Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area

Urbanization process is a major factor of change in the Mediterranean region where pre-urban cities and new urban settlements have raised over the past decades. Several cities rapidly became regional centres or international nodes according to economic and political pressures. Urbanization (and informal settlement) causes land cover changes which can lead to deeper social, economic and environmental changes. The main objective of this paper is to provide time-series information to define and locate the evolution trends of the Tunis Metropolitan Area. In a first step, satellite imagery has been used (1986-1996 SPOT XS) to extract the land cover, identify the urbanization processes and estimate the changes. One of the main aspects is to locate informal settlement areas that grow significantly along the roadway networks in the Tunisian capital. Results show a global progression of the built-up areas of 13% in 10 years. In a second step, the urban growth evolution has been approached by using a potential model that provides general trends of feasible urban expansion, taking into account protection laws of natural and agricultural land. This type of model has not been tested on developing cities and as such it corresponds to a new planning contribution for planners who have no concept of spatially how their urban areas have changed over time and where the growth is occurring. In this case, it has been calibrated over the period of 1986-1996, and then applied to predict the location of the built-up growth over the next 10 years (1996-2006). These results can provide local authorities and other stakeholders with information towards decision support documents for future planning and monitoring plans. Moreover, they can be updated systematically through the integration of remote sensing data.     

Analysing the consistency between built-up areas and human activities and the impacts on the urbanization process: a case study of Zhengzhou,China

Urbanisation is accompanied by drastic urban sprawl and populations gathering in cities. The new urban districts in China have gradually become the most effective form of built-up area expansion, but the corresponding human activities within these built-up areas have not increased at the same rate, which has led to the emergence of urbanisation-related problems, such as ghost cities. However, few studies have focused on the evolution of this inconsistency within a city, especially at multiple scales. Based on Landsat images and the Defense Meteorological Satellite Programme-Operational Linescan System (DMSP-OLS) data, our study calculated the barycentre, the agglomeration degree and the consistency index to quantitatively analyse the spatio-temporal consistency between built-up areas and human activities within a city from 1993 to 2013. A mainstream urban example in China, Zhengzhou, which accepted financial and policy support from the government and underwent rapid urbanisation, was chosen as a case study. The results of this study showed that (1) the consistency between built-up areas and human activities was characterised by an approximately S-shaped curve that divided the city’s development into three stages, namely, relative stability (1993–1998), rapid development exhibiting inconsistency (1998–2008) and the optimisation and matching of spatial factors (2008–2013); (2) the inconsistency stage had two diametrically opposed patterns: the increase in human activities lagged behind the expansion of built-up areas, and the opposite occurred in older areas, which resulted in the emergence of ghost cities in new districts and crowded populations in the old cities; and (3) the inconsistency stage occurs easily; thus attention should be paid during the promotion of urbanisation processes in rapidly developing cities such as Zhengzhou; however, as the urban population increases, under economic development and a policy of unceasing expansion, the area of inconsistency was mitigated after 2010. Our findings illustrated the influence of consistency on built-up areas and human activities in terms of coordinated development within cities and provided a better understanding of the urbanisation process in Chinese cities.     

Evaluation of Urban Expansion in Chongqing City based on the Nighttime Light Data

Urbanization is an important embodiment of regional economic development.Its progress reflects the level of economic development in the region.Since Chongqing being directly under the Central Government in 1997,it has enjoyed rapid economic growth and rapid urbanization.Looking for an intuitive and efficient method of urbanization is of great practical significance to correctly grasp the achievements of urbanization in Chongqing from the overall situation.Through the comparison of statistical data on night lighting data,we can extract the built-up area of Chongqing year after year.The city gravity model is based on the results.A night light index is established and used to calculate the speed of urban expansion.The result indicates that the urban distributions in Chongqing is spatially distributed and expanded in all directions.There are great differences between urban areas and rural areas and urban area urbanization level is higher.The city gravity gradually shifted from Yuzhong district to Yubei district.The speed of city expansion maintains high.Our study can afford refers for the planning and construction of the Chongqing city.     

基于夜间灯光的2000—2018年成渝地区城市化过程研究

成渝城市群正逐步成为我国西部经济发展的增长极,探索其城市化时空格局对区域协调发展具有指引作用。基于2000—2018年整合夜光遥感数据提取城市群多期建成区空间范围,运用夜光规模统计、标准椭圆、位序—规模法则以及空间自相关等指标、模型定量分析了成渝区域城市化时空过程。结果表明：(1)灯光与统计数据配合下的建成区提取多年平均误差为1.27%,重庆、成都和绵阳市提取验证效果好;(2)19年间成渝各城市夜光规模显著增长,整体累计增长5.6倍,2010年后成渝城市群灯光规模扩张速度显著;(3)区域内各城市的位序—规模（rank-size）由高位序城市集中发展转向区域协调均衡发展,中小城市均有不同程度的扩张;(4)城市群规模重心位于四川资阳市安岳县,重心移动整体上以东南方向为主,空间格局整体呈现以“成都—重庆”为轴线沿西北向东南演变,空间范围逐渐扩张,说明以重庆为主的东南都市圈的社会经济形势更显著,对城市群发展更具影响力;(5)成渝城市群扩展的空间集聚程度逐渐加强,冷热点格局整体呈现冷点区占比大,热点区占比低的特征,热点区主要出现在位于成都与重庆主城区及其周边城镇。研究揭示了成渝城市群均衡发展的特...     

中缅边境典型城市扩张遥感动态监测及驱动力分析

在“一带一路”背景下,当前针对边境城市建成区发展的研究尚不充分,特别境内外口岸毗邻城市的演化交互关系更有待挖掘。以中缅边境地区典型的口岸毗邻城市——瑞丽市和木姐市作为研究区,选取2012、2015和2018年遥感影像,采用城镇建筑用地指数提取两市建成区,从建成区扩张数量、空间分布格局和内部经济规模3个方面,分析瑞丽市开发开放试验区设立后的两市建成区动态变化特征,并对两市建成区演化的交互关系和扩张驱动因素进行探讨。结果表明：在自然环境、经济产业、政策规划、交通、口岸等因素的综合作用下,两市建成区扩张明显。瑞丽市城市西部扩张最为明显,东部次之,呈现“一体两翼”式扩张格局,逐渐从建成区外延转为内部填充及基础设施完善阶段;木姐市主要向西北和正东方向扩张,建成区保持较为明显的外延特征,但其内部土地集约利用水平不高。在交互协同发展过程中,瑞丽市占主导地位,木姐市处在依附地位。两市的快速协同扩张也体现了中缅两国政治经济的紧密交流和广阔的合作空间。     

福清市城镇空间扩展规律及其驱动机制分析

福清市是福建省沿海经济发达的城市。特别是上世纪90年代以来,福清市的经济得到快速发展,其经济结构已由以农业为主逐渐转向以工业为主。随着经济的进一步发展,福清市的城市化水平有了稳步的提高,其城镇用地也发生了很大的变化。城市扩展的空间数据是评估城市化进程的一个重要指标。因此,综合应用遥感和地理信息系统技术分别提取了福清市1991年和1996年的城镇用地信息,并发现在这5年中福清市城镇用地面积增加了1.2倍。经过对城市区域扩展进行空间分
析,并结合社会经济统计数据,推断福清市城镇用地扩展的驱动力主要为工业大发展(工业区的成片建设)。     

基于多源时空信息的西宁和拉萨近70年来城市扩展过程重现及地表覆盖变化分析

西宁和拉萨城市作为青藏高原人类活动的热点地区,其发展历程对青藏高原社会经济发展具有重要影响。研究基于遥感影像、城市规划图和历史地图等资料重建了西宁和拉萨城市1949基准年、1978基准年、1990年、2000年、2010年和2018年城市扩展及2000年以来城市不透水层和绿地空间组分数据,分析了1949基准年以来西宁和拉萨主城区城市扩展的时空特征,揭示了社会经济因素和政策因素对城市土地利用/覆盖变化的影响。研究结果表明：(1)新中国成立以来,西宁和拉萨主城区持续扩展,均呈现非线性的增长态势,城市土地面积分别从1949基准年的1.98 km²和1.10 km²增长到2018年的75.65 km²和76.04 km²;西宁主城区城市扩展呈现十字状的扩展态势,拉萨呈现出圈层外延式的扩展模式;(2)自2000年来,西宁和拉萨城市绿化水平显著提升。2000～2018年,西宁和拉萨城市不透水层面积分别从36.91 km²和21.56 km²增加到55.34 km     

Urban expansion simulation from the perspective of land acquisition-based on bargaining model and ant colony optimization

As a major method to serve the demand for land requirement in Chinese urban construction, land acquisition has been intensified since the speeding up of urbanization. However, clashes arise during the process of land acquisition, out of conflicts of interests, which to some extent have affected the development and direction of urban expansion, as well as social harmony and stability. Therefore, the urban expansion simulation should be based on smooth land acquisition technique. In this study, urban expansion was simulated from the perspective of land acquisition, based on coupled bargaining model and modified ant colony optimization (ACO) algorithm. First, the bargaining model with fairness preferences should be set up for the government and farmers, to search for the candidate areas for urban expansion, both sides of which could reach a consensus without conflicts of interest. Then, urban expansion simulation is applied to all the candidate areas, taking advantage of the modified ACO algorithm. The model considered the built-up area in Wuhan city as the demonstration area and simulated conditions of land use in 2016 and 2026. The result showed that the coupled model could simulate decision-making behavior of the government and farmers in land acquisition veritably, so as to protect farmers' economic interests, with an increase of over 50% on average, and ensure government's appropriate profit. Moreover, the simulation accuracies of the coupled model was found to be better than that of the traditional cellular automata model, and the Kappa coefficient was 0.65, which supports the effectiveness of the model in simulating urban expansion. Further, it was estimated that the urban land use of Wuhan will cover 516.22 km² in 2026, and the southeastern part of the city will be the hot spot area of urban expansion.     

特大型山地城市动态扩展的遥感研究

以2000年、2003年的SPOT图像和2006年的中巴资源卫星（CBERS-1）图像为主要数据源,在运用RS及GIS技术对遥感图像进行一系列预处理的基础上,提取不同时期重庆主城核心区的建成区面积,同时分析城市扩展的速度和空间差异。结果表明,2000~2006年建成区的面积增长了114.14 km2,年增长率为10.48%。城区扩展的主要方位是正北和东北方向,占到总增长面积的60%左右。农用地为扩展用地的主要地类来源。经济发展、政策与规划、自然地理环境、交通基础设施等合力作用是城市空间格局演变的主要驱动力。     

Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015

To analyse changes in human settlement in Shenzhen City during the past three decades, changes in land use/land cover (LULC) and urban expansion were investigated based on multi-temporal Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images. Using C4.5-based AdaBoost, a hierarchical classification method was developed to extract specific classes with high accuracy by combining a specific number of base-classifier decisions. Along with a classification post-processing approach, the classification accuracy was greatly improved. The statistical analysis of LULC changes from 1988 to 2015 shows that built-up areas have increased 6.4-fold, whereas cultivated land and forest continually decreased because of rapid urbanization. Urban expansion driven by human activities has considerably affected the landscape change of Shenzhen. The urban-expansion pattern of Shenzhen is a mixture of three urban-expansion patterns. Among these patterns, traffic-driven urban expansion has been the main form of urban expansion for some time, especially in the Non-Special Economic Zone. In addition, by taking 8 to 10 year periods as time intervals, urban expansion in Shenzhen was divided into three stages: the early-age urbanization stage (1988–1996), the rapid urbanization stage (1996–2005), and the intensive urbanization stage (2005–2015). For different stages, the state of urban expansion is different. In long-term LULC dynamic monitoring and urban-expansion detection, it was possible to obtain 11 LULC maps, which took 2 to 4 years as a research interval. With regard to the short research periods, LULC changes and urban expansion were investigated in detail.     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

一种基于指数的新型遥感建筑用地指数及其生态环境意义

城市建筑用地是一种复杂的土地利用类型,在电磁波反射光谱上表现出明显的异质性。因此,很难用简单的方法将其从遥感影像中准确地提取出来。在详细研究了城市建筑用地的光谱特征以后,创建了一种不直接采用影像的原始波段,而是采用由它们衍生的3个指数波段来构成新型建筑用地指数(IBI)。通过对ASTER和Landsat ETM+两种多光谱影像进行的实验表明,新指数除了能够有效地增强影像中的建筑用地信息外,还能和植被指数、水体指数一样,用于进行数值运算,从而实现了建筑用地对城市生态环境影响的定量研究。对厦门、福州两城市的实例分析表明,新的建筑指数与地表温度呈正相关关系,而与植被指数、水体指数呈负相关关系。研究进一步发现,建筑指数与地表温度的关系不是简单的线性关系,而是一种指数函数关系,说明高建筑用地比例地区的升温效应要明显高于低建筑用地比例地区,因此,对城市热岛的形成起着更大的作用。     

改进卷积网络的高分遥感图像城镇建成区提取

目的 城镇建成区是城镇研究重要的基础信息,也是实施区域规划、落实城镇功能空间布局的前提。但是遥感影像中城镇建成区的环境复杂,同时不同城镇建成区在坐落位置、发展规模等方面存在许多差异,导致其信息提取存在一定困难。方法 本文基于面向图像语义分割的深度卷积神经网络,使用针对特征图的强化模块和通道域的注意力模块,对原始DeepLab网络进行改进,并通过滑动窗口预测、全连接条件随机场处理方法,更准确地实现城镇建成区提取。同时,针对使用深度学习算法容易出现过拟合和鲁棒性不强的问题,采用数据扩充增强技术进一步提升模型能力。结果 实验数据是三亚和海口部分地区的高分二号遥感影像。结果表明,本文方法的正确率高于93%,Kappa系数大于0.837,可以有效地提取出大尺度高分辨率遥感影像中的城镇建成区,且提取结果最为接近实际情况。结论 针对高分辨率遥感卫星影像中城镇建成区的光谱信息多样化、纹理结构复杂化等特点,本文算法能在特征提取网络中获取更多特征信息。本文使用改进的深度学习方法,提出两种处理方法,显著提高了模型的精度,在实际大幅遥感影像的使用中表现优秀,具有重要的实用价值和广阔的应用前景。     

Remote Sensing Monitoring and Spatiotemporal Pattern of Land Use/Cover Change in Built-up Area of Tianjin in the Past 70 Years

Urban land use/cover changes have an important impact on urban ecosystem services functions and the environmental quality of human settlements. This study mapped urban expansion process using the historical data, remote sensing images and urban planning maps, and acquired the urban impervious surface and green space fraction based on big data platform. We analyzed the process of Tianjin urban expansion rate, intensity and urban land cover change since 1949, and revealed the driving factors in the process of urban expansion with social economic and policy factors. The results showed that the built-up area increased from 49.15 km² in 1949 to 663.39 km² in 2015. The expansion has undergone four stages of "acceleration – deceleration – acceleration - deceleration"; the urban expansion mode presents the filled with built-up area and along with the neighboring transportation trunk lines. The proportion of green space in the built-up area is increasing, indicating that the urban ecological greening in the main urban area of Tianjin has been improved.     

基于多源遥感数据的陕西2000～2015年城市扩展研究北大核心CSCD

夜间灯光数据已成为监测城市发展进程及空间格局演变的重要依据。利用覆盖陕西省的2000、2005、2010年3期DMSP/OLS数据及2015年无云复合NPP/VIIRS数据,结合MODIS等多源遥感数据构建VANUI、EANTLI两种指数模型,识别并提取陕西省城市建成区面积,经过对比分析后,选取较为精确的EANTLI指数重建陕西省2000～2015年城市扩展进程,并对西安市城市扩展的细节特征进行探究。结果表明:EANTLI指数在城市建成区识别与提取方面较VANUI指数稳定;2000～2015年陕西省城市扩展的不均衡性较为突出,关中各地市城市建成区面积增长速率最快,陕北次之,陕南地区较为缓慢。陕西省城市扩展的主要特征是以各地市中心城市为核心,呈同心圆或扇形模式向外蔓延;西安市城市扩展具有明显的外延性特征,城市重心逐渐向西移动,城市建成区向西北、西南方向持续扩展。     

基于遥感数据SOFM 网络分类结果的城市增长潜力模型研究——以北京1994～2003年城市扩展为例

在长时间序列多光谱遥感数据和高精度自组织神经网络分类方法的支持下, 对北京地区1994～ 2003 年城市化的基本过程进行了分析和研究, 5 种地物分类结果表明: 北京近10 年来城市扩展基本是以老城为中心围绕环线不断向外围扩展, 呈现一个中心多个环线的发展趋势。在此基础上建立了潜力模型, 计算潜力指数和生成潜力指数图, 潜力指数图呈现从四环以内向外围环线不断递增的趋势, 说明北京市发展主要是农业用地发展为城市用地类型, 这与北京北部2000 年～ 2003年航空照片反映的变化规律一致, 同时还介绍了利用潜力指数对2003 年～ 2012 年城市增长潜力的预测分析。     

Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta,China

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–Brazil 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.     

张掖市甘州区1959~2009年间城市扩展分析

利用锁眼、MSS、TM影像和地形图及其他辅助数据,提取张掖市1959、1968、1977、1986、1997、2009共6个年份的城市建成区范围和面积,利用空间重心转移模型、扩展强度指数、扩展速度指数、紧凑度指数、面积变化、城市用地扩展系数等指标,分析了近50 a来城市土地利用扩张的空间变化特征。结果表明,50 a间张掖市城市建成区扩展5.71倍,其中1986年之前属于缓慢扩展阶段,1986年之后属于快速扩展阶段。在1959~1986的27 a间,建成区面积仅扩展258.42 hm²。1986~2009年期间,随着改革开放和西部大开发战略的实施,建成区面积增加了1 995.11 hm²,是前27 a的7.73倍。1959~1977年期间,城市重心偏移量小,1977~1986年期间,虽然城市扩展面积较小,但城市主要向西和西北方向扩展,重心开始向西北偏移,偏移量较大;1977~2009年期间,由于城市向东和东北方向扩展,城市重心开始向东和东北方向偏移,偏移量都很大,最大达到629.79 m。城市紧凑度在1959~1968年间急剧下降;在1968~1997年间逐渐趋于稳定后又略有下降,1997~2009年开始慢慢上升,该市后期土地的集约化以及城市基础设施和公共资源的利用效率有所提高。城市用地扩展系数在1959~1986年期间小于1.12,城市用地规模扩展不足(1977年前后比较特殊),1986~2009年期间,城市用地扩展系数大于1.12,城市用地规模扩展过快。     

利用长时序流域不透水面数据集探索城市增长模式——以秦淮河流域为例

南京作为秦淮河下游的中心城市,在快速城镇化进程中面临着下垫面条件急剧变化带来的生态环境效应。不透水面作为衡量区域城镇化发展状况的关键指标,搭建了城市开发与环境质量的桥梁,可为当前空间治理与统筹城乡发展提供新的研究视角。在我国海绵城市建设背景下,以南京所在的秦淮河流域为研究区,通过半自动决策树分类模型从1988～2017年9景卫星影像提取流域基础不透水面数据集,利用多重滤波器构建连续变化的不透水地表,采用扩展强度指数和景观扩展指数定量分析30 a秦淮河流域不透水面时空扩展特征与城镇增长模式,揭示流域内城市发展轨迹及其成因。研究结果初步表明：(1)流域城镇化进程十分迅速。不透水面占比从1988年的3.09%增至2017年的26.49%,特别是2006年以来不透水面处于快速扩展期;(2)流域内不同城市的不透水面扩展进程差异明显。初期集中在南京城区和江宁城区,进入21世纪后则以江宁区、溧水区和句容市为主;(3)“多核扩展”和“点—轴扩展”是秦淮河流域城镇形态组建和增长的主要模式。初期以城区边缘式扩张为主,后期逐渐转向填充式增长,城镇一体化水平不断提升;(4)流域不透水面扩展是自然环境、经济发展...     

Monitoring annual urbanization activities in Guangzhou using Landsat images (1987–2015)

Rapid land-use/land-cover (LULC) changes such as urbanization have tremendous impacts on regional climate and environment. Satellite images acquired by fast-developing remote-sensing techniques provide frequent observations of the land surface, thereby allowing for continuous mapping of urbanization activities. In this study, we investigated the annual urbanization activities over the past three decades in Guangzhou, one of the largest metropolises in China. To enhance the efficiency of training sample extraction in long-term land-cover mapping, we developed a three-step method: 1) three spectral indices were derived to extract the candidates of training samples based on decision trees; 2) a spatial filter was used to extract homogenous samples for each land-cover type; and 3) temporal consistency checking was performed for the samples of urban areas. We applied the developed method to time-series Landsat images and produced annual land-cover maps of Guangzhou from 1987 to 2015. We evaluated the produced land-cover maps and found an average overall accuracy of 89.80% for all studied years. Our results show that dramatic urbanization has occurred in the region of the Guangzhou city, where built-up areas have mostly expanded to the northwest, east, and south of the central regions of Guangzhou. The average growth rate of urban areas in Guangzhou from 1987 to 2015 was at 38.72 km² per year, which was generally consistent with the government survey data. Future studies are required to understand how rapid urbanization in Guangzhou influences social economy and environmental sustainability.