基于RS与GIS的宁波市鄞州区土地利用变化分析

利用1990年土地详查变更调查数据和2006年SPOT遥感影像数据,借助GIS空间分析能力,通过计算土地利用类型动态度、转移矩阵、扩展强度指数分析了鄞州区的城镇土地利用变化的时空特征。研究结果表明:1)鄞州区城市、建制镇、交通运输用地、工矿用地扩展的大部分来源于耕地;2)区各街道城镇建设用地扩展强度指数大于建制镇;3)建设用地扩展以新城区及宁波城区周边的街道镇为主。     

青藏高原少数民族聚居地区城镇用地扩展时空演变分析——以青海省海东市为例

基于多期遥感影像数据,采用GIS空间叠加分析技术,选择扩展强度指数、紧凑度、分形维数、重心转移指数和扩展方位指数,定量分析了海东市1990—2016年间城镇用地扩展时空分异特征。研究结果表明,海东市城镇用地持续增加,扩展强度呈现先降低后升高的中速持续扩展趋势,城镇从空间无序转向空间协同发展;城镇用地紧凑度持续下降,分形维数持续增大,城镇用地不规则性增加;城镇用地重心转移呈现出“有序—无序—有序”的特征,城镇用地扩展方位分异特征明显,并表现出单翼、双翼和扇形多种扩展模式并存的特点。在海东市城镇用地扩展中,高原河谷地形为自然制约因素,园区新区的设立为主要路径,政策诱变为主要动力,重大基础设施建设为动力保障。     

徐州城市空间扩展特征分析

基于多时相TM影像和GIS空间技术,计算了徐州城市空间形态的分形维数和紧凑度指数,运用象限方位分析和缓冲区分析方法,对研究区城镇建设用地的空间分异特征进行定量分析。结果表明,1983~2013年,徐州城镇建设用地扩展了1.4倍;全市范围内,中心点西南区为建设用地扩展的核心区,而中心城区整体向东南方向移动;城镇扩展强度呈现中心向外围"圈层式"扩展趋势。     

赣榆县城镇用地扩展时空特征分析

赣榆县作为东部沿海城市城镇化发展尤为突出。本文选取1987年、2000年和2009年3期Landsat TM/ETM遥感影像作为主要数据源,利用ArcGIS软件数字化功能对城镇建成区进行提取,并将3期提取结果进行叠加分析,通过城镇扩展面积比重、城镇扩展指数、城镇用地扩展强度指数、城市紧凑度指数和城市用地分形维数模型对城镇扩展动态特征进行分析。     

1990年以来太原市主城区空间扩张特征研究

以太原市主城区为研究对象,利用1990、2000、2010、2019年建设用地的矢量数据,采用城市扩展速率、扩展强度、紧凑度、扩展弹性系数等指数,并结合土地利用动态模型、等扇分析等方法定量研究不同时期太原市主城区空间演变特征。研究发现:太原市扩展面积呈逐年增加的趋势,主城区的扩展速度与强度存在较大的波动,在研究时段内仅有2000—2010年主城区扩展速度与人口增长速度相对均衡;主城区用地结构变化较为显著,耕地面积大幅减少,并且多转向建设用地,土地覆被状况趋于稳定;城市扩展类型主要以填充式、外延式为主,受地形条件、经济开发以及政策支持的影响,扩展存在明显的方位性,主要沿汾河呈轴向型非均衡扩展,扩展重心向东南方向偏移。     

建设用地扩展时空变化特征研究——以重庆市巴南区为例

建设用地对城市发展、经济发展、提高人居环境有重要影响.选择重庆市巴南区为研究区,以美国陆地卫星遥感数据为数据源,应用3S技术分析重庆市巴南区2000—2015年建设用地扩展速率、扩展强度指数、城市扩展方位、重心转移方向.结果表明:2000—2015年巴南区建设用地扩展为20.87 km2,呈正态增长;扩展速度最快为2005—2010年,扩展速率达到2.28 km2/a,最慢为2000—2005年,扩展速率仅有0.36 km2/a;巴南区建设用地主要集中于西部地区,并由西向东方向发展,主要受地形地势影响.建设用地扩展变化特征分析可为巴南区可持续发展提供科学、合理的参考.     

长株潭地区建设用地扩张遥感时空特征分析

利用遥感、GIS一体化技术,获取长沙、株洲、湘潭3市(长株潭地区)2000年、2005年和2010年3期陆地卫星图像,提取建设用地扩张及其空间分布信息。运用扩张速度指数和强度指数分别对2000—2010年间10 a变化以及2005年为界的前后两个5 a区域建设用地时序特征进行分析;利用优势度指数分析了区域建设用地扩张的空间趋向性。结果表明:长株潭地区建设用地在不同时期都处于增长的趋势,其中前5 a建设用地扩张速度和强度都要明显高于后5 a。2000—2010年间建设用地面积增加约3.94万hm2,前后2个时期的扩张面积分别占增加总量的57.30%和42.70%。对长株潭地区建设用地扩张进行时空特征分析,认为建设用地扩张引起区域内耕地和林地在数量和空间格局上的变化,城区周围9~12 km范围内耕地极易转入建设用地;2个时期河流附近耕地流入建设用地随距离变化而呈现不同特征;前5 a城区周围林地转入建设用地强度大,距离交通用地3 km附近的林地易转入建设用地。     

城镇扩张遥感动态监测的方法研究

利用遥感技术动态监测城镇扩展己成为一个重要的研究领域和应用方向。本论文基于多源多时相遥感影像和地形数据,以福建漳州市区为示范区,探讨城镇建筑用地扩张遥感动态监测的方法。作者认为基于ASTER影像,综合利用非监督分类、多时相植被指数、城镇建筑用地的地形分布等知识建立分类决策规则,可以有效提取城镇建筑用地信息,准确度不低于90%。同时提出利用城镇建筑用地时空分布知识、多时相植被指数知识,改进TM三指数城镇建筑用地提取方法的思路,最终提取的城镇建筑用地信息满足城镇建筑用地动态变化分析的精度要求。     

江西土地利用/覆盖变化研究

本文利用了2000年和2004年的LandsatTM/MSS数据,对江西地区几年来的土地利用/覆盖变化进行了研究。结果表明从2000～2004年,江西地区发生了快速城市化的大规模土地利用/覆盖变化,表现出城镇用地通过大量占用平原区耕地扩展,非城镇用地间结构变化明显的基本特征。同时土地利用/覆盖变化区域差异明显,近郊区和远郊区的土地利用/覆盖变化强度发生了逆转,远郊区高密度城镇用地的重心发生了一定程度的移动,显示出同期城镇用地有扩展的趋势。     

基于STIRPAT模型的长株潭城市建设用地扩展驱动因素分析

通过岭回归分析方法,尝试构建STIRPAT定量模型,进而分析社会经济因素对长株潭城市建设用地扩展的作用程度。首先,利用统计数据,从城市建设面积扩展动态度和扩展强度两个指标分析了1995—2014年长沙、株洲和湘潭3个城市建设用地扩张的阶段性特征;然后选定非农业人口、GDP、工业总产值、固定投资总额和第三产业增值作为建设用地扩张的驱动因子,应用岭回归分析方法拟合STIRPAT模型,构建出长株潭建设用地扩展驱动的量化模型。结果表明:工业总产值、固定资产投资总额和非农业人口分别是长沙、株洲和湘潭建设用地扩张的第一驱动因子;非农人口增长引发对城市建设用地的需求使得其在3个城市的驱动模型结果中均表现出较强的影响力。     

甘肃省主要城市空间扩张协调性分析

在全国地级以上城市以及典型城市群空间格局监测项目中,根据城市用地增长弹性系数判断城市空间扩展的协调性。结果表明,2000-2016年全国大多数城市用地增长弹性系数大于最优值1.12。分析了城市用地增长弹性系数最优值的来源以及城市空间扩张与人口增长关系的相关研究成果,将人均城市用地面积与城市用地增长弹性系数结合起来,用于城市空间扩张协调性分析,并在甘肃省主要城市进行了空间扩张分析实验。     

Land Use/Cover Change Detection of Tiruchirapalli City, India, Using Integrated Remote Sensing and GIS Tools

The rapid growth of urban population in India is a cause of concern among country??s urban and town planners for efficient urban planning. The drastic growth of urban areas has resulted in sharp land use and land cover changes. In recent years, the significance of spatial data technologies, especially the application of remotely sensed data and geographical information systems (GIS) has been widely used. The present study investigates the urban growth of Tiruchirapalli city, Tamilnadu using IRS satellite data for the years 1989, 1992, 1995, 1998, 2001, 2004, 2007, and 2010. The eight satellite images are enhanced using convolution spatial enhancement method with Kernel (7?×?7) edge enhance function. Supervised classification method is used to classify the urban land use and land cover. The GIS is used to prepare the different layers belonging to various land uses identified from remotely sensed data. The analysis of the results show the drastic increase of built up area and reduced green cover within the city boundary limit.     

Incorporating sprawl and adjacency measures in land‐use forecasting model: A case study of Collin County,TX

Sprawl measures have largely been neglected in land‐use forecasting models. The current approach for land‐use allocation using optimization mostly utilizes objective functions and constraints that are non‐spatial in nature. Application of spatial constraints could take care of the contiguity and compactness of land uses and can be utilized to address urban sprawl. Because a land‐use model is used as an input to transportation modeling, a better spatial allocation strategy for more compact land‐use projections will promote better transportation planning and sustainable development. This study formulates a scenario‐based approach to normative modeling of urban sprawl. In doing so, it seeks to improve the land‐use projections by employing a spatial optimization model with contiguity and compactness consideration. This study incorporates urban sprawl measures based on smart growth principles together with a mixed‐use factor, and adjacency consideration of nearby land uses. The objective function used in the study maximizes net suitability based on imposed constraints. These constraints are based on smart growth principles that enhance walkability in neighborhoods, promote better health for residents, and encourage mixed‐use development. The formulated model has been applied to Collin County, TX, a fast‐developing suburban county located to the north of the Dallas–Fort Worth metroplex. The suitability of land cells indicates the probability of conversion, which is calculated using spatial discrete choice analysis with Moran eigenvector spatial filtering for vacant cells at a resolution of 150 × 150 m employing factors of the built environment, and socioeconomic and demographic characteristics. This study demonstrates how spatial proximity between land uses, which has been ignored to date, can be used to control sprawl, resulting in better mixing of different land uses based on constraints imposed in a spatial optimization problem.     

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe ‘urban land growth types’ and ‘fluctuations in urban land growth’ within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of ‘urban land growth types’ and ‘fluctuations in urban land growth’ follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.     

A Bayesian characterization of urban land use configurations from VHR remote sensing images

The composition and arrangement of spatial entities, i.e., land cover objects, play a key role in distinguishing land use types from very high resolution (VHR) remote sensing images, in particular in urban environments. This paper presents a new method to characterize the spatial arrangement for urban land use extraction using VHR images. We derive an adjacency unit matrix to represent the spatial arrangement of land cover objects obtained from a VHR image, and use a graph convolutional network to quantify the spatial arrangement by extracting hidden features from adjacency unit matrices. The distribution of the spatial arrangement variables, i.e., hidden features, and the spatial composition variables, i.e., widely used land use indicators, are then estimated. We use a Bayesian method to integrate the variables of spatial arrangement and composition for urban land use extraction. Experiments were conducted using three VHR images acquired in two urban areas: a Pleiades image in Wuhan in 2013, a Superview image in Wuhan in 2019, and a GeoEye image in Oklahoma City in 2012. Our results show that the proposed method provides an effective means to characterize the spatial arrangement of land cover objects, and produces urban land use extractions with overall accuracies (i.e., 86% and 93%) higher than existing methods (i.e., 83% and 88%) that use spatial arrangement information based on building types on the Pleiades and GeoEye datasets. Moreover, it is unnecessary to further categorize the dominant land cover type into finer types for the characterization of spatial arrangement. We conclude that the proposed method has a high potential for the characterization of urban structure using different VHR images, and for the extraction of urban land use in different urban areas.     

Assessment of Land use/land cover Change in the North-West District of Delhi Using Remote Sensing and GIS Techniques

Land is one of the prime natural resources. A city grows not only by population but also by changes in spatial dimensions. Urban population growth and urban sprawl induced land use changes and land transformation. The land transformation is a natural process and cannot be stopped but it can be regulated. Many geographical changes at the urban periphery are associated with the transfer of land from rural to urban purpose. There is an urgent need for fast growing areas like Delhi, which can be easily done by high-resolution remote sensing data. Land use/land cover of North West of Delhi has been analyzed for the time period of 1972?C2003. The remote sensing data used in study is Aster image of 2003 with a spatial resolution of 15?m and other data of 1972 Survey of India (SOI) toposheet at the scale of 1:50,000. Supervised digital classification using maximum likelihood classifier was applied for preparing land use/land cover. A change detection model was applied in ERDAS Imagine to find out the land use/land cover during 1972 to 2003. Eight land use classes was identified but main dominated classes were built up and agricultural land. A drastic change has been recorded during 30 years of time i. e. (1972-2003). In 1972, 92.06% of the land was under agricultural practice, which reduced to 64.71% in 2003. This shows 27.35% decrease in agricultural land in three decades. On the other hand built up area was 6.31% in 1972, which increased to 34% in 2003. One of the main cause of this land use change is the population growth due to the migration in the district from small cities and rural areas of Delhi.     

Evaluation and development of sustainable urban land use plans through spatial optimization

Along with rapid global urbanization, cities are challenged by environmental risks and resource scarcity. Sustainable urban planning is central to address the dilemma of economic growth and ecosystem protection, where the use of land is critical. Sustainable land use patterns are spatially explicit in nature, and can be structured and addressed using spatial optimization integrating GIS and mathematical models. This research discusses prominent sustainability concerns in land use planning and suggests a generalized multi‐objective spatial optimization model to facilitate conventional planning. The model is structured to meet land use demand while satisfying the requirements of the physical environment, society and economy. Unlike existing work relying on raster data, due to its simple data structure and ease of spatial relationship evaluation, this research develops an approach for identifying land use solutions based on vector data that better reflects the actual shape and spatial layout of land parcels as well as the ways land use information is managed in practice. An evolutionary algorithm is developed to find the set of efficient (Pareto) solutions given the complexity of vector‐based representations of space. The proposed approach is applied in an empirical study of Dafeng, China in order to support local urban growth and development. The results demonstrate that spatial optimization can be a powerful tool for deriving effective and efficient land use planning strategies. A comparison to results using a raster data approach supports the superiority of land use optimization using vector data as part of planning practice.     

基于GIS和空间逻辑回归模型的城市空间扩展预测——以深圳市龙岗区为例

本文从土地利用的角度出发,分析了城市扩展的基本原理和过程,提出城市空间扩展直接原因在于城市发展对土地的需求,城市空间扩展预测可分为城市发展用地总体需求预测和空间扩展预测的观点,并采用优化组合预测模型对城市用地总体规模扩展进行建模,利用空间逻辑回归模型对空间分布扩展进行建模,两者共同构建了城市空间扩展预测模型,最后以深圳市龙岗区为例,进行了试验,验证了这种思路。     

Tourism land use simulation for regional tourism planning using POIs and cellular automata

Previous studies on tourism land use primarily focus on the spatial distribution, and its related impacts on the environment. Here, we propose a future tourism land use simulation model for mountain vacations based on the cellular automata and Markov chain methods, having verified and simulated tourism land use in Emeishan city at a spatial resolution of 30 × 30 m using remote sensing and GIS. In addition, we introduced a tourism land use intensity index to study the spatial expansion mode of tourism land use. The results have confirmed the validity of the model and demonstrated its ability to simulate future tourism land use. The average growth rate of tourism land use from 2010 to 2015 is 33.36%, and tourism land use will rise from 1.26% of Emeishan city’s land area in 2015 to 2.95% in 2030. Tourism land use shows a spatial expansion pattern along channels from scenic spots to the urban area. The growth of tourism land use in the protected area has an increasing trend when there is no restriction on development, especially in the Eshan region. The simulation results can provide useful implications and guides for regional tourism planning and management.