上海城市热场与植被覆盖的关系研究

以TM和ETM+遥感数据,反演了自20世纪80年代以来的6个特定年份的上海市地表温度,并以此来分析上海城市热岛扩展的时空演变格局,结果表明:上海城市热岛范围不断扩大,强度不断增加,分布格局逐渐由集中分布呈现片状破碎化分布;上海市建成区的扩展是导致城市热岛范围扩大、强度加大的最直接,也是最根本的原因之一。相关分析和回归分析的结果表明:植被覆盖与地表温度具有明显的负相关关系,植被分布面积的增加对城市热岛强度的降低具有非常积极的 作用。     

Urban growth and change analysis using remote sensing and spatial metrics from 1975 to 2003 for Hanoi,Vietnam

Since the beginning of the ‘Doi Moi’ policy in 1986, Hanoi has witnessed significant changes in its urban areas. Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes were used to identify built-up areas in Hanoi, and spatial metrics were used to characterize urban change patterns from 1975 to 2003. Firstly, a spatial metric called the ‘percentage of like adjacency’ was used to discern urban growth patterns, which were classified into three sub-patterns: expansion growth, infill growth and outlying growth. Secondly, the driving force underlying the urbanization of the city for the 1975–1984, 1984–1992, 1992–2001, 2001–2003 periods was investigated using a spatial metric analysis programme (FRAGSTATS). The expansion of urban areas along major transportation routes in the latter 1980s was identified as the main form of urbanization in Hanoi. This paper shows the potential application of spatial metrics as secondary sources of information for supporting remotely sensed data and their use to characterize urban growth patterns.     

基于混合光谱分解的兰州城市热岛与下垫面空间关系分析

利用Landsat ETM+数据,采用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面,通过单窗算法反演地表真实温度,对兰州市中心城区的夏季城市热岛强度与城市下垫面的空间分布关系进行相关分析。结果显示,利用中等分辨率ETM+影像对兰州中心城区不透水面和植被盖度分布提取,其成本较低,精度令人满意;兰州城区植被覆盖、不透水面与热岛强度的分布呈空间正自相关,地表温度的空间依赖性极强,与植被盖度和不透水面在空间方向上的相关性差异较大。     

Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China

Urbanization is taking place at an unprecedented rate around the world, particularly in China in the past few decades. One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). Understanding the effects of landscape pattern on UHI is crucial for improving the ecology and sustainability of cities. This study investigated how landscape composition and configuration would affect UHI in the Shanghai metropolitan region of China, based on the analysis of land surface temperature (LST) in relation to normalized difference vegetation index (NDVI), vegetation fraction (Fv), and percent impervious surface area (ISA). Two Landsat ETM+ images acquired on March 13 and July 2, 2001 were used to estimate LST, Fv, and percent ISA. Landscape metrics were calculated from a high spatial resolution (2.5 × 2.5 m) land-cover/land-use map. Our results have showed that, although there are significant variations in LST at a given fraction of vegetation or impervious surface on a per-pixel basis, NDVI, Fv, and percent ISA are all good predictors of LST on the regional scale. There is a strong negative linear relationship between LST and positive NDVI over the region. Similar but stronger negative linear relationship exists between LST and Fv. Urban vegetation could mitigate the surface UHI better in summer than in early spring. A strong positive relationship exists between mean LST and percent ISA. The residential land is the biggest contributor to UHI, followed by industrial land. Although industrial land has the highest LST, it has limited contribution to the overall surface UHI due to its small spatial extend in Shanghai. Among the residential land-uses, areas with low- to-middle-rise buildings and low vegetation cover have much high temperatures than areas with high-rise buildings or areas with high vegetation cover. A strong correlation between the mean LST and landscape metrics indicates that urban landscape configuration also influences the surface UHI. These findings are helpful for understanding urban ecology as well as land use planning to minimize the potential environmental impacts of urbanization.     

An integrated soft and hard classification approach for evaluating urban expansion from multisource remote sensing data: a case study of the Beijing–Tianjin–Tangshan metropolitan region,China

Integrating soft and hard classification to monitor urban expansion can effectively provide comprehensive urban growth information to urban planners. In this study, both the impervious surface coverage (as a soft classification result) and land cover (as a hard classification result) in the Beijing–Tianjin–Tangshan metropolitan region (BTTMR), China, were extracted from multisource remote sensing data from 1990 to 2015. Then, we evaluated urban expansion based on centre migration, standard deviation ellipse, and spatial autocorrelation metrics. Furthermore, the differences between the soft and hard classification results were analysed at the landscape scale. The results showed that (1) the impervious surface area increased considerably over the past 25 years. Notably, the areas of urban built-up land and industrial production land increased rapidly, while those of ecological land and agricultural production land seriously decreased. (2) The distribution of impervious surfaces was closely related to the regional economic development plan of ‘One Axis, Two Wing, and Multi-Node’ in the BTTMR. (3) The contributions of different land use types to impervious surface growth ranked from high to low as follows: urban built-up land, rural residential land, industrial production land, agricultural production land, and ecological land. (4) The landscape metrics varied considerably based on the hard and soft classification results and were sensitive to different factors.     

Quantifying livestock effects on bunchgrass vegetation with Landsat ETM+ data across a single growing season

Grassland systems provide important habitat for native biodiversity and forage for livestock, with livestock grazing playing an important role influencing sustainable ecosystem function. Traditional field techniques to monitor the effects of grazing on vegetation are costly and limited to small spatial scales. Remote sensing has the potential to provide quantitative and repeatable monitoring data across large spatial and temporal scales for more informed grazing management. To investigate the ability of vegetation metrics derived from remotely sensed imagery to detect the effect of cattle grazing on bunchgrass grassland vegetation across a growing season, we sampled 32 sites across four prescribed stocking rates on a section of Pacific Northwest bunchgrass prairie in northeastern Oregon. We collected vegetation data on vertical structure, biomass, and cover at three different time periods: June, August, and October 2012 to understand the potential to measure vegetation at different phenological stages across a growing season. We acquired remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) data closest in date to three field sampling bouts. We correlated the field vegetation metrics to Landsat spectral bands, 14 commonly used vegetation indices, and the tasselled cap wetness, brightness, and greenness transformations. To increase the explanatory value of the satellite-derived data, full, stepwise, and best-subset multiple regression models were fit to each of the vegetation metrics at the three different times of the year. Predicted vegetation metrics were then mapped across the study area. Field-based results indicated that as the stocking rate increased, the mean vegetation amounts of vertical structure, cover, and biomass decreased. The multiple regression models using common vegetation indices had the ability to discern different levels of grazing across the study area, but different spectral indices proved to be the best predictors of vegetation metrics for differing phenological windows. Field measures of vegetation cover yielded the highest correlations to remotely sensed data across all sampling periods. Our results from this analysis can be used to improve grassland monitoring by providing multiple measures of vegetation amounts across a growing season that better align with land management decision making.     

Verification of the analysis method for extracting the spatial continuity of the vegetation distribution on a regional scale

Vegetation plays a key role in not only improving urban environments, but also conserving ecosystems. The spatial continuity of vegetation distributions can be expected to make green corridors for landscape management, wind paths against heat island phenomena. In this paper, we develop a spatial analysis method of vegetation distributions using remotely sensed data on a regional scale. The method consists of a spatial autocorrelation analysis, an overlay analysis, and a hydrological analysis with the Normalized Difference Vegetation Index (NDVI) adopted as the proxy of vegetation abundance. Application of the method leads to the extraction of the lines between the core areas and sparse areas of vegetation. The purpose of this study is to verify our method through applying a vegetation map digitized from aerial photographs. The map contained three vegetation types of land cover: grasslands, agricultural fields, and tree-covered areas. We use remotely sensed data collected at four different time periods at the regional scale, along with information on the seasonal fluctuations of the vegetation. As a result, the exclusion of seasonal land-cover changes, as in the reaping of agricultural fields, in the process of applying the proposed method produces an effect. The analysis reveals steady areas unaffected by the seasonal fluctuation of vegetation along the lines extracted by applying the proposed method.     

Effects of land‐use categorization on landscape metrics: A case study in urban landscape of Shenzhen,China

Using remotely sensed data, landscape pattern analysis based on landscape metrics has been one of the major topics of landscape ecology, and more attention has been focused on the effects of spatial scale and the accuracy of remotely sensed data on landscape metrics. However, few studies have been conducted to assess the change of landscape metrics under the influence of land‐use categorization. In this paper, we took the Bao'an district of Shenzhen city as the study area, to analyse how land‐use categorization would influence changes in 24 landscape metrics. The results showed a significant influence, and based on the characteristics of the response curves of landscape metrics associated with the change in land‐use categorization in regression analysis, and the predictability of these relations, the 24 landscape metrics fell into three groups. (1) Type I included 12 landscape metrics, and showed a strong predictability with changing of land‐use categorization with simple function relations in regression analysis. (2) Type II included seven indices, and exhibited complicated behaviours against changing of land‐use categorization. The response curves of these metrics, which were not easy to predict, consisted of two subsections and could not be described by a single function. (3) Type III included five indices, and showed unpredictable behaviours against the change of the land‐use categorization. Their response curves could not be described by a certain function. This study highlights the need for the analysis of effects of land‐use categorization on landscape metrics so as to clearly quantify landscape patterns, and provides insights into the selection of landscape metrics for comparative research on a given area under different land‐use categorizations.     

Evaluation of urban sprawl pattern in the tribal-dominated cities of Jharkhand state,India

The patterns of urban sprawl over a 20-year period presented in the study indicate unplanned development in the urban agglomerations of Ranchi, Jamshedpur and Dhanbad. The visual interpretation of Landsat (1986, 1991, 1996 and 2001) and IRS-P6 (2005) was used to map land use/land cover and analyse urban sprawl. The saturation of urban areas within municipal limits, along with pressure from the growing population, resulted in the densification of the core urban areas within Dhanbad and Jamshedpur. Comparatively, Ranchi exhibited a very high rate of built-up growth with a reducing population density, indicating a low density of built-up development. The development of built-up land at the expense of agricultural land in Ranchi Urban Agglomeration indicates poor land-transformation practices. An area of 103.6 km² (165.66% growth) was transformed to built-up land in these cities during 1986–2005. Any future built-up development of these agglomerations should involve the use of the government city development plan.     

The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia

Information on the rate and pattern of urban expansion is required by urban planners to devise proper urban planning and management policy directions. This study evaluated the dynamics and spatial pattern of Mekelle City’s expansion in the past three decades (1984–2014). Multi-temporal Landsat images and Maximum Likelihood Classifier were used to produce decadal land use/land cover (LULC) maps. Changes in LULC and spatial pattern of urban expansion were analysed by post-classification change detection and spatial metrics, respectively. The results showed that in the periods 1984–1994, 1994–2004, and 2004–2014, the built-up area increased annually by 10%, 9%, and 8%, respectively; with an average annual increment of 19% (100 ha year^?1), from 531 ha in 1984 to 3524 ha in 2014. Between 1984 and 2014, about 88% of the gain in built-up area was from conversion of agricultural lands, which decreased by 39%. Extension of existing urban areas was the dominant growth type, which accounted for 54%, 75%, and 81% of the total new development during 1984–1994, 1994–2004, and 2004–2014, respectively. The spatial metrics analyses revealed urban sprawl, with increased heterogeneity and gradual dispersion in the outskirts of the city. The per capita land consumption rate (ha per person) increased from 0.009 in 1984 to 0.014 in 2014, indicating low density urban growth. Based on the prediction result, the current (2014) built-up area will double by 2035, and this is likely to have multiple socioeconomic and environmental consequences unless sustainable urban planning and development policies are devised.     

Spatial pattern analysis of land cover change trajectories in Tarim Basin,northwest China

This study attempts to develop a methodology to quantify spatial patterns of land cover change using landscape metrics. First, multitemporal land cover types are derived based on a unified land cover classification scheme and from the classification of multitemporal remotely sensed imagery. Categorical land cover change trajectories are then established and reclassified according to the nature and driving forces of the change. Finally, spatial pattern metrics of the land cover change trajectory classes are computed and their relationships to human activities and environmental factors are analysed. A case study in the middle reach of Tarim River in the arid zone of China from 1973 to 2000 shows that during the 30‐year study period, the natural force is dominant in environmental change, although the human impact through altering water resources and surface materials has increased dramatically in recent years. The human‐induced change trajectories generally show lower normalized landscape shape index (NLSI), interspersion and juxtaposition index (IJI) and area‐weighted mean patch fractal dimension (FARC_AM), indicating greater aggregation, less association with others and simpler and larger patches in shape, respectively. The results suggest that spatial pattern metrics of land cover change trajectories can provide a good quantitative measurement for better understanding of the spatio‐temporal pattern of land cover change due to different causes.     

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。     

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.     

基于GIS的西安市城镇建设用地扩展研究

基于1988年TM影像、2002年ETM影像和2007年TM影像提取了西安市主城区和远郊区城镇建设用地信息,利用GIS技术对三期城镇建设用地进行了叠加得到西安市城镇扩展数据。利用主要道路交通图和行政边界图对扩展数据进行裁切,得到了西安市绕城高速内和远郊区的城镇扩展信息。运用扩展强度指数、城镇建设用地相对变化率和分形维数等模型对西安市的城镇扩展进行了分析研究。研究结果表明,研究区内的城镇建设用地面积从1988年的151 796 493.8 m2增加到2007年的365 180 608.0 m2,总体扩展了2.4倍。其中主城区的扩展主要集中在二环与绕城高速之间,二环内的城镇建设用地面积扩展速度相对较慢;西安市的3个远郊区中长安区扩展倍数最大,在1988~2007年扩展了7.39倍,其次为临潼区和阎良区;1988~2002年西安市的主城区和远郊区的分形维数都呈增加的趋势,城市边缘形态趋于复杂,而2002~2007年分形维数呈减少的趋势。西安市远郊区的城镇建设用地相对变化率高于主城区,未来城市发展的格局逐渐呈现为主城区城镇扩展速度的相对放缓和远郊区的规模不断增大的趋势。     

Impacts of Urban land surface temperature on tract landscape pattern,physical and social variables

ABSTRACT

This paper first focuses on the study of the relationship between the urban heat island (UHI) and the selected physical variables (percentage of urban surface covers, Normalized Difference Vegetation Index (NDVI)) and social variables (population density (PDEN)), and then concentrates on the study of the relationship between UHI and the landscape spatial geometric patterns. The researched results discover that urban Land Surface Temperature (LST) is not only impacted by land cover composition, i.e. land use/cover, which is expressed in this paper as the PURB (commerce/industry/transportation), but also its spatial geometric configuration, i.e. various landscape geometric pattern metrics, which in this paper are expressed by compositional percentage of landscape area (PLAND), configurational edge density (ED), patch density (PD), landscape shape index (LSI), clumpiness index (CI), and Shannon’s diversity index (SHDI). The results show that the proportion of vegetation coverage out of a tract impacts its contribution to an entire UHI in Washington District of Columbia (DC), in particular, interspersing vegetation within a tract is capable of making a stronger mitigation effect to UHI than its concentrated form. Thus, a scatter spatial arrangement and distribution of vegetation is proposed to mitigate UHI effect.     

Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale

Remote sensed imagery can both describe urban sprawl on a watershed scale and provide essential information for modeling the impact of sprawl on watercourses. This paper looks at six watersheds in greater Cleveland, OH: two urban; two rural; and two undergoing rapid urbanization. Thematic Mapper imagery from 1984, 1988, 1994, to 1999 was classified into functional classes describing each watershed in terms of the position of each pixel along continua of [1] percentage permeability and [2] canopy cover. Because the functional classes represent positions along independent continua rather than thematic land-cover classes, they can easily be compared from image to image, and they provide quantitative estimates of parameters at 30-m resolution suitable for spatial simulation models. The imagery classified in this way makes it possible to observe the progress of urban sprawl both within these watersheds and over a study area which extends from the inner city to its rural surroundings.     

Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)

The concentration of people in densely populated urban areas, especially in developing countries like India and China, calls for the use of sophisticated monitoring systems, like remote sensing and Geographical Information Systems (GIS). Time series of land use/cover changes can easily be generated using sequential satellite images, which are required for the prediction of urban growth, verification of growth model outputs, estimation of impervious area, parameterization of various hydrological models, water resources planning and management and environmental studies. In the present work, urban growth of Ajmer city (India) in the last 29 years has been studied at mid‐scale level (5–25 m). Remote sensing and GIS have been used to extract the information related to urban growth, impervious area and its spatial and temporal variation. Statistical classification approaches have been used to derive the land use information from satellite images of eight years (1977–2005). The Shannon's entropy and landscape metrics (patchiness and map density) are computed in order to quantify the urban form (impervious area) in terms of spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative factors. Results reveal that land development (200%) in Ajmer is more than three times the population growth (59%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the sprawl.     

Understanding urban wetland dynamics: cross-scale detection and analysis of remote sensing

This study aimed to detect and understand remotely sensed urban wetland dynamics as a sensitive indicator of the combined effects of human disturbances and climate impacts in the course of global change. To address this objective, the study developed technical approaches to detect and interpret wetland changes across spatial scales in complex urban landscapes. Using a series of Satellite Pour l’Observation de la Terre (SPOT) images covering 1992–2010, the study was conducted in the Kansas City metropolitan area of the USA, which has experienced significant urban sprawl in recent decades. As a fine-tuning of the traditional supervised image classification, a knowledge-based classification algorithm was developed to identify fine-scale, hidden wetlands that cannot be appropriately detected based on their spectral differentiability. The analyses of wetland change were implemented at the metropolitan, watershed, and sub-watershed scales as well as being based on the size of surface water bodies in order to reveal real pictures of urban wetland change trends in relation to major driving factors. The results of the study indicated that the knowledge-based classification approach improved the detection capability and accuracy of urban wetlands by fine-tuning the traditional classification results. The cross-scale analysis of detected land covers revealed that wetland dynamics varied in trend and magnitude from metropolitan, watersheds, to sub-watershed scales. The study found that increased precipitation swelled wetlands, which inflated the findings of remotely sensed wetland cover and related trend interpretation. During an 18 year study period, human development activities in the study area resulted in a large increase in impervious surfaces, which was mainly at the expense of farmland/grassland areas and some small wetlands in all urban watersheds. In contrast, increased precipitation in the region swelled large wetlands in particular. This mixed picture of urban wetland dynamics, associated with the analysis of underlying driving factors, provides a new baseline for relevant urban planning, management, and research in a global change perspective.     

辽宁抚顺近10年来市域热场与植被变化分析

利用1999年和2010年的TM卫星遥感影像,定量反演了抚顺市域的热场和植被指数,并对其变化进行了分析。结果表明,11 a全市的平均热场温度升高了1.53 ℃,城市热岛主要集中在抚顺市的城市建成区以及苏子河河谷和黑大线沿线地带,但强热岛和极强热岛的空间分布范围2010年较1999年压缩幅度空前。从植被盖度总体情况来看,高覆盖度植被覆盖面积均在60%以上,而全市低覆盖度等级以下的植被面积比例很小,其面积比例都在1.5％以下。从植被盖度的变化看,高覆盖度和较高覆盖度的植被面积比例分别下降了3.22％和2.31％;而中覆盖度的植被面积比例增加了4.94％,其变化最大的区域在抚顺市区,该区域变化的比率是全市变化的3~5倍。从热场与植被的变化原因来看,首先是受植物生长季节气候的暖干化变化趋势的影响,其次还与土地利用类型中耕地和草地的减少以及建设用地的快速增加有关,此外,抚顺市生态建设工作对其也有一定程度的影响。     

Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data

Land surface temperature (LST) is essentially considered to be one of the most important indicators used for assessment of the urban thermal environment. It is quite evident that land-use/land-cover (LULC) and landscape patterns have ecological implications at varying spatial scales, which in turn influence the distribution of habitat and material/energy fluxes in the landscape. This article attempts to quantitatively analyse the complex interrelationships between urban LST and LULC landscape patterns with the purpose of elucidating their relation to landscape processes. The study employed an integrated approach involving remote-sensing, geographic information system (GIS), and landscape ecology techniques on bi-temporal Landsat Thematic Mapper images of Southwestern Sydney metropolitan region and the surrounding fringe, taken at approximately the same time of the year in July 1993 and July 2006. First, the LULC categories and LST were extracted from the bi-temporal images. The LST distribution and changes and LST of the LULC categories were then quantitatively analysed using landscape metrics and LST zones. The results show that large differences in temperature existed in even a single LULC category, except for variations between different LULC categories. In each LST zone, the regressive function of LST with fractional vegetation cover (FVC) indicated a significant relationship between LST and FVC. Landscape metrics of LULC categories in each zone in relation to the other zones showed changing patterns between 1993 and 2006. This study also illustrates that a method integrating retrieval of LST and FVC from remote-sensing images combined with landscape metrics provides a novel and feasible way to describe the spatial distribution and temporal variation in urban thermal patterns and associated LULC conditions in a quantitative manner.