Special Issue "The Application of Thermal Urban Remote Sensing to Understand and Monitor Urban Climates"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2016).

Special Issue Editors

Dr. Benjamin Bechtel
E-Mail Website
Guest Editor
Institut für Geographie, Universität Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
Tel. +49 (0) 40 42838 4962
Interests: urban remote sensing; land surface temperature; downscaling; time series analysis; annual temperature cycle; surface urban heat island; urban climates
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Dr. Iphigenia Keramitsoglou
E-Mail Website
Guest Editor
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Vas. Pavlou & I. Metaxa, Penteli, Athens, GR-15236, Greece
Interests: Thermal satellite remote sensing; surface urban heat islands; land surface temperature; LST downscaling; heatwaves; decision support systems; urban resilience
Special Issues and Collections in MDPI journals
Dr. Simone Kotthaus
E-Mail Website
Guest Editor
Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading, RG6 6BB, UK
Interests: urban climates, boundary layer processes, surface energy fluxes, thermal remote sensing, urban building materials, emissivity and thermal anisotropy
Dr. James A. Voogt
E-Mail Website
Guest Editor
Department of Geography, Western University, London, ON N6A 5C2, Canada
Tel. 519-661-2111 x85018
Interests: measurement and modeling of urban surface temperatures, urban thermal anisotropy, urban heat islands, how modifications to urban surface temperatures affect urban climates
Dr. Klemen Zakšek
E-Mail Website
Guest Editor
Institute of Geophysics, University of Hamburg, Bundesstrasse 55, 20146 Hamburg, Germany
Interests: satellite and terrestrial remote sensing of volcanic thermal anomalies, satellite observations of urban heat islands, influence of terrain on satellite data, advanced terrain visualizations and photogrammetry of clouds
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

For about four decades, thermal infrared (TIR) remote sensing has been a promising source of information concerning surface urban heat island (SUHI) intensity and its spatial distribution. However, operational thermal monitoring of urban areas remains elusive. Three major obstacles restrict the routine exploitation of TIR data. First, the high spatiotemporal variability of the UHI implies specific requirements regarding both the spatial and temporal resolutions of observations that are not yet fulfilled by any of the space-borne sensors currently available. Second, the interpretation and application of thermal remote sensing observations are complicated by biased sampling of complex three-dimensional urban surfaces and by variations in surface emissivity. Third, only the land surface (skin) temperature (LST), from which the SUHI is defined, can be measured by these instruments, whereas it is air temperature that mostly impacts human activities. The retrieval of the latter from space measurements remains a scientific challenge despite great advances in the development of urban energy balance (UEB) models and urban canopy schemes of differing complexities. These models can comprehensively simulate the radiative and turbulent transfers between the surface and the urban atmosphere, but they are dependent on a large number of meteorological and surface parameters as boundary conditions that are rarely available. Additionally, many of those models lack independent evaluation.

Simultaneously, a notable paradigm shift has recently been made in how the urban heat island is defined within the area of urban climatology. Specifically, there is a move away from the traditional dichotomy between urban and rural towards defining urban structures with homogeneous thermal responses, called local climate zones (LCZ). Remote sensing inherently has the advantage of providing a more holistic view of the entire city and LCZs can well be classified by moderate resolution multi-temporal multi-spectral data. Hence, this concept can be readily adopted for investigating the SUHI phenomenon and therefore define a better standard than do the numerous definitions of urban and rural applied in SUHI studies.

Both the urban remote sensing and the urban climatology community are invited to contribute to this Special Issue, which focuses on multi-temporal analyses of remote sensing data as well as remote sensing-modeling interfaces. We invite you to submit articles concerning your recent research with respect to the following topics:

-          Validation of UEB models via remote sensing LST;

-          Assimilation and other possible uses of satellite-derived LST in urban canopy schemes;

-          Downscaling / disaggregation of LST data over urban areas;

-          Parametrization of urban air temperatures from remote sensing data;

-          Application of the LCZ concept in remote sensing SUHI studies;

-          Derivation of surface parameters for urban canopy models;

-          Urban surface structure and its linkage to thermal anisotropy and emissivity;

-          Multi-temporal SUHI analysis that use large datasets;

-          Diurnal and / or seasonal evolution of the SUHI;

-          Operational retrieval of urban temperatures and high-level services;

-          Derivation of surface energy fluxes in cities based on LST observations.

Authors are required to check and follow the specific Instructions to Authors; see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Dr. Benjamin Bechtel
Dr. Iphigenia Keramitsoglou
Dr. Simone Kotthaus
Dr. James A. Voogt
Dr. Klemen Zakšek
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (16 papers)

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Research

Open AccessArticle
Urban–Rural Contrasts in Central-Eastern European Cities Using a MODIS 4 Micron Time Series
Remote Sens. 2016, 8(11), 924; https://doi.org/10.3390/rs8110924 - 06 Nov 2016
Cited by 2
Abstract
A primary impact of urbanization on the local climate is evident in the phenomenon recognized as the Urban Heat Island (UHI) effect. This urban thermal anomaly can increase the health risks of vulnerable populations to heat waves. The surface UHI results from emittance [...] Read more.
A primary impact of urbanization on the local climate is evident in the phenomenon recognized as the Urban Heat Island (UHI) effect. This urban thermal anomaly can increase the health risks of vulnerable populations to heat waves. The surface UHI results from emittance in the longer wavelengths of the thermal infrared; however, there are also urban anomalies that are detectable from radiance in the shorter wavelengths (3–5 micron) of the Middle Infrared (MIR). Radiance in the MIR can penetrate urban haze which frequently obscures urban areas by scattering visible and near infrared radiation. We analyzed seasonal and spatial variations in MIR for three Central European cities from 2003 through 2012 using Moderate Resolution Imaging Spectrometer (MODIS) band 23 (~4 micron) to evaluate whether MIR radiance could be used to characterize heat anomalies associated with urban areas. We examined the seasonality of MIR radiance over urban areas and nearby croplands and found that the urban MIR anomalies varied due to time of year: cropland MIR could be larger than urban MIR when there was more exposed soil at planting and harvest times. Further, we compared monthly mean MIR with the Normalized Difference Vegetation Index (NDVI) to analyze contrasts between urban and rural areas. We found that the seasonal dynamic range of the MIR could exceed that of the NDVI. We explored the linkage between meteorological data and MIR radiance and found a range of responses from strong to weak dependence of MIR radiance on maximum temperature and accumulated precipitation. Our results extend the understanding of the anomalous characteristics of urban areas within a rural matrix.
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Open AccessArticle
Urban Heat Islands as Viewed by Microwave Radiometers and Thermal Time Indices
Remote Sens. 2016, 8(10), 831; https://doi.org/10.3390/rs8100831 - 10 Oct 2016
Cited by 1
Abstract
Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower [...] Read more.
Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower temporal coverage due to revisit frequency, limited swath width, and cloud cover. Algorithms have recently been developed to retrieve near-surface air temperatures using microwave radiometer data, which enables characterization of UHIs in metropolitan areas, major conurbations, and global megacities at regional to continental scales using temporally denser time series than those that have been available from TIR sensors. Here we examine how UHIs appear across the entire Western Hemisphere using surface air temperatures derived from the Advanced Microwave Scanning Radiometers (AMSRs), AMSR-E onboard the National Aeronautics and Space Administration’s (NASA’s) Aqua and AMSR2 onboard the Japan Aerospace eXploration Agency’s Global Change Observation Mission-Water1 (JAXA’s GCOM-W1) satellites. We compare these data with station observations from the Global Historical Climate Network (GHCN) for 27 major cities across North America (in 83 urban-rural groupings) to demonstrate the capability of microwave data in a UHI study. Two measures of thermal time, accumulated diurnal and nocturnal degree-days, are calculated from the remotely sensed surface air temperature time series to characterize the urban-rural thermal differences over multiple growing seasons. Daytime urban thermal accumulations from the microwave data were sometimes lower than in adjacent rural areas. In contrast, station observations showed consistently higher day and night thermal accumulations in cities. UHIs are more pronounced at night, with 55% (AMSRs) and 93% (GHCN) of urban-rural groupings showing higher accumulated nocturnal degree-days in cities. While urban-rural thermal gradients may vary according to different datasets or locations, day-night differences in thermal time metrics were consistently lower (>90% of urban-rural groupings) in urban areas than in rural areas for both datasets. We propose that the normalized difference accumulated thermal time index (NDATTI) is a more robust metric for comparative UHI studies than simple temperature differences because it can be calculated from either station or remotely sensed data and it attenuates latitudinal effects. Full article
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Open AccessArticle
Effects of Urbanization and Seasonal Cycle on the Surface Urban Heat Island Patterns in the Coastal Growing Cities: A Case Study of Casablanca, Morocco
Remote Sens. 2016, 8(10), 829; https://doi.org/10.3390/rs8100829 - 10 Oct 2016
Cited by 19
Abstract
The urban heat island (UHI) phenomenon is a harmful environmental problem in urban areas affecting both climatic and ecological processes. This paper aims to highlight and monitor the spatial distribution of Surface UHI (SUHI) in the Casablanca region, Morocco, using remote sensing data. [...] Read more.
The urban heat island (UHI) phenomenon is a harmful environmental problem in urban areas affecting both climatic and ecological processes. This paper aims to highlight and monitor the spatial distribution of Surface UHI (SUHI) in the Casablanca region, Morocco, using remote sensing data. To achieve this goal, a time series of Landsat TM/ETM+/OLI-TIRS images was acquired from 1984 to 2016 and analyzed. In addition, nocturnal MODIS images acquired from 2005 to 2015 were used to evaluate the nighttime SUHI. In order to better analyze intense heat produced by urban core, SUHI intensity (SUHII) was computed by quantifying the difference of land surface temperature (LST) between urban and rural areas. The urban core SUHII appears more significant in winter seasons than during summer, while the pattern of SUHII becomes moderate during intermediate seasons. During winter, the average daytime SUHII gradually increased in the residential area of Casablanca and in some small peri-urban cities by more than 1 °C from 1984 to 2015. The industrial areas of the Casablanca region were affected by a significant rise in SUHII exceeding 15 °C in certain industrial localities. In contrast, daytime SUHII shows a reciprocal effect during summer with emergence of a heat island in rural areas and development of cool islands in urban and peri-urban areas. During nighttime, the SUHII remains positive in urban areas year-round with higher values in winter as compared to summer. The results point out that the seasonal cycle of daytime SUHII as observed in the Casablanca region is different from other mid-latitude cities, where the highest values are often observed in summer during the day. Full article
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Open AccessArticle
Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities
Remote Sens. 2016, 8(10), 788; https://doi.org/10.3390/rs8100788 - 22 Sep 2016
Cited by 28
Abstract
The main factors influencing the spatiotemporal variability of urban climate are quite widely recognized, including, for example, the thermal properties of materials used for surfaces and buildings, the mass, height and layout of the buildings themselves and patterns of land use. However, the [...] Read more.
The main factors influencing the spatiotemporal variability of urban climate are quite widely recognized, including, for example, the thermal properties of materials used for surfaces and buildings, the mass, height and layout of the buildings themselves and patterns of land use. However, the roles played by particular factors vary from city to city with respect to differences in geographical location, overall size, number of inhabitants and more. In urban climatology, the concept of “local climate zones” (LCZs) has emerged over the past decade to address this heterogeneity. In this contribution, a new GIS-based method is used for LCZ delimitation in Prague and Brno, the two largest cities in the Czech Republic, while land surface temperatures (LSTs) derived from LANDSAT and ASTER satellite data are employed for exploring the extent to which LCZ classes discriminate with respect to LSTs. It has been suggested that correctly-delineated LCZs should demonstrate the features typical of LST variability, and thus, typical surface temperatures should differ significantly among most LCZs. Zones representing heavy industry (LCZ 10), dense low-rise buildings (LCZ 3) and compact mid-rise buildings (LCZ 2) were identified as the warmest in both cities, while bodies of water (LCZ G) and densely-forested areas (LCZ A) made up the coolest zones. ANOVA and subsequent multiple comparison tests demonstrated that significant temperature differences between the various LCZs prevail. The results of testing were similar for both study areas (89.3% and 91.7% significant LST differences for Brno and Prague, respectively). LSTs computed from LANDSAT differentiated better between LCZs, compared with ASTER. LCZ 8 (large low-rise buildings), LCZ 10 (heavy industry) and LCZ D (low plants) are well-differentiated zones in terms of their surface temperatures. In contrast, LCZ 2 (compact mid-rise), LCZ 4 (open high-rise) and LCZ 9 (sparsely built-up) are less distinguishable in both areas analyzed. Factors such as seasonality and thermal anisotropy remain a challenge for future research into LST differences. Full article
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Open AccessArticle
Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data
Remote Sens. 2016, 8(8), 656; https://doi.org/10.3390/rs8080656 - 13 Aug 2016
Cited by 14
Abstract
Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this [...] Read more.
Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001–2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R2) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18–2.60 K in RMSE and 0.00–0.15 in R2). Full article
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Open AccessArticle
A Comprehensive Statistical Study on Daytime Surface Urban Heat Island during Summer in Urban Areas, Case Study: Cairo and Its New Towns
Remote Sens. 2016, 8(8), 643; https://doi.org/10.3390/rs8080643 - 05 Aug 2016
Cited by 4
Abstract
Surface urban heat island (SUHI) is defined as the elevated land surface temperature (LST) in urban area in comparison with non-urban areas, and it can influence the energy consumption, comfort and health of urban residents. In this study, the existence of daytime SUHI, [...] Read more.
Surface urban heat island (SUHI) is defined as the elevated land surface temperature (LST) in urban area in comparison with non-urban areas, and it can influence the energy consumption, comfort and health of urban residents. In this study, the existence of daytime SUHI, in Cairo and its new towns during the summer, is investigated using three different approaches; (1) utilization of pre-urbanization observations as LST references; (2) utilization of rural observations as LST references (urban–rural difference); and (3) utilization of the SIUHI (Surface Intra Urban Heat Island) approach. A time series of Landsat TM & ETM+ data (46 images) from 1984 to 2015 was employed in this study for daytime LST calculation during summer. Different statistical hypothesis tests were utilized for the evaluation of LST and SUHI in the case studies. The results demonstrated that there is no significant LST difference between the urban areas studied, and their corresponding built-up areas. In addition, daytime LST in new towns during the summer is 2 K warmer than in Cairo. Utilization of a pre-urbanization observations approach, alongside an evaluation of the long-term trend, demonstrated that there is no daytime SUHI during the summer in the study areas, and construction activities in the study areas do not result in cooling or warming effects. Utilization of the rural observations approach showed that LST is lower in Cairo than its surrounding areas. This demonstrates why the selection of suitable rural references in SUHI studies is an important and complicated task, and how this approach may lead to misinterpretation in desert city areas with significant landscape and surface difference with their most surrounding areas (e.g., Cairo). Results showed that, although SIUHI technique can be representative for the changes of variance of LST in urban areas, it is not able to identify the changes of mean LST in urban areas. Full article
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Open AccessArticle
Characterizing Urban Fabric Properties and Their Thermal Effect Using QuickBird Image and Landsat 8 Thermal Infrared (TIR) Data: The Case of Downtown Shanghai, China
Remote Sens. 2016, 8(7), 541; https://doi.org/10.3390/rs8070541 - 24 Jun 2016
Cited by 7
Abstract
The combined usage of high-resolution satellite images and thermal infrared (TIR) data helps understanding the thermal effect of urban fabric properties and the mechanism of urban heat island (UHI) formation. In this study, three typical urban functional zones (UFZs) of downtown Shanghai were [...] Read more.
The combined usage of high-resolution satellite images and thermal infrared (TIR) data helps understanding the thermal effect of urban fabric properties and the mechanism of urban heat island (UHI) formation. In this study, three typical urban functional zones (UFZs) of downtown Shanghai were chosen for quantifying the relationship between fine-scale urban fabric properties and their thermal effect. Nine land surfaces and 146 aggregated land parcels extracted from a QuickBird image (dated 14 April 2014) were used to characterize urban fabric properties. The thermal effect was deduced from land surface temperature (LST), intra-UHI intensity, blackbody flux density (BBFD) and blackbody flux (BBF). The net BBF was retrieved from the Landsat 8 TIR band 10 dated 13 August 2013, and 28 May 2014. The products were resampled to fine resolution using a geospatial sharpening approach and further validated. The results show that: (1) On the UFZ level, there is a significant thermal differential among land surfaces. Water, well-vegetated land, high-rises with light color and high-rises with glass curtain walls exhibited relatively low LST, UHI intensity and BBFD. In contrast, mobile homes with light steel roofs, low buildings with bituminous roofs, asphalt roads and composite material pavements showed inverse trends for LST, UHI intensity, and BBFD; (2) It was found that parcel-based per ha net BBF, which offsets the “size-effect” among parcels, is more reasonable and comparable when quantifying excess surface flux emitted by the parcels; (3) When examining the relationship between parcel-level land surfaces and per ha BBF, a partial least squares (PLS) regression model showed that buildings and asphalt roads are major contributors to parcel-based per ha BBF, followed by other impervious surfaces. In contrast, vegetated land and water contribute with a much lower per ha net BBF to parcel warming. Full article
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Open AccessArticle
Impact of MODIS Quality Control on Temporally Aggregated Urban Surface Temperature and Long-Term Surface Urban Heat Island Intensity
Remote Sens. 2016, 8(5), 374; https://doi.org/10.3390/rs8050374 - 13 May 2016
Cited by 12
Abstract
Surface Urban Heat Island (SUHI) is a phenomenon of high spatial and temporal variability. However, studies that investigate urban land surface temperature (LST) observed in different seasons frequently utilize a single satellite measurement and do not incorporate temporal composites. Temporally aggregated data increase [...] Read more.
Surface Urban Heat Island (SUHI) is a phenomenon of high spatial and temporal variability. However, studies that investigate urban land surface temperature (LST) observed in different seasons frequently utilize a single satellite measurement and do not incorporate temporal composites. Temporally aggregated data increase clear sky coverage, which is important in many aspects of urban climatology. However, it is critical to account for possible errors and quality of the data that are utilized. The objective of this paper is to analyze the impact of MODIS Quality Control (QC) and the view angle on temporally aggregated urban surface temperature and long-term SUHI intensity. To achieve this, a weighted arithmetic mean was utilized whose weights were based on the view angle of satellite observation and the MODIS QC flags; namely, LST retrieval errors and emissivity errors. In order to investigate the impact of the MODIS QC on long-term LST composites, five exponential powers were applied to weights during the temporal aggregation process, resulting in five thresholds of best quality pixel promotion. It was found that there are significant differences between temporal composites that take into account the MODIS QC and the view angle and those that do not (obtained by means of a simple arithmetic mean with no weights applied), in terms of spatial distribution and density distribution of urban and rural LST. The differences were more distinctive in spring or daytime cases than in autumn or nighttime cases. The impact of the MODIS QC and the view angle on temporal composites was highest in the city center. Ten SUHI indicators were utilized. It was found that the impact on long-term SUHI intensity is weaker than on the spatial pattern of LST and that SUHI indicators are inconsistent. Full article
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Open AccessArticle
Seasonal Variations of the Surface Urban Heat Island in a Semi-Arid City
Remote Sens. 2016, 8(4), 352; https://doi.org/10.3390/rs8040352 - 21 Apr 2016
Cited by 53
Abstract
The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer [...] Read more.
The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of −4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient: −0.76), night LST and IS in spring (0.60), night LST and albedo in winter (−0.53) and day LST with fractional vegetation cover in summer (−0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators. Full article
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Open AccessArticle
An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas
Remote Sens. 2016, 8(4), 306; https://doi.org/10.3390/rs8040306 - 06 Apr 2016
Cited by 15
Abstract
The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities and impacts human health. The study of this effect for monitoring and mitigation purposes is crucial, but it is hampered by the lack of [...] Read more.
The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities and impacts human health. The study of this effect for monitoring and mitigation purposes is crucial, but it is hampered by the lack of high spatiotemporal temperature data. This article presents the work undertaken for the implementation of an operational real-time module for monitoring 2 m air temperature (TA) at a spatial resolution of 1 km based on the Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This new module has been developed in the context of an operational system for monitoring the urban thermal environment. The initial evaluation of TA products against meteorological in situ data from 15 cities in Europe and North Africa yields that its accuracy in terms of Root Mean Square Error (RMSE) is 2.3 °C and Pearson’s correlation coefficient (Rho) is 0.95. The temperature information made available at and around cities can facilitate the assessment of the UHIs in real time but also the timely generation of relevant higher value products and services for energy demand and human health studies. The service is available at http://snf-652558.vm.okeanos.grnet.gr/treasure/portal/info.html. Full article
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Open AccessArticle
A Comparison of Multiple Datasets for Monitoring Thermal Time in Urban Areas over the U.S. Upper Midwest
Remote Sens. 2016, 8(4), 297; https://doi.org/10.3390/rs8040297 - 31 Mar 2016
Cited by 11
Abstract
Traditional studies of urban climate used air temperature observations from local urban/rural weather stations in order to analyze the general pattern of higher temperatures in urban areas compared with corresponding rural regions, also known as the Urban Heat Island (UHI) effect. More recently, [...] Read more.
Traditional studies of urban climate used air temperature observations from local urban/rural weather stations in order to analyze the general pattern of higher temperatures in urban areas compared with corresponding rural regions, also known as the Urban Heat Island (UHI) effect. More recently, satellite remote sensing datasets of land surface temperature have been exploited to monitor UHIs. While closely linked, air temperature and land surface temperature (LST) observations do not measure the same variables. Here we analyze land surface temperature vs. air temperature-based characterization and seasonality of the UHI and the surface UHI (SUHI) from 2003 to 2012 over the Upper Midwest region of the United States using LST from MODIS, and air temperature from the Daymet modeled gridded daily air temperature dataset, and compare both datasets to ground station data from first-order weather stations of the Global Historical Climatology Network (GHCN) located in eleven urban areas spanning our study region. We first convert the temperature data to metrics of nocturnal, diurnal, and daily thermal time and their annual accumulations to draw conclusions on nighttime vs. daytime and seasonal dynamics of the UHI. In general, the MODIS LST-derived results are able to capture urban–rural differences in daytime, nighttime, and daily thermal time while the Daymet air temperature-derived results show very little urban–rural differences in thermal time. Compared to the GHCN ground station air temperature-derived observations, MODIS LST-derived results are closer in terms of urban–rural differences in nighttime thermal time, while the results from Daymet are closer to the observations from GHCN during the daytime. We also found differences in the seasonal dynamics of UHIs measured by air temperature observations and SUHIs measured by LST observations. Full article
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Open AccessArticle
Assessing the Capability of a Downscaled Urban Land Surface Temperature Time Series to Reproduce the Spatiotemporal Features of the Original Data
Remote Sens. 2016, 8(4), 274; https://doi.org/10.3390/rs8040274 - 25 Mar 2016
Cited by 18
Abstract
The downscaling of frequently-acquired geostationary Land Surface Temperature (LST) data can compensate the lack of high spatiotemporal LST data for urban climate studies. In order to be usable, the generated datasets must accurately reproduce the spatiotemporal features of the coarse-scale LST time series [...] Read more.
The downscaling of frequently-acquired geostationary Land Surface Temperature (LST) data can compensate the lack of high spatiotemporal LST data for urban climate studies. In order to be usable, the generated datasets must accurately reproduce the spatiotemporal features of the coarse-scale LST time series with greater spatial detail. This work concerns this issue and exploits the high temporal resolution of the data to address it. Specifically, it assesses the accuracy, correct pattern formation and the spatiotemporal inter-relationships of an urban three-month-long downscaled geostationary LST time series. The results suggest that the downscaling process operated in a consistent manner and preserved the radiometry of the original data. The exploitation of the data inter-relationships for evaluation purposes revealed that the downscaled time series reproduced the smooth diurnal cycle, but the autocorrelation of the downscaled data was higher than the original coarse-scale data. Overall, the evaluation process showed that the generation of high spatiotemporal LST data for urban areas is very challenging, and to deem it successful, it is mandatory to assess the temporal evolution of the urban thermal patterns. The results suggest that the proposed tests can facilitate the evaluation process. Full article
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Open AccessArticle
Quantifying the Daytime and Night-Time Urban Heat Island in Birmingham, UK: A Comparison of Satellite Derived Land Surface Temperature and High Resolution Air Temperature Observations
Remote Sens. 2016, 8(2), 153; https://doi.org/10.3390/rs8020153 - 17 Feb 2016
Cited by 34
Abstract
The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an [...] Read more.
The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an ongoing limitation for studies. The availability of remote sensing data has therefore helped fill a scientific need by providing high resolution temperature data of our cities. However, satellite-mounted sensors measure land surface temperatures (LST) and not canopy air temperatures with the latter being the key parameter in UHI investigations. Fortunately, such data is becoming increasingly available via urban meteorological networks, which now provide an opportunity to quantify and compare surface and canopy UHI on an unprecedented scale. For the first time, this study uses high resolution air temperature data from the Birmingham Urban Climate Laboratory urban meteorological network and MODIS LST to quantify and identify the spatial pattern of the daytime and night-time UHI in Birmingham, UK (a city with an approximate population of 1 million). This analysis is performed under a range of atmospheric stability classes and investigates the relationship between surface and canopy UHI in the city. A significant finding of this work is that it demonstrates, using observations, that the distribution of the surface UHI appears to be clearly linked to landuse, whereas for canopy UHI, advective processes appear to play an increasingly important role. Strong relationships were found between air temperatures and LST during both the day and night at a neighbourhood scale, but even with the use of higher resolution urban meteorological datasets, relationships at the city scale are still limited. Full article
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Open AccessArticle
Daytime Thermal Anisotropy of Urban Neighbourhoods: Morphological Causation
Remote Sens. 2016, 8(2), 108; https://doi.org/10.3390/rs8020108 - 30 Jan 2016
Cited by 31
Abstract
Surface temperature is a key variable in boundary-layer meteorology and is typically acquired by remote observation of emitted thermal radiation. However, the three-dimensional structure of cities complicates matters: uneven solar heating of urban facets produces an “effective anisotropy” of surface thermal emission at [...] Read more.
Surface temperature is a key variable in boundary-layer meteorology and is typically acquired by remote observation of emitted thermal radiation. However, the three-dimensional structure of cities complicates matters: uneven solar heating of urban facets produces an “effective anisotropy” of surface thermal emission at the neighbourhood scale. Remotely-sensed urban surface temperature varies with sensor view angle as a consequence. The authors combine a microscale urban surface temperature model with a thermal remote sensing model to predict the effective anisotropy of simplified neighbourhood configurations. The former model provides detailed surface temperature distributions for a range of “urban” forms, and the remote sensing model computes aggregate temperatures for multiple view angles. The combined model’s ability to reproduce observed anisotropy is evaluated against measurements from a neighbourhood in Vancouver, Canada. As in previous modeling studies, anisotropy is underestimated. Addition of moderate coverages of small (sub-facet scale) structure can account for much of the missing anisotropy. Subsequently, over 1900 sensitivity simulations are performed with the model combination, and the dependence of daytime effective thermal anisotropy on diurnal solar path (i.e., latitude and time of day) and blunt neighbourhood form is assessed. The range of effective anisotropy, as well as the maximum difference from nadir-observed brightness temperature, peak for moderate building-height-to-spacing ratios (H/W), and scale with canyon (between-building) area; dispersed high-rise urban forms generate maximum anisotropy. Maximum anisotropy increases with solar elevation and scales with shortwave irradiance. Moreover, it depends linearly on H/W for H/W < 1.25, with a slope that depends on maximum off-nadir sensor angle. Decreasing minimum brightness temperature is primarily responsible for this linear growth of maximum anisotropy. These results allow first order estimation of the minimum effective anisotropy magnitude of urban neighbourhoods as a function of building-height-to-spacing ratio, building plan area density, and shortwave irradiance. Finally, four “local climate zones” are simulated at two latitudes. Removal of neighbourhood street orientation regularity for these zones decreases maximum anisotropy by 3%–31%. Furthermore, thermal and radiative material properties are a weaker predictor of anisotropy than neighbourhood morphology. This study is the first systematic evaluation of effective anisotropy magnitude and causation for urban landscapes. Full article
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Open AccessArticle
The Morphology, Dynamics and Potential Hotspots of Land Surface Temperature at a Local Scale in Urban Areas
Remote Sens. 2016, 8(1), 18; https://doi.org/10.3390/rs8010018 - 30 Dec 2015
Cited by 12
Abstract
Current characterization of the Urban Heat Island (UHI) remains insufficient to support the effective mitigation and adaptation of increasing temperatures in urban areas. Planning and design strategies are restricted to the investigation of temperature anomalies at a city scale. By focusing on Land [...] Read more.
Current characterization of the Urban Heat Island (UHI) remains insufficient to support the effective mitigation and adaptation of increasing temperatures in urban areas. Planning and design strategies are restricted to the investigation of temperature anomalies at a city scale. By focusing on Land Surface Temperature of Wuhan, China, this research examines the temperature variations locally where mitigation and adaptation would be more feasible. It shows how local temperature anomalies can be identified morphologically. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. They are first considered as noisy observations of the latent temperature patterns. The continuous latent patterns of the temperature are then recovered from these discrete observations by using the non-parametric Multi-Task Gaussian Process Modeling. The Multi-Scale Shape Index is then applied in the area of focus to extract the local morphological features. A triplet of shape, curvedness and temperature is formed as the criteria to extract local heat islands. The behavior of the local heat islands can thus be quantified morphologically. The places with critical deformations are identified as hotpots. The hotspots with certain yearly behavior are further associated with land surface composition to determine effective mitigation and adaptation strategies. This research can assist in the temperature and planning field on two levels: (1) the local land surface temperature patterns are characterized by decomposing the variations into fundamental deformation modes to allow a process-based understanding of the dynamics; and (2) the characterization at local scale conforms to planning and design conventions where mitigation and adaptation strategies are supposed to be more practical. The weaknesses and limitations of the study are addressed in the closing section. Full article
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Open AccessArticle
An Unmanned Airship Thermal Infrared Remote Sensing System for Low-Altitude and High Spatial Resolution Monitoring of Urban Thermal Environments: Integration and an Experiment
Remote Sens. 2015, 7(10), 14259-14275; https://doi.org/10.3390/rs71014259 - 27 Oct 2015
Cited by 6
Abstract
Satellite remote sensing data that lacks spatial resolution and timeliness is of limited ability to access urban thermal environment on a micro scale. This paper presents an unmanned airship low-altitude thermal infrared remote sensing system (UALTIRSS), which is composed of an unmanned airship, [...] Read more.
Satellite remote sensing data that lacks spatial resolution and timeliness is of limited ability to access urban thermal environment on a micro scale. This paper presents an unmanned airship low-altitude thermal infrared remote sensing system (UALTIRSS), which is composed of an unmanned airship, an onboard control and navigation subsystem, a task subsystem, a communication subsystem, and a ground-base station. Furthermore, an experimental method and an airborne-field experiment for collecting land surface temperature (LST) were designed and conducted. The LST pattern within 0.8-m spatial resolution and with root mean square error (RMSE) value of 2.63 °C was achieved and analyzed in the study region. Finally, the effects of surface types on the surrounding thermal environment were analyzed by LST profiles. Results show that the high thermal resolution imagery obtained from UALTIRSS can provide more detailed thermal information, which are conducive to classify fine urban material and assess surface urban heat island (SUHI). There is a significant positive correlation between the average LST of profiles and the percent impervious surface area (ISA%) with R2 around 0.917. Overall, UALTIRSS and the retrieval method were proved to be low-cost and feasible for studying micro urban thermal environments. Full article
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