the 2004 Urban Master Plan on 16 districts in Beijing were estimated to identify the spatial variations of UPI. Within the context of China's booming economy, the results indicate that the effects of urban planning during the urban expansion increase over time, and are significantly stronger in exurban areas than in central cities and suburban areas. In addition, based on logistic regression results, we adopted an existing urban expansion simulation model to geographically visualize the impact of urban planning on future urban expansion, namely urban planning implementation effectiveness.

DOI

[21]
宗跃光, 王蓉, 汪成刚, 等. 城市建设用地生态适宜性评价的潜力—限制性分析: 以大连城市化区为例. 地理研究, 2007, 26(6): 1117-1126.城市建设用地生态适宜性评价是合理利用我国有限土地资源的一项基础性工作,本研究的特色是以大连城市化区为例在归纳GIS方法应用的基础上,将目前国内广泛采用的单纯权重叠加法推广到加权的潜力&mdash;限制性分析法。该方法的主要优点是把评价要素分为生态潜力和生态限制性两大类,通过取大原则和成对明智比较法分别确定权重,从而更加科学确定土地利用生态适宜性等级。结果表明,大连可做高强度开发的城市建设用地面积约为850.46 km<sup>2</sup>约占总面积的6.28%;可在指导下进行适度开发利用的土地面积为1835.97 km<sup>2</sup>占总市域面积的13.56%;而不宜作为建设用地的土地(不适宜和中低适宜区)面积为10851.92km<sup>2</sup>约占总市域面积的80.16%,即城市建设用地的影响范围应该控制在区域的20%土地利用类型中,另外80%的土地不适宜进行城市建设的开发。根据上述结果,全区规划分为优化建设区、重点建设区、限制建设区和禁止建设区,并提出用地分区发展管制对策。

[Zong Yueguang, Wang Rong, Wang Chenggang, et al.Ecological suitability assessment on land use based on potential-constrain approach: The case of urbanized areas in Dalian city, China. Geographical Research, 2007, 26(6): 1117-1126.]

[22]
尹海伟, 孔繁花, 罗震东, 等. 基于潜力—约束模型的冀中南区域建设用地适宜性评价. 应用生态学报, 2013, 24(8): 2274-2280.区域建设用地适宜性评价是区域规划空间布局的重要前提和基础，是区域土地资源合理利用的重要依据.基于GIS软件平台，采用区域综合实力与空间可达性分析方法对冀中南区域发展潜力进行了空间定量分析，采用生态环境敏感性方法对研究区发展的生态约束进行定量评价，进而借鉴损益分析法，构建了由发展潜力和生态约束构成的潜力-约束模型，并通过相互作用判别矩阵，得到不同发展理念下的建设用地适宜性情景方案.结果表明： 研究区综合实力与空间发展潜力均呈首位分布，且点轴发展模式明显；生态环境敏感性总体上呈西高东低的分布格局；区域经济发展理念对区域生态安全格局和城市建设用地增长空间规模具有重要影响.潜力-约束模型重新构建了区域用地发展适宜性的评判原则与方法，能够较为科学地实现区域综合发展潜力的空间栅格化，获取研究区未来用地的发展趋势和空间布局，可以为城市与区域规划提供科学依据，是实现区域&ldquo;精明增长&rdquo;与&ldquo;精明保护&rdquo;的有效途径.

[Yin Haiwei, Kong Fanhua, Luo Zhendong, et al.Suitability assessment of construction land in the central and southern parts of Hebei province, China based on potential-limitation model. Chinese Journal of Applied Ecology, 2013, 24(8): 2274-2280.]

[23]
尹海伟, 孔繁花. 城市与区域规划空间分析实验教程(第二版). 南京: 东南大学出版社, 2016.

[Yin Haiwei, Kong Fanhua.Lab Manual For Spatial Analysis in Urban and Regional Planning (second edition). Nanjing: Southeast University Press, 2016.]

[24]
尹海伟, 张琳琳, 孔繁花, 等. 基于层次分析和移动窗口方法的济南市建设用地适宜性评价. 资源科学, 2013, 35(3): 530-535.快速城市化使城市建设用地快速增长,但城市土地资源的有限性要求 城市土地必须集约利用和合理开发,以实现城市可持续发展.进行城市建设用地适宜性分析有助于优化城市土地配置.本文以济南市为例,应用SPOT和ALOS 高分辨率遥感数据进行土地利用解译与分类,基于ArcGIS、Fragstats软件平台,采用层次分析(AHP)与移动窗口相结合的方法,将景观指标融 入建设用地适宜性评价因子中,并结合城市土地利用的自然和其他限制因子,对济南市2004年城市建设用地的适宜性进行了评价和等级划分,最后利用2009 年土地利用现状图对评价结果进行有效性和可靠性分析.评价和分析表明,2004年适宜于城市建设用地的区域主要分布在城市中心以及东西部区域,2009年 新增加的建设用地分布范围与2004年适宜性评价结果大致吻合,评价结果具有一定准确性和实用性.研究结果可为城市建设用地增长管理提供参考依据和决策支 持.

[Yin Haiwei, Zhang Linlin, Kong Fanhua, et al.Suitability evaluation of urban construction land in Jinan city based on AHP and moving window methods. Resources Science, 2013, 35(3): 530-535]

[25]
尹海伟, 徐建刚, 陈昌勇, 等. 基于GIS的吴江东部地区生态敏感性分析. 地理科学, 2006, 26(1): 64-69.区域可持续发展的基础是生态环境的可持续，而生态敏感性区划足制定生态环境规划的前提和基础。文章借助GIS技术，选择有区域代表性的生态因子，采用因子叠加法，埘吴江东部地区的生态敏感性进行了深入分析，按生态敏感度的高低将研究区划分为5级；极高敏感区、高敏感区、中敏感区、低敏感区和非敏感区，并提出了分区保护与建设的建泌，为研究区生态环境保护和产业经济布局提供有价值的参考。研究结果表明，极高和高生态敏感区面积占研究区的48．63％，说明研究区生态敏感性总体上很高。

DOI

[Yin HaiWei, Xu JianGang, Chen ChangYong, et al. GIS-based ecological sensitivity analysis in the east of Wujiang city. Scientia Geographica Sinica, 2006, 26(1): 64-69.]

[26]
Clarke K C, Gaydos L J.Loose coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 1998, 12: 699-714.Prior research developed a cellular automaton model, that was calibrated by using historical digital maps of urban areas and can be used to predict the future extent of an urban area. The model has now been applied to two rapidly growing, but remarkably different urban areas: the San Francisco Bay region in California and the Washington/Baltimore corridor in the Eastern United States. This paper presents the calibration and prediction results for both regions, reviews their data requirements, compares the differences in the initial configurations and control parameters for the model in the two settings, and discusses the role of GIS in the applications. The model has generated some long term predictions that appear useful for urban planning and are consistent with results from other models and observations of growth. Although the GIS was only loosely coupled with the model, the model's provision of future urban patterns as data layers for GIS description and analysis is an important outcome of this type of calculation.

DOI PMID

[27]
Dietzel C, Clarke K C.Toward optimal calibration of the SLEUTH land use change model. Transactions in GIS, 2007, 11(1): 29-45.Abstract Abstract SLEUTH is a computational simulation model that uses adaptive cellular automata to simulate the way cities grow and change their surrounding land uses. It has long been known that models are of most value when calibrated, and that using back-casting (testing against known prior data) is an effective calibration method. SLEUTH's calibration uses the brute force method: every possible combination and permutation of its control parameters is tried, and the outcomes tested for their success at replicating prior data. Of the SLEUTH calibration approaches tried so far, there have been several suggested rules to follow during the brute force procedure to deal with problems of tractability, most of which leave out many of the possible parameter combinations. In this research, we instead attempt to create the complete set of possible outcomes with the goal of examining them to select the optimum from among the millions of possibilities. The self-organizing map (SOM) was used as a data reduction method to pursue the isolation of the best parameter sets, and to indicate which of the existing 13 calibration metrics used in SLEUTH are necessary to arrive at the optimum. As a result, a new metric is proposed that will be of value in future SLEUTH applications. The new measure combines seven of the current measures, eight if land use is modeled, and is recommended as a way to make SLEUTH applications more directly comparable, and to give superior modeling and forecasting results.

DOI

[28]
Rafiee R, Mahiny A S, Khorasani N, et al.Simulating urban growth in Mashad city, Iran through the SLEUTH model (UGM). Cities, 2009, 26(1): 19-26.Abstract Mashad is the capital city of Khorasan Razavi Province, in the North East of Iran. The city has witnessed rapid growth in the last two decades, mostly because of its economic, social and religious attractions. We implemented this study to understand Mashad City growth dynamics, to forecast its sprawl for the next two decades and to provide a basis for urban management. We used the SLEUTH urban growth simulation and forecasting model. We calibrated it with historical data derived from a time series of satellite images. Three scenarios were designed to simulate the spatial pattern of urban growth under different conditions. The first scenario was historical urban growth, which allowed continual urban area expansion, similar to historical trends, without any limitation. The second scenario was environmentally-oriented in which urban growth was limited. The third scenario was a specific compound urban sprawl situation in which growth was allowed to continue, similar to the historical trend, but a limitation was applied to construction on steeper slopes. The results showed the utility of the modeling method in explaining the spatial pattern of urban growth. The result of the second scenario illustrated that valuable land in suburban areas, including flourishing farms, could be saved. Urban growth under the third scenario showed the harnessing effect of slope-limited growth. We conclude that the results of the modeling under the three scenarios for Mashad City growth are of great potential use to city managers. We also showed that the environmental scenario is preferable for Mashad City development.

DOI

[29]
Dezhkam S, Amiri B J, Darvishsefat A A, et al.Simulating the urban growth dimensions and scenario prediction through sleuth model: a case study of Rasht county, Guilan, Iran. GeoJournal, 2014, 79(5): 591-604.Urban growth models (UGM) as regional planning tools are of great interest for quantitative analysis of urban complex systems. In this study, the SLEUTH UGM has been calibrated through a sequential multistage automated method to derive the pattern of urban growth in Rasht County from 1975 up to year 2011. Evaluation of model goodness of fit confirms that the model is adjusted properly to the area under investigation. Four growth rules of spontaneous, new spreading center, edge and road influenced growth as well as five coefficients of diffusion, breed, spread, road gravity and slope resistance are responsible to detect quantitative aspects of urban dynamics from control years. According to the results, successive improvement of the model parameters during the calibration mode indicates applicability of the model for forecasting of future urban growth mechanism until the year 2050. Accordingly, two growth scenarios were developed mainly with the aim of investigating the coefficients’ role in controlling the nature of urban dynamics. In this concern, the spread and road gravity coefficients’ value, as two major driving forces of urban sprawl in the study area were reduced to dictate compact and infill growth, compared to their original values derived from calibration for historical prediction. Comparison between two forecasted scenarios indicates insignificant difference in total amount of the urban area, which denotes there is a threshold to urbanization and the current trend of urban growth could not be maintained. Finally, we conclude that Rasht County with considerable industrial and agricultural attractions, will witness noticeable expansion from 20,31002ha in 2011, up to 34,74502ha in 2050, accounting to 7102% increase in total area of manmade surfaces.

DOI

[30]
Onsted J A, Chowdhury R R.Does zoning matter? A comparative analysis of landscape change in Redland, Florida using cellular automata. Landscape and Urban Planning, 2014, 121: 1-18.Landscape change is a key feature of social-揺cological change, and is especially marked in, urbanizing regions. Planning institutions use land use zoning to control and direct such changes. Urban growth models are commonly used to better understand past landscape changes as well as, forecast and plan for future landscape changes. Many of these models, however, do not utilize zoning, information in their deployment because many model designers do not believe zoning to be a relevant, criterion for the prediction of urban growth. This research offers a novel methodology for integrating, zoning information into a cellular automaton urban growth model, SLEUTH. It additionally tests the, utility of such information by comparing metrics of fit with past data under different zoning inclusion, conditions in a community of Miami-Dade County, Florida. These conditions include one scenario, where zoning is ignored and three others where it is included. The latter three test different methods, of including zoning data for three generalized zoning categories - arbitrarily guessing, measuring urban, growth in each zoning category for the entire study area, and measuring urban growth in each zoning, category only in those areas more likely to experience growth. Results indicate that this final condition, generates the highest model performance metric and creates a more fair comparison since remote, areas in the study area, less likely to experience growth, exaggerate differences in urban growth rates, across the different zoning categories. We conclude that zoning information, when utilized, appropriately, improves model performance and is therefore relevant for landscape change.

DOI

[31]
赵耀龙, 张珂, 彭勇俊, 等. 基于地理模拟方法的昆明市空间扩展情景分析. 地理研究, 2014, 33(1): 119-131.改革开放以来，西部高原湖滨城市经历的快速城镇化进程给湖泊流域带来了较为严重的生态环境问题，未来城市空间发展政策的调整需要关注城市空间拓展对区域生态环境的影响。以位于滇池湖滨地区的昆明市为例，设定6种不用的城市空间拓展政策情景，应用SLEUTH模型预测了6种情景下未来20年的城市空间格局，采用空间指数和空间分析方法对预测结果进行了分析评价。结果表明昆明市城市建成区具有典型的摊大饼式空间拓展模式，城市道路网对城市形态具有重要的影响。6种情景模式下未来昆明市建成区空间格局既有相似性，也表现出显著的差异。城市建设用地空间格局总体上呈集约、紧凑型的发展趋势。生态保护与城市发展管制相结合的政策情景对滇池湖滨地区的景观影响最小。多中心城市发展格局和城市发展管制相结合的政策情景对城市总体空间规模的控制具有明显的效果，但不宜在湖滨地区实施。滇池湖滨地区需要划定景观或生态保护区，严格禁止城市建设用地对湖滨用地景观的占用与分割。滇池湖滨以外的区域，适宜执行生态保护与城市发展管制相结合的多中心发展模式。

DOI

[Zhao Yaolong, Zhang Ke, Peng Yongjun, et al.Scenario analysis of urban growth in Kunming based on geosimulation system. Geographical Research, 2014, 33(1): 119-131.]

[32]
吴巍, 周生路, 魏也华, 等. 中心城区城市增长的情景模拟与空间格局演化: 以福建省泉州市为例. 地理研究, 2013, 32(11): 2041-2054.基于1993．2008年间4个时相的遥感影像，应用SLEUTH模型模拟与预测自组织和规划引导两类情景下泉州中心城区的城市用地增长过程，并借助空间关联法分析其城市增长的空间格局演化特征，为“两规”空间协调提供科学依据。结果表明：①SLEUTH模型适用于研究区的城市增长模拟与预测，其对城市用地扩展的数量拟合要优于空间匹配，可作为多方案情景模拟的一个技术手段。②规划引导预案的MPS、ED、AWMSI、MPI四类景观指数均优于自组织预案，城市用地斑块的整体性、连接性较优，未来城市发展较为紧凑，利于实现土地的集约利用与城市的集聚发展。③随预测时间推移，研究区城市用地扩展的速率以及空间集聚性将有所减弱，城市增长的热点区也会发生演变与迁移。2008-2020年，热点区分布总体呈现“圈层式”结构，局部以“跨江发展”为主要特征；2020-2030年，热点区总体布局较为发散，局部则呈“环湾发展”与“孤立分布”特征。本研究将情景模拟、景观指数、空间分析等方法有效结合，有助于深刻理解研究区的城市空间增长过程，可为城市管理工作提供决策支持。

DOI

[Wu Wei, Zhou Shenglu, Wei Yehua, et al.Scenario simulation and changes of urban growth patterns in central cities: A case study of Quanzhou, China. Geographical Research, 2013, 32(11): 2041-2054.]

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