徐州市土地利用CLUE-S模型变化模拟

对徐州市1987年、1994年和2000年3期遥感影像图进行分析,利用面向对象的思想,采用多尺度分割法对影像进行分类,通过遥感影像的光谱特征,确定分类目标,获取该区过去13年间的土地利用/覆被时空变化特征。运用CLUE-S模型以1994年土地利用数据模拟了2000年的土地利用空间变化状况,并通过2000年实际遥感影像分类数据加以验证,结果计算出Kappa指数为0.846,达到精度要求,实例证明CLUE-S模型较好地模拟了徐州市的土地利用/覆被变化。最后运用CLUE-S模型以6年为1个时空尺度模拟该区未来12年的自然状态和生态保护状态下的土地利用/覆被变化特征。     

基于Landsat-8 OLI的西安市土地利用类型遥感分类研究

近年来,由于区域人口的增加和社会经济的快速发展,西安市的土地利用类型发生了明显变化。土地利用分类可为生态系统模型、水资源模型和气候模型等提供重要信息,遥感技术为土地利用分类提供了有效的工具。本文以西安市2016年Landsat-8卫星的OLI多光谱数据为基础资料,参考国家土地利用分类标准和西安市土地利用现状,将西安市的土地类型分为建设用地、裸地、水体、草地、耕地、林地6类,采用监督分类中常用的最大似然分类法和决策树分类方法对研究数据进行解译,利用总体分类精度和Kappa系数等指标对各分类精度加以评价,并结合实际用地情况对分类结果进行了总结分析。     

多目标土地利用优化模型与应用

针对传统的土地利用优化配置存在对多目标支持不足和空间布局优化实现困难以及遥感影像监督分类结果存在错分漏分的问题,该文提出了一种结合兴趣点(POI)数据进行多目标土地利用优化的模型,即先利用POI数据对遥感影像监督分类的结果进行纠正,然后通过非支配排序遗传算法(NSGA-Ⅱ)实现多目标土地利用数量结构优化,最后利用未来土地利用模拟模型将变化斑块合理分布,从而实现土地利用空间布局优化。以江苏省徐州市为例进行实验,结果表明,该文所提方法对土地分类结果的精度有一定提高并能有效地进行多目标土地利用优化,能够为区域不同的发展目标提供决策支持,在新时期国土空间规划实践中有着较好的应用前景。     

基于1985—2015年Landsat MSS/TM/OLI影像的面向对象SVM分类的威海市土地利用及变化分析

威海的城市发展是滨海城市的代表,利用Landsat MSS/TM/OLI影像采用面向对象SVM分类技术提取了威海市1985—2015年7期的土地利用类型,并基于土地利用分类结果计算了土地利用动态度和土地利用的协方差矩阵.结合当地经济发展,分析土地利用的时空变化特征,研究结果表明:1)面向对象的SVM分类方法精度较高.2)近30年威海市的土地利用变化较大.3)土地利用类型的变化与经济发展密切相关,不同的土地利用类型的变化与经济发展的相关性不一样.     

利用遥感影像探索土地利用与城市热岛的关系

以1996年、2002年和2014年武汉市TM/ETM和OIL影像以及不同时期土地利用现状为基础,对土地利用现状进行了分类;采用城市空间格网研究了城市增长的时序动态变化情况;探索了在不同土地利用分类标准下,城市局部特征对城市热岛效应的影响以及土地利用变化与城市热岛的驱动关系,证明了城镇用地变化与城市热岛有显著的正相关性。     

基于CLUE-S模型的南京市土地利用变化模拟

本文以南京市为研究区,以南京市1986年的土地利用现状图为基础,分析研究区概况并根据数据的可获取性,选取13类土地利用变化驱动因素,利用逻辑斯蒂回归分析求解土地利用变化驱动因素作用系数矩阵。在此基础上运行CLUE-S模型,对南京市1996年的土地利用空间格局进行模拟。将模拟结果与南京市1996年土地利用现状图与进行对比,结果较为理想,模拟正确率达88.57%,KAPPA指数0.86。这说明CLUE-S模型具有成功模拟区域土地利用时空动态变化的能力,对土地利用预测、规划具有重要的指导作用。     

改进CA-Markov模型的武汉市土地利用变化模拟

针对传统的CA-Markov模型未充分考虑土地利用变化的多因素驱动影响,难免预测精度过低的问题,该文以武汉市为研究区域,采用2005、2010和2015年土地利用现状数据,结合Logistics回归模型,综合考虑土地利用变化多种影响因子,构建了改进的CA-Markov模型,对武汉市2020年土地利用空间格局进行了模拟预测及分析。结果表明:改进的CA-Markov模型可以有效提高土地利用现状预测精度;根据预测结果,武汉市2020年土地利用总体结构仍以耕地为主,2005—2020年耕地面积持续减少,建设用地面积持续增长,增长幅度达到76.36%;耕地转换为建设用地是地类转换的主要模式。预测结果为武汉市的国家空间规划与和谐发展提供了参考。     

近20年哈尔滨市呼兰区土地利用覆盖变化及驱动分析

本文基于哈尔滨市呼兰区1998—2018年Landsat TM影像数据和社会经济数据,通过ENVI、ArcMap等软件,采用监督分类、土地利用变化模型、典型相关分析法等方法,科学系统地分析了1998—2010、2010—2018、1998—2018年3个不同时段的土地利用覆盖变化特征,并对土地利用覆盖变化驱动因素进行了分析。结果表明,哈尔滨市呼兰区20年间呈居民地和水域面积增加、耕地变化不大、林草不断减少的土地利用覆盖变化格局,且前8年的变化速度和幅度大于后12年。该区土地利用覆盖变化的影响因素包括自然环境因素、社会经济因素及人口因素。     

基于RS技术的城市土地利用变化研究

本文利用1999年、2008年和2017年共三期LANDSAT数据影像,采用最大似然法对其进行监督分类,并利用混淆矩阵对分类结果进行精度评判,利用监督分类结果制作研究区域土地利用分类图,对该地区土地利用数量、程度及多样性变化等情况进行综合分析,得到其土地利用类型的整体变化规律,并从多个角度分析土地利用类型变化原因,提出...     

多源遥感数据结合的土地利用变化研究

针对多源遥感数据结合监测土地利用变化,本文以辽宁省盘锦市2005和2015年的TM和Landsat-8影像为实验数据,对该地区土地利用变化情况进行深入研究和探讨,重点分析了该研究区域的土地数据处理与分类、土地利用变化幅度模型、单一土地动态变化模型、综合土地动态变化模型、土地利用程度指数等,并分析了研究区域的转移矩阵及动态分析。结果表明:利用多源遥感影像处理,能够很好地总结和分析土地利用变化情况,使研究区域各项指标变化情况得到准确统计。     

顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法

稀疏多项式逻辑回归在分类中仅利用图像光谱信息,导致分类效果不太理想.本文提出了一种顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法.首先利用加权均值滤波与拓展形态学多属性剖面对原始高光谱图像进行局部与结构特征提取;然后对二者进行加权平均特征级融合以获取更具唯一性的像元特征;最后由稀疏多项式逻辑回归分类器对融合结果进行分类.结果表明,本文方法能有效地提高分类精度,而且具有较强的稳健性.     

关中地区土地利用格局模拟与驱动力分析

选取陕西省关中地区海拔高度、坡度、多年平均降雨量、河网密度、交通网密度、GDP和人口密度等自然和社会经济数据,通过随机空间采样,建立2000年关中地区土地利用类型的空间分布概率多元逻辑回归模型.根据所建立的模型,运用GIS技术和结合2000年相关数据,模拟出2000年该区域的土地利用空间分布格局,研究结果表明,林地的模...     

New urban settlements in a perspective of public and private interests. Case study: a Swedish municipality within the hinterland of the Stockholm city

The changes of land use patterns and urban structures could be seen as the dynamic result of the trade off between public and private interests. Thereby the land use change is to some extent unpredictable. The focus in the current study is to measure the importance of spatial location factors regarding new residential and commercial buildings in relation to existing urban amenities and political guidelines. The relative importance of the location factors was studied by multinomial regression analysis. Results from this study reveal that the location profiles of new urban object types attained here indicate strong correspondence with local political land use guidelines and to clustering. The spatial distribution of new urban settlements does not in general correspond to the monocentric urban scheme where firms and residents locate in spatial proximity to urban centres.      

城市扩展极限学习机模型

城市空间结构及其扩展的模拟是城市科学管理与规划的重要前提,本文基于极限学习机提出了顾及不同非城市用地转化为城市用地差异与强度的城市扩展元胞自动机模型（ELM-CA）。模型验证表明：①ELM-CA模型的模拟精度达到70.30%,相比于逻辑回归和神经网络分别提高了2.21%和1.54%,FoM系数分别提高了0.025 9和0.017 9,Kappa系数分别提高了0.024 7和0.016 9,且Moran I指数接近于实际值,说明极限学习机模型较逻辑回归和神经网络能更有效模拟城市扩展的空间形态及其变化;②ELM模型的训练时间仅为神经网络的1/3左右,体现了ELM学习速度的优势;③在小样本情况下,逻辑回归和神经网络都受到明显的影响,而极限学习机还能保持良好的性能,这个特点使其在样本难以获取的情况下具有明显的优势。两个时相的城市扩展模拟与真实数据的比较表明：基于极限学习机的城市扩展元胞自动机模型（ELM-CA）,简化了CA模型的复杂度,并在小样本情况下能有效提高模拟精度,适合于复杂土地利用条件下城市扩展模拟与预测。     

Integration of logistic regression,Markov chain and cellular automata models to simulate urban expansion

This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades.     

Illegal land use change assessment using GIS and remote sensing to support sustainable land management strategies in Taiwan

Land use is changing at accelerated rates in Taiwan, and illegal land use change practices (ILP) are regularly observed within conservation areas. For this reason, we map high-potential areas of ILP within the Soil and water conservation zone (SWCZ) as an aid for effective land management and conducted an exploratory analysis of explanatory variables to evaluate their variability within ILP hot spots. We used variables relevant to hot spots to develop a logistic regression model and identified seven statistically significant variables. We re-applied the logistic regression approach to produce spatially explicit predictions of ILP. High probability areas are distributed along the coastal regions, covering 26% of the SWCZ, and their major drivers are related to accessibility and topography. The results from this research provide relevant information on the major drivers of ILP and high-potential areas, which can support officials in monitoring efforts for better planning and governance within the SWCZ.     

Modeling Urban Growth Using GIS and Remote Sensing

Based on remote sensing and GIS, this study models the spatial variations of urban growth patterns with a logistic geographically weighted regression (GWR) technique. Through a case study of Springfield, Missouri, the research employs both global and local logistic regression to model the probability of urban land expansion against a set of spatial and socioeconomic variables. The logistic GWR model significantly improves the global logistic regression model in three ways: (1) the local model has higher PCP (percentage correctly predicted) than the global model; (2) the local model has a smaller residual than the global model; and (3) residuals of the local model have less spatial dependence. More importantly, the local estimates of parameters enable us to investigate spatial variations in the influences of driving factors on urban growth. Based on parameter estimates of logistic GWR and using the inverse distance weighted (IDW) interpolation method, we generate a set of parameter surfaces to reveal the spatial variations of urban land expansion. The geographically weighted local analysis correctly reveals that urban growth in Springfield, Missouri is more a result of infrastructure construction, and an urban sprawl trend is observed from 1992 to 2005.     

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.     

Urban growth pattern modeling using logistic regression

Transformation of land use/land cover change occurs due to the numbers and activities of people.Urban growth mod-eling has attracted substantial attention because it helps to comprehend the mechanisms of land use change and thus helps relevant policies made.This paper tends to apply logistic regression to model urban growth in the Jiayu county of Hubei province,China.It is applied in a GIS environment to calculate variables and,then,in SPSS to discover the relationships between urban growth and the driving forces.The relative operating characteristic(ROC) shows the modeling accuracy with the curve 0.891 with standard er-ror 0.001.A probability map is generated finally to predict where urban growth will occur as a result of the computation.The result shows the model simulates urban growth well in the county scale.