Measuring accessibility of urban scales: A trip-based interaction potential model

In the interaction process of city space and citizens’ activity pattens, the regular travel behavior under the cumulative impacts of urban land use is required to be assessed. The aim of this study is to formulate and estimate attraction choice models that provide measurements of accessibility on various scales reflecting the choice of people to travel to facilities or activity places and characterize the interaction between land use patterns and transportation facilities. Based on a dataset inclusive of big data from varied sources, measurement methodologies are proposed encompassing the multidimensional aspect of the accessibility estimation issues. We sketch the characteristics of service facilities and travel impedance in the calibration processes. Logit models and gravity models are applied to simulate the impacts of different scales of trip length on the accessibility scores. Accessibility is aggregated over spatial elements of different scales and trips and herein the spatial accessibility of study zones is estimated as well as the potential of citizens’ travel choices and activity patterns. Such spatial interaction models have potential implication for enhancing our understanding of the cumulative environmental influences on citizen’s travel behavior and vice versa. It can be a substantial part of a more composed proposal of life convenience of residential citizens reflecting the happiness of living in an urban community.     

A GIS toolset for automated partitioning of urban lands

Urban growth models operating at finer spatial scale usually incorporate a subdivision module that carries out automated partitioning of the lands selected for future development. In this paper, we describe the development and implementation of Parcel-Divider – a GIS toolset for automated subdivision of land parcels. In addition to automating the process of generating urban layouts such as city blocks, streets and cadastral lots, the toolset is capable of extending roads to new subdivisions. Researchers can integrate the toolset into their modeling while planners can use it as a standalone application to visualize scenarios of infrastructure arrangements in growing areas of the city. Our tool-generated subdivision configurations closely match the subdivision styles observed in real-world cities when compared visually as well as statistically.     

基于GIS的城市土地利用分析与建模框架研究

本文针对城市土地利用数据的时空特性,依托地理信息系统(GIS)丰富的空间分析工具以及对海量空间数据的高性能计算优势,围绕城市土地利用研究有关数据的处理、分析、建模等方面问题设计了一个基于GIS的城市土地利用分析与建模框架;框架主体结构中有关城市土地利用变化的驱动力机制建模方法选取逻辑回归模型,对地理数据的空间自相关性处理则根据Getis自相关系数构建滤波模型;具体应用则结合深圳市国土资源局的"城市土地利用虚拟政策实验室"项目,取得良好效果     

Land surface phenology of North American mountain environments using moderate resolution imaging spectroradiometer data

Monitoring and understanding plant phenology is becoming an increasingly important way to identify and model global changes in vegetation life cycle events. High elevation biomes cover twenty percent of the Earth's land surface and provide essential natural resources. These areas experience limited resource availability for plant growth, development, and reproduction, and are one of the first ecosystems to reflect the harmful impact of climate change. Despite this, the phenology of mountain ecosystems has historically been understudied due to the rough and variable terrain and inaccessibility of the area. In addition, although numerous studies have used synoptically sensed data to study phenological patterns at the continental and global scales, relatively few have focused on characterizing the land surface phenology in mountainous areas. Here we use the MODIS/Terra + Aqua satellite 8-day 500 m Nadir BRDF Adjusted Reflectance product to quantify the land surface phenology. We relate independent data for elevation, slope, aspect, solar radiation, and temperature as well as longitude and latitude with the derived phenology estimates. We present that satellite derived SOS can be predicted based on topographic and weather variables with a significant R²adj between 0.56 and 0.62 for the entire western mountain range. Elevation and latitude exhibit the most significant influences on the timing of SOS throughout our study area. When examined at both the local and regional scales, as well as when accounting for aspect and temperature, SOS follows closely with Hopkins' Bioclimatic Law with respect to elevation and latitude.     

Importance of spatially distributed hydrologic variables for land use change modeling

Land use changes have a pronounced impact on hydrology. Vice versa, hydrologic changes affect land use patterns. The objective of this study is to test whether hydrologic variables can explain land use change. We employ a set of spatially distributed hydrologic variables and compare it against a set of commonly used explanatory variables for land use change. The explanatory power of these variables is assessed by using a logistic regression approach to model the spatial distribution of land use changes in a meso-scale Indian catchment. When hydrologic variables are additionally included, the accuracies of the logistic regression models improve, which is indicated by a change in the relative operating characteristic statistic (ROC) by up to 11%. This is mostly due to the complementarity of the two datasets that is reflected in the use of 44% commonly used variables and 56% hydrologic variables in the best models for land use change.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

土地利用最佳模拟尺度选择及空间格局模拟

土地利用变化是一个受到多重因素相互影响的动态过程。目前,已经成为全球环境变化和可持续发展的重要内容,而区域土地利用空间格局模拟已成为LUCC研究的关键内容。以2000年以及2010年的TM遥感影像解译数据以及数字高程模型、水系、铁路、公路、降雨量和气温等数据为基础,运用二元逻辑斯蒂回归模型对黄土台塬区的土地利用最佳模拟尺度进行了选择,并在此基础上对研究区的各种土地利用进行了空间格局模拟。研究结果显示：（1）在土地利用格局模拟的十个空间尺度上,土地利用变化空间格局与其驱动力因子之间存在着一定的尺度相关性特征;（2）黄土台塬区耕地、林地、草地的ROC值在十个空间尺度上均呈现出先增加后减少的趋势,转折点在400 m尺度附近,说明黄土台塬区的土地利用在尺度效应和尺度转换的效应下,400 m×400 m是此区域土地利用格局优化的最佳模拟尺度;（3）在400 m最佳模拟尺度上所模拟出的草地和林地的分布格局都与人均GDP和地形综合指数两个变量显著相关,而对耕地的分布影响最为明显的变量则是地形综合指数。     

基于空间数据库的地类知识的发现

基于空间数据库的知识发现是地理信息系统应用研究的重要方向。以四川省石棉县为例,对基于空间数据库的地类知识发现进行了研究。利用了森林资源二类调查数据构建了GIS数据库,并从中提取出地类图层,利用地形图生成了DEM,结合年平均温度和年平均降雨量等栅格数据构成了空间数据库。在该空间数据库的支持下,探讨出了发现各地类分布的海拔、年平均温度,年平均降雨量等带谱知识,以及其空间配置知识的技术方法。揭示出了各地类分布的海拔、年平均温度和年平均降雨量等背景知识,以及各地类间的空间配置关系知识。该知识可用于支撑土地利用优化决策以及各土地利用的遥感识别提取。其方法还可以广泛用于自然、社会、经济和人口等空间分布带谱知识和空间配置知识的发现。     

基于遥感和GIS的人口数据空间化研究进展及案例分析

人口数据对于全球、洲际、区域尺度的人与环境交互作用研究的重要性已经被广泛地认知。但基于行政单位的人口统计数据难以与基于自然单位的环境数据相匹配,必须通过建模对人口数据进行空间化分布。人口数据空间化建模的基本思路是将人口数据和地球表面的地理因子关联起来,遥感和地理信息系统提供了有效的工具。介绍了国内外基于遥感和GIS的人口空间化研究的主要项目和方法思路。以黑河流域为研究区,在流域尺度上把典型的人口估计结果GPW、UNEP/GRID、LandScan和中国1 km格网的人口资料与政府人口统计数据进行比较分析,可以看出国内外的研究机构和研究人员已做了大量的相关工作。国外研究主要包括从遥感解译信息反演人口数据、从DMSP-OLS夜间灯光数据反演人口数据和从遥感获取的光谱特征直接反演人口数据。国内研究尽管起步较晚但发展很快,主要是根据土地利用数据和其它地理因子(如高程、道路、居民区等)建立回归模型。黑河流域人口空间分布的比较结果表明中国1 km格网人口数据是几种数据中与实践情况最相符合的。     

GIS技术在区域土地开发适宜性分区中的应用——以江苏省为例*

重点讨论了GIS技术在区域土地开发适宜性分区方法中的应用。区域土地开发适宜性分区,要求将评价单元的自然生态要素与经济社会要素在空间上反映出来,并对各单元的生态保护价值和经济开发需求进行聚类分析。GIS技术在获取、处理和集合各基本空间单元的要素数据上发挥了重大作用。应用GIS技术建立空间基础信息数据库,进行面状要素叠加分析、线状要素网络分析、统计要素链接分析和赋值要素追加分析,使分区数据的采集、组织和分析更加简单;专题地图的应用,也使分区方案的表现更为直观;尤其是GIS空间叠加分析功能通过图形处理与空间数据     

Fusion of point-based postal data with IKONOS imagery

Research in urban remote sensing has been recently reinvigorated by both the continuing fusion with GIS and the advent of high spatial resolution satellite sensor data. Both will be examined by this paper in terms of how GIS data at the point level can assist the identification and interpretation of urban land use patterns from classified land cover. Specifically, how spatial statistics can be used to summarise the two-dimensional patterns of point data representing residential and commercial buildings. In this paper point data refer to the location of postal addresses known as ADDRESS-POINT^TM and collected by the Ordnance Survey of Great Britain and COMPAS^TM in Northern Ireland. Groups of these postal points are characterised using standard nearest-neighbour and linear nearest-neighbour indices in terms of the spacing and arrangement of residential and commercial buildings. The indices then form the basis for the interpretation of urban pixels classified from IKONOS imagery at the 4 m spatial resolution. In addition, the paper will outline an agenda for constructing an automated pattern recognition system that would ultimately identify and characterise the physical arrangement of buildings in terms of density (compactness versus sparseness) and linearity. Preliminary results so far are most encouraging. Using ground truth from aerial photographs at 15 cm spatial resolution, classified IKONOS imagery representing two cities in the United Kingdom, Bristol and Belfast, have been investigated. In both, spatial patterns have demonstrated the ability to identify misclassified urban pixels and characterise a variety of building arrangements. Also, using the software e-Cognition, a spatial classification based on nearest neighbour contextual rules produced accuracies of 95.4% compared to 90.7% from a multispectral-only classification. Further, more extensive testing is continuing.     

Establishing the relationship between urban land-cover configuration and night time land-surface temperature using spatial regression

Studies suggest that urban form can influence microclimate regulation. Remote sensing studies have contributed to these findings through analysis of high-resolution land cover maps, landscape ecology metrics, and thermal imagery. Collectively, these have been referred to as land cover configuration studies. There are three objectives to this study. The first is to assess the relationship between nighttime land surface temperatures (LST) and land cover configuration and composition. The second objective is to outline a comprehensive methodology that includes ordinary least squares (OLS), spatial regression, variable selection, and multicollinearity analysis. Our last objective is to test three hypotheses about the relationship between LST and land cover, which can briefly be described as: 1) the importance of land-use regimes in modeling LST from land cover composition and configuration variables; 2) the strength of the correlation between LST and roads, buildings, and vegetation; and 3) the improved quality of models using landscape metrics in modeling the relationship between LST and land cover. Based on 16 different models (8 OLS, 8 spatial regression) we could confirm the above hypotheses, but we found that the configuration of buildings, roads, and vegetation have a complex relationship with LST. Our interpretation of this complexity, combined with the strength of composition variables, is that parsimonious models, for now, are more useful to urban planners because they are more generalizable. Finally, spatial regression models of land cover configuration and LST demonstrated an improvement over non-spatial linear models (OLS). Spatial regression models reduced heteroskedasticity and clusters of residuals, and tempered coefficients, suggesting that the OLS models could be biased. OLS models were still found to be a valuable tool for exploratory analysis.     

县(市)级土地利用现状数据库关键技术实现

结合自身建立土地利用现状数据库系统的实践，探讨了GIS基础上建立县(市)级土地利用现状数据库系统的技术流程、质量控制及数据转换等问题，提出了具体可行的技术方法。     

基于城镇居民用地再分类的人口数据空间化方法研究——以长江中游4省为例

基于土地利用数据的人口统计数据空间化方法,在处理过程中会出现同一土地利用类型下人口难以细分的情况,从而影响人口空间数据精度。引入夜间灯光信息并提出了一种基于夜间灯光强度对城镇居民地再分类的人口空间化方法,以改善人口空间数据精度。基于DMSP/OLS夜间灯光及土地利用数据,以长江中游4省为研究区进行方法试验。研究结果显示:利用夜间灯光数据对城镇居民地再分类后,各分区模型的调整R2都提高到了0.8以上,人口空间数据总体平均相对误差较重分类前降低了12.32%。说明该方法在提高传统人口数据空间化模型精度的基础上能够细化城镇居民地人口空间分布。     

基于TM和GIS的合肥市热环境研究

利用2007年10月5日覆盖合肥市的TM影像反演了NDVI和LST,并结合GIS技术和城市形态分维理论,分析了合肥市热环境布局以及不同热环境等级与各土地利用类型的关系。结果表明:合肥市地表温度具有东南高、西北低的热岛效应存在,热岛效应的中心并未出现在城市中心区域。LST与NDVI呈明显负相关关系(相关系数为-0.734),NDVI值每升高0.1,LST约降低0.93℃。城建面积加权环境效应贡献指数(WHI)为1.14,对热环境的作用(正向)程度最高;耕地的WHI为-0.51,对热环境的作用(负向)程度最高,且耕地在合肥市各热环境等级中都发挥了独特的作用。      

基于“3S”技术的县级土地资源动态监测技术系统

土地资源日新月异的变化使得传统的土地资源管理手段无法满足土地资源信息及时更新的要求。探讨了利用遥感、地理信息系统和全球定位系统为代表的“3S”技术进行县级土地资源动态监测和更新的原理和方法。遥感是进行土地利用变化动态监测,发现土地利用变化区域的主要手段。全球定位系统接收仪用于对变化区域进行现场精确定位和实测。实测的变更数据可用于对原有的地理信息系统本底数据库进行修改,从而完成对土地资源的动态监测和数据库的及时更新。福清市土地资源动态监测技术系统就是利用“3S”技术建立起来的业务运行系统。系统所具备的功能可以满足县级土地资源的动态监测和及时更新。     

Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS

We used three Landsat images together with socio‐economic data in a post‐classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Nairobi city. Land use/cover statistics, extracted from Landsat Multi‐spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images for 1976, 1988 and 2000 respectively, revealed that the built‐up area has expanded by about 47?km². The road network has influenced the spatial patterns and structure of urban development, so that the expansion of the built‐up areas has assumed an accretive as well as linear growth along the major roads. The urban expansion has been accompanied by loss of forests and urban sprawl. Integration of demographic and socio‐economic data with land use/cover change revealed that economic growth and proximity to transportation routes have been the major factors promoting urban expansion. Topography, geology and soils were also analysed as possible factors influencing expansion. The integration of remote sensing and Geographical Information System (GIS) was found to be effective in monitoring land use/cover changes and providing valuable information necessary for planning and research. A better understanding of the spatial and temporal dynamics of the city's growth, provided by this study, forms a basis for better planning and effective spatial organization of urban activities for future development of Nairobi city.     

Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia

Changes in species composition and diversity are the inevitable consequences of climate change, as well as land use and land cover change. Predicting species richness at regional spatial scales using remotely sensed biophysical variables has emerged as a viable mechanism for monitoring species distribution. In this study, we evaluate the utility of MODIS-based productivity (GPP and EVI) and surface water content (NDSVI and LSWI) in predicting species richness in the semi-arid region of Inner Mongolia, China. We found that these metrics correlated well with plant species richness and could be used in biome- and life form-specific models. The relationships were evaluated on the basis of county-level data recorded from the Flora of Inner Mongolia, stratified by administrative (i.e., counties), biome boundaries (desert, grassland, and forest), and grouped by life forms (trees, grasses, bulbs, annuals and shrubs). The predictor variables included: the annual, mean, maximum, seasonal midpoint (EVI_mid), standard deviation of MODIS-derived GPP, EVI, LSWI and NDSVI. The regional pattern of species richness correlated with GPP_SD (R² = 0.27), which was also the best predictor for bulbs, perennial herbs and shrubs (R² = 0.36, 0.29 and 0.40, respectively). The predictive power of models improved when counties with > 50% of cropland were excluded from the analysis, where the seasonal dynamics of productivity and species richness deviate patterns in natural systems. When stratified by biome, GPP_SD remained the best predictor of species richness in grasslands (R² = 0.30), whereas the most variability was explained by NDSVI_max in forests (R² = 0.26), and LSWI_avg in deserts (R² = 0.61). The results demonstrated that biophysical estimates of productivity and water content can be used to predict plant species richness at the regional and biome levels.     

The relationship between centrality and land use patterns: Empirical evidence from five Chinese metropolises

The relationship between location and land use patterns is one of the classic theoretical issues in urban studies. Classic models based on the monocentricity hypothesis have limitations in the interpretation of modern urban structure. China has experienced institutional transformation in recent decades, and the interaction of national government policy, market forces and the natural environment has influenced urban planning in Chinese metropolises, resulting in urban structures with special characteristics. This paper examines the distribution of location and land use intensity, and tested the Alonso model by the relationship between them in five Chinese metropolises using Point of Interest data, space syntax methodology, the grid weighted statistical method and the Geographically Weighted Regression (GWR) model. Universal patterns about the scaling relation between intensity of land use types and the centrality of location are revealed. The elasticity of land use types to location, from high to low sensitivity, is commercial, residential then industrial land in most of the five metropolises studied. The sensitivity sequence of land use studied suggests that the hypothetical model based on the classical Alonso model can explain the spatial structure of modern metropolises in China to some extent, especially for the commercial land. But the order of sensitivity of residential land and industrial land to location does not conform to the model. The spatial heterogeneity in land use intensity and centrality were explored and the factors embedded were discussed. It can be found that the relation between centrality and land use intensity conforms to power law. In most of the metropolises studied, when the scaling relation between land use intensity and centrality is super linear, the sequence of the frequency value from high to low are commercial, residential and industrial land; when the scaling relation is sublinear, the sequence of the frequency value is industrial, residential and commercial land.