兰州新区土地扩张及驱动力分析

针对新区建设过程中建成区土地快速扩张的问题,该文对建成区土地扩张时空规律及驱动力进行分析,使用不同时期的TM和Landsat 8影像,通过目视解译提取了兰州新区建成区2010、2013、2014和2015年土地扩张信息,并从政策、经济两方面对土地扩张的驱动力进行分析。结果表明:兰州新区建成区土地扩张在2010年7月至2013年12月,扩张强度为36.449%,呈慢速增长;2013年12月—2014年12月,扩张强度为74.546%,呈快速增长;2014年12月至2015年6月,扩张强度为43.614%,呈稳定增长;城市几何中心向东北方向偏移。驱动因子中,政策影响起到引导作用,经济影响起到驱动作用。其中,最大驱动力为招商引资。     

基于长时间序列遥感数据的太原市建成区近50年扩张规律分析

本文基于太原市1973-2020年遥感影像,利用人工目视解译结合Google历史影像校正提取建成区,引入紧凑度、扩张强度、扩张速度及重心迁移模型等指标对太原市建成区近50年的时空演变特征进行深入分析,并分析太原市建成区扩张的主要驱动力因素。研究表明：①1973-2020年太原市建成区面积扩张了15.12倍,平均年增长面积为7.85 km²;建成区紧凑度普遍较低,在南北方向扩展明显,重心沿南西约30°方向迁移了约3500 m。②引起太原市建成区扩张的主要因素有经济、人口扩张等,其中国民生产总值与人均GDP起到关键性作用。     

应用遥感和GIS技术提取城市建成区及驱动力分析——以南京市为例

本文以南京市为例,应用2001年11月16日Landsat7 ETM+和2005年中巴资源卫星遥感影像(校正后的4、3、2波段假彩色合成影像),采用遥感和GIS相结合的技术,对南京市建成区进行动态监测,并结合相关资料分析南京市建成区扩张特点以及驱动力。结果表明,南京市建成区主要是向西南和东北方向扩展,扩张特点为近郊城市化、郊区近郊化、农村城镇化。这种变化主要是受区位因素、社会经济和政策等因素影响。     

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。     

基于DMSP/OLS夜光遥感影像的城市扩张政策驱动因素分析

城市扩张的速度和方向已成为社会关注的焦点,利用夜光遥感进行城市扩张驱动力成因分析是近年来的研究热点。本文采用DMSP卫星所获取的年平均中国区域夜光影像数据,首先对1992—2012年共21年的时序夜光影像进行相对辐射定标;其次通过经验阈值法进行城市建成区面积提取,并通过Landsat影像进行精度验证;最后引入计量经济模型,以地级市主政官员政治周期为解释变量,对全国1992—2012年地级市扩张的政策驱动力因素进行了归因分析。通过本文的分析可知,地级市主政官员政治周期的更替对城市扩张方向存在显著影响。     

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...     

基于地理国情数据的天津滨海新区建成区动态监测

以天津滨海新区地理国情数据、遥感影像为数据源,提取2004—2015年间8期建成区信息,结合建设用地、社会经济等数据,利用相关模型分析城市建成区形态演变特征和规律,揭示建成区扩张驱动力,为城市发展提供技术和数据支持。     

基于遥感影像的武汉市2003～2015年城区扩张及驱动力分析

以Landsat TM/OLI遥感影像为数据源,将武汉市土地利用分类情况主要分为建成区、植被、水体三类.通过统计对比2003～2015年的各类土地面积变化发现,武汉市建成区的面积呈逐年增加的趋势.结合分析各类统计数据得出影响武汉市城区扩张的驱动力因子中,地理环境、交通因素和人口因素为其扩张的基础条件,经济结构的转变是推动主城区向外扩张的内在动力,宏观规划政策的调控起着外在的引导作用.     

郑州城市空间扩展特征及其驱动因素分析

以遥感与GIS为技术支撑,对郑州市1976-2004年城市建成区面积进行了动态监测,并对郑州28 a来城市空间扩展特征和驱动因素进行了分析。结果表明,郑州市建成区从1976-2004年面积增加了5.81倍,平均每年扩展10.75 km^2。其扩展占用的土地利用类型主要是耕地,其次为农村居民点和其他建设用地,还有一些林地、水库和沼泽地。社会经济因素是建成区扩展的内在推动力,经济发展、政策和规划等是建成区扩展的主要驱动力。通过郑州建成区遥感监测,了解城市空间扩展规律,对正确处理城市扩展与占用土地的关系具有重要意义。     

广州市建成区空间扩张与驱动因子研究

利用Landsat与DMSP/OLS相结合的建成区提取方法分析了1993-2010年广州市建成区扩张的时空特点及其驱动因子。首先采用突变检测法提取DMSP/OLS夜间灯光数据的建成区范围,并对利用SVM监督分类方法提取的Landsat影像建成区进行过滤,得到广州市城市建成区空间分布;再通过统计数据的相关性分析验证该方法的有效性;然后采用紧凑度、扩张速度、重心迁移等指标分析了广州市建成区扩张的时空特点;最后通过建成区面积与统计数据中经济参量的回归分析探讨了建成区扩张的驱动因子。     

An analysis of urban expansion and its associated thermal characteristics using Landsat imagery

There has been an increasing interest in mapping and monitoring urban land use/land cover using remote sensing techniques. However, there still exist quite a number of challenges in deriving urban extent and its expansion density from remote sensing data quantitatively. This study utilized Landsat TM/ETM+ remote sensing data to assess urban expansion and its thermal characteristics with a case study in the city of Changsha, China. We proposed a new approach for quantitatively determining built-up area, its expansion density and their respective relationship with land surface temperature (LST) patterns. An urban expansion metric was also developed using a moving window mechanism to identify urban built-up area and its expansion density based on selected threshold values. The study suggested that urban extent and its expansion density, as well as surface thermal characteristics and patterns could be identified through quantitatively derived remotely sensed indices and LST, which offer meaningful characteristics in quantifying urban expansion density and urban thermal pattern. Results from the case study demonstrated that: (1) the built-up area and urban expansion density have significantly increased in the city of Changsha from 1990 to 2001; and (2) the differences of urban expansion densities correspond to thermal effects, where a high percentage of imperviousness is usually associated with the area covered by high surface temperature.     

Spatio-temporal analysis of urban growth in selected small,medium and large Indian cities

This study attempts to ascertain the spatial and temporal variations in the evolution of Indian cities using multi-date remote sensing data. A two-stage object-based nearest neighbour classification approach with hierarchical segmentation was used to extract built-up area in selected small, medium and large cities, whereas object-based temporal inversion was applied for change analysis. The temporal trend of net population density, degree of scattered development and compactness of urban core in each city was determined using the built-up area. The study observed a declining trend in growth rate of built-up area in small and medium sized-cities, in contrast to large cities. However, the net population density in cities of all types is decreasing as urban growth has outpaced the corresponding population growth. Furthermore, small and medium cities indicated greater tendency for scattered development in comparison to large cities, whereas the core urban areas of the later appeared relatively less compact.     

一种基于车辆轨迹数据的城市建成区边界识别方法

城市建成区边界是城市土地资源管理和城市扩张管理的重要依据。本文提出了基于出租车轨迹数据，利用多分辨率下规则格网对比分析法及Densi-Graph阈值确定法，来自动提取城市建成区边界的方法。试验采用北京市2008年出租车GPS轨迹点数据，依据该方法模型计算提取城市建成区并进行结果准确度评价，将提取结果与北京市2008年土地覆盖空间分布图进行比对，所提取建成区边界范围与城镇、建设用地区域范围基本相符；将提取结果与中国城市统计年鉴数据进行比对，该年鉴中给定2008年北京市建成区总面积为1 310.94 km²，本文方法提取建成区总面积约为1 077.33 km²，提取准确度约为82.18%，表明本文方法可以获得准确的建成区范围。     

SARBuD 1.0： 面向深度学习的GF-3 精细模式SAR建筑数据集

合成孔径雷达SAR（Synthetic Aperture Radar）是开展城市建筑区信息获取与动态监测的重要数据源。本文建立了一个面向深度学习建筑区提取的中高分辨率SAR建筑区数据集SARBuD1.0 （SAR BUilding Dataset）。该数据集包含了覆盖中国不同区域的27景高分三号（GF-3）精细模式SAR图像,并从中获取了建筑区共计60000个SAR样本数据,结合光学图像与专家解译,制作了与样本数据对应的标签图像。SARBuD1.0数据集包含了不同地形场景类型、不同分布类型、不同区域的建筑区。该数据集可支持研究者对建筑区进行图像特征分析、辅助图像理解,并可对当前热点深度学习方法提供训练、测试数据支持。本文以山区建筑为例,使用传统纹理特征与深度学习特征对建筑区进行了特征分析与比较,相比于传统的人工设计的纹理特征,卷积神经网络具有更深、更多的特征,利用网络模型浅层的不同卷积核采样可得到各种纹理特征,在网络的深层卷积结构中可获取代表着类别的深层语义特征,使得分类器能更好地检测并提取图像中指定的目标。基于本数据集利用深度学习方法对不同地形区域的建筑区进行提取实验。实验结果表明基于本数据集训练的深度学习模型,对建筑区提取可以取得良好的结果,说明该数据集可以很好支持面向大数据的深度学习方法。其他学者可以基于SARBuD1.0数据集开展建筑区图像特征分析与语义分割提取等方面的研究。     

西安咸阳一体化进程中城市扩张及驱动力研究

本文以RS和GIS技术为支撑,以西咸一体化进程为研究对象,基于多时相TM/ETM+及相关统计数据,采用监督分类和归一化裸露指数(NDBI)等方法提取西安、咸阳两市的城市空间特征信息,结合地学统计方法,定量研究了城市扩张强度、紧凑度等指标,并探讨了影响西咸一体化进程的因子.得出:西咸一体化政策的出台是主导西咸两市对向不断...     

利用高分影像和珞珈一号数据进行住房空置率高精度空间估算

本文主要利用珞珈一号夜间灯光数据和高分遥感影像数据,提出了一种对研究区按建成区高层区、建成区低层区和非建成区住房进行空置率分区块估算的方法,采用夜晚实地记录的方法对估算结果进行精度检验,并通过LISA聚集图分析其空间聚集情况。表明结果：①研究区整体住房空置率为17.88%,均方根误差为0.14,其中非建成区的住房空置率整体上要高于建成区,而满置率却低于建成区;②研究区住房空置率呈高高集聚和低低集聚两种空间集聚特征,为进一步大范围了解农村地区住房情况及整体的空间分布规律提供了参考。     

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.     

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.