基于GIS的西安市城镇建设用地扩展研究

基于1988年TM影像、2002年ETM影像和2007年TM影像提取了西安市主城区和远郊区城镇建设用地信息,利用GIS技术对三期城镇建设用地进行了叠加得到西安市城镇扩展数据。利用主要道路交通图和行政边界图对扩展数据进行裁切,得到了西安市绕城高速内和远郊区的城镇扩展信息。运用扩展强度指数、城镇建设用地相对变化率和分形维数等模型对西安市的城镇扩展进行了分析研究。研究结果表明,研究区内的城镇建设用地面积从1988年的151 796 493.8 m2增加到2007年的365 180 608.0 m2,总体扩展了2.4倍。其中主城区的扩展主要集中在二环与绕城高速之间,二环内的城镇建设用地面积扩展速度相对较慢;西安市的3个远郊区中长安区扩展倍数最大,在1988~2007年扩展了7.39倍,其次为临潼区和阎良区;1988~2002年西安市的主城区和远郊区的分形维数都呈增加的趋势,城市边缘形态趋于复杂,而2002~2007年分形维数呈减少的趋势。西安市远郊区的城镇建设用地相对变化率高于主城区,未来城市发展的格局逐渐呈现为主城区城镇扩展速度的相对放缓和远郊区的规模不断增大的趋势。     

基于GIS的西安市城镇建设用地扩展研究

基于1988年TM影像、2002年ETM影像和2007年TM影像提取了西安市主城区和远郊区城镇建设用地信息,利用GIS技术对三期城镇建设用地进行了叠加得到西安市城镇扩展数据.利用主要道路交通图和行政边界图对扩展数据进行裁切,得到了西安市绕城高速内和远郊区的城镇扩展信息.运用扩展强度指数、城镇建设用地相对变化率和分形维数等模型对西安市的城镇扩展进行了分析研究.研究结果表明,研究区内的城镇建设用地面积从1988年的151 796 493.8 m2增加到2007年的365180 608.0 m2,总体扩展了2.4倍.其中主城区的扩展主要集中在二环与绕城高速之间,二环内的城镇建设用地面积扩展速度相对较慢;西安市的3个远郊区中长安区扩展倍数最大,在1988～2007年扩展了7.39倍,其次为临潼区和阎良区;1988～2002年西安市的主城区和远郊区的分形维数都呈增加的趋势,城市边缘形态趋于复杂,而2002～2007年分形维数呈减少的趋势.西安市远郊区的城镇建设用地相对变化率高于主城区,未来城市发展的格局逐渐呈现为主城区城镇扩展速度的相对放缓和远郊区的规模不断增大的趋势.     

利用夜间灯光数据的武汉城市空间格局演化

鉴于夜间灯光数据在市级尺度精度不足等问题,提出了一种基于NDVI修正的阈值提取法。在此基础上,采用空间扩展模式分析、景观格局指数、重心迁移模型方法,系统性分析近16年武汉市城市扩张时空特征,以期为武汉未来城镇建设用地布局及相关政策制定提供决策依据。研究结果表明,基于NDVI修正的阈值提取法,可用于地级市建成区提取及城市扩展研究;武汉城市扩张模式表现为以主城区为中心的面状发展为主,点状城镇发展所占比例相对较小,主要受黄陂区、新洲区的牵引;城镇发展经历了高度破碎化时期之后不断趋于集约化发展,破碎度逐步减小;武汉建成区重心移动幅度越来越大,城市化过程总体处于较快发展状态。     

近20年来南通市区城镇用地扩展遥感监测

借助RS和G IS技术,利用TM卫星影像资料对近20年来南通市区城镇用地扩展过程进行了研究。结果表明,1984~2003年主城区扩展趋于缓慢,市区边缘带乡镇扩展速度加快。在扩展模式上,主城区先是沿着老城区填充式零星的扩展,然后逐步与周边乡镇连成一片,并形成新的扩展中心。城镇用地扩展的主要驱动力是工业化水平的提高。把市区城镇用地扩展图与土壤图相叠加,并参照1987年土地利用现状调查资料,结果表明南通市区城镇用地扩展主要侵占城市周边优质的菜地和农田。     

近30 a来蒙古国乌兰巴托市城镇扩张及其驱动力分析

蒙古国自20世纪90年代政体改革以来,城镇化发展迅速,认识其区域发展特征与城镇化特点对于我国"一带一路"倡议实施及"中蒙俄经济走廊"建设具有重要意义。基于遥感影像,采用面向对象的分类方法,获取了蒙古国乌兰巴托市1990、2001、2010、2017年土地覆盖数据集,总体分类精度分别为86.00%、89.00%、91.6%、94.80%。利用转移矩阵对其土地覆盖变化信息进行了挖掘,结果表明:草地—林地、草地—建筑用地、林地—草地之间的转移占绝对优势。建筑用地增幅最大,扩张趋势显著,面积从99.87 km~2增加到了216.16 km~2,增幅达到了216.44%,扩张速率8.01 km~2/a,属于快速扩展模式。扩张方向以中北部、东北部和西部为主。其中,中北部主要是"summer house"度假房屋建设为主,东北部以传统的家—户—院的房屋为主(蒙古包与低层建筑混搭),西部以工业用地与居民用地为主。乌兰巴托市城镇用地扩张是外部社会经济发展和国家制度政策共同作用发生的,其中,土地私有化、市场经济化与人口数量是城镇用地扩张最主要的驱动因素。     

基于灯光数据的西南生态屏障区建设用地扩展的空间分布特征

基于 1998 年、2003 年、2008 年和 2013 年 4 期夜间灯光影像数据，本文对西南生态屏障区建设用地的扩展情况进行了分析。结果表明：(1) 1998 年到 2013 年，研究区建设用地面积共增加了 19282.31 km2。其中，广西和四川建设用地面积增加最多。从时间尺度上看，1998-2003 年该地区的建设用地扩展强度最大，为 5.23%。与全国相比，研究区建设用地扩展强度处于中等水平。(2) 研究时段内，研究区建设用地的重心呈南移趋势。(3) 整个研究区建设用地扩展以边缘型扩展为主，并呈逐渐增加的趋势。飞地型扩展不断减小，内填型扩展减小迅速。各地区建设用地仍以边缘型扩展为主，不同地区变化情况不同。(4) 交通运输，尤其是铁路运输的发展对研究区建设用地的扩展至关重要。城镇人口的增加、游客的增多及经济的发展对建设用地的扩展具有一定的影响。     

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、交通建设和政策规划等多种因素综合导致的结果。     

基于图像分析的中国城市化空间过程研究

针对城市扩张的加速引起的一系列问题,利用DMSP/OLS夜间灯光图像采用图像处理技术分析了中国大陆在省级尺度上的城市化空间过程。基于区域标记的种子填充算法从全球夜间灯光图像中提取各省市灯光图像;以1992、1996、1998年为例基于统计数据,利用直方图结合二分法快速确定各省市城镇可用地面积的阈值,提取城镇可用地面积,证明与统计数据的相对误差不超过10%;分析1992—2010年夜间灯光图像亮度总值与地区生产总值、人口之间在省级尺度上的相关性,构建相应的模型,证明各省灯光亮度值与其地区生产总值、人口之间存在较强的相关性且不同城市化空间扩展模式的灯光亮度值与其地区生产总值、人口之间存在不同的相关性。     

高分卫星在甘肃省城镇扩展研究中的应用

遥感和地理信息相结合是实现对变化快速、空间分布范围广的城镇扩展进行实时、准确监测的最佳手段之一。获取城镇扩展的基础数据,把握城镇扩展的空间分异特征及城镇体系结构特征,揭示城镇扩展与耕地保护之间矛盾有助于政府管理部门做出正确的决策。以高分卫星影像为主要数据源,结合土地利用数据对甘肃省87个主要城(市)镇2008~2014年城镇扩展进行监测,对监测结果进行分析。结果表明:甘肃省城镇化发展较快,城(市)镇扩展面积达232.01km~2,扩展强度为25.3%;城镇扩展空间分异大,位于河西绿洲区、兰白城镇带以及陇东能源基地的经济基础好,交通要道沿线的城(市)镇发展迅速,而在甘南山地和祁连山高山地区的城镇规模小,发展落后;城镇体系结构不协调,城镇体系呈二元结构,整体发展水平较低;46.95%的城镇扩展用地源自耕地,耕地保护压力大,提高城镇建设用地节约集约利用水平是协调城镇发展和耕地保护矛盾的有效途径。     

基于TerraSAR-X和Landsat影像的城镇用地提取——以常州市为例

城市扩张作为一个重要的社会和经济现象,正以前所未有的速度和规模在世界各地不断推进。但是盲目的城镇扩张会对社会、生态及经济等产生一定的影响。因此准确高效地提取城镇用地,为城镇规划等提供决策依据变得尤为重要。以常州市为例,对基于单极化Terra SAR-X影像提取建成区方法进行了改进和完善,结合光学卫星影像辅助提取城镇范围,以提高这一类城市城镇提取的精度。该方法首先通过分析局部散斑特性和强度信息,采用基于阈值的方法提取城市和非城市区域,然后再利用Landsat8图像,采用最大似然法和分类后处理来提取城镇范围内的拆迁区域,最后结合提取的城市区域和拆迁区域来完成城镇用地的提取。最后,该研究中,利用Google Earth的遥感影像图进行交叉验证生成混淆矩阵来评价城镇用地的提取精度,其总体分类精度约为89%,表明该方法行之有效。     

Land Change Patterns and Driving Mechanism in Beijing since 1990 based on GEE Platform

Land-cover and land-use dynamics is a key component for global change,and it is a significant form of the impact of human activities on physical environment.Basing Google Earth Engine platform and Classification And Regression Tree method,selected seven types of cultivated land,forest,grassland,wetland,water body,artificial surface and bare land as classification system,the paper used Landsat 5 TM and Landsat 8 OLI images to interpret the land\|cover and land\|use since 1990 of Beijing.Simultaneously,the paper analyzed and summarized the character of land\|use changing and driving force.The results show that：(1) GEE has outstanding advantages in remote sensing data analysis and processing at regional scales.(2) The CART method has high accuracy of remote sensing classification,and the overall accuracy of validation of 6 land cover products is above 93%.The spatial consistency of 2010 products and GlobeLand30\|2010 data showed that the spatial consistency ratios of woodland,water body and cultivated land were 84.28%,74.75%and 73.56% respectively.The spatial consistency of the distribution is 74.0%.(3) The main land types in Beijing were cultivated land,woodland and artificial surface,and the area accounted for about 90%.During the period from 1990 to 2016,the artificial surface and woodland area increased,and the cultivated land and water were shrinking.The artificial surface area increase of 1 371 km²,and cultivated land shrinkage 40%;On Beijing plain area,artificial surface by the circle of “spread pie” expansion trend to “blossom everywhere” expansion trend;The expansion of the artificial surface is mainly achieved through the encroachment of cultivated land.We constructed a multidimensional stepwise linear equation model to analyze the driving force of land type change,indicated that rapid population growth,rapid economic development,government\|related policies and other socio\|economic development factors jointly drive the Beijing land-cover/land-use evolution process.     

Urban Expansion and Driving Force Analysis of Ulaanbaatar in Mongolia in Recent Thirty Years

Since the reform of the regime in the 1990s，Mongolian urban experienced a rapid development.Understanding the characteristics of urbanization and development in Mongolia is much of significance to China’s implementation of the “Belt and Road” strategy and “China-Mongolia-Russia Economic Corridor”.This study was based on theLandsat TM/OLI remote sensing image，using object-oriented classification method，and obtained 1990，2001，2010，2017 land cover data set，the overall classification accuracy were 86%，89%，91.6%，94.80% respectively，Kappa coefficient were 0.83，0.869，0，898，0，935.based on the transfer matrix，the information of land cover change from 1990 to 2017 in Ulaanbaatar was mined，the results showed that：① the areas of built area，barren，and water showed an increasing trend，and the built area was increased most.On the contrary，the area of forest，cropland，and grassland showed a tendency to decrease，and the forest area decreased most.② the transfer between grassland and forest，grassland and built area，forest and grassland played a major role in land cover change of Ulaanbaatar.The change area from 1990 to 2001，2001 to 2010 and 2010 to 2017 accounted for nearly 71%，74%，and 79% of the total change area.③ the expansion trend of built area was significant，the area has increased from 99.87 km2to 216.16 km2，the growth rate has reached 216%，the expansion rate was 8.01 km2/a，which belonged to the rapid expansion mode.The middle-north with the type of summer house，northeast with the types of traditional houses based on the structure of home-household-yard，mixture of Mongolian yurts and low buildings，west with the types of industrial land and residential land.The urbanization in Ulaanbaatar caused by the interaction of external social economic development and national policies，among of which，land privatization and market economic were the main policy driving forces of urbanization     

Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS

We used three Landsat images together with socio‐economic data in a post‐classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Nairobi city. Land use/cover statistics, extracted from Landsat Multi‐spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images for 1976, 1988 and 2000 respectively, revealed that the built‐up area has expanded by about 47?km². The road network has influenced the spatial patterns and structure of urban development, so that the expansion of the built‐up areas has assumed an accretive as well as linear growth along the major roads. The urban expansion has been accompanied by loss of forests and urban sprawl. Integration of demographic and socio‐economic data with land use/cover change revealed that economic growth and proximity to transportation routes have been the major factors promoting urban expansion. Topography, geology and soils were also analysed as possible factors influencing expansion. The integration of remote sensing and Geographical Information System (GIS) was found to be effective in monitoring land use/cover changes and providing valuable information necessary for planning and research. A better understanding of the spatial and temporal dynamics of the city's growth, provided by this study, forms a basis for better planning and effective spatial organization of urban activities for future development of Nairobi city.     

基于格网划分的城市区域土地利用景观格局时空变化遥感监测

城市化引起的土地利用变化已成为城市问题研究热点。多时相遥感变化检测能够监测到土地利用变化的数量,被广泛地用来进行城市扩张研究。对于城市化引起的城市空间结构变化,最新研究引入景观格局分析法,大量涌现的景观指标为景观格局定量化表达提供了基础。目前对于城市化过程中景观格局时空变化的描述过于笼统,一般是对整个研究区域提取全局景观格局及其时间变化。通过提出一种基于网格划分的景观格局提取与时空变化检测方法,并运用此方法研究了北京市城市化进程中景观格局的时空变化。结果表明:基于网格划分的景观格局变化检测方法能够检测出城市空间结构变化的数量、位置和模式,为理解城市扩张行为以及城市扩张建模提供了相比较于遥感土地利用变化检测之外的另一种知识。     

哈尔滨城乡梯度建设用地结构变化及不透水面遥感监测分析

在快速城镇化背景下,客观掌握城市扩张进程中的城乡建设用地及内部不透水面变化特征,有利于优化大都市城乡用地结构及空间融合发展。基于城乡建设用地及不透水面遥感监测数据集,应用空间分析模型,对哈尔滨2000~2015年城乡建设用地规模、结构及内部不透水面时空演变特征进行分析,探究城市扩张格局、区位差异、建设用地利用强度及城乡之间的差异。结果表明：①2000年以来城乡建设用地快速扩张了158.32 km²,年变化率和动态度均呈先增大后减小趋势;从城市核心区至远郊区方向其扩张规模依次增大,建设重点不断向城市周边推移,呈现出较明显的空间异质性;②城镇建设用地和独立工矿用地面积及占比逐年增加,扩张来源均以耕地为主;农村居民点占比降低了13.14%,城乡建设用地结构特征发生了较明显变化;③2000~2015年城乡建设用地内部不透水面面积和比例分别增加了145.32 km²和10.04%,城镇建设用地利用强度已达到较高水平,农村居民点用地利用强度快速提高,城市和农村之间的差距不断缩小;自城市核心区至远郊区方向不透水面比例不断降低,可利用潜力越大,不透水面面积增量、比例增量、比例增长率及扩张强度大体呈增加趋势,不透水面与城乡建设用地规模变化趋势相近,可在一定程度上揭示城市扩张轨迹。     

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.     

A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta,China

The Zhujiang Delta of South China has experienced a rapid urban expansion over the past two decades due to accelerated economic growth. This paper reports an investigation into the application of the integration of remote sensing and geographic information systems (GIS) for detecting urban growth and assessing its impact on surface temperature in the region. Remote sensing techniques were used to carry out land use/cover change detection by using multitemporal Landsat Thematic Mapper data. Urban growth patterns were analysed by using a GIS-based modelling approach. The integration of remote sensing and GIS was further applied to examine the impact of urban growth on surface temperatures. The results revealed a notable and uneven urban growth in the study area. This urban development had raised surface radiant temperature by 13.01 K in the urbanized area. The integration of remote sensing and GIS was found to be effective in monitoring and analysing urban growth patterns, and in evaluating urbanization impact on surface temperature.     

基于TM影像的城郊景观土地利用初步研究--以北京昌平沙河区为例

北京市是全国的政治、经济和文化中心，城镇扩张速度较快。中国加入世界贸易组织和北京申办奥运会的成功，将使城镇进一步扩展。随着城市向郊区的扩张，城郊景观成为城市和郊区的过渡带，该过渡带的景观土地利用发生了巨大变化。借助TM遥感影像采用两种方法来解译北京昌平沙河区景观土地利用：其一是利用TM影像的4、5、3波段的假彩色合成来该地区的土地利用解译；其二是借助TM影像3和4波段计算的NDVI来判定土地利用，并与土地统计数据对比，结果表明第一种方法解译城郊景观的土地利用类型效果较好，而第二种方法对有植被覆盖的土地利用类型解译较好。     

1988年～2014年莆田市城市扩展及其驱动力分析

鉴于城市扩张及其驱动力分析在协调城市扩张与土地资源保护的矛盾、促进城市可持续发展中的重要作用,该文利用多时相遥感数据和莆田市社会经济统计数据,结合野外调查,采用室内解译方法来获取1988年～2014年莆田城市扩张数据,以分析不同时期莆田各城区扩张的面积、速度、方向主要驱动力。1988年～2002年莆田市建成区扩张面积快速增长,而2002年以后增长速度明显减小;仙游县、梧塘镇、大济镇和度尾镇建成区扩张整体呈现出“增 减”趋势,其余城镇建成区扩张则呈现“减 增”趋势。同时,人口增长、经济发展和社会投资是莆田城市扩张发展的主要驱动力,其中,GDP增长是最主要驱动力。该研究有效地揭示了莆田城市扩张的发展规律,为该地区城市规划与管理提供参考与借鉴。     

Error Sources and Post Processing Method for Land Use/cover Change Estimation of Dongguan Citybased on Landsat Remote Sensing Imagery with SVM

Urbanization is the world developing trend in the past century,which significantly changed the land use/cover of the urbanized area,and caused a series negative impacts,such as water shortage,flood increase,environment pollution,ecosystem degradation.How to estimate the land use/cover change more accurately has the prerequisite of studying the urbanization processes and its impacts,and is the research hot and challenge of the remote sensing and application communities.Dongguan city expressed the rapidest urbanization in China since China’s reform and opening door,and transferred from an agriculture county to a modern international metropolitan in less than 30 years,which has made a miracle in the world urbanization process.To prepare a high accuracy land use/cover change dataset for studying Dongguan’s urbanization process and its impacts,this paper first estimated the land use/cover change dataset by employing Support Vector Machine auto\|classification algorithm based on 12 Landsat remote sensing imageries from 1987 to 2015 at an average interval of 3 year.Then the error sources is analyzed by comparing the results estimated by using auto\|classification algorithm and visual interpretation,and a post data processing algorithm is proposed for refining the auto\|classification results.The final dataset of land use/cover change of Dongguan City is produced with the above method with an average accuracy of 86.87% and a Kappa coefficient of 0.83,which implies this product has a very good accuracy for analyzing the urbanization process of Dongguan city and its impacts.