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GEOGRAPHICAL RESEARCH >

2017 , Vol. 36 >Issue 3: 518 - 528

DOI: https://doi.org/10.11821/dlyj201703010

Articles

Simulation and analysis of urban land expansionconducted by ecological security

  • WEN Ya , 1 ,
  • GONG Jianzhou , 2 ,
  • HU Yingen 3 ,
  • HU Zhiren 2
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  • 1. College of Natural Resource & Environment, South China Agricultural University, Guangzhou 510642, China;
  • 2. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
  • 3. College of Land Management, Huazhong Agricultural University, Wuhan 430070, China

Received date: 2016-09-15

  Request revised date: 2017-01-17

  Online published: 2017-03-20

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《地理研究》编辑部
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Abstract

Urban land expansion can be considered as direct manifestation of the regional space of urbanization, which is also a key ecological process that affects the urban ecological security patterns. It is of great significance to optimize urban ecological security patterns through simulation and forecast researches on the basis of ecological security oriented spatial expansion of cities. This paper tried to forecast urban landscape transition by establishing an ecological security oriented simulation model with Guangzhou as the sample site based on the framework of Dyna-CLUE model so as to explore the urban land expansion patterns. The results lead to three main conclusions: (1) The quantitative regulation of land use by government is more important than space zoning with the goal of ecological security under present urbanization level. (2) The percentage of adjacent index indicates a trend of aggregation of construction land patches while the patch density index shows another trend of increase then decrease, which reveals some kind of variation of urban spatial characteristics. (3) The results of analysis on landscape patterns and urban land expansion modes reveal that three urban expansion modes, namely infilling, edge-expanding and leapfrogging, have common effects on the city's space. And during 1990-2005, edge-expanding and leapfrogging are the main urban expansion modes while from 2005 to 2020 edge-expanding and infilling have become the main modes. In the former period, urban expansion results in a large number of construction land patches due to the increase of aggregation and patch number per unit area. In the latter period, urban expansion continues with land-use transition from non-construction to construction land use with the trend of aggregation. However, as urban expansion modes are changing, many patches of construction land, including existing patches and newly added patches, will be connected with others. That is the reason why no increase of patch quantity per unit area has been observed for the patch density index. Together with pattern analysis, these results show that the urban expansions have changed from edge-expanding mode in external surrounding area to in-filling mode with intensive and economical internal expansion, which indicates a preliminary success of construction of urban ecological civilization.

Key words： urban expansion; Dyna-CLUE model; modes of urban expansion; ecological security oriented; Guangzhou

Cite this article

WEN Ya , GONG Jianzhou , HU Yingen , HU Zhiren . Simulation and analysis of urban land expansionconducted by ecological security[J]. GEOGRAPHICAL RESEARCH, 2017 , 36(3) : 518 -528 . DOI: 10.11821/dlyj201703010

1 引言

据联合国经济和社会事务部的全球城市化发展报告（2014）,1990年、2015年和2050年发达国家（地区）的城市化率分别为54.6%、78.3%和85.4%,欠发达国家（地区）的城市化率分别为17.7%、49%和63.4%,这意味着接下来的三十多年里,世界范围内的城市化趋势仍然不可逆转,并且将有超过63%以上的人口生活在城市区域[1]。快速城市化过程必然伴随着城市空间扩展,即在城市地域内的自然地表不断转化为人工地表,城市空间范围不断增大[2]。城市空间扩展为经济发展提供支撑、为人民生活提供保障的同时,也改变了城市景观结构,引发热岛效应、环境污染、交通拥挤等生态环境安全问题[3]。
采用情景模拟方法,人们可以基于历史发展趋势或假设,生成未来不同的城市景观结构,为城市规划或环境管理者的决策提供辅助依据[4,5],Bryan等[6]认为情景分析更是进行长期可持续性评价的一种特别有效工具。城市扩展模式是城市扩展时空特征的重要内容之一,也是城市空间形态的重要甚至决定性因素[7]。普遍认为城市空间扩展有飞地式、外缘式和内填式三种模式[7,8]。不同的扩展模式将形成不同的城市空间形态,其中飞地式、外缘式扩展模式将有利于形成松散的城市空间形态,内填式形成的城市空间相对紧凑,继而对城市功能和过程有着不同的影响[9]。例如Holden[10]对挪威的研究发现居住密度大的城镇可持续性更高;Muñiz等[11]在巴塞罗那大都市区的研究表明松散的城市形态使得能源消耗量和CO2排放量的增加,加剧了城市热岛效应。
作为城市生态安全问题的强有力回应,土地利用规划者和资源环境管理者已十分关注城市生态系统退化的现象[4],城市总体规划也越来越强调城市生态环境的规划。如《广州市城市总体规划（2001-2010）》,下文简称为《规划》（2001-2010）,已确定城乡生态安全格局、城乡生态良性循环的生态建设目标。该《规划》不仅确定未来十年的城市土地利用数量结构,还进行生态管护区、生态控制区和生态协调区等生态政策区划。在接下来的《广州城市总体发展战略规划（2010-2020）》中,下文简称为《规划》（2010-2020）,进一步确定贯彻生态优先、优化生态结构、提升城市环境品质的实施发展策略。
本文以广州市为例,基于《规划》（2001-2010）拟定的城市发展基本生态底线以及《规划》（2010-2020）拟定的2020土地利用结构指标,构建生态安全导向下的情景,基于Dyna-CLUE模型进行城市空间扩展模拟,探讨城市扩展模式及其空间配置,研究结果有利于改善或维持城市生态安全、优化城市空间结构,也为制定城市生态安全保障与可持续发展政策提供参考依据。

2 研究区概况与数据来源

2.1 研究区概况

广州是一座具有两千多年历史的文化名城,是珠江三角洲、广东省和华南地区的中心城市,中国对外开放的重要门户[12]。改革开放以来,凭借国家政策和区位优势,广州城市化发展极为迅速,同时也产生了一系列城市生态环境问题。保护城市基本生态用地,维持城市生态系统健康,已成为保障城市可持续发展的基本要求[13]。广州市位于112°57ʹE~114°03ʹE、22°26ʹN~23°56ʹN,属亚热带季风气候,具有温暖多雨、光热充足、夏季长、霜期短等特征。广州市地处珠江三角洲的北部边缘,是三角洲平原与低山丘陵区的过渡地带,地形总体特征是东北高、西南低。其东北部是由花岗岩与变质岩组成的低山丘陵区;西部是由河流堆积组成的冲积平原;南部则是稍向南倾的珠江三角洲平原,市域南端与中国南海相接[14]。根据2014年最新行政区划调整方案,市辖越秀、荔湾、天河、海珠、白云、花都、黄埔、番禺、南沙、从化和增城共11个行政区（图1）。据2015年广州市国民经济和社会发展统计公报,2015年末常住人口为1350.11万,户籍人口为854.19万,城镇人口占比85.53%;全年实现地区生产总值（GDP）18100.41亿元。
Fig. 1 Study area and its administrative districts

图1 研究区域及其行政区示意图

2.2 数据来源

（1）基于Landsat TM影像,解译得到1990年和2005年广州市景观类型图。影像成像时间分别是1990年10月14日和2005年10月22日,轨道号为（122,43）,空间分辨率为30 m;考虑城市空间扩展这一核心研究内容和研究区的特点,兼顾Landsat影像上地物的可判性,采用林地、农用地、建设用地、河流水库、其他水体共五类的景观分类体系,其中以基塘为主的类型是研究区独特的人工景观类型,因此作为一个独立的类型。在1990年TM和2005年谷歌地球分别选取验证点（每种景观类型分别选25个）,用于计算分类结果的分类混淆矩阵,计算结果显示1990年和2005年的影像解译总体精度分别为79.2%和86.4%,Kappa系数分别为0.75和0.84。
（2）2014年广州市最新的城市行政区划图和1 ∶ 5万地形图。
（3）其他资料：《规划》（2001-2010）、《规划》（2010-2020）、土地利用总体规划（2006-2020）以及广州市统计年鉴网站提供的人口数据和GDP数据。

3 研究方法

3.1 Dyna-CLUE模型及城市空间模拟过程

3.1.1 Dyna-CLUE模型介绍及建模思路 基于土地利用与驱动力的量化关系,以及不同土地利用类型之间的竞争关系,CLUE（The Conversion of Land Use and its Effects modelling framework）模型最初被开发用于模拟土地利用变化[15],综合考虑生物物理及人类影响驱动力的模拟模型[16],进行土地利用变化模型模拟的一个操作实施框架[16]。Steyaert认为CLUE是通过相互关系和反馈机制而将多个学科联结在一起的交叉学科模型[17],但认为作为一种量化方法,需要多个主题的大量数据来验证CLUE模型的有效性[15]。
CLUE模型最初开发用于国家和大区域尺度的土地利用变化模拟,之后研究者对模型进行修正,于是就有CLUE-S和Dyna-CLUE两个版本[18]。修订模型可用于小区域尺度的土地利用变化预测,也在驱动因子选择和土地需求计算等方面得以完善[19]。已有开发的Dyna-CLUE程序,该程序包括非空间和空间两个模块,其中非空间模型完全独立于Dyna-CLUE模块,而主程序主要是用于进行空间位置配置的模块（图2）。
Fig. 2 Modelling framework of Dyna-CLUE

图2 Dyna-CLUE模型框架

3.1.2 模拟过程 针对图2的模型框架以及研究思路与目标,选取影响城市生态安全格局的交通、河流、水库、人口和GDP等驱动因素,构建空间逻辑回归模型;结合城市总体规划拟定的生态管护区、生态控制区和生态协调区等生态区划政策,设定了城市生态区划保护和无生态区划保护的情景。基于生态安全导向下的Dyna-CLUE模型构建步骤如下：
（1）非空间的模块——需求数据（Demand）。非空间模块是一个以年为基础的土地利用需求（面积）的逐年文件,也叫需求数据。基于这个文件,空间模块将各类土地利用面积配置到合适的空间位置上。为进行情景分析,共生成两个Demand文件：一是在没有土地利用逐年面积数据的情况下,基于遥感影像解译获得的1990年和2005年土地利用数据,以及土地利用总体规划（2006-2020）获取2020年各土地利用数据,采用内插法来生成1990-2005年、2005-2020年规划目标下的逐年土地利用数据[20];二是采用外推法,基于1990-2005年的数据趋势,外推生成2006-2020逐年的土地利用数据,作为不考虑规划目标的土地利用趋势数据。
（2）逻辑回归。已有研究表明,土地利用类型的空间分布（适宜性）受诸多因素的驱动,各空间位置的分布与驱动因素之间存在某种定量关系[19]。逻辑回归方法已经被广泛用于量化自变量与因变量之间的这种关系[21]。基于CLUE模型的要求、研究区社会经济和地理环境,兼顾数据可得性,选用13个驱动力因子,逻辑回归分析结果如表1所示。
Tab. 1 Driving factors and beta-coefficients from logistic regression for each land use type in 2005

表1 2005年各土地利用类型逻辑回归系数

驱动力 土地利用类型
林地（x1） 农用地（x2） 建设用地（x3） 河流水库（x4） 其他水体（x5）
到建设用地的距离 0 -0.000018 0 0.000017 0.000055
坡度 0.052666 -0.032138 -0.029082 0.026092 -0.006542
DEM 0.024728 -0.012987 -0.013966 -0.016500 -0.027122
到河流的距离 0.000148 0.000073 0 -0.001224 0.000181
到水库的距离 -0.000079 0.000017 -0.000006 0.000024 0.000031
到高速公路的距离 -0.000012 0.000016 -0.000025 0 0.000013
到省道的距离 0 -0.000023 -0.000091 0.000093 0
到国道的距离 0.000029 -0.000025 -0.000043 0.000012 0.000012
城市人口密度 0.001466 0.001521 -0.002284 -0.000745 0
城市人口密度 -0.001716 0.003982 -0.003786 0.003289 -0.010564
GDP密度 0.000101 0.002849 -0.005863 -0.000246 0.002231
第二产业产值的密度 0 -0.004503 0.007512 0 0
第三产业产值的密度 0 -0.002616 0 0 -0.002215
常量 -3.176906 0.788880 0.443842 -2.357928 -4.306068
ROC 0.949659 0.791223 0.848557 0.877021 0.818971
（3）转换规则（Rules）。转换规则呈现为一个矩阵,用来定义各土地利用类型发生转换的次序以及发生转换的弹力系数。其中,次序以0和1两个值来表示,0表示不能转换,1表示能发生转换,假定所有土地利用类型都能发生转换,转换次序则呈现为5×5的单位矩阵。弹力系数,即各地类转换的弹性系数,用0~1之间的值表示,1值表示完全没有弹力,完全不发生转换,0则有100%的弹力,可以完全发生转换。将1990年和2005年的景观类型图进行叠加,计算出各景观类型的转换比例,再调入模型调试,最后确定林地、农用地、建设用地、河流水库和其他水体的转换弹性系数分别为0.8、0.7、0.95、0.95和0.7。
（4）情景设置。在拟定“优化、协调”的土地利用战略和创建环境友好型发展模式下,土地利用总体规划（2006-2020）里确定了2020年城市土地利用目标;基于建构与城市建设体系相平衡的自然生态体系战略目标,以保障、促进、引导城市可持续发展为城市生态建设目标,在《规划》（2001-2010）里,已将城市划分为生态管护区、生态控制区和生态协调区共三类生态政策限制区域。基于以上目标拟定的土地利用结构和生态政策区划的空间限制政策,设定河流水库不发生变化,结合1990-2005年研究区土地利用变化趋势,设定城市生态安全保护和无生态安全保护情景。其中,情景1为基于历年土地利用趋势的未来发展;情景2为基于土地利用规划目标的发展;情景3为基于生态政策区划限制和土地利用规划为目标的发展。其中,情景2和情景3属于城市生态安全导向下的两种情景。
（5）有效性检验。包括逻辑回归模型的检验和Dyna-CLUE模型有效性检验。各逻辑回归模型ROC检验值如表1。ROC值为0.79~0.95,均大于0.7,说明所有方程的拟合程度较好[22],各驱动因子可以较好地解释土地利用变化。
Dyna-CLUE模型有效性检验是将2005年土地利用现状图与模拟生成的图进行对比（图2）,运用成对数据卡方检验两图精度差异的显著性,结果如表2所示。从统计学上来看,可以接受零假设,认为两图之间的精度差异不明显。
Tab. 2 χ2 and pixel number of each land-use/cover type between actual and simulated results in 2005

表2 2005年模拟结果与同年土地利用图像的卡方检验

林地 农用地 建设用地 河流水库 其他水体 χ2
像元数 实际 121960 102250 61155 22773 16531 1.83
模拟 122002 102333 60948 22913 16475

注：χ2<χ2 (4, 0.05)=9.488,因此没有理由拒绝零假设。

3.2 城市景观动态及增长模式

3.2.1 城市景观动态 在景观类型水平上,选用景观百分比（percentage of landscape,PLAND）、斑块密度（patch density,PD）和相邻百分比（percentage of like adjacencies,PLADJ）共3个景观指数描述城市景观及变化特征。其中,PLAND是指某类型景观斑块占总面积的百分比（%）。PD是指单位面积上的景观斑块个数（个/100 hm2）。PLADJ为某景观类型的相似邻接斑块个数与该类型总斑块数之比（%）。具体计算及含义可以参见网站（http://www.umass.edu/landeco/research/fragstats/fragstats.html）。
3.2.2 城市扩展模式 在景观水平上,借用PLADJ指数同时采用三种城市空间扩展模式来描绘研究区的城市空间扩展[8]。首先,基于5×5像元的移动窗口和4邻域规则,以单元像元为步长,生成PLADJ图,表征景观类型在空间上的聚集程度。方法是对图像上的每一个像元,分别比较该像元与其4邻域的差异,如果与4个邻域都不是一个景观类型,认为该中心像元聚集度最低,则PLADJ=0;相反,如果与其4邻域是同一个景观类型,则PLADJ=100,表明已最大限度地聚集;PLADJ介于0~100,则聚集度介于最小和最大值之间。然后,将PLADJ与同时段始末两幅土地利用图进行逐像元比较,以PLADJ=65为阈值[8],并生成一幅新的栅格图。赋值方式：① 当末期为新增建设用地时,对应PLADJ小于65,则赋值为破碎型;PLADJ介于65~100,则为聚集型;PLADJ=100,则为内部型;② 当末期为非建设用地或水体时,赋值为非建设用地和水体;③ 初期和末期一直为建设用地的,赋值为建设用地。其中破碎型、聚集型和内部型三种景观变化类型,分别对应于飞地式、外缘式和内填式三种城市空间扩展模式。

4 结果分析

4.1 基于Dyna-CLUE模型的城市景观模拟结果

三种情景模拟结果如图3所示。可见,情景1（基于土地利用变化趋势和无空间限制）的城市增长更多的是城市向外扩展,如西北角、东偏南及东部增城区内新增加的建设用地（灰色椭圆框内）,说明如果没有进一步人类干预的城市发展,以增大城镇空间面积为主要趋势,将使城市空间不断地向外围扩展。而在对土地利用结构和空间政策进行约束和限制的条件下,城市空间扩展将会受到约束,如图3b、图3c所示。
Fig. 3 Land-use maps from scenario simulation for 2020

图3 2020年土地利用情景模拟结果

Tab. 3 Area percentage of scenario simulations of different land-use/cover types for 2020 (%)

表3 2020年模拟结果的土地利用面积百分比（%）

土地利用类型 情景1 情景2 情景3 规划用地
林地 34.72 35.60 35.59 35.56
农用地 24.80 27.45 27.46 27.45
建设用地 28.05 24.26 24.26 24.26
河流水库 6.73 5.93 5.94 5.99
其他水体 5.70 6.76 6.75 6.73
表3显示3种情景模拟的土地利用结构。可见,从数量结构来看,情景1模拟的土地利用结果与其他两种情景存在较大的差异,与土地利用总体规划（2006-2020）里确定的2020年用地目标值的差异也是最大的。情景2和情景3预测的土地利用结构之间的差异不大,与规划用地目标值也比较接近,说明模型有较好的模拟效果,在生态安全导向下的未来土地利用结构可能更趋于合理。

4.2 1990-2020年城市扩展的时空变化

选用PD和PLADJ共2个景观指数,从景观破碎度和聚集度方面考察城市扩展的时空特征。基于1990和2005年的土地利用类型图和情景3模拟的2020年土地利用类型图,将PD和PLADJ指数计算结果绘制成图4。根据PD曲线变化,可将PD曲线分成单调下降、单调上升和先升后降三种动态类型,分别对应于林地和河流水库、其他水体、建设用地和农用地,表明单位面积的林地和河流水库斑块数将持续减小,其他水体则持续增大,建设用地和农用地则先增后降的变化。
Fig. 4 Change in landscape pattern during 1990-2020

图4 1990-2020年城市景观特征时空变化

PLADJ曲线则表现出两种动态类型,其中建设用地、林地和其他水体的曲线单调上升,农用地和河流水库的曲线先抑后扬。以上结果表明,PD和PLADJ两指数结果揭示的城市扩展时空特征并不完全一致。例如：建设用地聚集度持续增大,单位面积内的斑块数则先增后减,表明在1990-2005年和2005-2020年,建设用地空间上趋于更聚集,呈飞地式扩展的建设用地斑块数量可能先增多,之后填充式建设用地数量增多,更多的建设用地连接成大斑块。林地斑块聚集度和单位面积内的斑块数正好呈相反方向的变化,前者持续小幅度增加,后者则持续减小。以基塘为主的其他水体,其聚集度和单位面积内的斑块数曲线则都持续上升,表明该类型斑块数一直有增加,为此导致其聚集度持续增大。河流水库和农用地景观类型两种景观类型的聚集程度呈先减后增变化趋势,但单位面积的河流水库斑块数持续减小,而单位面积内的农用地斑块数则呈先增后减的波动变化,表明空间特征的变化较复杂。

4.3 城市空间扩展模式

广州城市空间扩展模式图（图5）显示,1990-2005年,广州城市空间扩展模式以飞地式和外缘式为主,表现为红色斑块较密集地分布于自城市中心（初期建设用地）向南和向北的区域,黄色斑块围绕原来的建设用地向外延伸。在接下来的15年里,外缘式和内填式斑块较明显,飞地式斑块比较小,主要在花都区、从化区和番禺境内,表明在不同的发展阶段,城市空间扩展模式具有不同的时空特征。
Fig. 5 Urban growth modes in Guangzhou during 1990-2020

图5 1990-2020年广州城市扩展模式

Fig. 6 Area percentage of different urban growth modes

图6 城市扩展模式的数量结构

城市空间扩展不同模式的数量结构如图6所示。在研究的第一个阶段（1990-2005年）,外缘式、飞地式和内填式三种扩展模式的面积百分比分别为53.6%、41.4%和5%,说明城市扩展早期,城市主要是围绕原有建设用地向外围延伸,同时由于政府、规划部分的主动参与,飞地式也成为一种主要的扩展模式。研究的第二个阶段（2005-2020年）,三种城市空间扩展模式的占比悬殊减少,外缘式、飞地式和内填式三种增长模式的面积百分比分别为47%、20.4%和32.6%,说明城市发展到现阶段之后,城市内部土地整理（城市更新）将成为一种重要的扩展模式。

5 讨论

情景模拟结果显示,无序的城市扩展将继续增大城市空间,并以占用农用地为代价（图3a）。城市化发展必将导致生态安全问题出现,使得政府和城市规划部门对城市发展的引导作用将日益突出[2],并且主要表现在对城市土地利用数量限制和空间政策的引导方面。情景1和2的模拟结果显示,基于土地利用规划目标这一数量限制（图3b）,模拟的城市景观明显有别于无数量限制的历史趋势的城市景观（图3a）,但与叠加生态政策空间引导下的情景模拟（图3c）城市景观差异不明显。这种结果与已有研究相吻合,Batisani等认为城市区划政策并不是城市土地利用空间配置的主要驱动力[4];相对于社会经济因子而言,地形地貌特征对城市土地利用空间分布所起的作用更加突出[23]。因此,当城市发展到现阶段时,政府对城市土地利用数量调控和空间区划的引导作用具有不同等的重要性。
从斑块破碎度（PD）和相似斑块邻接性（PLADJ）两个不同的角度考察景观特征,其结果之间并不一定相互印证,说明景观变化和景观特征的多样性与复杂性,多个指数联合运用将更有力于从多视角揭示景观特征与规律。例如PD指数显示（图4a）,建设用地破碎度先增加,之后减少,表明建设用地斑块的破碎化程度呈增大到减小的波动变化。但从其斑块邻接性来看（图4b）,建设用地斑块的聚集度又持续增大,说明建设用地斑块之间呈越来越聚集的趋势,然而其单位面积内的斑块数量并非持续增加,这种似非而是的现象可能暗示着某种或几种城市空间扩展方式占主导作用。
Pham等[8]认为,通过阈值划分,确定城市增长模式的阈值是60或70。在本文的研究时段（1990-2005年）内,遥感影像解译获取的土地利用数据表明,研究区城市扩展了大致65.5%,表明进行同时段城市扩展模拟时,将增加约65%的城市建设用地。正因为此,在城市扩展模式划分时,采用65作为阈值划分。由此进行的城市空间扩展模式结果符合实际情况,表明借用PLADJ进行城市扩展模式研究的方法可行,参照实际的城市扩展程度确定阈值也不失有效性。
本文采纳三种城市空间扩展模式方案,其中外缘和飞地式扩展的结果将使城市空间范围扩大,属于大多数中国城市的高速增长—外延式水平空间扩展（“摊大饼”外延式）;内填式的结果使城市更为聚集,属于缓慢增长—内涵式垂直空间扩展或内部填充（内填式）[24,25],往往在城市化中后期,伴随着土地整理（城市更新）。在1990-2005年和2005-2020年两个时段内,广州城市空间扩展模式差异比较明显。其中,在1990-2005年里,外缘和飞地式扩展的面积占新增建设用地的95%,内填式新增建设用地仅占比5%;接下来2005-2020年,内填式新增面积占比增大至32.6%,说明城市扩展已由“摊大饼”外延式逐渐转向集约节约的内填式。

6 结论

基于覆盖广州市的1990年和2005年TM影像,通过构建生态安全导向的Dyna-CLUE模型,进行2020年城市景观与城市空间模式分析。得到以下主要结论：
不同景观类型的PD和PLADJ指数典线变化不一样,两指数结果揭示的同一景观类型时空特征也并不一致,正好可补充说明景观的时空特征。如建设用地聚集度持续增大,其斑块在空间上趋向于越来越聚集;但是单位面积内的斑块数却先增多后减少。这种现象可以从城市空间扩展模式分析结果得到解释: 城市空间是三种扩展模式（外缘、飞地式和内填式）共同作用的结果,但在1990-2005年,以外缘和飞地式为主要模式;之后的2005-2020年间,则以外缘和内填式为主要扩展模式;外缘和飞地式两种主要扩展模式导致建设用地斑块数量先增多,之后外缘和填充式两种主要扩展模式引致更多的建设用地数量,但此时更多的建设用地连接成大斑块;可见,不同的城市发展阶段有其占主导地位的一种或两种城市发展模式,形成不同的城市景观格局特征。
城市化发展到现阶段,受城市地域及地形地貌的限制,政府进行的土地利用数量调控对城市空间扩展的作用更为突出。在1990-2005年和2005-2020年两个时段内,城市空间扩展的模式发生变化,由“摊大饼”外延式转向集约节约的内填式,最终将使城市土地利用配置优化,从而构建城市的生态安全格局。进一步的城市生态安全格局变化的研究将是一项十分重要和有意义的工作。

The authors have declared that no competing interests exist.

References

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[Xie Wenxuan, Huang Qingxu, He Chunyang.A study on urban expansion modes and regional ecological footprints in the Shandong Peninsula urban agglomeration. Acta Ecologica Sinica, 2017, 27(3): 969-978.]

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DOI

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Wilson E F, Hurd J D, Civco D L, et al.Development of a geospatial model to quantify, describe and map urban growth. Remote Sensing of Environment, 2003, 86: 275-285.ABSTRACT In the United States, there is widespread concern about understanding and curbing urban sprawl, which has been cited for its negative impacts on natural resources, economic health, and community character. There is not, however, a universally accepted definition of urban sprawl. It has been described using quantitative measures, qualitative terms, attitudinal explanations, and landscape patterns. To help local, regional and state land use planners better understand and address the issues attributed to sprawl, researchers at NASA's Northeast Regional Earth Science Applications Center (RESAC) at The University of Connecticut have developed an urban growth model. The model, which is based on land cover derived from remotely sensed satellite imagery, determines the geographic extent, patterns, and classes of urban growth over time.Input data to the urban growth model consist of two dates of satellite-derived land cover data that are converted, based on user-defined reclassification options, to just three classes: developed, non-developed, and water. The model identifies three classes of undeveloped land as well as developed land for both dates based on neighborhood information. These two images are used to create a change map that provides more detail than a traditional change analysis by utilizing the classes of non-developed land and including contextual information. The change map becomes the input for the urban growth analysis where five classes of growth are identified: infill, expansion, isolated, linear branch, and clustered branch.The output urban growth map is a powerful visual and quantitative assessment of the kinds of urban growth that have occurred across a landscape. Urban growth further can be characterized using a temporal sequence of urban growth maps to illustrate urban growth dynamics. Beyond analysis, the ability of remote sensing-based information to show changes to a community's landscape, at different geographic scales and over time, is a new and unique resource for local land use decision makers as they plan the future of their communities.

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[10]
Holden E.Ecological footprints and sustainable urban form. Journal of Housing and the Built Environment, 2004, 19: 91-109.Based on two large surveys in the Norwegiantowns of Greater Oslo and F酶rde, the studyteam collected data on housing-relatedconsumption from 537 households. Ecological Footprinting was then used asan analytical tool to analyse the environmentalconsequences of this consumption. Theseecological footprint analyses suggest thatsustainable urban development points towards decentralized concentration , i.e.,relatively small cities with a high density andshort distances between the houses andpublic/private services.

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[11]
Muñiz I, Galindo A.Urban form and the ecological footprint of commuting. The case of Barcelona. Ecological Economics, 2005, 55: 499-514.ABSTRACT One of the most controversial ideas in the debate on urban sustainability is that urban sprawl causes problems of ecological stress. This widespread assumption has been tested by measuring the ecological footprint left by commuters in the 163 municipalities of the Barcelona Metropolitan Region (BMR). This paper explores the determinants of the ecological footprint of commuting municipal variability by using the following regressors: population density, accessibility, average household income, and job ratio. The results confirm that urban form appears as the main determinant of ecological footprint variation among the municipalities of BMR.

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[12]
张智林. 改革开放以来广州市中心城市地位的变迁研究. 西北师范大学学报: 自然科学版, 2006, 42(4): 92-96.通过对改革开放以来广州市发展轨迹的考察,研究了广州市中心城市地位的变迁.将广州市中心城市地位的变迁分为3个阶段,由稳步巩固到相对削弱再到逐步上升.认为这种变迁是由于国内外的大经济环境和国家政策的变化、广州市自身的产业结构变化、城市发展规划与发展战略的实施以及广州市城市发展的传统优势条件的利用等因素所决定的.

DOI

[Zhang Zhilin.Research on the change of the key city status of Guangzhou city. Journal of Northwest Normal University: Natural Science, 2006, 42(4): 92-96.]

[13]
张林波, 李伟涛, 王维. 基于GIS的城市最小生态用地空间分析模型研究: 以深圳市为例. 自然资源学报, 2008, 23(1): 69-78.21世纪,全球范围内城市化已经成为人类社会发展的必然趋势,城市扩张不可避免地将大量的森林、农田、草地、湿地和水域等发挥着重要生态服务功能的生态用地转化为城市建设用地,对城市、区域乃至全球的生态系统造成较大的影响。在未来快速城市化过程中,保护必需的生态用地对于维持城市自身生态系统健康、改善城市居民生活质量和城市可持续发展有着重要意义。研究以中国经济特区深圳市为例,将景观生态概念模型与生态系统服务功能价值评估方法结合起来,在GIS技术的支持下,构建了城市最小生态用地空间分析模型,并分别按照保留城市面积30%、40%、50%和60%生态用地的4种情景,分析最小生态用地空间分布的合理性,结果表明论文所构建的最小生态用地模型能够很好地将城市当中具有重要生态系统服务功能的土地提取出来。

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[Zhang Linbo, Li Weitao, Wang Wei, et al.Research on space modeling for minimum urban ecological land based on GIS: A case in Shenzhen. Journal of Natural Resources, 2008, 23(1): 69-78.]

[14]
彭卫平, 容穗红. 广州市水文地质特征分析. 城市勘测, 2006, (3): 59-63.综合分析了广州市地下水形成的自然条件、地下水的类型、分布状况、水文地质特征和补给径流排泄条件。为有效管理广州市地下水资源，实施地下水资源的监测与保护提供了详实的依据。

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[Peng Weiping, Rong Suihong.Analysis of hydrogeology characteristic in Guangzhou. Urban Geotechnical Investigation & Surveying, 2006, (3): 59-63.]

[15]
Veldkarnp A, Fresco L O.CLUE: A conceptual model to study the conversion of land use and its effects. Ecological Modelling, 1996, 85: 253-270.A dynamic model to simulate Conversion of Land Use and its Effects (CLUE) is presented. For an imaginary region, CLUE simulates land use conversion and change in space and time as a result of interacting biophysical and human drivers. Within CLUE regional land use changes only if biophysical and human demands cannot be met by existing land use. After a regional assessment of land use needs, the final land use decisions are made on a local grid level. Important biophysical drivers are local biophysical suitability and their fluctuations, land use history, spatial distribution of infrastructure and land use, and the occurrence of pests and diseases. Important human land use drivers in CLUE are population size and density, regional and international technology level, level of affluence, target markets for products, economical conditions, attitudes and values, and the applied land use strategy. Initial CLUE simulations suggest that the integrated land use approach of CLUE can make a more realistic contribution to predictions of future land cover than currently used biophysical equilibrium approaches.

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[16]
陆文涛, 代超, 郭怀成. 基于Dyna-CLUE模型的滇池流域土地利用情景设计与模拟. 地理研究, 2015, 34(9): 1619-1629.以滇池流域为研究对象,基于1999年、2002年两期TM遥感解译数据和区域自然与社会经济数据,应用Dyna-CLUE模型模拟2008年滇池土地利用空间分布。结合滇池流域土地利用变化趋势与退耕还林政策,设定了三种土地需求情景,模拟2022年区域土地利用空间分布情况。研究结果表明①两期模拟结果Kappa系数分别为0.6814与0.7124,具有高度的一致性,表明Dyna-CLUE模型在滇池流域有较强的适用性。②三种情景的模拟结果显示,至2022年流域内未利用地、耕地显著减少,建设用地、林地显著增加,水域与草地相对变化较小,因加大退耕还林政策实施力度,三种情景中,位于官渡区、呈贡区、嵩明县及晋宁县的耕地与林地呈现不同的变化情况。③滇池流域建设用地扩张,增加了滇池非点源污染负荷。不合理的土地利用布局,将会恶化滇池水质,加剧水环境压力。研究结果可为未来滇池流域土地利用合理规划与非点源污染控制提供参考依据和决策支持。

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[Lu Wentao, Dai Chao, Guo Huaicheng.Land use scenario design and simulation based on Dyna-CLUE model in Dianchi Lake Watershed. Geographical Reasearch, 2015, 34(9): 1619-1629.]

[17]
Steyaert L T.A perspective on the state of environmental simulation modelling. In: Goodchild M F, Bradley O P, Steyaert L T. Environmental Modelling with GIS. Oxford: Oxford University Press, 1993: 16-30.

[18]
Verburg P H, Overmars K P.Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 2009, 24: 1167-1181.Land use change is the result of interactions between processes operating at different scales. Simulation models at regional to global scales are often incapable of including locally determined processes of land use change. This paper introduces a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes. The model is illustrated with an application for European land use. Interactions between changing demands for agricultural land and vegetation processes leading to the re-growth of (semi-) natural vegetation on abandoned farmland are explicitly addressed. Succession of natural vegetation is simulated based on the spatial variation in biophysical and management related conditions, while the dynamics of the agricultural area are determined by a global multi-sector model. The results allow an exploration of the future dynamics of European land use and landscapes. The model approach is similarly suitable for other regions and processes where large scale processes interact with local dynamics.

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[19]
王慧敏, 曾永年. 青海高原东部土地利用的低碳优化模拟: 以海东市为例. 地理研究, 2015, 34(7): 1270-1284.人类活动导致的土地利用变化及其碳效应在区域及全球碳循环研究中具有举足轻重的作用。以位于青海高原东部的海东市为研究区,在探讨土地利用碳循环系数的基础上,预测海东市2020年低碳情景下的土地利用结构,并运用CLUE-S模型模拟了海东市土地利用低碳优化空间格局,然后对比分析低碳情景与规划情景下,海东市2020年土地利用格局的差异。研究表明①尽管低碳情景下研究区域水浇地和旱地持续减少,但相对于规划情景,优质耕地的流失量减少。②有林地稳定增加,疏林草地缓慢增加,区域固碳能力逐步提升。③建设用地适度扩张,增加幅度小于规划情景,其中城镇用地相对于规划情景碳排放减少14.03万t。低碳视角的区域土地利用结构调整与空间布局优化,为区域土地利用科学规划与管理提供决策支持。

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[Wang Huimin, Zeng Yongnian.Land use optimization simulation based on low-carbon emissions in eastern part of Qinghai Plateau. Geographical Reasearch, 2015, 34(7): 1270-1284.]

[20]
Verburg P, Veldkamp A M, Espaldon R L V, et al. Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental management, 2002, 30(3): 391-405.Abstract Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation

DOI PMID

[21]
Lakes T, Müller D, Krüger C.Cropland change in southern Romania: A comparison of logistic regressions and artificial neural networks. Landscape Ecology, 2009, 24: 1195-1206.Changes in cropland have been the dominating land use changes in Central and Eastern Europe, with cropland abandonment frequently exceeding cropland expansion. However, surprisingly little is known about the rates, spatial patterns, and determinants of cropland change in Eastern Europe. We study cropland changes between 1995 and 2005 in Arges抬 County in Southern Romania with two distinct modeli...

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[22]
Pontius Jr R G, Schneider L C. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture Ecosystems & Environment, 2001, 85(1-3): 239-248.Abstract Scientists need a better and larger set of tools to validate land-use change models, because it is essential to know a model prediction accuracy. This paper describes how to use the relative operating characteristic (ROC) as a quantitative measurement to validate a land-cover change model. Typically, a crucial component of a spatially explicit simulation model of land-cover change is a map of suitability for land-cover change, for example a map of probability of deforestation. The model usually selects locations for new land-cover change at locations that have relatively high suitability. The ROC can compare a map of actual change to maps of modeled suitability for land-cover change. ROC is a summary statistic derived from several two-by-two contingency tables, where each contingency table corresponds to a different simulated scenario of future land-cover change. The categories in each contingency table are actual change and actual non-change versus simulated change and simulated non-change. This paper applies the theoretical concepts to a model of deforestation in the Ipswich watershed, USA.

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[23]
Pontius Jr R G,Spencer J J. Uncertainty in extrapolations of predictive land-change models. Environment and Planning B: Planning and Design, 2005, 32: 211-230.This paper gives a technique to extrapolate the anticipated accuracy of a prediction of land-use and land-cover change (LUCC) to any point in the future. The method calibrates a LUCC model with information from the past in order to simulate a map of the present, so that it can compute an objective measure of validation with empirical data. Then it uses that observed measurement of predictive accuracy to anticipate how accurately the model will predict a future landscape. The technique assumes that the accuracy of the model will decay to randomness as the model predicts farther into the future and estimates how fast the decay in accuracy will occur based on prior model performance. Results are presented graphically in terms of percentage of pixels classified correctly so that nonexperts can interpret the accuracy visually. The percentage correct is budgeted by three components: agreement due to chance, agreement due to the predicted quantity of each land category, and agreement due to the predicted location of each land category. The percentage error is budgeted by two components: disagreement due to the predicted location of each land category and disagreement due to the predicted quantity of each land category. Therefore, model users can see the sources of the accuracy and error of the model. The entire analysis is computable for multiple resolutions, so users can see how the results are sensitive to changes in scale. We illustrate the method with an application of the land-use change model Geomod to Central Massachusetts, where the predictive accuracy of the model decays to 90% over fourteen years and to near complete randomness over 200 years.

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[24]
宗跃光. 大都市空间扩展的廊道效应与景观结构优化: 以北京市区为例. 地理研究, 1998, 17(2): 119-124.由于存在城市中心梯度场和廊道效应梯度场，在单纯经济利益趋动下，城市本质上存在推大饼倾向，这将严重破坏城市合理景观结构与生态平衡。中运用廊道效应原理，研究人工廊道与自然廊道相互作用过程，结合北京中心市区不同时期空间扩展格局，分析城市景观８个方位廊道的扩展量、扩展速度及变化趋势，提出将自然廊道体系钠入北京大都市区规划，形成人工廊道与自然廊道相间分布的星状分散集团式景观格局，以有效防止建成区推大饼过程

DOI

[Zong Yueguang.The corridor effects and optimization of landscape structure in a metropolitan area: A case study on Beijing. Geographical Research, 1998, 17(2): 119-124.]

[25]
Guo Guanhua, Wu Zhifeng, Xiao Rongbo, et al.Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape & Urban Planning, 2015, 135:1-10.The spatio-temporal pattern of biophysical composition significantly affects land surface temperature (LST). Previous studies, however, mostly characterized urban heat island (UHI) clusters being spatially homogeneous. The landscape spatial heterogeneity in urban across UHI clusters challenges us to more accurately characterize the relationships between LST and corresponding urban biophysical composition. In this study, we introduced an innovative integrated approach that combined object-oriented image segmentation with local indicators of spatial autocorrelations (LISA) to extract UHI clusters from an LST image. We used a regression tree model to examine the nonlinear relationships between LST and each of three satellite-based indices within the UHI clusters: normalized differential vegetation index (NDVI), normalized differential build-up index (NDBI), and normalized difference bareness index (NDBaI). We found that both NDVI and NDBI are strongly correlated with the variations of LST whereas NDBaI has a weaker correlation with LST. We also found that the regression tree model built in this study enabled us to effectively detect the nonlinear relationship between LST and biophysical composition. Furthermore, based on a set of rules derived from a regression tree analysis, we found that urban landscapes strongly affect LST and its spatial heterogeneity within a UHI. These rules were used to detect the nonlinear impacts of complex urban biophysical composition on LST. The results of this study provided insights into how LST within UHI varies with urban surface characteristics at fine spatial scale and also a new method for investigating effects of land surface composition on LST in urbanized areas.

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