Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan,China

According to the UN Population Reference Bureau, 1.4 billion more people will have settled in urban areas by 2030. One of the key environmental effects of rapid urbanization is the urban heat island (UHI) effect. Understanding the mechanism of surface UHIs associated with land-use/land-cover (LULC) change patterns is important for improving the ecology and sustainability of cities. In this article, time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data were used to extract LULC data and land surface temperature (LST) data for the city of Jinan, China, from 1987 to 2011, a period during which the city experienced rapid urbanization. With the aid of a geographical information system (GIS) and remote sensing (RS) approach, the changes in this urban area’s LULC were explored, and the impact of these changes on the spatiotemporal patterns and underlying driving forces of the surface UHI effect were further quantitatively characterized. The results show that significant changes in land use and land cover occurred over the study period, with loss of farmland, forest, and shrub vegetation to urban use, leading to spatial growth of impervious surfaces. Consequently, the land surface characteristics and spatiotemporal patterns of the UHI have changed drastically. According to the seasonal and inter-annual variations in intensity of UHIs, mean differences in UHI intensity between city centre, peri-urban, and nearby rural areas were stronger during summer and spring and weaker during winter and autumn. Spatially, there were significant LST gradients from the city centre to surrounding rural areas. The city centre exhibited higher LSTs and remarkable variation in LSTs, while the surrounding rural areas exhibited lower LSTs and lower variation in LSTs. Moreover, the analysis of LSTs and indices showed that great differences of temperature even existed in a LULC type except for variations between different LULC types. In addition, a local-level analysis revealed that the intensity of the UHI effect is proportional to the size of the urban area, the population density, and the frequent occurrence of certain activities.     

Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Rapid changes of land use and land cover (LULC) in urban areas have become a major environmental concern due to environmental impacts, such as the reduction of green spaces and development of urban heat islands (UHI). Monitoring and management plans are required to solve this problem effectively. The Tabriz metropolitan area in Iran, selected as a case study for this research, is an example of a fast growing city. Multi-temporal images acquired by Landsat 4, 5 TM and Landsat 7 ETM+ sensors on 30 June 1989, 18 August 1998, and 2 August 2001 respectively, were corrected for radiometric and geometric errors, and processed to extract LULC classes and land surface temperature (LST). The relationship between temporal dynamics of LST and LULC was then examined. The temperature vegetation index (TVX) space was constructed in order to study the temporal variability of thermal data and vegetation cover. Temporal trajectory of pixels in the TVX space showed that most changes due to urbanization were observable as the pixels migrated from the low temperature-dense vegetation condition to the high temperature-sparse vegetation condition in the TVX space. The uncertainty analysis revealed that the trajectory analysis in the TVX space involved a class-dependant noise component. This emphasized the need for multiple LULC control points in the TVX space. In addition, this research suggests that the use of multi-temporal satellite data together with the examination of changes in the TVX space is effective and useful in urban LULC change monitoring and analysis of urban surface temperature conditions as long as the uncertainty is addressed.     

Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015

To analyse changes in human settlement in Shenzhen City during the past three decades, changes in land use/land cover (LULC) and urban expansion were investigated based on multi-temporal Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images. Using C4.5-based AdaBoost, a hierarchical classification method was developed to extract specific classes with high accuracy by combining a specific number of base-classifier decisions. Along with a classification post-processing approach, the classification accuracy was greatly improved. The statistical analysis of LULC changes from 1988 to 2015 shows that built-up areas have increased 6.4-fold, whereas cultivated land and forest continually decreased because of rapid urbanization. Urban expansion driven by human activities has considerably affected the landscape change of Shenzhen. The urban-expansion pattern of Shenzhen is a mixture of three urban-expansion patterns. Among these patterns, traffic-driven urban expansion has been the main form of urban expansion for some time, especially in the Non-Special Economic Zone. In addition, by taking 8 to 10 year periods as time intervals, urban expansion in Shenzhen was divided into three stages: the early-age urbanization stage (1988–1996), the rapid urbanization stage (1996–2005), and the intensive urbanization stage (2005–2015). For different stages, the state of urban expansion is different. In long-term LULC dynamic monitoring and urban-expansion detection, it was possible to obtain 11 LULC maps, which took 2 to 4 years as a research interval. With regard to the short research periods, LULC changes and urban expansion were investigated in detail.     

Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired during the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water – was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition, and rates among the three study areas and indicates the importance of analysing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g. urban expansion, roads, and land-use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.     

Dynamics of land-use and land-cover change in Freetown,Sierra Leone and its effects on urban and peri-urban agriculture – a remote sensing approach

This paper presents findings of a land-use and land-cover (LULC) change mapping exercise conducted in Freetown, Sierra Leone. Nine LULC classes were mapped from multi-temporal Landsat data of 1974, 1986 and 2000. Special attention was given to the growth or otherwise of agricultural land in relation to other LULC classes. Conversion of one land-use/-cover type to the other was identified, and its effects discussed. Major conversions occurred between agricultural lands, grasslands, evergreen forest, built-up areas and barren land. Built-up areas increased by at least 140% between 1974 and 2000, suggesting a high urbanization rate. About 882 ha (27%) of agricultural lands in 1986 were converted to residential purposes in 2000, especially at the urban fringes, in response to an increase in population. Some 14% of evergreen forest was found to have been converted to agricultural land. These major conversions suggest a strong linkage between urbanization, agriculture and deforestation.     

Monitoring annual urbanization activities in Guangzhou using Landsat images (1987–2015)

Rapid land-use/land-cover (LULC) changes such as urbanization have tremendous impacts on regional climate and environment. Satellite images acquired by fast-developing remote-sensing techniques provide frequent observations of the land surface, thereby allowing for continuous mapping of urbanization activities. In this study, we investigated the annual urbanization activities over the past three decades in Guangzhou, one of the largest metropolises in China. To enhance the efficiency of training sample extraction in long-term land-cover mapping, we developed a three-step method: 1) three spectral indices were derived to extract the candidates of training samples based on decision trees; 2) a spatial filter was used to extract homogenous samples for each land-cover type; and 3) temporal consistency checking was performed for the samples of urban areas. We applied the developed method to time-series Landsat images and produced annual land-cover maps of Guangzhou from 1987 to 2015. We evaluated the produced land-cover maps and found an average overall accuracy of 89.80% for all studied years. Our results show that dramatic urbanization has occurred in the region of the Guangzhou city, where built-up areas have mostly expanded to the northwest, east, and south of the central regions of Guangzhou. The average growth rate of urban areas in Guangzhou from 1987 to 2015 was at 38.72 km² per year, which was generally consistent with the government survey data. Future studies are required to understand how rapid urbanization in Guangzhou influences social economy and environmental sustainability.     

Quantifying the spatial differences of landscape change in the Hai River Basin,China, in the 1990s

Quantifying a landscape pattern and its change is essential for monitoring and assessing the ecological consequences of land-use/land-cover (LULC) change and human interference. In this study, a combination of landscape pattern indices and land-use dynamics indices based on remote-sensing images was employed to analyse and compare the spatial and temporal dynamics of the landscape pattern in the Hai River Basin (HRB), China, in the 1990s. During this decade, the change in the landscape pattern was mainly driven by intense human-induced alterations. Overall, the landscape pattern changed considerably, progressively becoming more fragmented and diverse, with widespread encroachment of cropland because of rapid urbanization. The change in the landscape pattern exhibited distinctive spatial differences between the mountains and the plains, as well as between urban and rural areas, with higher fragmentation in the plain region and urban fringes or newly urbanizing areas. The changes in the landscape pattern around Beijing resulted in more fragmented and diverse landscape types. A detailed examination of the Jing–Guang (Beijing–Guangzhou) railway line transect and two other subregions indicated that urbanization contributed mostly to the spatial differences of landscape change through population growth, transportation improvements and rapid economic development. Spatial differences in the change in the landscape pattern were induced by economic growth, population increase and government policy.     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization

ABSTRACT

The present study aims to quantify spatial relationship of land-use and land-cover (LULC) changes and land surface temperature (LST) using remote-sensing and geographical information system techniques over 10 major metropolitan cities of India. For this purpose, Landsat 7 Enhanced Thematic Mapper Plus images of these cities during winter period from 2001 to 2013 are used. Statistical analysis of the LULC classification has shown overall accuracy ranging between 85% and 88%. The LULC classification and estimated LST using the satellite imageries reveals the presence of multiple urban heat islands (UHIs) and their increase in number in all cities. Significant increase in built-up/urban areas are noticed at the expense of vegetated lands and barren lands over Lucknow, Nagpur, and Jaipur, whereas in Hyderabad and Bengaluru the built-up area and the dry/barren lands are observed to be increasing, at the expense of crop/grass lands. Higher UHI intensities in the range of 8.9–10.3°C are noticed over Mumbai, Nagpur, and Hyderabad compared to the other cities. Higher temperature zones (hotspots) are found to be increasing in the built-up area as well as in barren lands. Varying increase in UHI intensity among the cities is noticed which may have implications in the regional climate over the cities.     

Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology

Abstract

NOAA AVHRR satellite infra-red data are used to display the surface radiant temperature heat islands of Vancouver, British Columbia, Seattle, Washington, and Los Angeles, California. Heat island intensities are largest in the day-time and in the warm season. Day-time intra-urban thermal patterns are strongly correlated with land-use; industrial areas are warmest and vegetated, riverine or coastal areas are coolest. Nocturnal heat island intensities and the correlation of the surface radiant temperature distribution with land use are less. This is the reverse of the known characteristics of near-surface air temperature heat islands. Several questions relating to the interpretation and limitations of satellite data in heat island analysis and urban climate modelling are addressed.     

The role of rapid urbanization in surface warming over eastern China

Using satellite imaging of the Earth at night, we quantitatively assess the rapid growth of urban areas and investigate its impact on long-term surface warming at 214 medium and large cities over eastern China. Urban growth intensity is measured using the size of the area experiencing fast night-time light increase and the distance to temperature-observation sites. Surface warming, related closely to city size, also exhibits a strong association with urban growth. A rapid increase of surface temperature is observed mainly for cities undergoing rapid urbanization (RU). Such a relation is evident over Central, South, and Northwest China, but it is weak over Northeast China, implying regional variation of temperature responses to urban growth. Satellite-derived land-surface temperature analysis suggests that cities experiencing RU are more subject to the effects of urban heat island expansion, which explains the variations of warming rate among cities within the same region. These results underscore that surface warming induced by RU might be an important component of urban climate change in eastern China.     

Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures

The term urban heat island describes the phenomenon of altered temperatures in urban areas compared to their rural hinterlands. A surface urban heat island encompasses the patterns of land surface temperatures in urban areas. The classical indicator to describe a surface urban heat island is the difference between urban and rural surface temperatures. However, several other indicators for this purpose have been suggested in the literature. In this study, we compared the eleven different indicators for quantifying surface urban heat islands that were most frequently used in recent publications on remote sensing-based urban heat island assessments. The dataset used here consists of 263 European cities with monthly mean temperatures from MODIS data products for July 2002, January 2003 and July 2003. We found that (i) the indicators individually reveal diurnal and seasonal patterns but show rather low correlations over time, and (ii) for single points in time, the different indicators show only weak correlations, although they are supposed to quantify the same phenomenon. Differentiating cities according to thermal climate zones increased the relationships between the indicators. Thus, we can identify temporal aspects and indicator selection as important factors determining the estimation of urban heat islands. We conclude that research should take into account the differences and instabilities of the indicators chosen for quantifying surface urban heat islands and should use several indicators in parallel for describing the surface urban heat island of a city.     

A multi-scale urban integrated assessment framework for climate change studies: A flooding application

In order to assess the potential future impacts of climate change on urban areas, tools to assist decision-makers to understand future patterns of risk are required. This paper presents a modelling framework to allow the downscaling of national- and regional-scale population and employment projections to local scale land-use changes, providing scenarios of future socio-economic change. A coupled spatial interaction population model and cellular automata land development model produces future urbanisation maps based on planning policy scenarios. The framework is demonstrated on Greater London, UK, with a set of future population and land-use scenarios being tested against flood risk under climate change. The framework is developed in Python using open-source databases and is designed to be transferable to other cities worldwide.     

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.     

TM 图像的城镇用地信息提取方法研究

从对城镇用地的遥感信息机理分析入手, 分析了城镇用地在Landsat TM 2、TM 3、TM 4、TM 5等各个波段上与其它地类的可分性。发现利用归一化植被指数NDVI 和归一化水体指数NDWI,设定合适的阈值, 能够有效地提取植被信息和水体信息, 得到二值化图像。由谱间结构特征可知, 城镇用地和裸地的TM 3- TM 2> 0, 城镇用地的TM 4- TM 3< 0, 而裸地的TM 4- TM 3> 0, 利用此特性可以区分城镇用地和裸地。对上述二值图像进行二值逻辑运算, 得到城镇用地专题信息。研究结果表明, 该方法的提取效率和精度都较高, 与监督分类法和屏幕数字化方法相比, 是一种行之有效的方法。     

Assessing the long-term impact of urbanization on run-off using a remote-sensing-supported hydrological model

Land use/land cover (LULC) change in Dongguan, a city belonging to the Pearl River Delta metropolitan area, and its impact on the run-off of this city since the rapid urbanization period starting in 1979 are analysed in this article. Historic remote-sensing imagery (acquired in 1979, 1989, 2000, 2006, and 2013) was processed by the Support Vector Machine (SVM) method to obtain LULC data, and a long-term hydrologic impact assessment (L-THIA) model was applied to evaluate the long-term effects of LULC changes on surface run-off. The results show the urbanized area in Dongguan has increased more than 52% from 1979 to 2013 and the percentages of annual surface run-off depth and annual surface run-off coefficient are 58% and 5.83%, respectively. The increase in annual surface run-off is related to urbanization, and the centre area of the city has experienced the largest increase in annual surface run-off. The 32 towns’ local indicators of spatial association show centre towns are hot spots of increases in annual surface run-off in Dongguan city.     

基于Landsat 8影像和能量平衡的西安市热场格局研究!

基于2014年8月15日的Landsat 8影像,通过劈窗算法反演西安中心城区地表温度,定量测算热岛中心范围。估算多种地表能量分量,分析热环境格局与地表能量分量的关系。结果表明:(1)西安中心城区城市热岛集中分布在人口、居住、商业密集区、经济技术开发区以及植被覆盖较差的区域;(2)感热、波文比与地表温度呈正相关,人为热与温度呈不显著正相关,净辐射、潜热与地表温度呈显著负相关;(3)城市热岛的地表能量结构中感热与潜热差异是构成城市热岛差异的主要原因。     

Spatial–temporal land-use/land-cover dynamics and their impacts on surface temperature in Chongming Island of Shanghai,China

Land-use/land-cover (LULC) changes are occurring at rapid rates on the Chongming Island of Shanghai, China, giving rise to a major concern about environmental impacts. We herein carried out a sound analysis of the LULC dynamics, the conversions among different LULC classes, and land-surface temperature (LST) distribution using remote-sensing data from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) time series spanning the last 35 years (1979–2014). Based on LULC class information and LST, we constructed a temperature/vegetation index space to study the temporal variability of thermal data, vegetation cover, and LULC. The results showed that the LULC change dynamics in Chongming Island have strongly impacted the LST in the recent decade. The spatial position conversion and quantitative change of vegetation cover totalled about 44.4% of LULC-type areas over the Island, and the comprehensive LULC dynamicity changed from 2.97 to 3.95 during the investigated period. Accordingly, significant LST changes took place in the portion of the Chongming Island showing normal temperature range, which accounted for 85.94% of the whole Island’s area as of 1 August 2000 and that decreased to 50.79% on 6 May 2009, while the surface extents under low- and with ultra-high-temperature ranges increased, respectively, both from 0 of 2000 to 6.67% and 0.41% of 2009. The results indicate that the pixel classes including vegetation cover, wetland, and waterbody, which have larger dynamicity and maximum change vector magnitudes, played a large role in alleviating the effect of the land-surface thermal environment, and were key driving factors contributing to the increasing trend of non-normal temperature range ratio over time. Our findings are expected to provide valuable information for decision-making regarding the development and construction of Chongming Island into an eco-region.     

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.     

基于MODIS 数据的南京市夏季城市热岛分析

城市热岛效应是当前城市环境与气候主要研究内容之一。地表温度与气温之间有紧密的联系, 通过遥感反演地表温度已成为研究城市热岛的有效手段。利用MODIS 数据, 获取地表比辐射率与大气透过率2 个基本参数, 运用劈窗算法反演南京市夏季地表温度。基于不同时相的MODIS数据, 对4 幅南京市地表温度反演图像作对比分析, 较好地显示了南京市城市热岛的空间分布、热岛范围和城市热岛强度, 结果表明南京市夏季热岛问题较为严重。