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基于高分辨率遥感影像的2000-2015年中国省会城市高精度扩张监测与分析
张翰超1,2,3,, 宁晓刚2, 王浩2,, 邵振峰4
1. 武汉大学遥感信息工程学院,武汉 430072
2. 中国测绘科学研究院,北京100830
3. 城市空间信息工程北京市重点实验室,北京 100038
4. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079

作者简介:张翰超(1991-), 男, 博士生, 研究方向为城市扩张监测和分析。E-mail: zhc_geo@whu.edu.cn

通讯作者:王浩(1985-), 男, 博士, 助理研究员, 研究方向为城市地理国情和生态环境遥感监测。E-mail: wanghao@casm.ac.cn
摘要

21世纪以来,中国城镇化水平迅速提高,城市扩张监测成为地学应用研究的热点,但同时存在着城市区域概念不统一,城市边界提取精度较粗等问题。选取全国31个省会级城市为监测对象,利用高分辨率遥感影像进行统一标准、高精度的扩张监测及分析。基于高分辨率遥感影像数据进行2000年、2005年、2010年、2015年4期城市区域边界提取,与利用中低分辨率影像提取的成果进行比较,并开展城市规模分布和城市扩张分析。结果表明,与其他研究成果相比,本文拥有更高的精度和可靠性;2000-2015年,中国省会城市保持了高速增长趋势,总面积增加了90.15%;省会城市体系接近捷夫模式的等级规模分布;城市扩展情况地区差异显著,东部扩展速度逐步放缓,西部、东北地区加速扩张,中部地区稳步扩张;2010年确定建设的5个国家中心城市(北京、天津、上海、广州、重庆)在2015年城市区域面积位序中排名前5,15年间扩展了82.45%,单个城市年均扩展30.66 km2,其中北京扩展了三成以上,天津、上海扩展了一倍左右,广州扩展了近六成,重庆扩展了两倍以上。本研究成果为中国城镇体系的发展和规划提供了直观准确的数据,对国家全面认知城市扩张状况,掌握城镇建设方针政策实行效果,进行城市体系科学规划具有十分重要的指导意义。

关键词: 城市扩张; 高分辨率影像; 城市区域; 省会城市; 位序—规模法则;;
High accuracy urban expansion monitoring and analysis of China's provincial capitals from 2000 to 2015 based on high-resolution remote sensing imagery
ZHANG Hanchao1,2,3,, NING Xiaogang2, WANG Hao2,, SHAO Zhenfeng4
1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
2. Chinese Academy of Surveying and Mapping, Beijing 100830, China
3. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China
4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Abstract

China has undergone a rapid urbanization since the beginning of the 21st century. Urban expansion monitoring has become a hotspot in the field of geographical science. However, methods of urban boundary extraction were inconsistent, and the precision of previous urban boundary products is relatively low due to the coarse image resolution. In this paper, a method of high-precision and unified urban expansion monitoring and analysis of China's 31 provincial capitals was carried out based on high-resolution remote sensing images. First, the urban boundaries of 2000, 2005, 2010 and 2015 were extracted with a series of unified rules by urban landscape characteristics and geographical knowledge based on high-resolution images. Then, urban boundary result was compared with other urban boundary products based on low and mid-resolution images to assess the accuracy. Finally, urban size distribution and urban expansion were analyzed based on urban area and urban boundary results. Results showed that the proposed method of urban boundary extraction was superior to other researches. From 2000 to 2015, China's provincial capitals witnessed a rapid growth trend, and the total urban area increased by 90.15%; the provincial capitals system approximated size distribution of the rank-size law. Urban expansion had a significant regional difference. Urban expansion rate in the eastern region gradually slowed down, while that in the western and northeastern regions had an accelerating mode, and that in the central region expanded steadily. Beijing, Tianjin, Shanghai, Guangzhou and Chongqing, which were designated as the national central cities in 2010, ranked the top five of urban area size in 2015. The five cities increased by 82.45% during the 15 years, and the average annual urban expansion area was 30.66 km2. Urban area of Beijing, Tianjin, Shanghai, Guangzhou and Chongqing increased by about 30%, 100%, 100%, 60% and 200%, respectively. This research provides unified and high-precision spatial urban boundaries data and urban expansion results for local governments and the public, which are useful for scientific urban development and planning of China's urban system.

Keyword: urban expansion; high-resolution image; urban area; provincial city; rank-size law;
1 引言

1978年改革开放以来,中国经历了一个起点低、速度快、规模大的城镇化发展过程,取得了重大进展[1]。快速增长的城市人口和粗放的城市空间利用给城市可持续发展带来了巨大的压力,为应对快速城镇化带来的挑战,新型城镇化[2]、智慧城市[3]、生态城市[4]、精明增长[5]等城市发展理念应运而生。这些理念要求城市可持续发展必须要划定合理的城市开发边界,优化空间布局,实现土地集约节约利用[6,7]。开展城区边界提取,进行城市扩张监测分析,对于准确把握城市化进程,科学开展城市规划和建设,促进城市可持续发展有着极为重要的作用。

遥感技术具有真实客观、现势性强、成本低等优势,已经成为城市扩张监测重要技术手段[8,9]。城市扩张遥感监测的首要任务是划定城市边界即城区范围,“城区”相关概念众多,包括市域[10]、市区[11]、主城区[12]、中心城区[12]、建成区[13]、城市建设用地[14]、城市区域[15]等,这些定义有着不同的出发点和特点,目前并没有一个定义明确的“城区”[16]。本文中提到的城市区域、建成区统称为城区,只作为一种指代说法,不做严格意义上的概念区分。不少学者在城市扩张监测中参考土地利用类型[17,18,19,20](包括建设用地、草地、林地、耕地、水域、未利用地等),将其中的建设用地或不透水面[21,22,23]作为城区,也有学者综合考虑城市用地功能[24],提出的城区定义更加符合认知。

城市边界提取和扩展监测采用的数据来源众多[25]。早期,城市扩张监测主要采用MODIS[26,27]作为数据源。在Global Land Cover 2000[28]中,以人工地表和相关区域作为城区,得出中国城区面积约为10000 km2,而Global Rural-Urban Mapping Project[29]以城市范围作为城区的监测结果显示,中国城区面积超过261000 km2,两者之间差异巨大,后续的精度评价研究[30,31]也受到了实地数据缺乏的阻碍。之后开始采用Landsat系列[32,33,34,35,36,37]为主的中分辨率卫星影像,王雷等[38]利用Landsat TM/ETM+影像采用人工目视解译为主的方法对中国1990年、2000年和2010年的城市建成区边界制图并进行用地效益分析,指出利用TM/ETM+影像提取城区存在混合像元、光谱混淆、空间分辨率低导致城区边界难以确定等问题。随着遥感技术发展,利用高分辨率影像的城市扩张监测近年来开始兴起,不少学者采用高分辨率影像对城市区域自动提取方法进行了尝试[39,40,41,42],但由于自动化提取方法适用性的限制,目前主要集中在城市尺度进行,难以反映中国城市整体的发展过程。另外,部分学者还采用DMSP/OLS夜光数据[43,44,45,46]、ALOS/AVNIR-2[47]、高分一号影像[48]、SAR数据[49]等,同时结合社会经济统计数据、数字高程模型、数字城市数据、规划数据、土地利用数据等辅助数据[50]开展分析。

城区的提取方法各不相同[51,52,53],不同方法提取的结果差异显著,主要为目视解译的方法[19,20],也有部分学者采用分割、分类的方法,包括最大似然分类法[54]、灯光阈值法[46]、 决策树[17, 55]等;监测范围包括主城区[20]、城市[22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]、城市群[52]、国家[38]、地区[56]乃至全球尺度[43]

众多学者的研究为了解中国城镇化时空扩展过程提供了丰富的方法,然而仍存在不足之处。主要体现在:首先,各研究对城区概念理解不一,提取标准不一致,监测结果差异大,难以进行精度验证和对比分析。其次,成果多基于MODIS、Landsat系列卫星影像制作,影像分辨率较低导致城区边界成果的精度较差。21世纪初,正是中国城市高速发展的阶段,但这一时期的高分辨率遥感影像较为匮乏[48],没有形成一套全国范围的高精度监测产品。

本文利用高分辨率遥感影像在统一标准下采集2000年、2005年、2010年和2015年4期全国31个省会城市市辖区范围内的城市区域边界,同采用中低分辨率影像提取的类似成果进行比较分析,开展城市规模分布变化和城市区域扩展分析,并提出对城市发展的建议。

2 研究区域和数据
2.1 研究区域

本研究监测区为中国31个省会城市(包括省会城市、自治区首府、直辖市,不包含港澳台),选取其市辖区作为城市区域提取范围,存在行政区划调整的城市以2015年市辖区为准。按经济区域划分为东部、中部、西部和东北四大地区(图1)。省会城市是省级行政单元的政治、经济、文化中心,各省会城市的发展基本上代表着该省发展的最高水平[57]。21世纪初期,这些城市经济社会快速发展,城区扩展迅速,在中国城镇化发展中极具代表性。

图1 中国省会城市分布及四大经济区域划分图 Fig. 1 The distribution of China's provincial capitals and four economic regions

2.2 研究数据

研究采用高分辨率遥感影像作为主要数据来源,覆盖整个研究区域,具体情况如表1所示,表1中数据类型按照使用的优先顺序进行排列。其中,研究区的2015年遥感影像以全国第一次地理国情普查标准时点核准影像为主要数据源,来源于国家基础地理信息中心。2010年遥感影像主要来源于原国土资源部,不足的影像通过收集全国第一次地理国情普查影像数据予以补充,多为当年度9-12月份的卫星影像,分辨率一般为优于1 m分辨率,且已经过正射纠正处理。2000年和2005年遥感影像以测绘地理信息部门存档高分辨率遥感影像和航片为主,不足的影像通过网络下载和购买的方式得到,优先选择9-12月份的影像。数据覆盖省会城市市辖区范围,由于研究区覆盖较广,时间跨度大,少量影像缺失区域以相邻年份的影像进行补充。为保证提取结果的一致性,影像数据统一重采样到2 m分辨率。辅助数据主要有第一次全国地理国情普查成果和基础地理信息成果,来源于原国家测绘地理信息局。

表1 遥感数据情况 Tab. 1 The remote sensing data
3 研究方法

利用2000年、2005年、2010年、2015年高分辨率遥感影像、第一次全国地理国情普查成果和基础地理信息成果等辅助数据,经过正射纠正、影像融合、影像镶嵌、裁切等预处理步骤,采用人工解译的方法进行城市区域边界提取,并与同类研究成果进行比较,同时开展城市规模分布及城市区域扩展分析。

3.1 城市区域提取方法

根据对城区理解和采用数据的不同,不同学者对城区范围有着不同的定义。在利用MODIS进行城区边界提取的研究中多采用建筑密集区域或不透水面作为城区。以世界银行组织在《东亚变化中的都市景观》[56]中发布的成果(记为A)为例,其将城区定义为被建筑环境所主导的区域,即建筑环境在250 km2为大小的景观单位中占50%以上的区域,建筑环境包括除植被以外的所有的人工构筑物(如道路、建筑物等)。在利用Landsat系列卫星影像进行城区制图的研究中,王雷等[38]将城市范围定义为该城市政府机关所在地的建成区,即实际已成片开发建设,市政公共设施基本具备的区域,不包括内部的成片耕地、贯穿城市的河流、以及山体。对于城乡结合带,在不考虑被道路连接的情况下,把间隔在5个像元(150 m)以内的成片居民地划为该城市建成区(记为B)。

参考各研究中城市区域的概念和包含的具体内容,考虑城市用地功能,在行政界线内部,以该城市政府所在地为基础,提取具有城市功能和景观特征(包括房屋建筑区、构筑物、城市道路、城市绿地、城市水域等)的集中连片的空间范围作为本文的城市区域。包括城市的中心城区;具有城市公共设施,符合城市景观特征的居民区以及大型的社区、高等院校、科研机构、高新开发区、工矿用地等。相比于中低分辨率影像提取的城市区域,本文的城市区域充分考虑了城市功能特征,包含内容更加具体细致,能够准确反映城市建设的真实情况。

图2展示了本研究的城市区域内部典型地物示例及与A、B的对比情况:影像分辨率越高,能够从影像上得到信息越丰富,从而定义的城区也更加详细具体。

图2 城市区域包含地物示例及与A、B成果示例对比图 Fig. 2 Comparative examples for surface features in urban area compared with A and B

由于大区域城市区域提取存在着影像差异大导致的提取标准难统一、城市地物复杂导致的光谱特征易混淆以及城市与农村交错导致的城市边界难确定等困难[38],本文采用目视解译的方法进行提取。城市区域提取要从城市实际建设完成情况出发,充分考虑遥感影像中展现出的以建筑物为核心、以自然景观为辅助、以道路为纽带形成的城市景观特征,遵循先宏观再精细的流程,先粗提取城市区域初始边界,再充分利用地理国情普查和基础地理信息成果,按照行政界线限定原则、城区边界走向原则、集中连片原则、城市景观判定原则以及飞地型城区判定原则等5项原则进行边界微观精细调整,得到最终边界(图3)。多期边界的提取顺序应从最新时相开始,依次提取前一时相的边界,直到完成所有时期的边界提取。

图3 城市区域边界提取流程 Fig. 3 The workflow of urban boundary extraction

城市区域提取原则包括以下5项:① 行政区划限定原则。必须在城市的行政区域内部提取城市区域;② 城区边界走向原则。城市区域边界禁止穿越房屋、典型构筑物、耕地、林地、园地、草地、水域等,城市区域边界优先采用建筑物和线状地物(河流、道路等)的自然边界。在没有线状地物边界采用时,沿已建成的完整地块的边界进行勾绘。若无已建成的完整地块,可根据实际情况按照地物的边界进行勾绘。当城市区域边界处存在耕地、林地、园地、草地等农用的非城市景观用地时,不纳入城市区域。当城市区域边界存在城市景观用地,且外侧为河岸时,沿河岸的外侧边界进行勾绘。③ 集中连片原则。城市区域应集中连片,中间不应被非建设用地如农用地、未利用地等隔断;或者不同的城市区域之间可以通过道路相连接。④ 城市景观判定原则。具备城市景观特征的地物主要包括具有城市特点的房屋建筑区、构筑物、城市道路、城市广场、公园、停车场、体育场、城市绿地、城市水域等[58,59],其中最主要的城市景观为被城市道路分割形成的街区。⑤ 飞地型城区判定原则。飞地型城区指与城市区域中心大块集中连片区相分隔但属于城市区域的地区,其特征有:a.与城市区域中心大块通过主干路相连通;b.具有明显的城市景观特征;c.城市的行政部门、居民区、大型的社区、高等院校、科研机构、高新开发区、工矿用地等特殊区域所在的大块集中连片区。

城市区域提取由长期从事遥感影像解译的专业人员进行,提取成果由专业质检部门进行内业核查,有疑问区域进行外业核实,将总体采集精度控制在实地5 m以内。

3.2 城市规模分布分析方法

城市规模分布[59,60,61]是指一个国家或地区内城市整体规模的层次分布情况,研究中常将城市人口、城区面积、经济指标、复合指标作为城市规模的表征指标,本文采用城区面积为城市规模指标。通过分析区域内城市从大到小的序列与其规模的关系,揭示区域内部的城市分布特征。一个城市体系的规模和位序的关系可以用位序—规模法则来进行考察。其公式为:

P i = P 1 × R i - q (1)

式中:Pi为城市的规模;Ri为城市的位序(按照规模从大到小排列);P1为首位城市规模的理论值;q为Zipf指数。

在城市体系研究中,为更加深入分析城市规模分布特点,常引入分形理论。根据分形理论[62,63,64],城市规模分布的分维值(D)和位序—规模法则公式中的q存在如下关系:

D × q = R 2 ( R 2 为判定系数 ) (2)

分维值(D)及Zipf指数(q)的大小均可反映城市体系的均衡程度。当q越大,D越小时,说明区域内城市规模分布整体趋于集中状态,大城市规模突出,中小城市不够发达。大中小城市之间规模差异大,城市规模体系不均衡。当q越小,D越大时,说明区域内城市规模分布整体趋于分散状态,大城市规模不突出,中小城市较发达;各城市规模差异不大,城市规模体系更为均衡。当qD同时接近于1时,城市规模分布接近于捷夫模式的理想状态,各类城市比例较为合理。

根据城市规模分布理论,当q = 1时,为有规则的序列分布,即捷夫模式;当q > 1时,为首位分布,城市人口集中,城镇体系中以大城市为主,中小城市不够发达;当q < 1时,为序列分布,城市人口分散,城镇体系中大城市不突出,中小城市发达;当q = 0时,所有城市人口数相等,属于平均分布;但q = ∞时,区域内只有一个城市。

lnP1被称为结构容量。结构容量越大,说明城市总体规模越大,体系越复杂;相反结构容量越小,说明城市总体规模越小,体系越简单。

3.3 城市区域扩展分析指标

在获取城区边界后,采用城区扩展速度、扩展强度对城区时空扩展进行分析。

(1)扩展速度。扩展速度Vi为城市i的城市区域面积年均增长率,表示单位时间内城市区域扩展面积变化的速度。

V i = U ij t j × 100 % (3)

式中:Vi为城市i的扩展速度;ΔUijj时段城市i的扩展面积;Δtjj时段以年为单位的时间跨度。

(2)扩展强度。扩展强度Ni为某一时间段内城市区域面积相对于最初面积的年均扩展比例,表示单位时间内城市区域扩展的程度。

N i = U ij t j × M i × 100 % (4)

式中:Ni为城市扩展强度;ΔUijj时段内城市i的扩展面积;Δtjj时段的时间跨度;Mjj时段初期城市i的城市区域总面积。

4 结果和分析
4.1 与同类研究成果比较分析

图4中,A指世界银行组织发布的《东亚变化中的都市景观》[56]中的成果,主要采用MODIS影像进行自动分类提取;B指王雷等在《中国1990-2010年城市扩张卫星遥感制图》[38]中公布的成果,主要采用Landsat TM/ETM+影像进行人工目视解译提取得到;C为本文的城市区域提取结果,省会城市区域面积和扩展具体情况如表2所示。其中,A中上海和广州的提取结果实际为长江三角,与其他成果范围不一致,不加入比较,A、B中拉萨的提取结果未公布,也不加入比较。从图4中展示的总体趋势可以看出,成果A的城区面积明显大于成果B和成果C的城区面积,成果B比成果C的城区面积稍大。根据成果A,中国省会城市区域2000年的平均面积为427.68 km2,2010年为636.00 km2,成果B显示2000年为206.68 km2,2010年为368.62 km2,本文结果2000年为187.35 km2,2010年为295.41 km2。从面积均值来看,成果B与本文结果相差19.17%,2000年差距较小,为10.32%,2010年差距较大,为24.78%,而成果A与两者差异较大,基本为两者各自面积的2倍左右。对3组提取结果分年份两两进行显著性检验(t检验),结果显示,成果A与成果B、成果C的差异均较大,成果B与本文结果通过0.01水平显著性检验。差异较大的城市主要有北京、重庆等,选择2010年的北京市及重庆市的3种成果进行比较(图5)。

图4 中国省会城市城区提取研究成果比较 Fig. 4 The comparison of extraction results of urban areas

表2 2000-2015年中国省会城市扩展情况综合情况表 Tab. 2 The urban expansion results of China's provincial capitals from 2000 to 2015

图5 成果A、B、C的2010年提取结果对比示例——北京、重庆 Fig. 5 Comparative examples for urban boundaries of A, B and C in 2010: Beijing and Chongqing

在北京市的成果对比中,根据提取结果对比图5a中结果显示,成果A的城区覆盖范围远超过成果B和成果C,从高分辨率影像中可以看出,成果A提取的城区范围包含大量耕地、林地,甚至山区也错误划分为城区,精度最差,主要由于MODIS的分辨率远低于后两者采用的影像,在精度和粒度上难以达到中高分辨率的提取水平,不再加入后续重庆地区的比较。成果B与本文结果比较结果显示,整体差异不大,通过图中放大结果显示,图5b中部分含有明显聚集的高层建筑的城市区域在成果B中被排除在外,图5c中城乡结合部的低矮建筑、图5d中的部分农村区域、图5e中的大片农田区域却在成果B中被错误的划为建成区,从对应的Landsat5 TM影像对比结果图5f、5g、5h、5i中可以看出,这些区域中除了图5f中能够明显看出成果B存在漏提现象,图5g、5h、5i中难以准确识别边界附近的地物,很容易造成误提。根据北京市的提取结果可以看出,成果A精度最差,成果B精度较高,本文结果精度最高。

在重庆市的成果对比中,造成成果B与本文成果的重庆市统计结果相差较大的原因是本文中重庆市辖区中各个区的城区均进行提取和统计,成果B中只提取了市政府所在地的建成区范围,从而造成统计数值上的偏差,选取本文成果中的主要城市区域统计面积为273.71 km2,与成果B的重庆建成区面积269.39 km2仅差1.60%。图5j展示了主要城市区域两者结果的整体情况,可以看出提取结果总体一致,在具体边界细节稍有不同。图5k、5l、5m、5n展示了更为具体的边界差异,从图5k、5n中可以看出,成果B存在部分城区漏提的现象,图5l显示成果B存在部分误提现象,将部分农田和裸地归入了建成区,图5m则展示了本文成果在建筑物边界提取的精细和准确程度方面优于成果B。通过将三类不同数据源提取的成果进行比较可以看出,数据源的分辨率对于城市区域提取结果精度有显著影响,低分辨率的数据源同中高分辨率的数据源得到的结果有近一倍的差距,利用高分辨率数据源进行城市区域提取能够有效避免中低分辨率数据源造成的误提、漏提、边界精度较差等现象,能够准确区分城区、城乡结合部、农村,也能够准确获取城市内部要素分布、形态和结构信息,提取结果对于把握城市发展具有重要的作用。

4.2 城市规模分布变化分析

2000年全国省会城市区域总面积为6520.17 km2,2005年为8217.72 km2,较2000年增长了26.04%,2010年为10361.08 km2,较2005年增长了26.08%,2015年为12398.31 km2,较2010年增长了19.66%,2015全国省会城市区域总面积是2000年的1.90倍。这一结果显示中国省会城市增长保持了指数级的增长趋势。采用中国省会城市区域面积作为城市规模指标,对2000年、2005年、2010年、2015年4期的提取结果进行城市位序-规模分析,得到中国省会城市体系的位序—规模图(图6)。

图6 2000-2015年中国省会城市体系位序—规模曲线图 Fig. 6 The rank-size curve of China's provincial capital system during 2000-2015

根据图6结果显示,各时期模型拟合判定系数 R 2 都在0.87以上,说明模型拟合度较好,位序—规模法则能够较好的描述中国省会城市规模的分布。各时期首位城市的规模均小于理论值,排名前几位的城市规模较为接近,说明中国省会城市体系的首位度不够突出,不属于典型的首位分布。Zipf指数值小于但接近1,分维值 D 极为接近1,说明中国省会城市规模分布的均衡度较好,城市体系接近捷夫模式的等级规模分布,首位城市规模与最小城市的规模之比接近城市总数,各规模等级城市数量比例较为合理。

2000-2005年,结构容量 ln P 1 随时间呈现逐步上升趋势,表明中国省会城市整体规模在不断扩大,城市体系越来越复杂。在对1990-2000年中国城市用地规模分布演进的已有研究中,普遍认为该阶段中国城市用地规模分布呈现Zipf指数下降,均衡度上升的特征趋势[66,67]。根据本文研究发现(图7),2000-2015年,中国省会城市体系Zipf指数值随着时间变化呈现先上升后下降的趋势,从2000年的0.858上升至2005年的最高值0.906,随后开始下降至2015年的0.873,即中国省会城市规模分布演进在2005年前后呈现不同的阶段性特征。

图7 中国省会城市体系Zipf指数、结构容量变化图 Fig. 7 Change of Zipf index and structural capacity in China's provincial capital system

结合中国城市发展政策[68],1996-2000年,中国实行“严格控制大城市规模,突出发展小城镇”的城市发展方针,而在2000-2005年期间执行“大中小城市和小城镇协调发展”的多样化城市发展方针,政策转变使得具有天然优势的大城市迅速而粗放的发展。根据本研究结果统计,在2000-2005年期间,原本面积在200 km2以上的10个大城市和超大城市的扩展面积占总扩展面积的73.64%,3个超大城市(面积大于500 km2)的扩展面积占总扩展面积的38.14%,可以看出在此期间中国城市规模排名靠前的大城市获得了巨大发展,远远超过中小城市,首位度上升,均衡度下降,城市规模分布更加接近捷夫模式。在2005年,中国提出要走集约发展的城镇化道路,在一定程度上约束了大城市的发展,在之后的5年中,排名前十的城市扩展面积占比降低至62.38%,排名前三的城市面积占比降低至25.96%,城市体系首位度有所下降,均衡度上升,Zipf指数下降。2011年之后坚持“以大城市为依托,中小城市为重点,逐步形成辐射作用大的城市群,促进大中小城市和小城镇协调发展”的政策,2015年排名前10的城市扩展面积占比保持在62.32%,排名前三的城市扩展面积占比进一步降低至19.29%,城市体系首位度、Zipf指数进一步下降,中国省会城市体系更加趋于均衡。

4.3 城市区域扩展分析

2000-2015年,31个省会城市区域面积均显著增加,单个城市区域面积平均增加达189.62,是2000年平均城市区域面积的90.15%,反映了这15年间中国省会城市的迅速发展,具体扩展情况见如图8所示。其中,城市区域扩展面积排名前十的城市依次为上海(760.44 km2)、天津(594.52 km2)、重庆(447.47 km2)、成都(415.92 km2)、杭州(368.2 km2)、西安(324.75 km2)、南京(258.8 km2)、广州(255.19 km2)、沈阳(253.05 km2)、北京(241.89 km2),这些城市中有一半以上(6个)属于东部地区,占东部省会城市总数的60%,反映了东部地区城市的快速扩张,西部地区有重庆、成都、西安,占西部省会城市总数的25%,东北地区3个省会城市中只有沈阳,中部地区则没有城市扩展超过200 km2,说明相比于东部地区,其他地区还有很大的发展空间,尤其是中西部地区,同时也反映了这些排名前十的城市在各自区域城镇化发展的引领作用。

图8 2000-2015年4个时期中国31个省会城市城市区域分布和扩展状况图 Fig. 8 Urban expansion and distribution of China's 31 provincial captials during 2000-2015

城市区域面积排名前5的城市中,北京、上海、天津、广州从2000年到2015年始终占据着城市区域面积前4的位置,武汉在2000年、2005年排名第5,在2010年和2015年被重庆取代。北京作为中国首都,城市区域面积排名始终保持在前3,2000-2015年保持着2.04%的扩展强度,共扩展241.89 km2,相当于2000年北京城区的1/3;上海城市区域扩展更为迅速,15年间以6.44%的扩展强度,共扩展760.44 km2,相比于2000年787.31 km2的城区面积,约扩展了一倍;天津则在15年间扩展了一倍多,广州和武汉均扩展了六成左右,重庆扩展了近两倍。2010年2月,中国住房和城乡建设部发布的《全国城镇体系规划》北京、天津、上海、广州、重庆被确定为中国国家中心城市,从国家层面肯定了五市在中国具备引领、辐射、集散功能,能获得国家中心城市的定位,对地区的发展,极具重大意义。研究结果显示,这5大国家中心城市15年间共扩展2299.51 km2,扩展了82.45%。另外,这5个国家中心城市正是2015城市区域面积位序中排名前5的城市,之后确定的成都、武汉、郑州、西安4个国家中心城市中,除郑州外也均排名前10,进一步印证了本文研究成果对国家全面认知城市扩张状况进行城市体系规划的现实指导意义。

2000年、2005年、2010年、2015年,东部地区的城市区域平均面积分别为350.67 km2、454.07 km2、551.85 km2、616.32 km2,东北地区为206.05 km2、255.03 km2、314.02 km2、389.10 km2,中部地区为162.41 km2、192.48 km2、246.72 km2、283.71 km2,西部地区为118.41 km2、146.42 km2、201.68 km2、280.46 km2。总体来看,中国东部地区的省会城市区域平均面积最大,东北次之,西部地区最小,说明中国东部地区的省会城市发展水平最高,西部地区有待进一步发展。

2000-2005年、2005-2010年、2010-2015年,全国省会平均扩展面积分别为54.76 km2、69.14 km2、65.72 km2,扩展速度总体上呈现先上升后下降的趋势。通过分时段、分城市、分区域计算扩展速率和扩展强度,得到各省会具体扩展情况(图9),并结合全国省会城市区域的面积分布情况开展分析。

图9 中国省会城市扩展情况区域分布图 Fig. 9 The regional distribution of urban expansion of China's provincial capitals

从扩展速度来看,2000-2005年、2005-2010年、2010-2015年3个时期中,西部地区省会城市扩展速度整体偏低,呈总体上升趋势;东部地区省会城市则呈现相反的趋势,扩展速度整体最高,但随着时间发展逐步降低,表明东部地区发展速度减缓;东北地区的3个省会城市扩展速度均保持着稳步上升的趋势;中部地区整体扩展速度较为缓慢,各个城市扩展速度差异不大,扩展速度阶段性变化也不明显。

从扩展强度来看,2000-2015年,全国省会城市城区扩展强度排名前十依次为:银川(15.41%)、成都(13.46%)、杭州(12.59%)、重庆(12.55%)、西安(10.97%)、合肥(10.63%)、南昌(10.50%)、西宁(10.20%)、南宁(8.84%)、南京(7.91%),其中有6个西部城市(共12个),2个东部城市(共10个),2个中部城市(共6个),没有东北地区的城市(共3个)。结合图9中分地区扩展强度以及城市区域扩展监测面积结果可知,2000-2015年间,西部地区城市发展起步低、但相对增长最快,中部地区次之,东北地区再次之,东部地区最小。

从分区域扩展面积和比例来看,东部地区的省会城市区域平均扩展面积最大,但相对扩展比例最小,说明东部地区省会城市发展的起点高、扩张速度快,但相对扩张速度较慢;中部地区平均扩张面积和相对扩展比例均为最小,表明了中部省会城市发展的相对缓慢;西部地区省会城市平均扩展面积偏低,只有成都、重庆等少数城市扩展面积很高,但平均扩展比例在全国4个区域中最高,表明了西部城市在15年间相对2000年的土地城镇化水平有着极大的提高;东北地区扩展面积和比例均处于中等位置,保持着稳定发展。

研究结果在一定程度上反映了国家的沿海地区经济发展战略、西部大开发、中部崛起和振兴东北等重大战略实施的效果。沿海地区经济发展战略是改革开放开始时期便提出并逐步形成的战略,东部地区也是中国最早发展起来,发展得最好的地区。监测结果显示,中国东部地区的省会城市平均面积在各区域中排名最高,15年间的平均扩展面积也最大,远超过其他区域,随着时间推移,东部城市发展趋于完善,尤其是在2005年国家提出集约发展的理念后,城市扩展速度逐步降低,向形成辐射作用大的城市群的方向转变。中国西部大开发战略从2000年开始实施,2000-2010年为奠定基础阶段,2010-2030年为加速发展阶段。研究结果表明,2000年以来,西部省会城市扩展速度逐步上升,2010-2015年的扩展速度最大,与西部大开发战略阶段相符合。自2004年提出中部崛起战略以来,具有政策和资源优势的中部地区省会城市率先受益,2005-2010年扩展速度迅速提升,达到一个高点,但在2010年到2015年又有所减缓,只有武汉一直保持着较高速度的发展。振兴东北战略也是在2004年提出并开始实施,2005年后,东北三省省会城市面积扩展速度不断提升,侧面反映了振兴东北战略实施的效果。

5 结论与建议

本文利用高分辨率遥感影像采集了2000年、2005年、2010年和2015年4期全国31个省会城市市辖区范围内的城市区域边界,与同类研究成果进行了比较,证明了本研究在方法、精度、可靠性等方面的优势,并对城市空间扩展所引起的城区面积的时空变化进行了分析,结果表明:

(1)2000-2015年,全国省会城市整体扩展迅猛,呈指数级的增长趋势。2000-2015年间,省会城市扩展后面积相当于2000年省会城市区域面积的1.90倍。

(2)中国省会城市规模分布接近捷夫分布模式,并随着时间推移趋于均衡。

(3)2000-2015年,全国省会城市扩展速度整体呈现先上升后下降的趋势,西部、东北地区扩展速度逐步上升,东部地区扩展速度逐渐放缓,中部地区稳步扩张。

(4)北京、上海、天津、广州城市区域面积在2000-2015年始终占据前4。2015年,重庆进入前5,与第一批确定的5个国家中心城市保持高度一致。

在研究中国省会城市规模分布和扩展情况的基础上,本文针对中国省会城市发展和规划,提出以下建议:① 坚持西部大开发和振兴东北战略,持续推动西部和东北地区城镇化发展;② 加快推进中部崛起战略实施,实现中部城市的快速发展;③ 控制东部特大城市规模,保证城市发展的健康可持续;④ 未来城市发展要以大城市为依托,重点发展中小城市,逐步形成辐射作用大的城市群,促进中国城市的协调发展。

本研究只从成果比较、城市规模分布和扩展面积3个方面进行了分析,下一步将结合经济、人口、土地利用等专题数据开展城市扩展协调性、用地效率、空间形态变化、占用土地类型等方面的综合分析,发掘更多的中国城市发展规律。

The authors have declared that no competing interests exist.

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本文选取哈尔滨市城乡结合部为研究区,以1984年、1993年、2002年和2010年的TM影像数据为数据源,利用混合像元分解方法提取了哈尔滨市城乡结合部的不透水面信息。分析了不透水面的时空变化特征。选取坡度、坡向、DEM、距河流距离、距高速公路距离、距铁路距离、距主要路距离和距1984年城区距离8个因子,利用增强回归树法进一步分析了1984-2010年哈尔滨市城乡结合部不透水面扩张的主要影响因素。结果表明:1984年哈尔滨市城乡结合部不透水面所占比例为3.9%、1993年为6.6%、2002年为9.0%、2010年为16.52%。高速公路、铁路、主要路等交通要素带动周边地区的发展,从而导致道路沿线城市扩张速度较快。
DOI:10.11821/dlxb201701009     
[22] Li X, Gong P, Liang L.A 30-year (1984-2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of Environment, 2015, 166(1): 78-90.
61An annual sequence of urban land has been produced in Beijing over a 30-year period.61Many Landsat images have been employed to make full use of their temporal contexts.61A temporal consistency check was conducted to make the sequence more reasonable.61The growth rates are different in Beijing during the past three decades.
DOI:10.1016/j.rse.2015.06.007      [本文引用:2]
[23] Zhang Tian, Wang Yanglin, Liu Yanxu, et al.Multi-temporal detection of landscape evolution in western Shenzhen City during 1987-2015. Acta Geographica Sinica, 2016, 71(12): 2170-2184.
[本文引用:2]
[张甜, 王仰麟, 刘焱序, . 1987-2015年深圳市主城区景观演变过程多时相识别. 地理学报, 2016, 71(12): 2170-2184.]
城市扩张具有典型的阶段性特征,城市化初期的不透水面快速蔓延和中后期的生态环境约束往往呈现非线性关系。基于多时相数据对典型城市发展中关键指标的变化拐点进行识别,将有助于认识城市景观演变的时间节点,理解城市化的生态响应过程。本文选用深圳市西部1987-2015年长时序Landsat影像共27期,逐年提取新构建的归一化裸露指数(MNDBI*)与归一化植被指数(NDVI*),从而在时间上寻找城市增长的转折点,在空间上识别不同空间位置的景观演变特征。研究结果表明,1987-2015年深圳市城市增长十分明显,并以2003年为拐点经历了"快速增长"至"平稳约束"的变化;与此对应,植被指数的关键拐点与城市化拐点基本重合,印证了城市建设用地扩张和生态系统响应的时空关联特征。此外,从空间分异来看,深圳市南部各区的城市化约束期出现相对更早,拐点一般在1995-1998年间;而北部各区的快速城市化时期持续更长,一般在2003-2006年后趋于平稳。在空间上,以市图书馆为中心刻画西、北、西北、东北剖面线方向的指标动态,发现采样中心附近的景观类型变化较小,而更远辐射半径经历了更大幅度的城市化;这说明深圳市南部城市发展较早达到饱和,且近30年的城市扩张以由南向北的放射式蔓延为主。面临城市化带来的生态威胁,相关环保措施的有力实施仅能延缓城市化导致的生境退化步伐,城市扩张所带来的生态破坏依然不容小觑,合理而有力的政策颁布、实施与监管在未来的城市发展中极为必要。
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[26] Friedl M A, Mciver D K, Hodges J C F, et al. Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 2002, 83(1/2): 287-302.
Until recently, advanced very high-resolution radiometer (AVHRR) observations were the only viable source of data for global land cover mapping. While many useful insights have been gained from analyses based on AVHRR data, the availability of moderate resolution imaging spectroradiometer (MODIS) data with greatly improved spectral, spatial, geometric, and radiometric attributes provides significant new opportunities and challenges for remote sensing-based land cover mapping research. In this paper, we describe the algorithms and databases being used to produce the MODIS global land cover product. This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP. To generate these maps, a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data. In addition to the IGBP class at each pixel, the MODIS land cover product provides several other parameters including estimates for the classification confidence associated with the IGBP label, a prediction for the most likely alternative class, and class labels for several other classification schemes that are used by the global modeling community. Initial results based on 5 months of MODIS data are encouraging. At global scales, the distribution of vegetation and land cover types is qualitatively realistic. At regional scales, comparisons among heritage AVHRR products, Landsat TM data, and results from MODIS show that the algorithm is performing well. As a longer time series of data is added to the processing stream and the representation of global land cover in the site database is refined, the quality of the MODIS land cover product will improve accordingly.
DOI:10.1016/S0034-4257(02)00078-0      [本文引用:2]
[27] Wan B, Guo Q, Fang F, et al.Mapping US urban extents from MODIS data using one-class classification method. Remote Sensing, 2015, 7(8): 10143-10163.
Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.
DOI:10.3390/rs70810143      [本文引用:2]
[28] Bartholomé E, Belward A S.GLC2000: A new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 2005, 26(9): 1959-1977.
A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre. The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on‐board SPOT 4, though mapping of some regions involved use of data from other Earth observing sensors to resolve specific issues. Detailed legend definition, image classification and map quality assurance were carried out region by region. The global product was made through aggregation of these. The database is designed to serve users from science programmes, policy makers, environmental convention secretariats, non‐governmental organizations and development‐aid projects. The regional and global data are available free of charge for all non‐commercial applications from http://www.gvm.jrc.it/glc2000.
DOI:10.1080/01431160412331291297      [本文引用:2]
[29] Arnell N W, Brown S, Gosling S N, et al.The impacts of climate change across the globe: A multi-sectoral assessment. Climatic Change, 2016, 134(3): 457-474.
The overall global-scale consequences of climate change are dependent on the distribution of impacts across regions, and there are multiple dimensions to these impacts. This paper presents a global...
DOI:10.1007/s10584-014-1281-2      [本文引用:2]
[30] Mayaux P, Eva H, Gallego J, et al.Validation of the global land cover 2000 map. IEEE Transactions on Geoscience & Remote Sensing, 2006, 44(7): 1728-1739.
The Joint Research Centre of the European Commission (JRC), in partnership with 30 institutions, has produced a global land cover map for the year 2000, the GLC 2000 map. The validation of the GLC2000 product has now been completed. The accuracy assessment relied on two methods: a confidence-building method (quality control based on a comparison with ancillary data) and a quantitative accuracy assessment based on a stratified random sampling of reference data. The sample site stratification used an underlying grid of Landsat data and was based on the proportion of priority land cover classes and on the landscape complexity. A total of 1265 sample sites have been interpreted. The first results indicate an overall accuracy of 68.6%. The GLC2000 validation exercise has provided important experiences. The design-based inference conforms to the CEOS Cal-Val recommendations and has proven to be successful. Both the GLC2000 legend development and reference data interpretations used the FAO Land Cover Classification System (LCCS). Problems in the validation process were identified for areas with heterogeneous land cover. This issue appears in both in the GLC2000 (neighborhood pixel variations) and in the reference data (cartographic and thematic mixed units). Another interesting outcome of the GLC2000 validation is the accuracy reporting. Error statistics are provided from both the producer and user perspective and incorporates measures of thematic similarity between land cover classes derived from LCCS
DOI:10.1109/TGRS.2006.864370      [本文引用:2]
[31] Bicheron P, Defourny P, Brockmann C, et al.GLOBCOVER: Products description and validation report. Foro Mundial De La Salud, 2011, 17(3): 285-287.
[本文引用:2]
[32] Bagan H, Yamagata Y.Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote Sensing of Environment, 2012, 127: 210-222.
78 Remote sensing data and census data were integrated in grid cells each with an area of 1 km2. 78 Urban expansion has strong correlation with population changes and cropland changes. 78 Population and urban/built-up area decreased in the city core during 1972–2011.
DOI:10.1016/j.rse.2012.09.011      [本文引用:2]
[33] Guindon B, Zhang Y, Dillabaugh C.Landsat urban mapping based on a combined spectral-spatial methodology. Remote Sensing of Environment, 2004, 92(2): 218-232.
Urban mapping using Landsat Thematic Mapper (TM) imagery presents numerous challenges. These include spectral mixing of diverse land cover components within pixels, spectral confusion with other land cover features such as fallow agricultural fields and the fact that urban classes of interest are of the land use and not the land cover category. A new methodology to address these issues is proposed. This approach involves, as a first step, the generation of two independent but rudimentary land cover products, one spectral-based at the pixel level and the other segment-based. These classifications are then merged through a rule-based approach to generate a final product with enhanced land use classes and accuracy. A comprehensive evaluation of derived products of Ottawa, Calgary and cities in southwestern Ontario is presented based on conventional ground reference data as well as inter-classification consistency analyses. Producer accuracies of 78% and 73% have been achieved for urban ‘residential’ and ‘commercial/industrial’ classes, respectively. The capability of Landsat TM to detect low density residential areas is assessed based on dwelling and population data derived from aerial photography and the 2001 Canadian census. For low population densities (i.e. below 3000 persons/km 2), density is observed to be monotonically related to the fraction of pixels labeled ‘residential’. At higher densities, the fraction of pixels labeled ‘residential’ remains constant due to Landsat's inability to distinguish between high-rise apartment dwellings and commercial/industrial structures.
DOI:10.1016/j.rse.2004.06.015      [本文引用:2]
[34] Gao F, De Colstoun E B, Ma R, et al. Mapping impervious surface expansion using medium-resolution satellite image time series: A case study in the Yangtze River Delta, China. International Journal of Remote Sensing, 2012, 33(24): 7609-7628.
Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China razil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived from this study agree well with an existing urban extent polygon data set that was previously developed independently. The overall mapping accuracy was estimated at about 92.5% with 3% commission error and 12% omission error for the impervious type from all images regardless of image quality and initial spatial resolution.
DOI:10.1080/01431161.2012.700424      [本文引用:2]
[35] Sun Z, Wang C, Guo H, et al.A modified normalized difference impervious surface index (MNDISI) for automatic urban mapping from landsat imagery. Remote Sensing, 2017, 9(9): 942.
Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales.
DOI:10.3390/rs9090942      [本文引用:2]
[36] Son N T, Chen C R.Urban growth mapping from Landsat data using linear mixture model in Ho Chi Minh City, Vietnam. Journal of Applied Remote Sensing, 2012, 6(1): 100-106.
Rapid urbanization in Ho Chi Minh City (HCMC), Vietnam, is creating societal impacts on the environment attributed to the increasing population. Understanding spatio-temporal dimensions of land-use changes that shape the urbanization is thus critical to the process of urban planning. We explore the urban growth in HCMC through Landsat images for 1990, 2002, and 2010 using the linear mixture model (LMM). The data are processed through four steps: (1) data pre-processing, (2) image classification by LMM using endmembers extracted from the original image using minimum noise fraction, (3) accuracy assessment of the classification results using field verification data, and (4) urban growth analysis to understand the spatial changes of land cover. The results achieved by comparisons between the classification results and ground reference data indicate that the overall accuracy and Kappa coefficient obtained for 1990 were 87.1% and 0.83, respectively, while those for 2002 were 92.5% and 0.89, and those for 2010 were 89.6% and 0.86. The results of urban growth analysis indicate that high albedo class (i.e., built-up areas) expanded from 12.3% in 1990 to 27.2% in 2002 and to 31.1% in 2010. When investigating land-cover conversions to high albedo class from 1990 to 2002, the largest conversion is observed for soil class (9.2%), followed by vegetation class (7.2%), and low albedo class (2.2%). From 2002 to 2010, 4.5% area of soil class was converted to high albedo class, while conversions from vegetation and low albedo classes were 3.5% and 2.5%, respectively.
DOI:10.1117/1.JRS.6.063543      [本文引用:2]
[37] Angiuli E, Trianni G.Urban mapping in Landsat images based on normalized difference spectral vector. IEEE Geoscience & Remote Sensing Letters, 2014, 11(3): 661-665.
In the last decades the number of natural and anthropic changes affecting population worldwide has raised dramatically. This fact, coupled with the increasing world population living in urban areas, requires the development of a detailed and reliable map of global urban extent. This letter reports on a new approach for urban mapping from Landsat images, based on the Normalized Difference Spectral Vector (NDSV). This spectral transformation allows the creation of a normalized signature that becomes peculiar of each land cover class within the scene. The urban extent classification is obtained by analyzing the NDSV data in conjunction with a Spectral Angle Mapper (SAM) based classifier. The experiments presented in this letter show the effectiveness of the proposed technique in detecting urban areas in extremely different environments. The results of the proposed methodology have been compared with the ones obtained by classifying the NDSV using other classifiers [namely, maximum likehood (ML) and support vector machines (SVM)], and also to the results obtained by classifying the calibrated data using the ML, SVM and SAM classifiers. The NDSV+SAM approach has provided the best results, with an overall accuracy of 97%.
DOI:10.1109/LGRS.2013.2274327      [本文引用:1]
[38] Wang Lei, Li Congcong, Ying Qing, et al.China's urban expansion from 1990 to 2010 determined with satellite remote sensing. Chinese Science Bulletin, 2012, 57(22): 2802-2812.
Based on the same data source of Landsat TM/ETM+ in 1990s, 2000s and 2010s, all urban built-up areas in China are mapped mainly by human interpretation. Mapping results were checked and refined by the same analyst with the same set of criteria. The results show during the last 20 years urban areas in China have increased exponentially more than 2 times. The greatest area of urbanization changed from Northeastern provinces in 1990s to the Southeast coast of China in Jiangsu, Guangdong, Shandong, and Zhejiang in 2010s. Urban areas are mostly converted from croplands in China. Approximately 17750 km croplands were converted into urban lands. Furthermore, the conversion from 2000 to 2010 doubled that from 1990 to 2000. During the 20 years, the most urbanized provinces are Jiangsu, Guangdong, Shandong and Zhejiang. We also analyzed built-up areas, gross domestic production (GDP) and population of 147 cities with a population of greater than 500000 in 2009. The result shows coastal cities and resource-based cities are with high economic efficiency per unit of built-up areas, resource-based cities have the highest population density, and the economic efficiency of most coastal provinces are lower than central provinces and Guangdong. The newly created urban expansion dataset is useful in many fields including trend analysis of urbanization in China; simulation of urban development dynamics; analysis of the relationship among urbanization, population growth and migration; studies of carbon emissions and climate change; adaptation of climate change; as well as land use and urban planning and management.
DOI:10.1007/s11434-012-5235-7      [本文引用:5]
[39] Chen Hong, Tao Chao, Zhou Zhengrong, et al.Automatic urban area extraction using a gabor filter and high-resolution remote sensing imagery. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1063-1067.
[本文引用:1]
[陈洪, 陶超, 邹峥嵘, . 一种新的高分辨率遥感影像城区提取方法. 武汉大学学报(信息科学版), 2013, 38(9): 1063-1067.]
利用城区特有的局部特征,提出了一种新的高分辨率遥感影像城区提取方法.该方法首先对原图像做多角度和多频率组合的Gabor变换,然后利用Ostu方法阈值分割所有变换结果,并在每个中心频率上对各个方向的阈值分割图做逻辑与运算,其次根据运算结果图上Gabor特征的分布情况,确定适合影像的最优中心频率,最后利用滤波器在最优中心频率上的特征提取结果,结合高斯函数构建空间投票矩阵,最终提取城市区域.实验表明,该方法可以成功地提取高分辨率影像城市区域,且具有较高的准确度.
[40] Tao Chao, Tan Yihua, Cai Huajie, et al.Object-oriented method of hierarchical urban building extraction from high-resolution remote-sensing imagery. Acta Geodaetica et Cartographica Sinica, 2010, 39(1): 39-45.
[本文引用:1]
[陶超, 谭毅华, 蔡华杰, . 面向对象的高分辨率遥感影像城区建筑物分级提取方法. 测绘学报, 2010, 39(1): 39-45.]
提出一种高空间分辨率遥感影像城区建筑物自动提取方法。该方法将面向对象的思想融入到基于邻域总变分的建筑物分割方法中,并通过分析分割后不同类型建筑物提取的难易程度,提出一种多特征融合的建筑物对象分级提取策略:首先通过形状分析检测一部分分割完整的矩形建筑物目标,然后采用新提出的多方向形态学道路滤波算法将建筑物与邻近光谱相似的道路目标分离,确保每一个候选建筑物目标都是独立的对象,最后利用初提取的建筑物对象和已剔除的非建筑物对象作为样本建立概率模型,根据贝叶斯准则进行建筑物后提取。实验表明:该方法可以检测同一幅影像中具有不同形状结构和光谱特性的建筑物目标,准确率高、鲁棒性好。
[41] Lin Xiangguo, Ning Xiaogang.Extraction of human settlements from high resolution remote sensing imagery by fusing features of right angle corners and right angle sides. Acta Geodaetica et Cartographica Sinica, 2017, 46(1): 83-89.
[本文引用:1]
[林祥国, 宁晓刚. 融合直角点和直角边特征的高分辨率遥感影像居民点提取方法. 测绘学报, 2017, 46(1): 83-89.]
提出了一种融合直角点和直角边两种特征的高分辨率遥感影像居民点提取方法:首先,分别检测高分辨率遥感影像的角点和直线段,通过两种特征交叉验证确定直角点和直角边,并对二者进行栅格化;然后,基于局部直角点和直角边点的密度和距离特征生成居民点指数图像;最后,通过指数图像二值化、栅格转矢量、剔除小图斑等操作确定居民点多边形。使用3景影像进行了试验。试验结果表明,本文方法提高了居民点提取精度,其正确率、完整率、质量等3个指标的平均值比已有方法的相关值分别高6.76%、10.12%、12.14%。
[42] Ni Huan, Lin Xiangguo, Ning Xiaogang.A method for extracting human settlements from remote sensing image using right angle corners features. Geomatics and Information Science of Wuhan University, 2017, 42(5): 648-655.
[本文引用:1]
[倪欢, 林祥国, 宁晓刚. 直角点特征引导的遥感影像居民地提取方法. 武汉大学学报(信息科学版), 2017, 42(5): 648-655.]
提出了一种基于直角点统计特征的高分辨率遥感全色图像居民地提取方法。首先,分别检测全色图像中的直角点、直线段;然后,利用直角点邻边的长度约束进一步优化直角点;接着,通过构建直角点缓冲区投影的方式生成特征图像;最后,二值化特征图像提取居民地信息。实验结果表明,与角点、直线以及未限制邻边长度直角点的统计特征对比,邻边长度约束的直角点统计特征更能反映居民地信息,有效克服了同样具有较多角点与直线特征的道路、车辆、农田田埂等地物对居民地提取的负面影响。与已有的PanTex指数方法相比,所提方法不依赖于纹理的复杂程度,有效削弱了纹理复杂的非居民地区域对居民地提取的负面影响。
[43] Zhou Y, Smith S J, Zhao K, et al.A global map of urban extent from nightlights. Environmental Research Letters, 2015, 10(5): 054011.
Urbanization, a major driver of global change, profoundly impacts our physical and social world, for example, altering not just water and carbon cycling, biodiversity, and climate, but also demography, public health, and economy. Understanding these consequences for better scientific insights and effective decision-making unarguably requires accurate information on urban extent and its spatial distributions. We developed a method to map the urban extent from the defense meteorological satellite program/operational linescan system nighttime stable-light data at the global level and created a new global 1 km urban extent map for the year 2000. Our map shows that globally, urban is about 0.5% of total land area but ranges widely at the regional level, from 0.1% in Oceania to 2.3% in Europe. At the country level, urbanized land varies from about 0.01 to 10%, but is lower than 1% for most (70%) countries. Urbanization follows land mass distribution, as anticipated, with the highest concentration between 30 N and 45 N latitude and the largest longitudinal peak around 80 W. Based on a sensitivity analysis and comparison with other global urban area products, we found that our global product of urban areas provides a reliable estimate of global urban areas and offers the potential for producing a time-series of urban area maps for temporal dynamics analyses.
DOI:10.1088/1748-9326/10/5/054011      [本文引用:2]
[44] Huang X, Schneider A, Friedl M A.Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights. Remote Sensing of Environment, 2016, 175: 92-108.
61We estimated sub-pixel urban cover at 250m resolution in China for 2001 and 2010.61We fused 250m, 500m, and 1km MODIS data and DMSP/OLS nighttime lights data.61Separate regression models estimated for temperate and subtropical regions of China61City-level assessment showed good agreement with Landsat-based urban information.61Regional mapping demonstrated utility of this method for large-area application.
DOI:10.1016/j.rse.2015.12.042      [本文引用:1]
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A Tresholding technique was used to convert a prototype “city lights” data set from the National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAAINGDC) into a map of “urban areas” for the continental United States. Thresholding was required to adapt the Defense Meteorological Satellite Program's Operational Linescan System (DMSPIOLS)-based NGDC data set into an urban map because the values reported in the prototype represent a cumulative percentage lighted for each pixel extracted from hundreds of nighttime cloud screened orbits, rather than any suitable land-cover classification. The cumulative percentage lighted data could not be used alone because the very high gain of the OLS nighttime photomultiplier configuration can. lead to a pixel (2.7X2.7 km)
DOI:10.1016/S0034-4257(97)00046-1      [本文引用:1]
[46] Li Jingye, Gong Jian, Yang Jianxin, et al.Urban spatial pattern evolution of Wuhan City based on nighttime light. Remote Sensing Information, 2017, 32(3): 133-141.
[本文引用:2]
[李靖业, 龚健, 杨建新, . 利用夜间灯光数据的武汉城市空间格局演化. 遥感信息, 2017, 32(3): 133-141.]
鉴于夜间灯光数据在市级尺度精度不足等问题,提出了一种基于NDVI修正的阈值提取法。在此基础上,采用空间扩展模式分析、景观格局指数、重心迁移模型方法,系统性分析近16年武汉市城市扩张时空特征,以期为武汉未来城镇建设用地布局及相关政策制定提供决策依据。研究结果表明,基于NDVI修正的阈值提取法,可用于地级市建成区提取及城市扩展研究;武汉城市扩张模式表现为以主城区为中心的面状发展为主,点状城镇发展所占比例相对较小,主要受黄陂区、新洲区的牵引;城镇发展经历了高度破碎化时期之后不断趋于集约化发展,破碎度逐步减小;武汉建成区重心移动幅度越来越大,城市化过程总体处于较快发展状态。
[47] Asiyemu Tuerdi, Alimujiang Kasimu.Analysis of urban expansion in Kuitun City based on multi-source remote sensing data. Research of Soil and Water Conservation, 2013, 20(2): 233-237.
[本文引用:1]
[阿斯耶姆·图尔迪, 阿里木江·卡斯木. 基于多源遥感数据的奎屯市城市扩展分析. 水土保持研究, 2013, 20(2): 233-237.]
[48] Wang Tingting, Wang Yundong, Yang Qiang, et al. Urban expansion and its driving force for Putian City from 1988 to 2014. Remote Sensing Information, 2015(6): 111-115.
[本文引用:2]
[王婷婷, 王运动, 杨强, . 1988年-2014年莆田市城市扩展及其驱动力分析. 遥感信息, 2015(6): 111-115.]
鉴于城市扩张及其驱动力分析在协调城市扩张与土地资源保护的矛盾、促进城市可持续发展中的重要作用,该文利用多时相遥感数据和莆田市社会经济统计数据,结合野外调查,采用室内解译方法来获取1988年~2014年莆田城市扩张数据,以分析不同时期莆田各城区扩张的面积、速度、方向主要驱动力。1988年~2002年莆田市建成区扩张面积快速增长,而2002年以后增长速度明显减小;仙游县、梧塘镇、大济镇和度尾镇建成区扩张整体呈现出"增-减"趋势,其余城镇建成区扩张则呈现"减-增"趋势。同时,人口增长、经济发展和社会投资是莆田城市扩张发展的主要驱动力,其中,GDP增长是最主要驱动力。该研究有效地揭示了莆田城市扩张的发展规律,为该地区城市规划与管理提供参考与借鉴。
[49] Susaki J, Kajimoto M, Kishimoto M.Urban density mapping of global megacities from polarimetric SAR images. Remote Sensing of Environment, 2014, 155: 334-348.
61We estimated urban areas and density from a single polarimetric SAR image.61We calculated statistics from images to reduce orientation angle effects.61The estimated urban density has a high correlation with building-to-land ratio.61We compared the urban density patterns of global megacities.61Analysis using urban density maps indicates the patterns of urban development.
DOI:10.1016/j.rse.2014.09.006      [本文引用:1]
[50] Xiao Lin, Tian Guangjin.Study on spatial modes and driving mechanisms of Tianjin's urban expansion. Resources Science, 2014, 36(7): 1327-1335.
[本文引用:1]
[肖琳, 田光进. 天津城市扩展空间模式与驱动机制研究. 资源科学, 2014, 36(7): 1327-1335.]
首先引入定量识别城市扩展模式的城市扩展面积指数,结合象限方位法和缓冲带法分析空间扩展特征;其次在栅格尺度构建空间Logistic回归模型,研究不同时间序列城市增长驱动机制。结果表明:①1990-1995年天津城市扩展以主城区填充式、环城四区外延式为主,填充式和外延式主要分布在Ⅲ、Ⅶ、Ⅷ象限与16、56、104km缓冲带,距公路较近易发生扩展,道路沿线的城市扩展空间辐射范围广;②1995-2000年天津环城四区、塘沽、大港少量填充式扩展,集中在Ⅶ、Ⅷ象限与96~112km缓冲带,城市扩展与到城市用地距离显著负相关,主城区高密度建设用地抑制环城四区城市扩展;③2000-2005年天津城市扩展活跃,以主城区填充式、环城四区外延式、塘沽和大港"卫星城"式为主,主要分布在Ⅲ、Ⅶ、Ⅷ象限,填充式和外延式集中在8~24km缓冲带而"卫星城"式集中在16~56km缓冲带,城市扩展与到公路距离有最显著负相关性,道路建设是推动天津主城区、环城四区、武清及静海城市扩展主要驱动力,其次距城市用地较近易发生城市扩展,塘沽与大港分散的城市用地带动周边城市扩展。以上结果反映出1990-2005年天津城市扩展时空分异显著,西北-东南是城市空间扩展主要走向;城市扩展影响因素多元化,公路建设是主导驱动因子,交通区位优越地区最易发生城市扩展。
[51] Gong P.Settlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm. International Journal of Remote Sensing, 2010, 31(6): 1411-1426.
In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30–40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM65+65) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30–32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85.
DOI:10.1080/01431160903475332      [本文引用:1]
[52] Jie Y, Yin Z, Zhong H, et al.Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979-2009) in China. Environmental Monitoring & Assessment, 2011, 177(1-4): 609-621.
This study explored the spatio-temporal dynamics and evolution of land use/cover changes and urban expansion in Shanghai metropolitan area, China, during the transitional economy period (1979 2009) using multi-temporal satellite images and geographic information systems (GIS). A maximum likelihood supervised classification algorithm was employed to extract information from four landsat images, with the post-classification change detection technique and GIS-based spatial analysis methods used to detect land-use and land-cover (LULC) changes. The overall Kappa indices of land use/cover change maps ranged from 0.79 to 0.89. Results indicated that urbanization has accelerated at an unprecedented scale and rate during the study period, leading to a considerable reduction in the area of farmland and green land. Findings further revealed that water bodies and bare land increased, obviously due to large-scale coastal development after 2000. The direction of urban expansion was along a north-south axis from 1979 to 2000, but after 2000 this growth changed to spread from both the existing urban area and along transport routes in all directions. Urban expansion and subsequent LULC changes in Shanghai have largely been driven by policy reform, population growth, and economic development. Rapid urban expansion through clearing of vegetation has led to a wide range of eco-environmental degradation.
DOI:10.1007/s10661-010-1660-8      PMID:20824336      [本文引用:2]
[53] Dai X, Guo Z, Zhang L, et al.Spatio-temporal pattern of urban land cover evolvement with urban renewal and expansion in Shanghai based on mixed-pixel classification for remote sensing imagery. International Journal of Remote Sensing, 2010, 31(23): 6095-6114.
Research into pixel unmixing in remote sensing imagery led to the development of soft classification methods. In this article, we propose a possibilistic c repulsive medoids (PCRMdd) clustering algorithm which attempts to find c repulsive medoids as a minimal solution of a particular objective function. The PCRMdd algorithm is applied to predict the proportion of each land use class within a single pixel, and generate a set of endmember fraction images. The clustering results obtained on multi-temporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) images of Shanghai city in China reveal the spatio-temporal pattern of Shanghai land use evolvement and urban land spatial sprawl in course of urbanization from 1989 to 2002. The spatial pattern of land use transformation with urban renewal and expansion indicates the urban land use structure is gradually optimized during vigorous urban renewal and large-scale development of Pudong area, which will have an active influence on improving urban space landscape and enhancing the quality of the ecological environment. In addition, accuracy analysis demonstrates that PCRMdd represents a robust and effective tool for mixed-pixel classification on remote sensing imagery to obtain reliable soft classification results and endmember spectral information in a noisy environment.
DOI:10.1080/01431160903376407      [本文引用:1]
[54] Alimujiang Kasimu, Tang Bing, Gulikezi Tulake.Analysis of the spatial-temporal dynamic changes of urban expansion in oasis of Xinjiang based on RS and GIS. Journal of Blaciology and Geocryology, 2013, 35(4): 1056-1064.
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[阿里木江·卡斯木, 唐兵, 古丽克孜·吐拉克. 基于遥感和GIS的新疆绿洲城市扩展时空动态变化分析. 冰川冻土, 2013, 35(4): 1056-1064.]
[55] Mao Weihua, Hu Deyong, Cao Ran, et al.Monitoring urban expansion of Zhejiang Province using MODIS/EVI data products and DMSP/OLS nighttime light data. Geographical Research, 2013, 32(7): 1325-1335.
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[毛卫华, 胡德勇, 曹冉, . 利用MODIS产品和DMSP/OLS夜间灯光数据监测城市扩张. 地理研究, 2013, 32(7): 1325-1335.]
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[57] Yu Yongjun, Lu Yuqi.Studies on centrelity of provincial capitals. Economic Geography, 2005, 25(3): 352-357.
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[俞勇军, 陆玉麒. 省会城市中心性研究. 经济地理, 2005, 25(3): 352-357.]
城市中心性的研究内容分为两部分,一是中心性指数,另一为中心性程度。省会城市中心性的影响因素包括省区形状、省会位置、省会综合实力、省内地级市实力以及省内交通结构等。文章提出了更为简易的省区形状和省会位置的计量公式,计算了我国各省的形状指数和省会城市位置指数。利用GIS软件,计算出我国各省区经济重心、人口重心、几何重心,并根据这些重心与省会城市位置间的关系,对我国各省最高级中心城市的组合类型进行了探讨。提出中心性程度的概念并探讨了计算方法。文章将影响因素及中心性程度综合考察,对我国部分省会城市的中心性作用不强的原因进行了剖析。其结论对全国省级城市经济区的组织和行政区划调整有一定的借鉴意义。
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[59] Lu D, Weng Q.A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 2007, 28(5): 823-870.
Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non‐parametric classifiers such as neural network, decision tree classifier, and knowledge‐based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image‐processing chain to improve classification accuracy.
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[60] Gan L, Li D, Song S.Is the Zipf law spurious in explaining city-size distributions? Economics Letters, 2006, 92(2): 256-262.
The Zipf law, which states that that the rank associated with some size S is proportional to S to some negative power, is a regularity observed in natural and social sciences. One popular application of the Zipf law is the relationship between city sizes and their ranks. This paper examines the rank ize relationship through Monte Carlo simulations and two examples. We show that a good fit (indicated by a high R2 value) can be found for many statistical distributions. The Zipf law's good fit is a statistical phenomenon, and therefore, it does not require an economic theory that determines city-size distributions.
DOI:10.1016/j.econlet.2006.03.004      [本文引用:1]
[61] Anderson G, Ge Y.The size distribution of Chinese cities. Regional Science & Urban Economics, 2005, 35(6): 756-776.
This paper uses urban data to investigate two important issues regarding city sizes in China, the relative growth of cities and the nature of the city size distribution. The manner in which cities of different sizes grow relative to each other is examined and, contrary to the common empirical finding that the relative size and rank of cities remains stable over time, it is found that the Economic Reforms and the One Child Policy since 1979 have delivered significant structural change in the Chinese urban system. The city size distribution remains stable before the reforms but exhibits a convergent growth pattern in the post-reform period. The theoretical literature on city sizes highlights a link between log normal and Pareto distributions for city sizes prompting the employment of Pearson goodness-of-fit tests to examine directly which theoretical distribution provides the best approximation to the empirical city size distribution. Contrary to the evidence for other countries, a log normal rather than Pareto specification turns out to be the preferred distribution.
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[62] Gangopadhyay K, Basu B.City size distributions for India and China. Physica A Statistical Mechanics & Its Applications, 2009, 388(13): 2682-2688.
This paper studies the size distributions of urban agglomerations for India and China. We have estimated the scaling exponent for Zipf’s law with the Indian census data for the years of 1981–2001 and the Chinese census data for 1990 and 2000. Along with the biased linear fit estimate, the maximum likelihood estimate for the Pareto and Tsallis q-exponential distribution has been computed. For India, the scaling exponent is in the range of [1.88, 2.06] and for China, it is in the interval [1.82, 2.29]. The goodness-of-fit tests of the estimated distributions are performed using the Kolmogorov–Smirnov statistic.
DOI:10.1016/j.physa.2009.03.019      [本文引用:1]
[63] Zhou Xiaoyan, Han Liyuan, Ye Xinyue, et al.Change of city size distribution in China from 2000-2012. Journal of Central China Normal University (Natural Sciences), 2015, 49(1): 132-138.
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[周晓艳, 韩丽媛, 叶信岳, . 基于位序规模法则的我国城市用地规模分布变化研究(2000-2012年). 华中师范大学学报(自然科学版), 2015, 49(1): 132-138.]
我国已进入快速城市化时期,城市用地迅速增长,研究我国城市用地规模分布变化规律有助于在城市化进程中合理配置城市用地,促进城市体系协调发展.选择2000年~2012年我国地级及以上城市的建成区面积数据,利用位序-规模法则结合分形理论,研究我国城市用地规模分布的变化趋势并分析不同区域城市用地规模分布变化的差异性.研究结果表明:(1)以建成区面积衡量城市规模,我国城市规模分布符合位序-规模法则;(2)城市规模的位序-规模对数曲线呈平行向前推进趋势,城市用地规模总量增加,但城市用地规模分布Zipf指数呈上升趋势,城市用地规模分布的均衡度下降;(3)按建成区面积分类,超大、大城市的城市用地增长速度快于中小城市;(4)不同区域城市用地规模分布变化特征反映出差异性:4大区域中东部地区城市用地规模Zipf指数始终最接近理想值1,表明东部地区高中低位次城市用地发展较为协调,东北地区以及西部地区城市用地规模分布趋向于集中,高位次城市用地扩张明显快于中低位次城市,中部地区城市用地规模分布较为分散,但近几年高位次城市用地规模扩张趋势明显增加.
[64] Wu Zongqing, Dai Xuezhen, Yang Wuyang.On reconstruction of Parato formula and its relationship with development of urban system. Human Geography, 2000, 15(1): 15-19.
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[仵宗卿, 戴学珍, 杨吾扬. 帕雷托公式重构及其与城市体系演化. 人文地理, 2000, 15(1): 15-19.]
本文首次提出并论证了帕雷托公式关于城市体系规模结构“均衡度”和“结构容量”两个指数 ,并以此对传统的帕雷托公式进行重新构建。重构后的帕雷托公式不仅成为描述城市体系规模分布的一般性模式 ,还全面展示了城市体系的各种演化状态 ,使其应用范围得以扩展。最后本文成功地运用重构后的帕雷托公式对“八五”期间我国各大区城市体系的演化状态作了全方位的应用性研究
[65] Tan Minghong, Fan Cunhui.Relationship between Zipf dimension and fractal dimension of city-size distribution. Geographical Research, 2004, 23(2): 243-248.
[本文引用:0]
[谈明洪, 范存会. Zipf维数和城市规模分布的分维值的关系探讨. 地理研究, 2004, 23(2): 243-248.]
城市位序 规模理论和分形理论是研究城市系统的重要基础。前者可以较好地刻画城市的规模分布 ,后者可用来深入地解释城市规模的分布规律。其中 ,城市规模分布的分维值和Zipf维数是这两个基础理论中的重要参数。在研究我国城市规模的分布规律时 ,理论上可认为分维值和Zipf维数的乘积等于 1。但本文认为这种理论上的关系并不能直接套用到统计分析中去 ,如果城市规模分布的分维值和Zipf维数是利用对于样本的OLS (最小二乘法 )估计所得 ,两者的乘积应等于判定系数 (R2 )。最后我们对此结果进行了推导和证明 ,并对其所具有的理论意义和实践价值进行了简要阐述。
[66] Tan Minghong, Lu Changhe.Distribution of China city size expressed by urban built-up area. Acta Geographica Sinica, 2003, 58(2): 285-293.
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[谈明洪, 吕昌河. 以建成区面积表征的中国城市规模分布. 地理学报, 2003, 58(2): 285-293.]
选择1990~2000年中国城市用地面积位于前200位的地级及地级以上的城市用地资料,把运用在城市人口规模上的位序-规模法则移植到城市用地上,分析了城市土地利用规模的变化规律。然后运用分形理论,阐释了城市用地的位序-规模曲线。结果显示:(1)以建成区面积作为衡量城市规模的指标,中国城市规模分布符合位序-规模法则,拟合曲线的判定系数都在0.95以上;(2)根据位序-规模曲线的形态,中国城市按建成区面积可分为3类:用地面积>200 km2的大城市,50~200 km2的中等城市和<50 km2的小城市;(3)城市建成区用地的位序-规模曲线有平行向前推进的特点,这为预测我国未来城市建成区用地规模提供很好的基础;(4)中国位于前200位的城市用地规模分布的均衡度不断增强,城市建成区用地规模总量持续增加。
[67] An Qian, Li Xiaojian, Lv Kewen.A research on the spatial structure and efficiency of China's expansion of urban built-up area(1990-2009). Economic Geography, 2012, 32(6): 37-45.
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文章使用1990—2009年中国260个地级及地级以上的城市数据,采用位序—规模法则、K值、GIS等方法,分析了中国城市扩张的空间格局及演变特点,并引入K'衡量城市空间扩张的效率。主要结论有:①城市扩张呈现明显的阶段性特点,主因是国家城市化战略的调整。2000年后城市发展趋向不均衡,城市体系发生动荡变化。这种变化应该高度重视。②城市空间规模变大的趋势更加显著,城市类型向更高级别推进,城市区域空间分布极不均衡,城市所在地区差异导致城市扩张进程分化,城市地位也随之变化。③各地区城市扩张速度有显著差异,省级中心城市的扩张前慢后快,城市扩张形成区域性集团。城市扩张与城市人口增长不协调,且有拉大趋势,整体上城市空间扩张应当放缓。④不同阶段,不同类型、不同地区城市扩张的效率有很大差异,扩张效率的高低提示各城市在城市扩张上应选择适宜的发展道路,并制定相应的政策和措施。
[68] Fang Chuanglin.A review of Chinese urban development policy, emerging patterns and future adjustments. Geographical Research, 2014, 33(4): 674-686.
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[方创琳. 中国城市发展方针的演变调整与城市规模新格局. 地理研究, 2014, 33(4): 674-686.]
城市发展方针是指导城市持续健康发展、把握城市发展大局与方向的总体纲领。处在不同发展阶段的城市需要与之相适应的城市发展方针来指导,城市发展的阶段性规律决定了城市发展方针是随着城市发展阶段的变化而调整的,因而不是一成不变的。在对中国近60多年来城市发展总体方针演变过程与指导效果回顾总结的基础上,客观分析了中国现行城市发展方针的局限性,包括现行城市发展方针与国家城市发展的客观现实不相符合,缺少对城市化重点地区"城市群"的基本表述,对大、中、小城市的划分标准不尽合理,现行城市发展方针指导下的城市体系等级规模结构与行政区划不相协调等。最后提出了调整现行城市发展方针的建议方案,重新划分大、中、小城市的规模标准,将中国城市划分为超大城市(市区常住人口规模≥1000万人)、特大城市(介于500万~1000万人)、大城市(介于100万~500万人)、中等城市(介于50万~100万人)、小城市(介于10万~50万人)、小城镇(10万人)共六个规模等级标准;将新形势下中国城市发展方针调整为:引导发展城市群,严格控制超大和特大城市,合理发展大城市,鼓励发展中等城市,积极发展小城市和小城镇,形成城市群与大、中、小城市与小城镇协调发展的国家城市发展新格局。到2020年将形成由20个城市群、10个超大城市、20个特大城市、150个大城市、240个中等城市、350个小城市和19000个小城镇组成的6级国家城市规模结构新体系;重新构建建制市的设市标准,尝试建立民族自治市;鼓励发展小城市和小城镇,把其作为农业人口就近就地市民化的首选地,不断提升城镇化发展质量。
DOI:10.11821/dlyj201404008     
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