A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal landsat data

Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns.     

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.      

Class imbalance in unsupervised change detection – A diagnostic analysis from urban remote sensing

Automatic monitoring of changes on the Earth’s surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k‐means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes.     

决策级融合的多分辨率遥感影像变化检测

针对多时相、多分辨率遥感影像数据的特点,充分考虑不同分辨率数据和不同变化检测应用的需求,将由粗到精数据集分层检测和决策级融合的思想引入到变化检测,以多时相多分辨率ALOS遥感影像为例,构建并试验了由粗到精变化检测的技术流程.该方法将ALOS多光谱数据视为粗数据集,将全色数据和融合数据视为精数据集,通过对3种数据集变化检...     

结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测

不同时相遥感影像变化检测已成为土地利用变更调查、城市扩张分析、自然灾害分析及其他环境问题必不可少的技术手段之一。本文提出了一种结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测方法。该方法通过伪不变特征法完成两期影像的相对辐射校正,有效改善影像间的配准误差,并利用IR-MAD算法对校正后的影像进行迭代运算,采用均值漂移算法对迭代后的影像进行分割,同时运用形态学方法处理分割后的影像,最终提取变化图斑。试验结果表明,该方法可以有效检测出变化区域,可应用于城市地表覆盖的变化检测。     

DBSCAN空间聚类算法及其在城市规划中的应用

空间聚类是空间数据挖掘和知识发现的主要方法之一。DBSCAN算法可以从带有“噪声”的空间数据库中发现任意形状的聚类,是一种较好的聚类算法。本文介绍了DBSCAN算法的基本概念和原理,并应用GIS二次开发组件MapObjects予以了实现。然后,本文将该算法应用于城市规划中,对某城市中小学和商业网点等公共设施的分布进行了聚类分析,并根据聚类结果对城市规划设计规范中的某些条款进行了讨论。     

利用轨迹数据提取城市居民出行时空分布特征

介绍了利用出租车轨迹数据提取城市居民出行时空分布特征的过程,包括利用数理统计的方法对出租车上下客事件基于时间进行特征分析;给出了一种融合核密度估计(KDE)与兴趣点(POI)分类的密度聚类算法,实现了出租车上下客热点区域的挖掘以及居民出行活动规律与城市功能区之间关系的发现.?研究表明:居民的出行活动特征在"工-休"日之...     

顾及属性空间分布不均的空间聚类方法——以城市商业中心的提取为例

针对Delaunay三角网空间聚类存在的不足,提出一种顾及属性空间分布不均的空间聚类方法。首先将Delaunay三角网空间位置聚类作为约束条件,采用广度优先搜索方法,以局部参数"属性变化率"作为阈值识别非空间属性相似簇的聚类过程。以城市商业中心为例,验证了该方法能够更客观地识别非空间属性相似的簇,且自适应属性阈值可以满足不同聚类需求,为城市商业中心等空间实体的提取提供了一种有效方法。     

基于共享单车时空大数据的细粒度聚类

针对传统上单独采用K-means或DBSCAN等方法对共享单车位置数据聚类时造成的聚类结果与真实的聚类结构不符的问题,本文提出了一种基于共享单车时空大数据的细粒度聚类方法(FGCM)。该方法通过DBSCAN进行初始聚类,并在此基础上采用GMM-EM算法进行细部聚类,以提取细粒度层级的热点区域。试验表明,该方法可根据密度阈值排除噪声和离群值,无需指定细部聚类簇数,簇的形状和大小比较灵活。在对共享单车大数据位置特征进行聚类时,与传统的单独采用K-means或DBSCAN的方法相比,FGCM具备更高的精细程度,能够充分展现共享单车的实际聚集特征,可用于规划共享单车电子围栏等设施,在不降低通勤效率的基础上规范共享单车的停放问题。     

基于扫描统计的甲型H1N1流感聚集性分析

扫描统计已被广泛应用于地域性疾病的聚集性检测,且可检测这种聚集的差异显著性。文中使用扫描统计的方法,通过对2009~2012年全国各省的甲型H1N1流感数据进行时空扫描以及逐年的空间扫描,生成高发病率地区和低发病率地区聚类,并通过叠置分析反映各个地区归入聚类的频次。利用专题地图和统计表分析甲型流感在2009~2012年中的爆发情况和趋势,并对结果进行客观的分析。     

Proximity-based grouping of buildings in urban blocks: a comparison of four algorithms

Grouping of buildings based on proximity is a pre-processing step of urban pattern (structure) recognition for contextual cartographic generalization. This paper presents a comparison of grouping algorithms for polygonal buildings in urban blocks. Four clustering algorithms, Minimum Spanning Tree (MST), Density-Based Spatial Clustering Application with Noise (DBSCAN), CHAMELEON and Adaptive Spatial Clustering based on Delaunay Triangulation (ASCDT) are reviewed and analysed to detect building groups. The success of the algorithms is evaluated based on group distribution characteristics (i.e. distribution of the buildings in groups) with two methods: S_Dbw and newly proposed Cluster Assessment Circles. A proximity matrix of the nearest distances between the building polygons, and Delaunay triangulation of building vertices are created as an input for the algorithms. A topographic data-set at 1:25,000 scale is used for the experiments. Urban block polygons are created to constrain the clustering processes from topological aspect. Findings of the experiment demonstrate that DBSCAN and ASCDT are superior to CHAMELEON and MST. Among them, MST has exhibited the worst performance for finding meaningful building groups in urban blocks.     

Examining urban sprawl in Europe using spatial metrics

Urbanisation is a global phenomenon with an important impact on the quality of human life. Europe has been widely affected by urbanisation. One of the main characteristics of urban growth is sprawl, a negative form of urban expansion, which affects large cities and most types of urban landscapes. Spatial indicators are applied to CORINE Urban Morphological Zones (UMZ) changes in order to measure urban sprawl between 1990–2000 and 2000–2006 in 24 European countries. The indicators calculate urban morphological properties such as shape, aggregation, compactness and dispersion. The results revealed that the urban areas (UMZ) increased by 146% during 1990–2006 and the urbanisation becomes more circle-shaped and less complex where mostly sprawl occurs. Moreover, urban form becomes less clumped or aggregated. Therefore, due to accelerating rates of urban sprawl, European urban planning should intensify appropriate initiatives to avoid negative impacts on human life.     

植被指数时序数据距离测度方法评价

利用MODIS增强型植被指数(EVI)时序数据,基于中国陆地生态系统55种植被类型上的468个测试点和一个测试区进行了实验,综合比较欧氏距离、光谱信息离散度、光谱角余弦、核光谱角余弦、相关系数、光谱角余弦-欧氏距离6种距离测度方法对遥感植被指数时序数据聚类精度的影响,结果表明:相关系数方法的聚类精度最差;光谱角余弦-欧氏距离方法充分利用了植被指数时序数据的曲线幅度和形状特征,在这6种距离测度方法中表现出了最优的聚类效果;只对光谱亮度敏感的欧氏距离方法或只对曲线形状敏感的光谱角余弦方法,无论是在区分地物类型方面,还是在区域应用上,表现效果均较差;核光谱角余弦虽然在点数据测试上表现较差,但在区域应用上却有较好的表现;光谱信息离散度无论是在点数据测试上还是在区域应用上均表现出了较为适中的效果。     

利用GPS轨迹二次聚类方法进行道路拥堵精细化识别

针对当前在精细识别道路拥堵时空范围方面研究的不足,提出一种利用GPS轨迹的二次聚类方法,通过快速识别大批量在时间、空间上差异较小且速度相近的轨迹段,反映出道路交通状态及时空变化趋势,并根据速度阈值确定拥堵状态及精细时空范围。首先将轨迹按采样间隔划分成若干条子轨迹,针对子轨迹段提出相似队列的概念,并设计了基于密度的空间聚类的相似队列提取方法,通过初次聚类合并相似子轨迹段,再利用改进的欧氏空间相似度度量函数计算相似队列间的时空距离,最后以相似队列为基本单元,基于模糊C均值聚类的方法进行二次聚类,根据聚类的结果进行交通流状态的识别和划分。以广州市主干路真实出租车GPS轨迹数据为例,对该方法进行验证。实验结果表明,该二次聚类方法能够较为精细地反映城市道路的拥堵时空范围,便于管理者精准疏散城市道路拥堵,相比直接聚类方法可以有效提升大批量轨迹数据的计算效率。     

Graph distance and feature-guided multi-view clustering: A novel method for clustering urban buildings

Urban buildings are an integral component of urban space, and accurately identifying their spatial configurations and grouping them is vital for various urban applications. However, most existing building clustering methods only utilize the original spatial and nonspatial features of buildings, disregarding the potential value of complementary information from multiple perspectives. This limitation hinders their effectiveness in scenarios with intricate spatial configurations. To address this, this article proposes a novel multi-view building clustering method that captures cross-view information from spatial and nonspatial features. Drawing inspiration from both spatial proximity characteristics and nonspatial attributes, three views are established, including two spatial distance graphs (centroid distance graph and the nearest outlier distance graph) and a building attribute graph (multiple-attribute graph). The three graphs undergo iterative cross-diffusion processes to amplify similarities within each predefined graph view, culminating in their fusion into a unified graph. This fusion facilitates the comprehensive correlation and mutual enhancement of spatial and nonspatial information. Experiments were conducted using 10 real-world community-building datasets from Wuhan and Chengdu, China. The results demonstrate that our approach achieves 21.27% higher accuracy and 22.28% higher adjusted rand index in recognizing diverse complex arrangements compared to existing methods. These findings highlight the importance of leveraging complementary and consensus information across different feature dimensions for improving the performance of building clustering.     

复杂环境下高分二号遥感影像的城市地表水体提取

水体指数可以抑制背景噪声和提高地表水体的可分性,已经广泛用于地表水体提取。传统FCM聚类算法考虑了地物的不确定性,但没有顾及地物的邻域空间信息,对背景异质性比较敏感。针对传统FCM聚类算法的不足,提出一种可变邻域的区域FCM聚类算法。由于复杂环境下高分二号(GF-2)遥感影像的城市地表水体具有复杂异质背景和不确定性的特点,本文利用水体指数和区域FCM聚类算法的优点,提出一种整合水体指数和区域FCM的城市地表水体自动提取算法,该算法主要步骤包括:(1)去除影像阴影后计算归一化差分水体指数NDWI(Normalized Difference Water Index);(2)区域FCM聚类算法;(3)整合水体指数和区域FCM聚类的城市地表水体自动提取算法。最后采用两景GF-2高分辨率遥感影像(广州和武汉)进行实验,验证了该算法的有效性,并与经典地表水体提取算法进行对比分析。实验结果表明:该算法具有较高的水体提取精度,城市地表水体边界既具有较好的区域完整性又保持了局部细节,同时对城市地表水体复杂背景噪声具有较好的抑制作用,有效减少传统FCM聚类算法的"胡椒盐"现象。     

北京环线建设驱动的土地利用变化遥感检测与分析

近年来,北京市的环线建设大大改进了北京的交通状况,促进了环线周边的商业发展和房地产开发的繁荣,同时也带来了环线周边地区的土地利用的变更。在“科技奥运”科技攻关项目和在研自然科学基金项目的支撑下,选择了1988,1994,2001和2003年5—6月份的TM数据,在影像经过几何校正和辐射归一化校正后对其应用高精度的自组织神经网络分类和利用高分辨率的航空照片验证;在分类数据和变更信息的基础上,采用了空间自相关分析模型、频率指数模型和潜力模型分别对北京各种用地类型的频率指数和建设用地的Moran系数和Geary系数,以及城市增长的潜力指她进行计算。一系列计算显示了环线驱动的北京市土地利用变化情况。结果表明:在环线建设的驱动下,北京市的建设用地围绕环线呈快速增长趋势;绿地面积在四环、五环的环线一带也呈增加趋势;城市增长潜力从四环到六环呈逐渐增大趋势。     

An algorithm to classify and monitor seasonal variations in vegetation phenologies and their inter-annual change

The algorithm presented in this paper classifies vegetation from annual Normalized Difference Vegetation Index (NVDI) time series according to the shape of the temporal cycle. Shape is described using the Fourier components’ magnitude and phase. The degree of an NDVI cycle’s similarity to a predefined reference cycle is measured by the similarity in their amplitude ratios and in their phase differences. Tolerable deviations from the ideal ratios and differences can be set by the user depending on individual accuracy requirements. Tolerable vegetation coverage variation within a shape class is another user defined variable. The algorithm is invariant to cycle modifications including temporal shifts, vertical displacements, and intensity variations, modifications that may be caused by differences in climate, soil (-type, -water, -fertility), or topography, but are unrelated to the vegetation type. The output is a highly consistent clustering of NDVI cycles according to their shapes, which can be linked to distinct vegetation types or land use practices. Intra-class coverage variations in the form of continuous fields measured relative to the reference cycle provide additional information about vegetation covers. Based on the same principles, inter-annual vegetation changes can be monitored with the possibility to distinguish between coverage fluctuations and phenological variations/changes.The algorithms are independent from scene statistics and can be used to create spatially and temporally comparable classifications. Their potential is demonstrated using a 250 m MODIS NDVI time series (version 4) from the Middle East.     

基于多时相遥感影像的龙岩市景观格局变化分析

基于龙岩市多时相Landsat TM/ETM+数据,应用最大似然、决策树及支持向量机分类方法对龙岩市景观类型进行分类.从3种分类方法的比较得知,支持向量机分类方法表现出较高的性能,分类精度明显高于其他的分类方法.因此选择最佳的支持向量机分类结果,并结合景观生态学方法,分析了1992-2008年龙岩市新罗区景观格局及其动态变化信息.结果表明,1992-2008年新罗区主城区农业用地大幅度降低,相应转化为建筑用地,但是却保持着良好的森林覆盖率.同时城市景观组分经历了由扩散式增长过程到粘合式集聚增长过程的转变,城市形态由不稳定形态逐步向稳定形态演化.整体上而言,城市景观呈现出破碎度变小、多样性降低和聚集度升高的发展趋势,建筑用地是龙岩市新罗区的主要景观类型.     

一种基于双重距离的空间聚类方法

传统聚类方法大都是基于空间位置或非空间属性的相似性来进行聚类,分裂了空间要素固有的二重特性,从而导致了许多实际应用中空间聚类结果难以同时满足空间位置毗邻和非空间属性相近。然而,兼顾两者特性的空间聚类方法又存在算法复杂、结果不确定以及不易扩展等问题。为此,本文通过引入直接可达和相连概念,提出了一种基于双重距离的空间聚类方法,并给出了基于双重距离空间聚类的算法,分析了算法的复杂度。通过实验进一步验证了基于双重距离空间聚类算法不仅能发现任意形状的类簇,而且具有很好的抗噪性。