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.     

基于GIS城镇扩展的空间格局分析

以城镇扩展为核心内容的城市土地利用/土地覆盖变化已经成为目前国内土地利用/土地覆盖变化(LUCC)研究的热点.基于某地2002年至2007年土地利用数据,提取城镇用地信息,利用全局和局部空间关联模型,分析了区域城镇扩展的空间分布特征,得出了相关研究成果.     

延河流域土地利用变化特征分析

研究土地利用时空变化特征对促进区域生态经济协调发展具有重要意义.以延河流域为例,基于不同时期遥感影像,借助RS、GIS和ENVI软件平台技术,获取了1980-2015年延河流域土地利用变化状况,并进行了土地利用转移分析;在此基础上解译了不同时期的土地利用类型,分析了延河流域土地利用时空变化特征和影响因素.结果表明,1980-2000年流域内土地利用结构整体变化较小,耕地、林地、草地3类土地利用面积相互转化频繁但整体面积趋于稳定,建筑用地面积增加了1.25倍;2000-2015年流域内耕地、林地和草地发生了显著变化,其中耕地面积减少了28％,林地面积增加了40％,草地面积增加了15％,建筑用地面积增加了近两倍,但比例很小,未利用地和水体变化幅度不明显;2000年前后流域内土地利用变化空间特征存在明显不同,2000年之前各土地利用类型变化主要发生在延安市区,2000年之后整个流域地区均有不同土地利用类型的转移;2000-2015年国家"退耕还林、草"生态修复政策、产业经济生产总值不断上升、人口数量稳定增长、城镇化发展迅速等均对流域土地利用结构产生了影响.     

南昌市城区土地利用变化时空特征分析

在遥感技术与GIS挂术的支持下,对南昌市城区15年来土地利用的时间动态特征和空间动态特征进行了定量分析。具体表现为通过数学建模,利用土地利用动态度模型、土地利用开发度模型、土地利用耗减度模型等对土地利用时空特征进行了分析。研究结果表明:南昌市城区的土地利用格局分布越来越均匀,斑块间的面积差异在逐渐缩小,某一土地利用类型占优势的状况逐渐减小,土地利用受人类影响程度越来越大。而且还揭示出土地利用类型中除耕地面积大量减少、建设用地面积大量增加外,其他土地利用类型均有不同程度的变化。     

准噶尔盆地不同土地利用类型地表反照率研究

研究准噶尔盆地不同土地利用类型地表反照率特征,为揭示在区域尺度上不同土地利用类型对气候变化的生物地球物理机制提供科学依据。选取2000—2018年遥感反演地表反照率数据及2000年、2010年和2018年3期土地利用数据,运用统计学方法分析准噶尔盆地不同土地利用类型在短波(0.3～2.5μm)、近红外(0.76～3.0μm)、可见光(0.35～0.76μm)地表反照率的时空变化特征及其年际变化趋势。研究结果表明:不同土地利用类型在不同波段的地表反照率具有明显的差异特征,除了二级土地利用类型湖泊和水库坑塘外,其他一级和二级土地利用类型的地表反照率均满足近红外短波可见光这一特征; 2010—2018年3个波段的不同土地利用类型地表反照率年际变化趋势,在整体上稍显著于2000—2010年,且在2010—2018年间短波波段的一级土地利用类型均通过了0.05的显著性检验;准噶尔盆地18 a来土地利用类型地表反照率年际变化速率呈微弱的增长趋势,年际速率变化较小,整体保持稳定。研究结果将为研究区地表分光辐射及能量平衡研究奠定基础。     

环北京地区土地利用变化监测与分析

利用1992和2002年2期Landsat卫星影像,对环北京地区土地利用变化和退化情况进行监测,探讨和分析了北京地区土地利用变化、土地退化的特征和规律.结果表明,北京风沙源区土地利用变化的最大特点是耕地显著减少,林地和牧草地明显增加;整个风沙源区的土地退化格局宏观上受大兴安岭向南延长段和阴山山脉东段的地形地貌控制,并且随所处位置的不同而退化程度有较明显的差异.     

基于遥感与GIS的城市用地动态变化分析——以上海市南汇区为例

近几十年来,土地利用/覆盖变化研究成为全球变化研究的焦点.本文利用2000年和2003年两个不同时期的遥感影像数据,借助GIS强大的空间分析能力,首先对上海市南汇区的城市用地利用变化的时空扩展过程、特征、规律做了深入的探讨,然后结合研究区社会经济数据对土地利用动态变化的驱动力进行了分析.研究结果表明:(1)随着经济的快速发展,南汇区城市土地利用正发生着数量及空间上的变化;(2)城市用地扩展的大部分来源于耕地,在城市化进程中耕地资源被大量地占用了;(3)南汇城市用地加速扩展,但扩展速度在空间上并不均衡;(4)城市用地的扩展在空间上呈现出轴线性和带状分布特点;(5)经济的发展始终是南汇区用地扩展的主导影响因子,相关的政府政策和规划贯穿于南汇城市用地扩展的全过程.     

基于遥感与GIS技术的土地利用时空特征研究

为了研究土地利用、土地覆盖的时空变化，本文在遥感技术与GIS技术的支持下，对土地利用的的时间动态特征和空间动态特征进行了定量分析。具体表现为通过数学建模，以湖北省为例，对湖北省近五年来的土地利用类型、土地利用程度、耕地状况、森林植被覆盖、城市扩展、水域湖泊状况等时空特征进行了动态分析，同时对湖北省土地利用变化的驱动机制进行了分析，为定性定量研究我国的土地利用、土地覆盖的时空演变提供了一种思路与方法。     

河西走廊土地利用时空变化特征及驱动因素分析

利用2009—2019年河西走廊土地利用现状年度变更调查数据,采用土地利用变化速率、土地利用动态度、核密度等要素,对河西走廊土地利用10年间变化的特征进行分析,探讨土地利用变化的驱动因素,为河西走廊土地资源合理开发和生态环境保护提供科学依据。     

地形地貌对半山区土地利用动态变化影响分析——以天津市蓟县为例

以2001年和2011年2期TM图像为数据源,采用分类后比较法提取了10 a间天津市蓟县土地利用动态变化信息;利用空间分析算法生成地形起伏度和坡度2个地形因子,分析了不同地形特征上的土地利用类型分布及变化特征;从类型转换和动态度2个方面定量分析土地利用变化情况,并分析土地利用变化与地形起伏度和坡度的相关关系以及土地利用变化的影响因素。结果表明:地形地貌对土地利用的动态变化有显著的影响,在微缓起伏地形上,居民地增加最多,其次是水域,而林地减少最多;在低起伏和中起伏地形上,居民地增加最多;在山地起伏地形上,居民地有所增加;在高山起伏地形上,只有林地和未利用地有少量变化。该结果可以为天津市蓟县的生态保护以及半山区县土地利用规划提供科学依据。     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.     

Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng,China

Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.     

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.     

1990-2019年艾丁湖流域城市空间扩展遥感监测

基于卫星遥感影像,监测艾丁湖流域1990-2019年土地覆盖类型变化,并利用多种城市变化监测指数及雷达图方法,揭示以自然流域为研究范围的城市扩展变化过程,并探究其驱动机制。结果表明:(1)1990-2019年,流域内土地类型变化巨大,城市扩展明显,建成区面积扩大了近3倍;(2)流域内城市扩展经历了中速扩张和高速扩张两个阶段;(3)流域内3个主要城市建成区面积分别扩展了约40 km²、60 km²、40 km²,3个城市均在原基础上整体向外扩展,在2000年后托克逊县向西南方向扩展明显,吐鲁番市向西北和东方向扩展更为明显,鄯善县除原西南和东北方向外还增加了西北的扩展方向。自然条件是制约西部干旱地区城市发展的主要因素,同时西部地区的城市扩展受经济和政策因素的影响逐渐增强。     

遥感和GIS技术应用于伊拉克南部Basrah省土地利用/覆盖变化及城市扩张研究(英文)

In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map, and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250 000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km² in 1990 to 3 794.9 km² in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time. Supported by the Al-Basrah University, Iraq, the Geo-information Science and Technology Program (No. IRT 0438)China).     

深圳市30年土地利用时空变化及驱动力分析

利用1980~2010年时间序列遥感影像,利用土地变化的变化率、变化贡献率和转移矩阵,从时间和空间上分析了土地利用/土地覆盖的变化特征,并结合单因子相关分析和主成分分析等方法,探讨深圳市土地利用变化的驱动因子。结果显示,30年来深圳市的土地利用发生了巨大变化:城乡用地扩张剧烈,增加了55 077.24 hm2,增长率为265.19%;耕地、林地、水域面积迅速减少,耕地减少33 949.17 hm2,减少率为73.93%。研究时段内,人类活动增强以及影响范围扩大是引起深圳市城乡用地急速增长和耕地下降的主因,且土地利用变化受社会经济驱动力的影响逐步增强。     

基于多时相Landsat数据的城市扩张及其驱动力分析

 以长沙市为例，在多时相Landsat遥感数据支持下，采用监督分类、非监督分类和归一化裸露指数(NDBI)等方法提取城市用地信息。通过对比多期城市用地的熵值变化，定量分析城市扩张的时空特性； 运用叠加、缓冲区分析等方法，分析城市扩张和中心城区的关系，绘制城市扩张速度玫瑰图。研究结果表明，长沙市建成区总面积不断扩展，其中，1973~1986年扩张主要表现在东南方向，1987~1993年为西部方向，1994~2001年南和东南方向成为快速扩张方向； 对长沙城市扩张驱动力进行分析，认为人口迅速增加是城市扩张的最主要驱动力。     

融合地理探测器和地理加权回归的太原市建设用地变化因素研究

准确地识别城市化进程中建设用地的变化情况及其背后的驱动力,对城市后续的可持续发展具有重要意义。本文首先以2000—2020年遥感影像为基础,对太原市建设用地空间分布变化进行研究,然后结合地理探测器模型和地理加权回归模型,对研究区建设用地的空间分布影响驱动力因子进行研究,得到以下结论:除政策因素外,现有的城市建设用地空间分布变化还受到高程、交通、GDP、人口等因素的显著作用。太原市城市建设用地变化的布局不单是GDP变化、人口变化、海拔高度、公路网密度4个显著性因子均匀、独立、直接作用的结果,而是具有空间异质性的各因子两两交互作用后增效的产物。本文成果有望为城市建设用地驱动力研究提供一种新思路。     

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.     

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.