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Journal of Geo-information Science >

2019 , Vol. 21 >Issue 1: 59 - 67

DOI: https://doi.org/10.12082/dqxxkx.2019.180361

Spatiotemporal Characteristics of Urbanization in China from the Perspective of Remotely Sensed Big Data of Nighttime Light

  • MA Ting , 1, 2, *
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  • 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author: MA Ting, E-mail:

Received date: 2018-08-05

  Request revised date: 2018-09-14

  Online published: 2019-01-20

Supported by

National Natural Science Foundation of China, No.41590845, 41771418

Copyright

《地球信息科学学报》编辑部 所有
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Abstract

The rapid growth of nation's economy has driven the unprecedented pace of urbanization in China over the past several decades. Urbanization process is a complicated geographical phenomenon involving human-nature interactions, such as population aggregation, land use change, infrastructure construction and eco-environmental changes. Hence, an understanding of the spatiotemporal dynamics of urban development is increasingly important for a variety of issues including research, planning, management and policy decision making. Owing to a spatially and temporally explicit manner of sensed information with respect to the magnitude of socio-economic activity related to urban development, the recent emergence of satellite-derived nighttime light data provides new means for investigating urban patterns and urbanization processes. In the present study, four kinds of quantitative information, including the spatial lighting area, temporal turning point, the spatial transformation of different types of lit areas and the velocity of spatial disperse of nighttime lightings signals, have been obtained and quantitatively analyzed based on time series of big data of annual composite products of nighttime light radiances during the period 1992-2013 from the Defense Meteorological Satellite Program (DMSP). Analysis results reveal the spatiotemporal patterns of China's urbanization over the past 22 years from the perspective of remotely sensed big data of artificial nighttime lighting signals in context of the spatial expansion, the distribution of urbanization onset time, the evolution of spatial structure and the urbanization velocity. This study can provide new insights into the understating of the fundamental spatiotemporal features of the rapid urbanization process in the present-day China using the remotely sensed big data of observed anthropogenic nighttime lighting signals.

Key words： remote sensing of nighttime light; urbanization; spatiotemporal characteristics; quantitative analysis; China

Cite this article

MA Ting . Spatiotemporal Characteristics of Urbanization in China from the Perspective of Remotely Sensed Big Data of Nighttime Light[J]. Journal of Geo-information Science, 2019 , 21(1) : 59 -67 . DOI: 10.12082/dqxxkx.2019.180361

1 引言

随着社会经济的持续快速发展,中国正经历着人类历史上前所未有的最大规模和最快速率的城市（镇）化进程。2014年的《国家新型城镇化规划》指出[1],在过去的30多年内,中国城镇人口从1.7亿增加到7.3亿,相应的人口城镇化率也从18%快速提升到54%。全国规模城市数量从200多个增加到650多个,建制镇的数量从2千多个增加到2万多个,并且形成了以长江三角洲、珠江三角洲和京津冀地区为代表的典型特大城市群。这一城市化趋势在未来的一段时间内还会持续,这就意味着仍将有数亿的农村人口在未来较短的一段时间内持续迁移到不断增长的城市化区域。从全世界城市发展的角度来看,大规模城市化是社会经济发展到一定程度的必然趋势,也是非农产业与人口在高度工业化背景下在地理空间上聚集的自然过程。因此,理解城市化过程及其发展规律对于解决区域格局优化、资源环境可持续发展以及各类社会问题的相关规划和管理政策的制定具有十分重要的意义[1]。
从地理学的角度来看,典型的城市化进程是高强度和高密度人类活动区域在空间上的扩展与演变的复杂地理过程[2,3,4]。这是一个涉及到持续增强的社会经济活动、不断增长的人口规模、逐渐扩展的基础设施建设和相关的土地利用动态变化等多个要素、多个过程在多个时空尺度上相互作用和演化的典型复杂地理现象。因此,理解城市发展规律的主要内容之一就是研究各类型城市变量在时间和空间上的动态演化特征。在以往的研究中,人口和社会经济的普查与调查数据以及以可见光和近红外为主的遥感数据构成了城市化定量分析和建模的主要数据基础。其中,普查与调查数据有着明确的定量内涵,但其在空间上通常是以各级行政管理边界为基础,从而在精细尺度上缺乏清晰的空间特征,同时也存在更新周期长以及统计口径标准化的困难;虽然各类遥感数据在空间上具有精细的关于城市建筑和人工下垫面的纹理特征,但无法从这类数据中获取与城市化相关的社会经济活动强度的信息。最近不断涌现的包括各类轨迹和定位信息在内的城市大数据为研究城市功能和结构提供了新的途径,但此类数据缺乏在大范围内的长期持续记录,通常适用于对城市的日常动态进行定量描述,而无法刻画城市的长时间演化进程。
近年来,基于卫星获取的由人为活动引起的夜间辐射亮度数据正逐渐被广泛地应用到与城市和社会经济活动相关的各类应用研究中[5,6,7,8,9,10,11,12,13,14,15]。形成这一趋势的主要原因在于夜光辐射遥感数据的3个显著特点：① 虽然此类卫星遥感平台的设计初衷是为了探测在没有月光影响下的地球夜间背景辐射信息,但在随后的研究中发现类似于城市这样的高密度人类聚集区域的信号响应最显著,而其他背景辐射信号通常以噪声形式存在[5,6];② 进一步的研究发现,由人类活动直接引起的夜光辐射信号在区域尺度上与众多城市化和社会经济变量之间存在显著的定量关系,并且这种关系在时间和空间上通常是单调和稳健的[7,8,9,10,11,12];③ 持续长时间、较小年际波动以及空间清晰化的观测记录使得利用夜光遥感影像序列评价多尺度社会经济活动的时空演变成为可能[13,14,15,16,17,18,19,20,21]。由此可见,不同于社会经济普查和调查数据以及以地表纹理特征为感知基础的各类遥感数据,作为一种空间清晰的有关城市社会经济活动强度信息的综合代理测量,夜光遥感大数据能够提供不同的视角来定量评估和理解城市发展的时空特征。
目前,利用夜光遥感数据进行城市分析研究主要集中在以下2个方面：① 利用区域的夜光辐射强度总量与统计和调查的社会经济变量进行时间和空间上的相关分析和定量建模,进而从区域总量上对城市化速率进行估算;② 以统计的建成区面积或土地利用数据为基础,通过对夜光遥感影像进行阈值分割来提取建成区范围,通过提取的范围动态来分析城市化用地的空间扩展。虽然这些研究为利用夜光遥感数据分析城市化提供了基础的方法和区域尺度的分析结果,但仍然缺乏对于城市以及人类和社会经济活动强度的空间结构演化特征的综合性研究,特别是在像元级别上对于城市化的扩展特征和演化速率等方面的研究,这些对于深入理解中国过去几十年来的城市化进程是非常重要的。
综上所述,本研究的主要目的是利用1992-2013年的夜光遥感时间序列大数据,综合分析过去22年来中国城市化的时空特征。主要研究内容将在以往所发展的多种夜光遥感影像城市化信息提取和分析算法的基础上,开展包括城市空间范围扩展、城市发展时间特征、城市空间结构变化以及城市化速度评估4个方面的定量分析,进而从夜光辐射遥感的角度对整个中国的城市时空演化特征提供新的理解,为相关城市化研究和管理规划实践提供新的参考。

2 数据来源和研究方法

2.1 夜光遥感大数据

本文所使用的夜光遥感大数据来自DMSP/OLS第四版本的全球稳定夜光在可见光波段的年度合成产品（数据和详细信息参见https://ngdc.noaa.gov/eog/dmsp/dmsp.html）。该产品采用的是在没有月光和云遮盖影响下的年均值合成算法,时间跨度为1992年到2013年。DMSP/OLS影像的空间分辨率为WGS84空间参考坐标系下的30'（弧度,赤道经线方向为1 km,40 °N地区近似为0.8 km）。共计22年的影像时间序列分别由6个不同传感器收集（包括F10、F12、F14、F15、F16和F18）。由于缺乏在轨辐射定标和校正设施,DMSP/OLS影像上的夜光辐射信号被离散成数字化的辐射亮度值（以下简称为DN值,取值范围0-63）。该系列稳定夜光信号的合成产品去除了短时辐射源的影响,因此高亮度DN值覆盖的部分通常对应高密度人类居住和活动地区,并且感应信号会随着人类活动强度和范围的增加而升高和扩大[5],这就为研究城市演化的时空特征提供了具有社会经济活动强度空间分布信息的遥感大数据基础。
在夜光遥感影像大数据中,像元尺度上辐射亮度值的大小主要是受到所对应地表上夜间各类型人造光源亮度强弱的直接影响[12]。通常来说,城市的中央商务区、机场、港口和大型工业用等地方在影像上具有较高的DN值。而在区域尺度上,其DMSP/OLS总辐射亮度通常和具体的社会经济活动指标呈现正的单调相关关系[13],如地区生产总值、人口数量、建成区规模、电力消耗总量等,并且这种关系被广泛证明在空间维度和时间维度上都是显著的。因此,对于城市和城市化相关研究来说,DMSP/OLS夜光辐射信号是城市活动的综合代理测量,并且其信号响应在时空演化上具有一致和稳健的可比性[14]。

2.2 数据预处理

根据DMSP/OLS时间序列影像的特点和具体研究问题的需要,在应用分析之前要对影像进行校正和低光信号的去除。本研究通过广泛使用的基于二项式拟合的经验交叉校正法来消除年际和不同传感器之间的信号偏移和变差[22]。此外,由于人造夜间辐射源以及卫星传感器本身的特点,溢光和饱和效应是DMSP/OLS数据的2个重要现象[23]。对于这2个数据获取本身存在的问题,目前还没有通用的方法来进行统一校正和去除,本研究将在相应的计算和分析中分别进行具体的考虑与处理。本次研究不包括香港、澳门和台湾。

2.3 数据分析方法

2.3.1 照亮面积的计算
一个区域的照亮面积总和是定量衡量该区域城市化以及相应的社会经济活动强度的最直接指标,照亮面积的增长是对城市活动空间扩展的正单调响应的直接结果[13]。然而,由于DMSP/OLS遥感信号中溢光效应的存在以及城乡过渡带变化的不确定性和不同城市结构之间的差异性,很难找到单一的所有区域均适用的强度阈值进行照亮面积的统计计算。通过与高分辨可见光遥感影像的经验对比研究发现,通常DN值大于12以上的像元会包含一定强度的人类活动,而DN值在50以上的大多是高密集人类活动区[12]。因此,本研究按单位DN增长的阈值从12-58进行照亮面积的分别统计,选取代表性的结果进行对比分析。
2.3.2 时间变化的转折点计算
城市化过程最典型的表现之一就是其他类型的用地向城市用地的转变,并伴随着基础设施的建设和发展。具体表现在夜光影像上则是像元的DN时间序列存在显著的分段演化现象[24],即从先前的相对平稳的低亮度信号阶变为持续增长的高亮度信号,对应的时间点即为转折点,它是该地区城市化活动起始时间的显著标识。本研究使用逐步回归的方法对像元尺度的DN时间序列进行分段拟合,利用Davies检验测试是否存在显著的分段演化,使用Welch检验测试分段序列是否存在显著差异,并利用AIC准则寻找最优的分段拟合（即转折点的确定）。所有的统计检验都是在95%的显著水平下完成,原始的DN序列通过最近邻居平滑来减少年际信号波动所带来的噪声。本算法的主要计算表达如下：
NTL Year = β 0 + β 1 Year ( Year ≤ Yea r t ) β 0 + β 1 Year + β 2 Year - Yea r t ( Year > Yea r t ) （1）
式中：NTL表示夜光亮度（以下同）平;Yeart为所求的时间转折点; β 0 , β 2 为拟合系数。算法的详细描述可以参考文献[24]。
2.3.3 空间结构转换分析
对于给定的城市化区域来说,其空间剖面线上的DN数值呈现的是单峰（或局部单峰）形态分布,这意味着从城市中心最高辐射亮度区到乡村和自然地表等低辐射亮度区的逐渐转变,这其中呈现的是清晰的城市空间结构信息[25]。本研究利用亮度数值和亮度空间梯度之间的二次关系曲线进行城市空间结构的分割和提取,将城市和周边过渡区域在空间上分割为高、高-中、中和低4个不同等级的区域来描述城市的空间结构,并考察这4个不同活动强度的区域在时间上的演化趋势,进而分析城市化过程中空间结构的演变与发展规律。本算法的主要计算表达如下：
BG = β 0 + β 1 NTL + β 2 NT L 2 （2）
式中：BG为夜光亮度的空间梯度,可采用与地形坡度的相同的估算方法,区域的划分基于拟合二次曲线的极值点和其左右的2个中间分割点进行。算法的详细描述可以参考文献[25]。
2.3.4 城市扩展速度计算
从空间演化的角度来看,典型的城市化过程是高密度人类活动由城市中心向外不断传播和扩展的过程。从夜光影像的空间剖面线形态变化来看,是波峰态DN分布的波段宽度和尾部不断向两侧增加的过程,这一过程包含了时间和空间2个维度的变化信息。利用夜光辐射信号时间梯度和空间梯度的比值可以计算出城市化活动向外扩展的平均速度[26]。本研究中时间梯度的计算采用线性最小二乘拟合进行,空间梯度的计算是在1992-2013年平均亮度的基础上利用平均最大下降算法来估算。时间梯度的显著性通过双尾t检验完成,只有在95%置信水平下具有显著正线性趋势的像元才被用于速度的计算。此外,为了避免投影变形所带来的误差,空间梯度的计算基于大圆弧距离进行。
Velocity = N T L trend BG （3）
式中：BG的含义和计算与式（2）相同;NTLtrend为夜光亮度的时间趋势,采用一元线性回归拟合。算法的详细描述可以参考文献[26]。

3 中国城市化的时空特征

3.1 城市化的空间扩展特征

通过对比1992年和2013年中国夜间辐射强度的空间变化情况,可以发现1992-2013年整个中国的夜光辐射亮度无论是空间范围还是高亮度区域都有显著的上升和扩展,特别是在珠江三角洲、长江三角洲、京津冀、成渝和山东半岛等地区,甚至在西北、西南和东北地区也有着明显的空间扩展和亮度上升,这意味着整个中国地理空间上普遍增强的社会经济活动以及伴随其中的大规模城市化进程。由于无法在全国范围内确定统一的分割阈值,本研究统计了DN值为12-58之间的所有照亮面积的变化（图1）。如图1（a）所示,所有的阈值曲线下的照亮面积都呈现快速增长的趋势,表明了过去几十年,中国城市化所呈现出整体而普遍的上升趋势。
Fig. 1 Changes in lit areas detected by DMSP/OLS from 1992 to 2013

图1 1992-2013年 DMSP/OLS夜光照亮面积的变化

为了在空间上进行照亮面积变化的对比,本研究选择了DN值为35和55作为样例分析的阈值。需要说明的是：① 阈值的选取并不是与统计的建成区面积进行对照,而是为了不同亮度区域的空间扩展进行对比研究;② 如图1（a）所示,由于所有的阈值曲线都呈现出相同的时间趋势,因此类似的阈值选取都会得到近似的空间扩展结果;③ 以选取的2个阈值为基础,将照亮面积分为相对低亮度、中亮度和高亮度区域,对应着不同强度的人类和城市化活动,产生的是相对对比分析结果,而不是城市建成区的扩展分析结果,这也正是夜光遥感数据与其他类型的土地利用数据在分析城市化方面的不同和独特之处。图1（b）展示了以DN值为35和55为阈值情况下,珠江三角洲夜光照亮区域在空间和时间上的演化趋势。从图中可以看到,3种级别亮度区域的由城市内部到外部的替代扩展,并伴随着初始分散的高亮度城市斑块的空间联接和团聚,最后形成典型城市群区域的空间过程。
从全国范围内来看（图1(c)-(f)）,阈值为12的照亮面积从1992年的15万km2快速上升到2013年的51万km2,增长了近2.5倍。这样的增长速度与国内生产总值的增加呈显著的线性相关关系,体现的是整体社会经济活动在空间上的快速增长。其中,285个地级以上城市贡献了26%的增长,而其它地区贡献了近四分之三的增长。在这样的一个全面增长的社会经济活动的背景下,与城市化活动的相关趋势同样表现为快速的上升,其中阈值为35的照亮面积从3.5万km2增加到18万km2,而阈值为55的照亮面积从1万km2增加到6.6万km2。285个地级市的贡献率则呈现明显的递增趋势,分别贡献了35阈值下的48%和55阈值下的74%的照亮面积的增长。特别是,285个地级以上城市的阈值为55的照亮面积与统计的建成区面积在2002年前十分接近1:1的线性关系,而从2002年后该阈值下的照亮面积的增速要明显大于相应统计的建成区面积的增速。引起这一现象的主要原因可能是由于在郊区城市化趋势下各类高密度人类活动用地的在远郊区不断离散化出现有关,而这些区域通常不在城市建成区的统计范围之内。这个结果也从侧面表明了中国城市化正在朝持续地不断增强的空间范围发展,即面向近郊区的连续扩展和在远郊区的断续化出现,最终发展成空间上连续的更大范围的城市化区域。

3.2 城市化的时间特征

由夜光辐射时间序列所检测的转折点蕴含了对应地区的城市化进程中显著增强的人类活动的起始时间信息。利用前面介绍的时间变化点转折计算方法,对全国的城市化转折点进行了计算和统计。从图2（a）中转折点的空间分布可以发现2个显著的现象：①在既有城市化区域的基础上按照由内到外的顺序逐渐向外扩张;②近些年来分散的局部小规模城镇区域集中式出现的城市化趋势。如图2（b）所示,从全国范围的定量统计来看,自1997年开始,出现城市化起步发展的区域呈现加速增长的趋势,并在2005年左右达到最高值。从具体面积统计来看,城市化转折点的年度出现了面积从1997年的3800 km2快速发展到2006年的9300 km2,每年的增速接近550 km2。2003年以后,转折点出现面积的加速趋势有所放缓,但每年的发生面积仍然维持在一个平稳的高值区域。其中,285个地级以上城市区域和其他区域占据了几乎相近的比例。这些有关城市化的时间特征结果表明了中国大规模城市化进程从20世纪90年代末的起步阶段开始经历了近10年的加速增长,然后进入相对平稳的高速发展阶段,并且中小规模城市地区在城市化起步总量上与大规模城市地区有着相近的比例,但是结合上述关于照亮面积的分析结果,可以发现中小规模城市地区在总体的城市化进度方面仍然显著落后于大规模城市地区。
Fig. 2 The spatial distribution of the turning point of urbanization and its changes over time

图2 京津冀部分地区城市化转折点的空间分布与变化趋势

3.3 城市空间结构演化特征

利用以个体城市为单元的夜光遥感影像结构分割和提取算法,可以清晰地发现城市内部不同亮度区域在时间上的演化特征（图3）。其中,高亮度区通常是带有高强度社会经济活动和高密度不透水面的城市核心区域;高-中亮度区一般包含着城市核心区的周围地带,有较高强度的人为活动;中亮度区通常覆盖城市的近郊区;低亮度区通常是城乡过渡带以及乡镇一类的相对低密度的人类聚集活动区。从图3(a)展示的太湖地区的城市空间结构演化可以发现,在过去的1992-2013年的22年里,该地区中城市高亮度区急剧向外扩张,直接导致高-中和中亮度区在空间上向外围推移,而受到行政范围和自然条件的限制,低亮度区的面积显著减少并呈现破碎和斑块化。但在小规模城市地区,低亮度区通常会继续向外围推移甚至扩展。这些发现刻画了城市化过程中不同强度的人类活动区域在空间结构上演化的一般过程和基本特征。
Fig. 3 The spatial distribution of different types of lighted areas and their changes over time

图3 不同类型照亮区域的空间分布与变化趋势

从全国范围的定量统计结果来看（图3(b)）：覆盖285个地级以上城市的高亮度区域的面积从1992年的 0.7万km2急剧扩展到2013年的4.5万多km2,增加了5倍多;高-中亮度区的面积也从0.4万km2增加到1.5万km2;而中等亮度区的面积增加了1.4倍（从1.6万到3.8万km2）;低亮度区面积增加了25%。以上结果表明了不同亮度区域（代表了不同人类活动强度和城市化程度）在城市化空间结构的演化中所体现出来的差异性增长,这也表明随着城市核心区的不断扩张和近郊区的外推,低密度人类活动区会逐渐破碎化甚至完全转换,当这样的结构演化趋势跨越行政管理边界的时候就会形成更大范围的城市群区域。

3.4 城市化的速度特征

夜光遥感影像的信号特点和空间结构特征使得可以在像元的尺度上定量估算城市活动向周边扩散的速度特征。这种定量指标所刻画的并不是城市化边缘区域向外围扩展的快慢,而是不同强度的城市活动由中心向外逐渐扩散与空间增强的多年平均速率。从给定空间位置来看,该指标描述是在城市化的进程中相对强的城市活动到达周围邻居区域的速度。从图4（a）所示的长江三角洲地区城市化速度空间的分布来看,相对高速率的城市化区域通常发生在城市化局部中心的边缘地带,而毗邻城市中心的区域和远郊区域的城市活动传播速率相对较低。这种速率分布模式反映的是不同强度的城市活动在空间扩展上的不同,即毗邻城市的区域通常会以较快的速度转变为低强度人类聚集活动的区域,而低强度的区域通常会以相对慢的速度转换为高强度城市化活动的区域。
Fig. 4 The spatial distribution of the velocity of urbanization and its changes over time

图4 城市化速度的空间分布与变化趋势

定量的统计结果显示,全国范围内共有近 19万km2的面积上检测到在过去的22年里具有不同强度的城市化活动的传播,其中近4万km2面积已经发展为高强度的城市化区域（2013年的DN值大于55）,逐渐增强的人类活动仍然在剩余的区域内不断传播和增强。如图4（b）所示,1992-2013年全国的城市化速度的平均值估算为（199±125） m/y,而285个地级以上城市区域（204±126 m/y）则显示了比其他区域（186±121 m/y）更快的速度,整个速度的分布呈现右拖尾的形态,意味着少数部分区域（如各类工业园和高新区）存在着更为快速的城市化活动的空间传播。

4 结论与展望

相比传统的普查和调查数据在更新周期和空间分辨率方面的问题以及可见光和近红外遥感数据在城市化活动强度定量化方面的困难,夜光辐射遥感大数据以其清晰的空间特征和人类活动强度信息的定量化表达,正逐渐被广泛应用到城市以及城市化相关问题的研究与应用中。本研究利用DMSP/OLS夜光辐射强度数据作为城市化的定量代理量测指标,结合4种不同的计算方法,综合分析了中国从1992-2013年共22年的全国范围内城市演化和发展的时间与空间的定量化特征。研究结果发现以下主要结论：
（1）在城市化区域的空间范围扩展方面,高亮度（DN>55）城市化区域的全国总面积增加了5.5倍,全国年均增长率为0.23万km2,285个规模较大的地级市以上地区的年均增长率为0.17万km2,与城市化相关的中等亮度区（55>DN>35）的面积则显示了更加快速的增长,年平均增长率为0.40万km2。
（2）在城市化活动的起始时间分布方面,从1995-2006年累积在8.6万km2的土地上发现了开始逐渐增强的城市化活动开始,每年平均有0.7 万km2的区域表现出增长的社会经济活动,即城市化的迹象。
（3）在城市不同亮度区域的空间结构演化方面,高亮度区域（通常与建成区相当）在285个地级市以上地区由城市中心区域向外连续扩展了近3.8万km2,而毗邻的高-中亮度区（与近郊区相当）随之逐渐外移并增长了1.2万km2,并成为潜在的城市化区域。
（4）在城市化速度的定量评价方面,各类强度的城市活动由局部中心区域以平均每年近200 m的速度向邻居局域扩展,使得局部地区的城市活动逐年增强,最终演变为具有高强度社会经济活动的完全城市化区域。
总的来说,夜光辐射遥感为探索和理解城市化时空特征提供了独特的感知大数据基础和研究方式,使能够从与城市化有关的社会经济活动强度的空间分布与动态演化的角度对有关城市发展在时间和空间上的特征进行计算和分析,进而对城市发展和演化的时空规律进行定量评估和理解。最近,具有更高空间分辨率和绝对辐射校正的夜间辐射信号数据（Suomi-NPP VIIRS）的获取必将推动夜光遥感大数据在城市化问题研究中的进一步应用[16,19-20,27]。然而,无在哪种类型的夜间辐射遥感数据,在与城市格局和人造地物分布相关的纹理信息的感知和表达方面都是相对弱化的,这可能阻碍了在更精细尺度上对城市结构及其演化特征的分析。因此,探讨如何与高分辨率的可见光影像的融合必将会促进从夜光遥感大数据视角对城市化更精细的时空演化特征的深入感知和理解。

The authors have declared that no competing interests exist.

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[ Chen J, Zhuo L, Shi P J, et al.The study on urbanization process in China based on DMSP/OLS data: Development of a light index for urbanization level estimation[J]. Journal of Remote Sensing, 2003,7(3):168-175,241. ]

[10]
Doll C N H, Muller J P, Morley J G. Mapping regional economic activity from night-time light satellite imagery[J]. Ecological Economics, 2006,57:75-92.The recognition that the elements of the ‘anthropocene’ play a critical role in global change processes means that datasets describing elements of the socio-economic environment are becoming increasingly more desirable. The ability to present these data in a gridded format as opposed to the traditionally reported administrative units is advantageous for incorporation with other environmental datasets. Night-time light remote sensing data has been shown to correlate with national-level figures of Gross Domestic Product (GDP). Night-time radiance data is analysed here along with regional economic productivity data for 11 European Union countries along with the United States at a number of sub-national levels. Night-time light imagery was found to correlate with Gross Regional Product (GRP) across a range of spatial scales. Maps of economic activity at 5 km resolution were produced based on the derived relationships. To produce these maps, certain areas had to be excluded due to their anomalously high levels of economic activity for the amount of total radiance present. These areas were treated separately from other areas in the map. These results provide the first detailed examination of night-time light characteristics with respect to local economic activity and highlight issues, which should be considered when undertaking such analysis.

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[11]
Small C, Elvidge C D.Mapping decadal change in anthropogenic night light[J]. Procedia Environmental Sciences, 2011,7:353-358.

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[12]
Small C, Elvidge C D, Balk D, et al.Spatial scaling of stable night lights[J]. Remote Sensing of Environment, 2011,115:269-280.City size distributions, defined on the basis of population, are often described by power laws. Zipf's Law states that the exponent of the power law for rank-size distributions of cities is near 1. Verification of power law scaling for city size distributions at continental and global scales is complicated by small sample sizes, inappropriate estimation techniques, inconsistent definitions of urban extent and variations in the accuracy and spatial resolution of census administrative units. We attempt to circumvent some of these complications by using a continuous spatial proxy for anthropogenic development and treat it as a spatial complement to population distribution. We quantify the linearity and exponent of the rank-size distribution of spatially contiguous patches of stable night light over a range of brightnesses corresponding to different intensities of development. Temporally stable night lights, as measured by the Defense Meteorological Satellite Program-Operational Line Scanner (DMSP-OLS), provide a unique proxy for anthropogenic development. Brightness and spatial extent of emitted light are correlated to population density (Sutton et al., 2001), built area density (Elvidge et al., 2007c) and economic activity ( Doll et al., 2006; Henderson et al., 2009) at global scales and within specific countries. Using a variable brightness threshold to derive spatial extent of developed land area eliminates the complication of administrative definitions of urban extent and makes it possible to test Zipf's Law in the spatial dimension for a wide range of anthropogenic development. Higher brightness thresholds generally correspond to more intense development while lower thresholds extend the lighted area to include smaller settlements and less intensively developed peri-urban and agricultural areas. Using both Ordinary Least Squares (OLS) and Maximum Likelihood Estimation (MLE) to estimate power law linearity and exponent of the resulting rank-size distributions across a range of upper tail cutoffs, we consistently find statistically significant exponents in the range 0.95 to 1.11 with an abrupt transition to very large, extensively connected, spatial networks of development near the low light detection limit of the sensor. This range of exponents and transition are observed at both continental and global scales. The results suggest that Zipf's Law also holds for spatial extent of anthropogenic development across a range of intensities at both continental and global scales. The implication is that the dynamics of urban growth and development may be represented as spatial phase transitions when the spatial extent and intensity of development are treated as continuous variables rather than discrete entities.

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[13]
Zhang Q, Seto K C.Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data[J]. Remote Sensing of Environment, 2011,115:2320-2329.

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[14]
Elvidge C D, Baugh K E, Anderson S J, et al.The night light development index (NLDI): A spatially explicit measure of human development from satellite[J]. Social Geography, 2012,7:23-35.

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[15]
Small C, Elvidge C D.Night on earth: Mapping decadal changes of anthropogenic night light in Asia[J]. International Journal of Applied Earth Observation and Geo-information, 2013,22:40-52.The defense meteorological satellite program (DMSP) operational linescan system (OLS) sensors have imaged emitted light from Earth's surface since the 1970s. Temporal overlap in the missions of 5 OLS sensors allows for intercalibration of the annual composites over the past 19 years (Elvidge et al., 2009). The resulting image time series captures a spatiotemporal signature of the growth and evolution of lighted human settlements and development. We use empirical orthogonal function (EOF) analysis and the temporal feature space to characterize and quantify patterns of temporal change in stable night light brightness and spatial extent since 1992. Temporal EOF analysis provides a statistical basis for representing spatially abundant temporal patterns in the image time series as uncorrelated vectors of brightness as a function of time from 1992 to 2009. The variance partition of the eigenvalue spectrum combined with temporal structure of the EOF5 and spatial structure of the PCs provides a basis for distinguishing between deterministic multi-year trends and stochastic year-to-year variance. The low order EOF5 and principal components (PC) space together discriminate both earlier (1990s) and later (2000s) increases and decreases in brightness. Inverse transformation of these low order dimensions reduces stochastic variance sufficiently so that tri-temporal composites depict potentially deterministic decadal trends. The most pronounced changes occur in Asia. At critical brightness threshold we find an 18% increase in the number of spatially distinct lights and an 80% increase in lighted area in southern and eastern Asia between 1992 and 2009. During this time both China and India experienced a similar to 20% increase in number of lights and a similar to 270% increase in lighted area - although the timing of the increase is later in China than in India. Throughout Asia a variety of different patterns of brightness increase are apparent in tri-temporal brightness composites - as well as some conspicuous areas of apparently decreasing background luminance and, in many places, intermittent light suggesting development of infrastructure rather than persistently lighted development. Vicarious validation using higher resolution Landsat imagery verifies multiple phases of urban growth in several cities as well as the consistent presence of low DN (<similar to 15) background luminance for many agricultural areas. Lights also allow us to quantify changes in the size distribution and connectedness of different intensities of development. Over a wide range of brightnesses, the size distributions of spatially contiguous lighted area are consistent with power laws with exponents near -1 as predicted by Zipf's Law for cities. However, the larger lighted segments are much larger than individual cities; they correspond to vast spatial networks of contiguous development (Small et al., 2011). (C) 2012 Published by Elsevier B.V.

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[16]
Shi K F, Huang C, Yu B L, et al.Evaluation of NPP-VIIRS nighttime light composite data for extracting built-up urban areas[J]. Remote Sensing Letters, 2014,5(4):358-366.The first global night-time light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day-night band carried by the Suomi National Polar-orbiting Partnership (NPP) satellite were released recently. So far, few studies have been conducted to assess the ability of NPP-VIIRS night-time light composite data to extract built-up urban areas. This letter aims to evaluate the potential of this new-generation night-time light data for extracting urban areas and compares the results with Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data through a case study of 12 cities in China. The built-up urban areas of 12 cities are extracted from NPP-VIIRS and DMSP-OLS data by using statistical data from government as reference. The urban areas classified from Landsat 8 data are used as ground truth to evaluate the spatial accuracy. The results show the built-up urban areas extracted from NPP-VIIRS data have higher spatial accuracies than those from DMSP-OLS data for all the 12 cities. These improvements are due to the relatively high spatial resolution and wide radiometric detection range of NPP-VIIRS data. This study reveals that NPP-VIIRS night-time light composite data would provide a powerful tool for urban built-up area extraction at national or regional scale.

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[17]
Wei Y, Liu H X, Song W, et al.Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with pseudo invariant features[J]. Landscape and Urban Planning, 2014,128(1):1-13.Previous studies demonstrated that DMSP-OLS stable nighttime light data are useful data source for delineating urban areas. However, the nighttime light data acquired in different years are not directly comparable, due to the variations in atmospheric condition from year to year and the periodic change in satellite sensor. This makes it difficult to use the time series nighttime light data for urban growth analysis. This paper presents a novel technique for normalizing time series DMSP/OLS nighttime light data and deriving urban detection threshold using Pseudo Invariant Features (PIFs). Our technique consists of three steps: (1) estimate an optimal threshold value for urban area detection for a reference year, when high resolution image data are available for some local areas. (2) Based on the irreversible nature of urbanization process, determine a set of PIFs, which are deemed as urban areas and did not exhibit significant change in nighttime light condition during the study period. (3) Normalize the time series DMSP-OLS data sets based on the PIFs and linear regression, determine optimal threshold values for urban area detection for all years based on the reference year threshold value, and extract urban areas accordingly. This technique was successfully applied to time series DMSP-OLS nighttime light images of the Central Liaoning region in China. Patterns of this urban agglomeration's spatial emporal evolution from 2000 to 2010 were mapped and analyzed. The reliability and spatial accuracy of this technique were evaluated with multitemporal Landsat TM images. The technique was proved accurate and effective.

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[18]
Yu B L, Shu S, Liu H X, et al.Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: A case study of China[J]. International Journal of Geographical Information Science, 2014,28(11):2328-2355.Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units 鈥 urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.

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[19]
Yu B L, Shi K F, Hu Y J, et al.Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015,8(3):1217-1229.Poverty has appeared as one of the long-term predicaments facing development of human society during the 21st century. Estimation of regional poverty level is a key issue for making strategies to eliminate poverty. This paper aims to evaluate the ability of the nighttime light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite in estimating poverty at the county level in China. Two major experiments are involved in this study, which include 1) 38 counties of Chongqing city and 2) 2856 counties of China. The first experiment takes Chongqing as an example and combines 10 socioeconomic variables into an integrated poverty index (IPI). IPI is then used as a reference to validate the accuracy of poverty evaluation using the average light index (ALI) derived from NPP-VIIRS data. Linear regression and comparison of the class ranks have been employed to verify the correlation between ALI and IPI. The results show a good correlation between IPI and ALI, with a coefficient of determination (R2) of 0.8554, and the class ranks of IPI and API show relative closeness at the county level. The second experiment examines all counties in China and makes a comparison between ALI values and national poor counties (NPC). The comparison result shows a general agreement between the NPC and the counties with low ALI values. This study reveals that the NPP-VIIRS data can be a useful tool for evaluating poverty at the county level in China.

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[20]
Chen Z Q, Yu B L, Song W, et al.A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017,55(11):6305-6319.Urban spatial structure affects many aspects of urban functions and has implications for accessibility, environmental sustainability, and public expenditures. During the urbanization process, a careful and efficient examination of the urban spatial structure is crucial. Different from the traditional approach that relies on population or employment census data, this research exploits the nighttime light (NTL) intensity of the earth surface recorded by satellite sensors. The NTL intensity is represented as a continuous mathematical surface of human activities, and the elemental features of urban structures are identified by analogy with earth's topography. We use a topographical metaphor of a mount to identify an urban center or subcenter and the surface slope to indicate an urban land-use intensity gradient. An urban center can be defined as a continuous area with higher concentration or density of employments and human activities. We successfully identified 33 urban centers, delimited their corresponding boundaries, and determined their spatial relations for Shanghai metropolitan area, by developing a localized contour tree method. In addition, several useful properties of the urban centers have been derived, such as 9% of Shanghai administrative area has become urban centers. We believe that this method is applicable to other metropolitan regions at different spatial scales.

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[21]
李小敏,郑新奇,袁涛. DMSP/OLS夜间灯光数据研究成果知识图谱分析[J].地球信息科学学报,2018,20(3):351-359.利用CitesSpace软件对1997-2017年DMSP/OLS夜间灯光数据进行知识图谱分析,梳理国内外研究热点与演化历史,发掘研究难点,为后来研究者提供方向。本文选取Web of Science 核心数据集数据库收录的文献,进行合著特征分析、关键词共现分析和文献共被引分析。结果表明：① 夜间灯光数据相关研究最活跃的国家、机构和作者分别是美国、中国科学院和Elvidge;② 社会经济条件估计（人口、人口密度和电力）和城市扩展变化监测一直是国内外研究的热点和前沿;③ 目前的研究难点是如何减少灯光溢出效应以及灯光过饱和现象对研究精度的影响;④ 研究学科交叉性强,涉及地理学、测绘科学与技术、应用经济学、社会学等领域。因此,未来的研究趋势主要表现在数据处理方法的优化、研究领域的拓展以及深化已有研究成果3个方面。

[ Li X M, Zheng X Q, Yuan T.Knowledge mapping of research results on DMSP/OLS nighttime light data[J]. Journal of Geo-information Science, 2018,20(3):351-359. ]

[22]
Elvidge C D, Ziskin D, Baugh, K E, et al.A fifteen year record of global natural gas flaring derived from satellite data[J]. Energies, 2009,2:595-622.

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[23]
卓莉,张晓帆,郑璟,等.基于EVI指数的DMSP/OLS夜间灯光数据去饱和方法[J].地理学报,2015,70(8):1339-1350.DMSP/OLS nighttime light (NTL) data has been widely applied to many studies on anthropogenic activities and their effects on the environment. Due to the limitations of the OLS sensor, NTL data suffers from saturation problem in the core of urban areas, which further influences researches based on nocturnal lights. The radiance calibrated nighttime light (RCNTL) products developed by the National Geophysical Data Center (NGDC) at NOAA partially solved the problem. However, they are only available for a very limited number of years. Recently, a vegetation adjusted NTL urban index (VANUI) has been developed based on the stylized fact that vegetation and urban surfaces are inversely correlated. Despite its simplicity of implementing and effectiveness in increasing variation to NTL data, VANUI does not perform well in some fast growing cities. In this paper, we proposed a new urban index, i.e., the Enhanced Vegetation Index (EVI) adjusted nighttime light index (EANTLI), which combined MODIS EVI with NTL to alleviate the saturation problem of NTL data. In order to evaluate the proposed EANTLI's capability in reducing NTL saturation, we first compared its spatial distributions in potential saturated areas (PSAs) of three metropolitan areas in China with that of the original NTL and VANUI, respectively. Then we randomly selected 30 latitudinal transects across these urban areas to verify EANTLI's similarity to the RCNTL. Finally, we tested EANTLI's effectiveness in assessing electric power consumption of 168 prefecture-level cities in China. Results from these experiments showed that EANTLI significantly increases spatial heterogeneity in the PSAs and effectively alleviates the NTL saturation problem. EANTLI's similarity to RCNTL is consistently higher than that of VANUI in the comparison of latitudinal transects. EANTLI also yields better results in the estimation of electric power consumption. In conclusion, the EANTLI can effectively reduce NTL saturation in urban centers and thus has great potential of wide range applications in the future.

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[ Zhou L, Zhang X F, Zheng Jing, et al.An EVI_based method to reduce saturation of DMSP/OLS nighttime light data[J]. Acta Geographica Sinica, 2015,70(8):1339-1350. ]

[24]
Xu T, Ma T, Zhou C H, et al.Characterizing spatio-temporal dynamics of urbanization in China using time series of DMSP/OLS night light data[J]. Remote Sensing, 2014,6:7708-7731.Stable nighttime light (NTL) data, derived from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS), are typically considered a proxy measure of the dynamics of human settlements and have been extensively used to quantitative estimates of demographic variables, economic activity, and land-use change in previous studies at both regional and global scales. The utility of DMSP data for characterizing spatio-temporal trends in urban development at a local scale, however, has received less attention. In this study, we utilize a time series of DMSP data to examine the spatio-temporal characteristics of urban development in 285 Chinese cities from 1992 to 2009, at both the local and national levels. We compare linear models and piecewise linear models to identify the turning points of nighttime lights and calculate the trends in nighttime light growth at the pixel level. An unsupervised classification is applied to identify the patterns in the nighttime light time series quantitatively. Our results indicate that nighttime light brightness in most areas of China exhibit a positive, multi-stage process over the last two decades; however, the average trends in nighttime light growth differ significantly. Through the piecewise linear model, we identify the saturation of nighttime light brightness in the urban center and significant increases in suburban areas. The maps of turning points indicate the greater the distance to the city center or sub-center, the later the turning point occurs. Six patterns derived from the classification illustrate the various characteristics of the nighttime light time series from the local to the national level. The results portray spatially explicit patterns and conspicuous temporal trends of urbanization dynamics for individual Chinese cities from 1992 to 2009.

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[25]
Ma T, Zhou Y K, Zhou C H, et al.Night-time light derived estimation of spatio-temporal characteristics of urbanization dynamics using DMSP/OLS satellite data[J]. Remote Sensing of Environment, 2015,158:453-464.61Quadratic curve is used for partitioning DMSP/OLS images at a local urban scale.61Different types of night light show diverse trends in multi-scale urban processes.61Type transitions in night light imagery show geographically explicit features.

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[26]
Ma T, Yin Z, Li B L, et al.Quantitative estimation of the velocity of urbanization in China using nighttime luminosity data[J]. Remote Sensing, 2016,8(2):94.Rapid urbanization with sizeable enhancements of urban population and built-up land in China creates challenging planning and management issues due to the complexity of both the urban development and the socioeconomic drivers of environmental change. Improved understanding of spatio-temporal characteristics of urbanization processes are increasingly important for investigating urban expansion and environmental responses to corresponding socioeconomic and landscape dynamics. In this study, we present an artificial luminosity-derived index of the velocity of urbanization, defined as the ratio of temporal trend and spatial gradient of mean annual stable nighttime brightness, to estimate the pace of urbanization and consequent changes in land cover in China for the period of 2000-2010. Using the Defense Meteorological Satellite Program-derived time series of nighttime light data and corresponding satellite-based land cover maps, our results show that the geometric mean velocity of urban dispersal at the country level was 0.21 kmyr(-1) across 88.58 x 10(3) km(2) urbanizing areas, in which similar to 23% of areas originally made of natural and cultivated lands were converted to artificial surfaces between 2000 and 2010. The speed of urbanization varies among urban agglomerations and cities with different development stages and urban forms. Particularly, the Yangtze River Delta conurbation shows the fastest (0.39 kmyr(-1)) and most extensive (16.12 x 10(3) km(2)) urban growth in China over the 10-year period. Moreover, if the current velocity holds, our estimates suggest that an additional 13.29 x 10(3) km(2) in land area will be converted to human-built features while high density socioeconomic activities across the current urbanizing regions and urbanized areas will greatly increase from 52.44 x 10(3) km(2) in 2010 to 62.73 x 10(3) km(2) in China's mainland during the next several decades. Our findings may provide potential insights into the pace of urbanization in China, its impacts on land changes, and accompanying alterations in environment and ecosystems in a spatially and temporally explicit manner.

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[27]
Elvidge C D, Baugh K E, Zhizhin M, et al.Why VIIRS data are superior to DMSP for mapping nighttime lights[J]. Proceedings of the Asia-Pacific Advanced Network, 2013,35:62-69.

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