县级尺度农村居民点景观格局时空分异研究——以徐州市睢宁县为例

以TM/ETM＋影像为基础数据源,基于3S技术及景观分析软件Fragstats对1995-2005年徐州市睢宁县的农村居民点景观格局进行动态分析,并使用马尔科夫模型对睢宁县2010年农村居民点景观格局进行预测。结果表明：1995-2010年徐州市睢宁县的农村居民点景观格局呈破碎化趋势,土地利用斑块数增加,斑块平均面积下降,面积加权平均形状指数上升,平均分维度小幅上升,散布与并列指数和香农多样性指数均在下降。随着城镇扩展、农田开发等人类干扰的不断加剧,景观生态环境保护与经济开发之间的矛盾将愈加突出。     

基于遥感技术的烟台市景观特征动态变化研究

基于ERDAS图像处理技术，对1988、1998年二期TM遥感图像进行了处理，获得烟台市各种景观类型及其动态变化信息。选用景观破碎度、均匀度、隔离度等指标，并建立景观转移矩阵，实现景观格局特征参数的计算和动态变化分析。结果表明，烟台市景观空间格局发生了巨大的变化，主要表现在城乡建设用地不断扩大，占用了大量的农田和林地，并使菜地、果园向外和向地势高的地方转移，农业用地出现了低收益向高收益转化的趋势。随着城市规模的扩大，景观格局的这种变化趋势还将继续下去。     

基于GIS的中国农村居民点用地分析

利用90年代初期至末期中国土地利用矢量数据提取农村居民点用地及动态变化数据，将其与自然区划图叠加，分析了各自然区农村居民点用地及其动态变化情况。中国农村居民点用地主要集中在华北平原、鲁中山地丘陵、辽东胶东山地丘陵、淮河与长江中下游及东北平原。这些地区是重要的农业区，城镇发达。农村居民点密集，人口密度高。淮河与长江中下游、华北平原、粤桂闽丘陵平原、华北山地丘陵是农村居民点用地扩展最快的地区，也是农村居民点扩展造成耕地资源流失的主要地区。农村居民点用地扩展造成的耕地流失甚至大于城镇用地扩展造成的耕地流失，并且是在农村人口绝对量逐渐减少的情况下发生的。因此，制定相关政策，加强农村居民点用地管理对于节约土地资源具有重要意义。     

基于遥感与GIS析农村居民点景观特征比较

以河北省阜平县、武邑县，福建省清流县、惠安县作为研究区，利用2000年TM遥感图像，通过解译、判读得到景观结构矢量图，然后利用生态学数量方法分析了研究农村居民点景观特征的差异及空间分布格局。研究表明，研究区农村居民点规模较小，平原地区平均面积不到20hm^2，山区农村居民点平均面积低于10hm^2。农村居民点距离较近，山区农村居民平均距离小于2.5km，平原地区农村居民点距离小于04.hm。农村居点规模小，分布零散，适应于农业经济的发展，为了节约居民点用地，应采取加快城镇及中心村发展的方针，促进农村居民点布置的优化。     

基于对象级分类的土地覆盖动态变化及趋势分析

以广东省东莞市2005~2008年SPOT 5遥感影像为主要数据源,采用对象级分类后比较的变化检测技术,从土地覆盖类型的面积总量、相互转移等方面多层次分析了研究区域4 a土地覆盖的变化情况。在此基础上,利用马尔科夫链模型对该区域未来5 a的土地覆盖动态变化及演变趋势进行了分析和预测,为广东省土地覆盖变化研究提供典型案例分析,以达到全面把握研究区土地覆盖变化规律的目标。结果表明:研究区域的土地覆盖变化基本趋势表现为城镇建设用地总量持续增加以及耕地和园林地面积总量减少,其中城镇建设用地和农业耕地变化幅度较大,其他类型土地变化相对稳定。增加的城镇建设用地主要来源于耕地的人为减少,农业垦殖环境将趋于恶化。研究结果可为研究区合理有效地利用土地以促进土地可持续发展、推进城市化进程奠定决策上的技术基础。     

基于遥感与GIS的农村居民点景观特征比较

以河北省阜平县、武邑县 ,福建省清流县、惠安县作为研究区 ,利用 2 0 0 0年 TM遥感图像 ,通过解译、判读得到景观结构矢量图 ,然后利用景观生态学数量方法分析了研究区农村居民点景观特征的差异及空间分布格局。研究表明 ,研究区农村居民点规模较小 ,平原地区平均面积不到 2 0 hm2 ,山区农村居民点平均面积低于 10 hm2。农村居民点距离较近 ,山区农村居民点平均距离小于 2 .5 km,平原地区农村居民点距离小于 0 .4 hm。农村居民点规模小 ,分布零散 ,适应于农业经济的发展。为了节约居民点用地 ,应采取加快城镇及中心村发展的方针 ,促进农村居民点布局的优化     

塔里木河下游输水区景观与植被变化分析

对塔里木河下游输水区植被分布主要区域2000、2002年ETM、2004、2006年ASTER影像景观特征值、景观格局分析,结果表明:2000年输水后,研究区(193726hm2)天然植被面积迅速扩大并呈连片趋势,沙地斑块数量增加但面积减少、聚集度明显减小;除了沙地外其他各地类斑块密度、数量减少,平均斑块面积扩大,各景观类型呈聚集趋势,景观分布趋于连片.转移矩阵计算表明,2000～2006年输水区4275hm2疏林地转化为有林地;4573hm2中、低覆盖度灌草地转化为高覆盖度灌草地;18729hm2低覆盖度灌草地转化为中覆盖度灌草地;2230hm2沙地转化为灌草地,输水后研究区植被面积增加了7345hm2.生态输水有效地遏制了沙漠化扩展.     

石羊河流域土地利用变化与景观格局分析

针对我国西部干旱半干旱区典型流域的土地利用与景观格局时空变化特征分析问题,以近35年来的5期Landsat卫星遥感影像为数据源,利用变化监测技术和景观生态学分析算法,对石羊河流域的土地利用和景观格局时空变化特征进行分析,结果表明:1)近35年来,由于人类的活动,石羊河流域居民地和耕地大幅增加,水体、草地、林地面积大幅减少,生态环境日益恶化。2)研究区不同地类之间出现不同程度的转移现象,主要是较大面积的未利用土地和草地转化成了耕地,部分草地转化成未利用土地。3)景观格局在各个研究时段也发生了明显的变化,整个景观的密度是先增后减,说明流域的开发已有所节制。     

基于面向对象分类的土地利用信息提取及其时空变化研究

基于面向对象的影像分类技术与土地利用变化模型,选取处于剧烈变化环境下的东江流域为研究对象,对其1980~2008年的土地利用变化特征进行了研究。结果表明:(1)面向对象的遥感分类方法在SPOT5高分辨率遥感影像分类中具有较高的精度(总体精度达87.7%),可以有效避免"椒盐现象"发生;(2)1980~2008年东江流域的土地利用方式和空间格局发生了显著变化。耕地面积急剧减少了2 854.4km2,流失的耕地主要转化为了林地、城镇建设用地;园地面积减少了667km2,流失的园地主要转化为了林地;林地面积增加了1 988.7km2,呈波动变化;草地面积比由4.9%缩减为2.0%;水域面积先减少后增加;城镇用地呈快速增长趋势,年增长率高达186.23%。加强耕地保护和适度限制城镇用地增长对区域可持续发展至关重要。     

基于遥感分类的博鳌地区生态变化评价

利用不同年份TM 数据, 使用遥感和景观分析的评价方法, 并对博鳌镇及周边区域的生态安全状况进行了研究。在充分利用影像波段信息的基础上选取不同的波段组合, 对最大似然方法和光谱角分类法做对比分析, 并将对比的最优结果用于景观评价指数的计算, 运用土地利用转移概率矩阵以及多种景观指标对博鳌地区景观格局变化和各个斑块类型的变化进行了研究. 结果表明, 从1988 年到2000 年, 该地区的未利用地面积明显下降, 土地利用开发效果明显; 与此同时耕地面积也有一定规模的下降, 明显存在耕地被建筑用地挤占情况; 林地面积有很大的扩张, 显示出极大的优势度, 就研究区域整体景观而言, 该区的破碎度得到一定的改善, 但其物种多样性和均衡性遭到一定破坏。     

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。     

基于多时相TM影像的城市边缘区划分及其变化监测

城市边缘区作为城市和农村之间的过渡地带,是城市扩张过程中土地利用变化最为活跃的部分。在城市边缘区,城市用地类型与其他的土地利用类型,比如耕地、林地、牧草地和水域等混合在一起,并且这些非城市用地类型随着城市化的进程很快转换为城市用地。城市边缘区被定义为城市内边界和外边界之间的环状区域,内边界分离城市核心区与城市边缘区,外边界分离城市边缘区与农村腹地。本研究采用一种新的方法来对城市边缘区进行界定,以及对其动态变化进行监测研究。通过多时相遥感数据的分类,提取城市及其周边的土地利用信息,并对其空间结构模式用地理景观指标进行定量的描述,最后借助空间聚类获取边界阈值来划分城市边缘区并对其变化进行监测。     

Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.     

Land Use Intensity and Radiation Feedback in Beijing from 2000 to 2015

Many current studies focus on urban expansion and its heat island effect, but the impact of different land use intensity on radiant energy needs further analysis. Based on the land use data of 2000 and 2015 in Beijing, this study divided the land use of Beijing into five types according to the influence degree of human activities and vegetation resilience, namely, the old urban areas, urban expansion areas, unchanged cropland areas, mixed pixel areas with changed gridcells, and unchanged pure pixel areas. On this basis, we calculated Radiative Forcing (RF) due to the change of surface albedo and explored the relationship between RF and vegetation cover. The results showed that: (1) In pure pixel areas, natural vegetation had a lower albedo, and the corresponding RF was larger than the other four land use type areas. However, under the influence of human activities, RF in the four land use type areas showed an obvious increasing trend during the research period, and the increment was also larger than RF in PP areas. (2) Comparing with unchanged pure pixel, the EVI within the other four human-affected land type areas (old urban areas, urban expansion areas, mixed pixel, and unchanged cropland) decreased but the LOS extended. The combined effect of LOS and EVI contributed to the decreasing trend of surface albedo, which prompted the increase of RF. Our finding highlights that human activities often enhances RF by affecting the intensity of land use. This study has important reference value for analyzing the climate feedback of land use change from physical mechanism.     

2000~2015年北京市土地利用强度及其辐射反馈评估

当前很多研究关注城市扩展及其热岛效应,但不同土地利用强度对辐射能量的影响尚有待进一步的分析。以北京市为例,基于2000和2015年的土地利用数据,按照人为活动对土地的利用程度,将北京市的土地利用变化划分为5类：即老城区、城市扩展区、混合变化区、耕地及自然纯像元区。在此基础上,在反照率和太阳辐射遥感反演数据的支持下,分析2000~2015年由地表反照率引起的辐射强迫(RF, Radiative Forcing),并探讨了RF与植被的关系。结果表明：相比自然纯像元区域,老城区、城市扩展区、混合变化区及耕地的RF在研究时段内均明显增加,后三年的RF均值比前三年增加了0.78 W/m²以上,远大于自然纯像元区的RF增量(0.19 W/m²)。本研究同时发现,植被绿度随土地利用强度的增加而逐年下降,但植被生长期长度却有所延长,两者综合作用于地表反照率,促使了RF的增加,说明单纯从辐射平衡来讲,北京市的土地利用变化在一定程度上增强了RF。     

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。     

An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region,China

Integrating soft and hard classification to monitor urban expansion can effectively provide comprehensive urban growth information to urban planners. In this study, both the impervious surface coverage (as a soft classification result) and land cover (as a hard classification result) in the Beijing–Tianjin–Tangshan metropolitan region (BTTMR), China, were extracted from multisource remote sensing data from 1990 to 2015. Then, we evaluated urban expansion based on centre migration, standard deviation ellipse, and spatial autocorrelation metrics. Furthermore, the differences between the soft and hard classification results were analysed at the landscape scale. The results showed that (1) the impervious surface area increased considerably over the past 25 years. Notably, the areas of urban built-up land and industrial production land increased rapidly, while those of ecological land and agricultural production land seriously decreased. (2) The distribution of impervious surfaces was closely related to the regional economic development plan of ‘One Axis, Two Wing, and Multi-Node’ in the BTTMR. (3) The contributions of different land use types to impervious surface growth ranked from high to low as follows: urban built-up land, rural residential land, industrial production land, agricultural production land, and ecological land. (4) The landscape metrics varied considerably based on the hard and soft classification results and were sensitive to different factors.     

Quantifying the spatial differences of landscape change in the Hai River Basin,China, in the 1990s

Quantifying a landscape pattern and its change is essential for monitoring and assessing the ecological consequences of land-use/land-cover (LULC) change and human interference. In this study, a combination of landscape pattern indices and land-use dynamics indices based on remote-sensing images was employed to analyse and compare the spatial and temporal dynamics of the landscape pattern in the Hai River Basin (HRB), China, in the 1990s. During this decade, the change in the landscape pattern was mainly driven by intense human-induced alterations. Overall, the landscape pattern changed considerably, progressively becoming more fragmented and diverse, with widespread encroachment of cropland because of rapid urbanization. The change in the landscape pattern exhibited distinctive spatial differences between the mountains and the plains, as well as between urban and rural areas, with higher fragmentation in the plain region and urban fringes or newly urbanizing areas. The changes in the landscape pattern around Beijing resulted in more fragmented and diverse landscape types. A detailed examination of the Jing–Guang (Beijing–Guangzhou) railway line transect and two other subregions indicated that urbanization contributed mostly to the spatial differences of landscape change through population growth, transportation improvements and rapid economic development. Spatial differences in the change in the landscape pattern were induced by economic growth, population increase and government policy.     

Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.