Local Climate Zones to Identify Surface Urban Heat Islands: A Systematic Review (original) (raw)
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Remote Sensing
This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat is...
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
Along with urbanization, sealing of vegetated land and evaporation surfaces by impermeable materials, lead to changes in urban climate. This phenomenon is observed as temperatures several degrees higher in densely urbanized areas compared to the rural land at the urban fringe particularly at nights, so-called Urban Heat Island. Urban Heat Island (UHI) effect is related with urban form, pattern and building materials so far as it is associated with meteorological conditions, air pollution, excess heat from cooling. UHI effect has negative influences on human health, as well as other environmental problems such as higher energy demand, air pollution, and water shortage. Urban Heat Island (UHI) effect has long been studied by observations of air temperature from thermometers. However, with the advent and proliferation of remote sensing technology, synoptic coverage and better representations of spatial variation of surface temperature became possible. This has opened new avenues for the observation capabilities and research of UHIs. In this study, "UHI effect and its relation to factors that cause it" is explored for İzmit city which has been subject to excess urbanization and industrialization during the past decades. Spatial distribution and variation of UHI effect in İzmit is analysed using Landsat 8 and ASTER day & night images of 2015 summer. Surface temperature data derived from thermal bands of the images were analysed for UHI effect. Higher temperatures were classified into 4 grades of UHIs and mapped both for day and night. Inadequate urban form, pattern, density, high buildings and paved surfaces at the expanse of soil ground and vegetation cover are the main factors that cause microclimates giving rise to spatial variations in temperatures across cities. These factors quantified as land surface/cover parameters for the study include vegetation index (NDVI), imperviousness (NDISI), albedo, solar insolation, Sky View Factor (SVF), building envelope, distance to sea, and traffic space density. These parameters that cause variation in intra-city temperatures were evaluated for their relationship with different grades of UHIs. Zonal statistics of UHI classes and variations in average value of parameters were interpreted. The outcomes that highlight local temperature peaks are proposed to the attention of the decision makers for mitigation of Urban Heat Island effect in the city at local and neighbourhood scale.
Satellite Based Analysis of Surface Urban Heat Island Intensity
Journal of Environmental Geography, 2016
The most obvious characteristics of urban climate are higher air and surface temperatures compared to rural areas and large spatial variation of meteorological parameters within the city. This research examines the long term and seasonal development of urban surface temperature using satellite data during a period of 30 years and within a year. The medium resolution Landsat data were (pre)processed using open source tools. Besides the analysis of the long term and seasonal changes in land surface temperature within a city, also its relationship with changes in the vegetation cover was investigated. Different urban districts and local climate zones showed varying strength of correlation. The temperature difference between urban surfaces and surroundings is defined as surface urban heat island (SUHI). Its development shows remarkable seasonal and spatial anomalies. The satellite images can be applied to visualize and analyze the SUHI, although they were not collected at midday and ear...
Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using one-year (April 2011–March 2012) land surface temperature (LST) data retrieved from the Communication, Ocean and Meteorological Satellite (COMS). The spatio-temporal variations of SUHI and the relationship between SUHI and vegetation activity were analyzed using hourly cloud-free LST data. In general, the LST was higher in low latitudes, low altitudes, urban areas and dry regions compared to high latitudes, high altitudes, rural areas and vegetated areas. In particular, the LST over the three megacities was always higher than that in the surrounding rural areas. The SUHI showed a maximum intensity (10–13 ° C) at noon during the summer, irrespective of the geographic location of the city, but weak intensities (4–7 ° C) were observed during other times and seasons. In general, the SUHI intensity over the three megacities showed strong seasonal (diurnal) variations during the daytime (summer) and weak seasonal (diurnal) variations during the nighttime (other seasons). As a result, the temporal variation pattern of SUHIs was quite different from that of urban heat islands, and the SUHIs showed a distinct maximum at noon of the summer months and weak intensities during the nighttime of all seasons. The patterns of seasonal and diurnal variations of the SUHIs were clearly dependent on the geographic environment of cities. In addition, the intensity of SUHIs showed a strong OPEN ACCESS Remote Sens. 2014, 6 5853 negative relationship with vegetation activity during the daytime, but no such relationship was observed during the nighttime. This suggests that the SUHI intensity is mainly controlled by differences in evapotranspiration (or the Bowen ratio) between urban and rural areas during the daytime.
An alternative method to characterize the surface urban heat island
International Journal of Biometeorology, 2014
An urban heat island (UHI) is a relative measure defined as a metropolitan area that is warmer than the surrounding suburban or rural areas. The UHI nomenclature includes a surface urban heat island (SUHI) definition that describes the land surface temperature (LST) differences between urban and suburban areas. The complexity involved in selecting an urban core and external thermal reference for estimating the magnitude of a UHI led us to develop a new definition of SUHIs that excludes any rural comparison. The thermal reference of these newly defined surface intra-urban heat islands (SIUHIs) is based on various temperature thresholds above the spatial average of LSTs within the city's administrative limits. A time series of images from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) from 1984 to 2011 was used to estimate the LST over the warm season in Montreal, Québec, Canada. Different SIUHI categories were analyzed in consideration of the global solar radiation (GSR) conditions that prevailed before each acquisition date of the Landsat images. The results show that the cumulative GSR observed 24 to 48 h prior to the satellite overpass is significantly linked with the occurrence of the highest SIUHI categories (thresholds of +3 to +7°C above the mean spatial LST within Montreal city). The highest correlation (≈0.8) is obtained between a pixel-based temperature that is 6°C hotter than the city's mean LST (SIUHI+6) after only 24 h of cumulative GSR. SIUHI+6 can then be used as a thermal threshold that characterizes hotspots within the city. This identification approach can be viewed as a useful criterion or as an initial step toward the development of heat health watch and warning system (HHWWS), especially during the occurrence of severe heat spells across urban areas.
Regarding Some Pitfalls in Urban Heat Island Studies Using Remote Sensing Technology
Remote Sensing, 2021
This paper attempts to illustrate the complexity of thermal infrared (TIR) data analysis for urban heat island studies. While a certain shift regarding the use of correct scientific nomenclature (using the term “surface urban heat island”) could be observed, the literature is full of incorrect conclusions and results using erroneous terminology. This seems to be the result of the ease of such literature implicitly suggesting that “warm surfaces” result in “high air temperatures”, ultimately drawing conclusions for urban planning authorities. It seems that the UHI is easy to measure, easy to explain, easy to find, and easy to illustrate—simply take a TIR-image. Due to this apparent simplicity, many authors seem to jump into UHI studies without fully understanding the nature of the phenomenon as far as time and spatial scales, physical processes, and the numerous methodological pitfalls inherent to UHI studies are concerned. This paper attempts to point out some of the many pitfalls i...
Analytical Study on The Intensity of Surface Urban Heat Island (1)
CSID Journal of Infrastructure Development, 2024
Urbanization and climate change are significant drivers of rising Land Surface Temperature (LST) and the formation of Surface Urban Heat Islands (UHIs), which have emerged as pressing environmental challenges in recent decades. This study examines the relationship between Land Use/Cover (LU/C) indices and LST in Kaduna, Nigeria, using multi-temporal Landsat imagery spanning five periods from 2001 to 2021. LST values were derived from the Landsat thermal bands, while LU/C dynamics were identified using the Maximum Likelihood Classifier algorithm. LU/C indices—including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil-Adjusted Vegetation Index (SAVI)—were generated to evaluate their correlation with LST. The results highlight a substantial increase in built-up areas over the 20-year period, accompanied by rising LST values, particularly over bare land and built-up regions, while water bodies exhibited the lowest LST values. Among the LU/C indices, NDBI showed the strongest positive correlation with LST, emphasizing the influence of urban expansion on surface thermal dynamics. The study underscores the critical need for sustainable urban planning and holistic design strategies to mitigate rising LST and SUHI effects in rapidly urbanizing regions like Kaduna.
This article presents the development and application to a set of French urban agglomerations of a method for Local Climate Zones (LCZ) attribution using the open-source language R. The LCZs classify the urban fabric at high spatial scale (such as a block of houses) according to its morphological characteristicsand land use. The LCZ classification is carried out for 42 urban agglomerations and is then related to urban heat island intensity (UHII) obtained from numerical simulations at a spatial resolution of 250m. The objective is to study the adequacy of the LCZ classification to characterise the impact of urban morphology on the UHII. The variance analysis (ANOVA) carried out confirms the highly significant relationship between LCZs and the UHII for a given urban agglomeration. For all the urban agglomerations in the sample, linear regression models show a significant correlation between the percentages of surface covered by different LCZ and the mean UHII for the time periods tes...
Journal of The Indian Society of Remote Sensing, 2018
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