基于DMSP-OLS和珞珈一号夜光遥感影像的粤港澳大湾区城市空间格局演变

夜光遥感影像数据可有效反映城市空间格局变化。本文基于1992—2012年的DSMP-OLS夜光遥感影像和2018年的珞珈一号遥感影像,利用分层阈值法提取粤港澳大湾区内各城市建成区;通过计算平均灯光强度、平均灯光增长速率、城市建成区面积、城市建成区增长速率、城市重心、城市重心偏移距离等一系列指数,揭示区内各城市的空间格局演变过程。研究结果表明:①1992—2018年,粤港澳大湾区的城市规模大幅增长,沿珠江口两侧形成了以澳门、广州、深圳和香港为核心的倒“U”形城市群,并呈辐射状向周边扩张。②以珠江口为界,粤港澳大湾区东部各个城市的发展水平整体高于西部各个城市,广州、深圳、香港等核心城市发展水平明显高于江门、肇庆、惠州等外围城市。③1992—2018年,粤港澳大湾区建成区的增长速率由小变大,最后逐渐趋于稳定,2002—2007年是城市扩张最迅猛时期。④1992—2018年,粤港澳大湾区的各城市重心迁移方式表现为3种类型:持续向区域中心迁移;持续向相邻城市邻接区迁移;持续向海洋方向迁移。大部分城市的重心迁移方向呈“震荡”特征。     

A comparative study on four major cities in Northeastern Thailand using urban land density function

Urban land density is an important factor to understand how cities expand. An “Inverse S-shape Rule” was implemented for the first time to analyze urban land density in Northeastern Thailand using the four cities Khon Kaen, Udon Thani, Nakhon Phanom, and Nong Khai as study sites. Land density function was tested using different data classification techniques from previous studies. Each city was investigated over two different time periods between 2002 and 2015. Declining pattern characteristics of metropolitan area density outward from city centers can be quantified by fitting the parameters to urban land density functions. An inverse S-shape function was identified as the best data fit. The four selected cities showed conventional density variation for decline in urban land area from city centers to outlying areas. Overall trend indicated that cities became more compact over time since the density differences between the urban core and urban fringe were greater with increasing infilling growth within the urban boundary. All four cities increased in size over time; however, the increasing amount of built-up land in the surrounding rural areas did not follow the same trend in each case. Some functional parameters required careful interpretation because of the linear shape of the city as in the case of Nakhon Phanom. Using highly detailed urban data resulted in lower densities of urban areas compared to the conventional pixel-based classification, and this affected the overall shape of the inverse S-shape function. The fitted parameters and their changing trends indicated that the urban land density function was useful for understanding urban form and urban sprawl in Thailand. Results can be used to develop a specific framework for other cities with similar attributes in the future.     

基于夜间灯光数据的河南省城市扩张及驱动力研究

以河南省为研究区,利用2000—2018年夜间灯光数据、NDVI数据和VANUI数据提取城市建成区.在建成区面积和灯光强度两个方面的变化进行论述,利用景观指数对河南省城市景观进行定量分析,采用主成分分析法对河南省建成区扩张驱动力进行分析.结果表明,河南省建成区面积从1937 km2增加到5155 km2;灯光强度从75...     

GIS-based sustainable city compactness assessment using integration of MCDM,Bayes theorem and RADAR technology

In recent decades, compact urban development and smart cities are recognised as most sustainable urban form in an effort to protect natural environment. Therefore, evaluation of existing compactness and sustainability of an area is an essential task before the real development takes place. In the literature, it is possible to see some studies on city’s compactness assessment and most of them have considered only a few aspects of compact development analysis. This paper aims to analyse urban sustainability of Kajang city (Malaysia) through a comprehensive city compactness assessment using geographical information system and radar remote sensing technology. To measure building density, a RADAR image of the study area was used to extract built-up areas with the aid of pixel-based and object-based classification schemes. Mixed land use development, urban density and intensity were the main indicators of the analysis. Finally, multicriteria decision-making and Bayes theorems were applied for overall compactness assessment. Building density analysis was validated using standard confusion matrix, which showed more than 90% accuracy. Similarly, the root-mean squared error showed 0.35 for object-based classification. The results classified the zones of the Kajang city in the range of least to most compact zones with the compactness value of 0.00273–0.0146, respectively. The results obtained in this study can help local government to improve the compactness of least compact zones to make Kajang city more sustainable. Furthermore, the results revealed that efficient public transportation and proper community facilities are the key factors to achieve sustainable urban development.     

Urban Built-up Area Assessment of Ranchi Township Using Cartosat-I Stereopairs Satellite Images

In the present study, the Cartosat-I digital elevation model (DEM) was utilized to deduce the vertical characteristics of Ranchi urban area and its relation to long term built-up expansion (1927–2010). The DEM represents moderate variation in terrain relief ranging from 595 m to 754 m with majority of area exhibiting upto 3° of slope and 3° to 6° indicating flat to undulating nature of terrain in Ranchi township. The DEM was used to generate location of sinks within urban area, which are generally delineated along the drainage channels, adjacent to high-rise built-up land and along the elevated road network. The pattern of urban sprawl over the eight decades (1927–2010) were examined with reference to terrain relief zones, which indicated that the built-up growth was mainly taken place over the elevation range of moderate (620–660 m) (67.0%) and high relief (660–680 m) (19.8%) zones. Although earlier preference for built-up development was more in high elevation zones (660–680 m), the low elevation zones (<600–620 m) are now preferred for multistoried built-up land development where better groundwater availability occur. The spatial pattern of vertical growth of built-up land was assessed using contour density obtained from Cartosat-I DEM. The results show that the high density contours predominately correspond to hilly area and high-rise buildings at majority of locations. The urban sprawl pattern and population trend exhibited rapid increase in vertical built-up growth after 1996 indicating beginning of urban densification in Ranchi township.     

Evaluating the Long-term Urban Expansion of Ranchi Urban Agglomeration,India Using Geospatial Technology

The present study analyzes the built-up expansion of Ranchi urban agglomeration over a period of about 8 decades from 1927–2005. Satellite images and topographical maps were used to evaluate land use dynamics during these periods. Built-up growth of 473.7% during 1927–2005 was primarily at the expense of agricultural land along with reduction of natural water bodies reflects negative impacts of built-up expansion, which increased many folds in recent decades. The built-up growth is also analyzed with reference to population growth, land consumption rate and land transformation. The land loss due to increasing built-up growth of Ranchi were compared with other capital regions and cities along with population increase to provide insight into the possible scenario of built-up expansion in Ranchi urban area.     

浙江城市群人口与用地规模的时空分异结构演变研究

结合夜间灯光与城市统计数据,应用城市位序-规模法则及人地异速生长模型,初步分析了浙江人口分布及人地关系,考虑到人口增长的特征,提出了加入滞后效应的修正模型,对浙江城市群长期的发展展开了动态研究.研究结果表明,浙江城市群结构体系较为成熟,但是随着经济发展,城市首位度逐步提高,人口逐渐呈现聚集状态;浙江城市群中的杭州、宁波...     

遥感影像与POI数据相结合的城市建成区提取适用性研究——以沈阳市为例

城市建成区的发展状况是地理国情监测的重要内容，本文基于遥感影像数据和POI数据对城市建成区进行提取，针对二者的适用性问题进行了研究。试验以沈阳市为研究区域，在研究区域内选择2016年遥感影像数据和POI数据作为数据源进行对比分析。首先，对遥感影像数据和POI数据进行预处理；其次，通过监督分类的方法对遥感影像进行建成区的提取；然后，采用核密度估计法分析POI数据并提取出建成区；最后，利用叠加分析法对比分析这两种数据的适用性。试验结果表明：使用遥感影像数据作为数据源可以较为全面客观地反映城市建成区的发展现状；利用POI数据提取出的城市建成区具有较强的经济属性，能够很好地反映出城市中的经济活跃区。     

粤港澳大湾区建成区扩张时空特征分析

本文首先基于1992-2019年的夜间灯光数据(DMSP-OLS、NPP-VIIRS),参考粤港澳大湾区建成区面积数据,对比确定灯光阈值,提取出大湾区建成区边界;然后从扩张模式、扩张速度、扩张程度3个方面定量分析了大湾区建成区扩展时空特征;最后结合景观生态学方法分析了大湾区建成区扩展的时空格局特征。研究表明:①粤港澳大湾区城市群重心1992年位于东莞市内,2019年迁移至广州市,形成了以广州市为中心的珠三角地区发展核心区域;②整区扩展区域一体化,蔓延速度有所下降,蔓延程度增强,珠三角地区向周围城市的辐射能力不断增强;③城市扩展主要以珠三角地区为中心沿交通线呈倒"V"发展。     

基于夜光遥感数据的中原城市群建成区时空变化分析

本文基于“类NPP-VIIRS”夜光遥感数据集,采用改进的统计数据比较法提取中原城市群建成区,利用重心迁移指标和典型景观格局指标,分析中原城市群建成区2002—2020年的时空变化特征。研究表明:①中原城市群建成区扩张强度先快后慢,整体呈下降趋势;建成区重心在经历多次偏移后最终指向东南方向,但一直位于郑州大都市区范围内。②中原城市群发展迅速,建成区总面积在2002—2020年增加了1.429倍;在2011—2012年出现大量新兴城镇;2014年之后趋于稳定,城镇之间的联系越来越紧密。③中原城市群建成区空间格局复杂度逐年上升,破碎程度总体降低,郑州大都市区建成区扩张速度明显快于中原城市群整体建成区扩张速度。     

Studying town morphology through Remote Sensing

Remotely-sensed data products have got unique advantage over conventional data-gathering techniques in the study of urban morphology. The physical parameters like built-up area density, street pattern, population density, urban structure as well as functional characteristics which can be derived from land use/land cover map, are clearly visible on aerial data products. This technique provides synoptic view of the area which makes the study comprehensive and uniform. Sequential aerial photographs and satellite imagery help in studying the growth of urban area and temporal changes in urban structure. These informations are very useful in the planning of city extension. Here an attempt has been made to study the urban morphology of Saharanpur city by using panchromatic aerial photographs on scale 1∶10,000, IRS-1B LISS II geocoded imagery on 1∶50,000 scale and photo-maps on 1∶4000 scale, and the results are very encouraging.     

基于夜间灯光影像的太原市城市扩张特征及驱动力分析

随着城市化进程的推进,城市建成区范围不断扩大,在此过程中也出现了诸多问题。为更好地了解城市发展规律,本文选取1992、1997、2002、2007、2012年5期的DMSP/OLS数据和2017、2019年2期的NPP-VIIRS两种夜间灯光影像作为数据源,采用统计数据比较法提取太原市建成区范围,通过对太原市建成区形态扩张指数的分析,得出城市扩张规律。研究结果表明:①太原市建成区从1992-2019年太原市建成区面积增长了249.73km²,扩大了2.7倍,且建成区增长的面积主要集中在太原市的东南部。②2002-2012年和2017-2019年进入加速型扩张模式,扩张的速度快;1992-2019年太原市重心偏移距离为5676.42m,平均偏移速度为202.73m/a,重心位置总体向东南方偏移。③太原城市扩张的驱动力主要是受经济发展因素、人口因素、交通因素和政策规划因素的影响。本文为太原城市规划和空间结构调整提供理论依据和数据支撑。     

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.     

Spatio-temporal analysis for monitoring urban growth – a case study of Indore City

Urban sprawl is characterized by haphazard patchwork of development, which leads to an improper development in any city. To prevent this kind of sprawl in future, it is necessary to monitor the growth of the city. Hence, an attempt has been made in the present study to monitor the urban growth over a period of time by employing Remote Sensing and Geographic Information System techniques in conjunction with Shannon entropy. Shannon entropy is a measure to determine the compactness or dispersion of built-up land growth in the urban areas. The growth patterns of urban built-up land have been studied initially by dividing the area into four zones. The observations have been made with respect to each zone. Then, the study area has been divided into concentric circles of 1 km buffers and the growth patterns have been studied based on urban built-up density with respect to each circular buffer in all four zones. These observations have been integrated with road network to check the influence of infrastructure on haphazard urban growth. It has been found from the study that Shannon entropy is a good measure to determine the spatial concentration or dispersion of built-up land in the city. The study also proved the potential of RS and GIS techniques in the spatio-temporal analysis of urban growth trends and their consequences in the lands adjoining to urban areas.     

成渝城市群城市建设用地扩张格局及规模体系分析

基于1990—2018年六期城市建设用地监测数据，本文利用城市用地年面积增长率指数、公共边测度、核密度估计、位序-规模法则等方法，刻画成渝城市群扩张演化特征及其发展规律，并对城市群规模体系进行分析。结果表明：成渝城市群城市建设用地扩张呈先快后慢的趋势，空间上呈“中部高速，南北低速”的特点，建设用地扩张密度高值主要集中在成都、重庆二市且城市群内部连片发展不明显。城市群新增城市用地呈现无序蔓延态势，并未趋于紧凑。2000—2010年，成渝城市群规模分布极化，呈现明显的双核首位特征，2010年后规模分布由非均衡趋于均衡。     

西安城市扩展时空特征与驱动因素分析

针对西安城市扩展研究当中研究时段跨度大、政府政策对城市扩张影响分析较少等问题,该文基于1988—2015年10期Landsat影像及统计数据,利用线性趋势、多元回归分析等方法,分析了西安1988—2015年城市扩展特征及驱动因素。结果表明:西安主城区建设用地增量大、非主城区增速快,城市扩展主要发生在先后建立的经济新区当中,而第一、第二产业,城镇固定资产投资,非农业人口增加是驱动西安城市扩展的主要社会经济因素。     

逻辑回归元胞自动机模型的鸡西市城市扩张分析及预测

城市的快速扩张导致人地矛盾激化,土地利用效率下降.分析并预测城市发展状态,可以实现土地资源的合理配置,为城市发展提供合理规划.本文以鸡西市市辖区为例,利用Logistic-CA模型进行城市扩张模拟及趋势特征分析.结果表明:1)鸡西市在2005—2015年间城市处于低速扩张阶段,土地利用效率较低,城市发展较为分散;2)通...     

Monitoring Urban Landscape Dynamics Over Delhi (India) Using Remote Sensing (1998–2011) Inputs

Urbanization is increasingly becoming a widespread phenomenon at all scales of development around the globe. Be it developing or developed nations, all are witnessing urbanization at very high pace. In order to study its impacts, various methodologies and techniques are being implemented to measure growth of urban extents over spatial and temporal domains. But urbanization being a very dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. This paper aims at quantifying urban expansion in Delhi, the capital city of India. The process has been studied using urban land cover pattern derived from Landsat TM/ETM satellite data for two decades (1998–2011). These maps show that built-up increased by 417 ha in first time period (1998–2003) and 6,633 ha during next period (2003–2011) of study. For quantification of metrics for urban expansion, the Urban Landscape Analysis Tool (ULAT) was employed. Land cover mapping was done with accuracy of 92.67 %, 93.3 % and 96 % respectively for years 1998, 2003 and 2011. Three major land covers classes mapped are; (i) built-up, (ii) water and (iii) other or non-built-up. The maps were then utilized to extract degree of urbanization based on spatial density of built-up area consisting of seven classes, (i) Urban built-up, (ii) Suburban built-up,(iii) Rural built-up, (iv) Urbanized open land, (v) Captured open land, (vi) Rural open land and (vii) Water. These classes were demarcated based on the urbanness of cells. Similarly urban footprint maps were generated. The two time maps were compared to qualitatively and quantitatively capture the dynamics of urban expansion in the city. Along with urbanized area and urban footprint maps, the new development areas during the study time periods were also identified. The new development areas consisted of three major categories of developments, (i) infill, (ii) extension and (iii) leapfrog.     

Urban growth analysis using spatial and temporal data

Urban growth identification, quantification, knowledge of rate and the trends of growth would help in regional planning for better infrastructure provision in environmentally sound way. This requires analysis of spatial and temporal data, which help in quantifying the trends of growth on spatial scale. Emerging technologies such as Remote Sensing, Geographic Information System (GIS) along with Global Positioning System (GPS) help in this regard. Remote sensing aids in the collection of temporal data and GIS helps in spatial analysis. This paper focuses on the analysis of urban growth pattern in the form of either radial or linear sprawl along the Bangalore — Mysore highway. Various GIS base layers such as built-up areas along the highway, road network, village boundary etc. were generated using collateral data such as the Survey of India toposheet, etc. Further, this analysis was complemented with the computation of Shannon’s entropy, which helped in identifying prevalent sprawl zone, rate of growth and in delineating potential sprawl locations. The computation Shannon’s entropy helped in delineating regions with dispersed and compact growth. This study reveals that the Bangalore North and South taluks contributed mainly to the sprawl with 559% increase in built-up area over a period of 28 years and high degree of dispersion. The Mysore and Srirangapatna region showed 128% change in built-up area and a high potential for sprawl with slightly high dispersion. The degree of sprawl was found to be directly proportional to the distances from the cities.     

利用Landsat卫星影像进行东莞市地表温度反演与变化分析

随着快速城市化进程,我国东南沿海一些城市新增了大量的城市建设用地,影响到了城市的人居环境,诱发了城市热的环境问题。探索城市热环境时空布局与变化特征,分析变化原因对城市可持续发展和人居环境质量改善具有重要的现实意义。本文以东莞市为例,以1986—2017年间8个时相Landsat系列卫星影像为数据源,利用单通道算法对东莞市地表温度进行定量反演,分析东莞城市热环境的时空分布特征及其变化,并探讨其影响因素。结果表明,东莞市30多年的城市热环境主要表现为热岛的分布格局,由零散的点状分布逐渐发展成沿着主要交通路线的带状分布;东莞市城镇/街道中心的地表温度明显高于郊区、村落,存在较强的热岛效应;1986—2017年东莞市的热岛比例指数总体呈上升趋势,从0.06上升到0.15,在2009年东莞市热岛效应达到最高。研究结果对合理规划东莞城市建设,促进城市建设可持续发展等方面具有重要意义。