基于ArcGIS ModelBuilder的LUCC转移矩阵自动计算模型研究

作为描述土地变化转移总体结构与数量的基础性模型,转移矩阵已广泛应用于土地利用/覆被变化领域.现有文献大多直接给出所研究区域的LUCC转移矩阵计算结果,但很少讨论分析其具体计算方法与步骤.针对这一情况,文中在界定总结转移矩阵概念和性质的基础上,详细描述A rcGIS平台下基于土地利用/覆盖栅格分类数据人工手动计算转移矩阵...     

基于AO的土地利用变化信息检测插件实现

基于Arc GIS10.1平台的Arc Objects组件,开发了一套可批量导入矢量土地利用斑块数据,并对指定类别的数据进行分类查询、统计分析、输出矢量地类斑块类型与面积等功能的本地GIS系统,可利用开发的单机软件快速获取图形与属性信息,并能对不同类别土地利用信息进行初步统计分析,快速获取土地利用变化信息。     

利用土地利用状态转移分析的变化检测

辅助数据在遥感影像变化检测中的应用研究目前还不充分,在分析已有变化检测方法的基础上提出了一种基于状态转移分析的面向对象的变化检测方法。该方法通过历史影像辅助数据估计地物土地利用类型的变化趋势,并利用生成的地物类型状态转移矩阵(STM)对变化检测结果进行修正。对不同时期历史影像生成的状态转移矩阵进行误差分析,结果与本文假设前提相符,而不同方法变化检测结果的对比也证明了本文方法的有效性。     

数字城市矢量地形图数据更新方法研究

在对矢量地形图数据更新算法进行分析的基础上,设计了数字城市GIS系统的矢量地形图数据更新策略与流程。针对不同比例尺的矢量地形图数据分别采取版本更新和增量更新的方式,提出根据比例尺、要素的凸包、属性等特征的匹配结果筛选出数据库中变化的要素。最后以贵州省兴义市矢量地形图数据为例,对提出的更新方法进行了验证,结果表明,所提出的更新方法可以应用于数字城市矢量地形图数据的更新。     

基于转移矩阵的城市土地利用变化分析

本文主要运用土地转移矩阵的方法分析土地利用的变化情况。土地转移矩阵相对于其他分析方法,其科学性及优势性在于矩阵的生成灵活多变,分析的结果不受地物类别、数量限制,同时又可以对多时期的数据进行分析处理。结果表明:2013年至2015年,研究区耕地、水域和草地面积呈减少趋势,而道路用地、房屋用地、附属设施用地和林地面积呈增加趋势。土地利用类型变化主要为草地变化为道路用地和房屋用地上。     

目标相似性的矢量数据联动更新方法

针对地理空间矢量数据更新和相似性在地理信息领域应用的研究现状,文章分析了不同几何类型目标之间的几何相似性,结合属性相似度,提出了针对数据联动更新的目标相似性的计算模型以及目标匹配方法;在此基础上,提出了数据变化发现和提取方法,并依据数据之间的映射关系实现多重表达矢量数据的联动更新。最后以某省矢量数据的水系要素之间联动更新为实例,证明了本数据联动更新方法能够完成多重表达地理空间矢量数据的更新,同时保证数据之间的一致性。     

武汉市土地利用/覆被与景观格局变化特征

武汉市作为武汉城市圈的核心城市,其土地利用/覆被和景观格局变化备受关注。本文基于1991年和2009年Landsat TM影像数据,采用RS、GIS和景观生态学方法,运用土地利用变化模型、土地利用转移矩阵和景观格局分析软件Fragstats3.3,对武汉市近19a的土地利用/覆盖及景观格局变化特征进行了动态研究,结果表明此成果能够为武汉市土地资源的合理开发利用和保护提供参考。     

顾及纹理特征贡献度的变化影像对象提取算法

遥感影像变化检测是全球变化研究的重要内容。基于两期遥感影像的变化检测方法存在数据条件要求苛刻、难以充分利用快速发展的多源遥感影像数据等问题。目前许多变化检测的参考数据中包含了一期分类矢量数据,矢量数据中往往包含了位置、形状、大小和类别属性等先验信息,充分利用这些先验信息将可提高变化检测精度。提取变化影像对象是结合矢量数据和遥感影像进行变化检测的核心步骤。本文提出了一种顾及纹理特征贡献度的变化影像对象提取方法。该方法利用矢量数据分割遥感影像,获取影像对象,计算影像对象纹理特征值。根据信息增益原理计算纹理特征参数的特征贡献度,选择特征参数。由贡献度指数大小确定纹理特征参数权重,计算影像对象与先验要素类别的相似度系数,提取变化影像对象。试验结果表明,基于纹理特征贡献度的特征参数选择,能有效地提高变化影像对象提取结果的精度。     

转角法实现长度约束矢量线要素化简

矢量线要素化简是矢量地理信息处理中的一种重要方法.在抽稀法化简算法基础上提出了转角法实现长度约束矢量线要素化简方法.在线要素合理形状和位置精度范围内,该方法可以实现化简前后线要素长度变化最小,提高了线要素长度属性的表示精度,从而使化简后地理数据在线要素长度统计与线要素长度实时分析等领域具有更高的应用精度和可信度.     

第三次全国国土调查土地利用矢量数据栅格化方法

第三次全国国土调查县级土地利用图斑层包含利用类型、耕地类型、坡度等级、种植属性等多属性信息,将县级土地利用矢量数据集成栅格化形成全国无缝的多尺度、多专题栅格产品存在一定技术难度。本文针对第三次全国国土调查土地利用栅格产品应用需求,探讨了多属性综合编码、全国基础地理网格框架、栅格化产品数据模型、矢量栅格化等技术方法,建立了全国土地利用栅格产品生产流程,开展了全国多尺度、多专题土地利用栅格产品生产验证。结果表明:本文方法能够实现第三次全国国土调查土地利用矢量图斑多属性信息集成栅格化和全国多尺度、多专题土地利用栅格产品工程化生产,可为今后的国土调查土地利用栅格产品生产和共享应用提供支撑。     

地表覆盖遥感产品更新完善的研究动向

近年来,多尺度地表覆盖遥感产品的不断涌现,为环境变化研究、地球系统模拟、地理国(世)情监测和可持续发展规划等提供了重要科学数据。为更好地满足广大用户日益增长的应用需求,应对地表覆盖遥感产品进行持续更新完善,保持其时效性、增强时序性、丰富多样性。针对大面积地表覆盖遥感产品更新完善所面临的主要问题,介绍和评述了国内外有关研究动向,包括影像与众源信息相结合的更新、数据类型细化与完善、地表覆盖真实性验证,并作了简要展望。     

An Evaluation of Four Different Wavelet Decomposition Procedures for Spatial Feature Discrimination in Urban Areas

Texture or spatial arrangement of neighborhood objects and features plays an important role in the human visual system for pattern recognition and image classification. The traditional spectral–based image processing techniques have proven inadequate for urban land use and land cover mapping from images acquired by the current generation of fine–resolution satellites. This is because of the high frequency spatial arrangements or complex nature of urban features. There is a need for an effective algorithm to digitally classify urban land use and land cover categories using high–resolution image data. Recent studies using wavelet transforms for texture analysis have generally reported better accuracy. Based on a high–resolution ATLAS image, this study illustrates four different wavelet decomposition procedures – the standard, horizontal, vertical, and diagonal decompositions – for urban land use and land cover feature extraction with the use of 33×33 pixel samples. The standard decomposition approach was found to be the most efficient approach in urban texture analysis and classification. For comparison purposes and to better evaluate the accuracy of wavelet approaches in image classification, spatial autocorrelation techniques (Moran's I and Geary's C ) and the spatial co–occurrence matrix method were also examined. The results suggest that the wavelet transform approach is superior to all other approaches.     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.     

大光斑波形数据在土地覆盖分类的适用性分析

针对GLAS地学激光测高系统是冰、云和陆地高程卫星(ICESat)的唯一监测工具,能够记录地表光斑内的地物信息,是否能应用于黄土高原土地覆盖分类的问题进行了研究。利用粒子群和最小二乘法相结合的方法对GLAS波形数据进行高斯分解,获取高斯波个数、波形总能量、波形信号起始和信号结束位置4个波形参数;基于波形自动分类方法对黄土高原水体、森林、城市用地、其他地类(裸地、低矮植被等)进行分类。通过基于覆盖相同研究区域的30 m地表覆盖数据(Globe Land30),验证分类的准确性。结果表明,GLAS大光斑波形数据对黄土高原的4种地类能够很好地进行区分,总分类精度高达87.68%,Kappa系数为65.79%。研究表明,GLAS波形数据可以作为获取土地覆盖信息的有效数据源,为研究黄土高原土地覆盖变化提供更丰富的数据支持。     

石羊河流域中游景观生态格局变化及驱动分析

以石羊河流域中游为研究区,首先利用多时相、多源遥感图像提取土地利用/土地覆盖信息,根据转移矩阵,定量研究了景观类型的变化趋势、变化面积、变化率及具体的转换类型;提取能够反映景观格局特征的相关指数,分析景观生态格局及景观异质性;最后,对其景观格局变化的驱动因素进行分析。通过本次研究可以增强对石羊河流域中游生态环境的科学认识,对促进该流域的综合治理具有一定的科学和实际意义。     

Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines – SVM), and hybrid (unsupervised–supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different depending on land use/cover classes. Early-growth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land use/cover classes were mapped with producer's and user's accuracies of ∼90%. Our classification approach seems very well suited to accurately map land use/cover of heterogeneous landscapes, thus having great potential to contribute to climate change mitigation schemes, conservation initiatives, and the design of management plans and rural development policies.     

一种基于联合特征的地表覆盖变化自动检测方法

地表覆盖的高效变化检测在地理国情监测中具有重要意义。本文针对当前地表覆盖检测人工目视解译方法效率低，以及软件自动解译错检率、漏检率较高的特点和现状，提出了一种基于联合特征的地表覆盖类型自动变化检测方法。该方法通过对比7种不同的特征联合方案，确立了联合灰度共生矩阵、灰度直方图、光谱统计特征、对象特征的最优组合形式，并设计支持向量机高维度分类器进行分类。试验结果表明，在浙江省复杂地表覆盖分布情况下，基于分辨率优于1 m的国产高分卫星影像，该方法对房屋建筑区、建筑工地等人工构筑物类型变化检测的正确率达到85%以上，对耕地、草地等植被类型也能取得较好的检测效果。     

中分辨率遥感图像土地利用与覆被分类的方法及精度评价

利用TM、SPOT及CBERS-1等中分辨率卫星图像,对土地覆被的专家系统分类方法、居民地的决策树提取方法以及水体的迭代混合提取方法进行了试验,其总体精度达到87.89%,与常用的监督分类方法相比精度可提高7.86%.专家系统分类的结果叠合居民地、水体等易于混分专题信息,可以形成精度较高的土地利用与覆被分类结果。     

基于变化向量分析的土地利用／覆盖变化动态监测（Ⅰ）——变化阈值的确定方法

本文以北京市海淀区为例，把变化向量分析方法引进到了当前国内的土地利用／覆盖变化监测中，针对变化向量分析方法中变化阈值确定的难题，提出了一种双窗口变步长阈值搜寻的新方法，从海淀区的实例研究结果看，变化的检测精度可以达到87％以上，表明变化向量分析法在解决阈值问题后，确实可以有效地提取土地利用／覆盖变化信息。