Special Issue "Satellite Remote Sensing of Urban Thermal Environment: Progresses, Challenges, and Opportunities"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2020).

Special Issue Editors

Dr. Yuyu Zhou
Website
Guest Editor
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011-1051, USA
Interests: ecosystem and human system dynamics; phenology indicators urban heat island; climate change; urbanization
Special Issues and Collections in MDPI journals
Dr. Weiqi Zhou

Guest Editor
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing, China
Interests: urban remote sensing, urban ecology, landscape ecology, urban heat island, object-based image analysis
Special Issues and Collections in MDPI journals
Dr. Bailang Yu
Website
Guest Editor
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China
Interests: nighttime light remote sensing; urban remote sensing; object-oriented analysis for remotely sensed images; LiDaR (Light Detection and Ranging)
Special Issues and Collections in MDPI journals
Dr. Wenfeng Zhan
Website
Guest Editor
International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Interests: thermal remote sensing, land surface temperature, urban remote sensing, urban heat island, urban climate
Dr. Decheng Zhou
SciProfiles
Guest Editor
Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, No.219 Ningliu Road, Nanjing 210044, China
Interests: urban heat island, urban ecology, land use and land cover change, satellite remote sensing

Special Issue Information

Dear Colleagues,

In the past several decades, the world has experienced fast urbanization, and this trend is expected to continue for decades to come. Urbanization plays an important role in the Earth system through modifying the terrestrial surfaces and atmospheric composition. Especially, urbanization changes the thermal environment in urban areas by creating the phenomenon of urban heat islands (UHI), with higher temperatures in urban areas compared to their surrounding rural areas. UHI has a significant socioeconomic and environmental impact, such as increasing cooling energy use, altering vegetation phenology, and affecting the health of urban dwellers, from local, to regional, and even global scales. As a result, UHI will pose challenges for environmental sustainability with its significant adverse impacts on human activities and natural processes. There is a growing need, from both the science and policy making communities, for science-based information and knowledge on the urban thermal environment and its drivers and impact from local to global scales. An improved understanding of the urban thermal environment can help us develop better practices in land use planning and management for urban sustainability.

Satellite remote sensing plays an irreplaceable role in understanding our urban thermal environment. With the rapid development of remote sensing technologies and algorithms, this Special Issue invites original manuscripts on the latest research and advancement in the remote sensing of the urban thermal environment. Potential topics include but are not limited to the following:

  • New methods for quantifying surface and air UHI;
  • Improvements in land surface and air temperate data for UHI studies;
  • Relationship between surface and air UHI;
  • Innovative findings on spatial and temporal patterns of UHI;
  • UHI studies in understudied regions or cities;
  • Evaluations of UHI drivers (e.g., urban composition and configuration);
  • Cooling effects of green and blue spaces;
  • Predicting and modelling UHI;
  • The investigation of UHI impacts on human activities (e.g., building energy use and heat related illness);
  • The investigation of UHI impacts on natural processes (e.g., vegetation phenology);
  • Mitigation and adaptation measures of UHI.

Dr. Yuyu Zhou
Dr. Weiqi Zhou
Dr. Bailang Yu
Dr. Wenfeng Zhan
Dr. Decheng Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Urbanization
  • Urban thermal environment
  • Urban heat island
  • Land surface temperature
  • Surface air temperature
  • Thermal remote sensing
  • Drivers and effects
  • Impervious surface area
  • Urban greenspace
  • Urban composition and configuration
  • Building energy use
  • Vegetation phenology
  • Heat-related illnesses
  • Mitigation and adaptation

Published Papers (10 papers)

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Research

Open AccessArticle
Human Activities Enhance Radiation Forcing through Surface Albedo Associated with Vegetation in Beijing
Remote Sens. 2020, 12(5), 837; https://doi.org/10.3390/rs12050837 - 05 Mar 2020
Abstract
The impact of human activities on vegetation has been the focus of much research, but the impact on radiation energy through surface albedo associated with vegetation greenness and length of the growth season is still not well documented. Based on the land cover [...] Read more.
The impact of human activities on vegetation has been the focus of much research, but the impact on radiation energy through surface albedo associated with vegetation greenness and length of the growth season is still not well documented. Based on the land cover data for the years 2000 and 2015, this study first divided the land cover change in Beijing from 2000 to 2015 into five types according to the impact of human activities and vegetation resilience, namely, old urban areas (OU), urban expansion areas (UE), cropland (CP), mixed pixel areas (MP, which means the land covers other than urban expansion which had changed from 2000 to 2015), and the residual vegetation cover areas (pure pixels (PP), dominated by natural and seminatural vegetation, such as grassland, forest, and wetland). Then, we calculated the direct radiative forcing from the albedo change from 2000 to 2015 and analyzed the effect of vegetation on the albedo under different land cover types based on multi-resource Moderate Resolution Imaging Spectroradiometer (MODIS) products of vegetation, albedo, and solar radiation. The results showed that the most typical changes in land cover were from urban expansion. By comparing the PP with the four human-affected land cover types (OU, UE, MP, and CP), we confirmed that the radiative forcing increment between 2001–2003 and 2013–2015 in PP (0.01 W/m2) was much smaller than that in the four human-affected land cover types (the mean increment was 0.92 W/m2). This study highlights that human activities affected vegetation growth. This, in turn, brought changes in the albedo, thereby enhancing radiative forcing in Beijing during 2000–2015. Full article
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Open AccessArticle
Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data
Remote Sens. 2020, 12(5), 801; https://doi.org/10.3390/rs12050801 - 02 Mar 2020
Abstract
Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data [...] Read more.
Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management. Full article
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Open AccessArticle
The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China
Remote Sens. 2020, 12(5), 794; https://doi.org/10.3390/rs12050794 - 02 Mar 2020
Cited by 8
Abstract
To improve land use efficiency, urban renewal must also consider urban microclimates and heat islands. Existing research has depended on manual interpretation of high-resolution optical satellite imagery to resolve land surface temperature (LST) changes caused by urban renewal; however, the acquired ground time [...] Read more.
To improve land use efficiency, urban renewal must also consider urban microclimates and heat islands. Existing research has depended on manual interpretation of high-resolution optical satellite imagery to resolve land surface temperature (LST) changes caused by urban renewal; however, the acquired ground time series data tend to be uneven and unique to specific frameworks. The objective of this study was to establish a more general framework to study LST changes caused by urban renewal using multi-source remote sensing data. Specifically, urban renewal areas during 2007–2017 were obtained by integrating Landsat and yearly Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and LST was retrieved from Landsat thermal infrared data using the generalized single-channel algorithm. Our results showed that urban renewal land (URL) area accounted for 1.88% of urban land area. Relative LST between URL and general urban land (GUL) of Liwan, Yuexiu, Haizhu, and Tianhe districts dropped by 0.88, 0.42, 0.43, and 0.10 K, respectively, whereas those of Baiyun, Huangpu, Panyu, and Luogang districts presented opposite characteristics, with a rise in the LST of 0.98, 1.03, 1.63, and 2.11 K, respectively. These results are attributable to population density, building density, and landscape pattern changes during the urban renewal process. Full article
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Open AccessArticle
Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China
Remote Sens. 2020, 12(3), 578; https://doi.org/10.3390/rs12030578 - 10 Feb 2020
Abstract
As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the [...] Read more.
As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design. Full article
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Open AccessArticle
Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants
Remote Sens. 2020, 12(3), 391; https://doi.org/10.3390/rs12030391 - 26 Jan 2020
Cited by 3
Abstract
Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely [...] Read more.
Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25–5 km) and spatial extents (20–60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect. Full article
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Open AccessArticle
Time-Series Analysis Reveals Intensified Urban Heat Island Effects but without Significant Urban Warming
Remote Sens. 2019, 11(19), 2229; https://doi.org/10.3390/rs11192229 - 25 Sep 2019
Cited by 1
Abstract
Numerous studies have shown an increased surface urban heat island intensity (SUHII) in many cities with urban expansion. Few studies, however, have investigated whether such intensification is mainly caused by urban warming, the cooling of surrounding nonurban regions, or the different rates of [...] Read more.
Numerous studies have shown an increased surface urban heat island intensity (SUHII) in many cities with urban expansion. Few studies, however, have investigated whether such intensification is mainly caused by urban warming, the cooling of surrounding nonurban regions, or the different rates of warming/cooling between urban and nonurban areas. This study aims to fill that gap using Beijing, China, as a case study. We first examined the temporal trends of SUHII in Beijing and then compared the magnitude of the land surface temperature (LST) trend in urban and nonurban areas. We further detected the temporal trend of LST (TrendLST) at the pixel level and explored its linkage to the temporal trends of EVI (TrendEVI) and NDBI (TrendNDBI). We used MODIS data from 2000 to 2015. We found that (1) SUHII significantly increased from 4.35 °C to 6.02 °C, showing an intensified surface urban heat island (SUHI) effect, with an annual increase rate of 0.13 °C in summer during the daytime and 0.04 °C in summer at night. In addition, the intensification of SUHII was more prominent in new urban areas (NUA). (2) The intensified SUHII, however, was largely caused by substantial cooling effects in nonurban areas (NoUA), not substantial warming in urban areas. (3) Spatially, there were large spatial variations in significant warming and cooling spots over the entire study area, which were related to TrendNDBI and TrendEVI. TrendNDBI significantly affected TrendLST in a positive way, while the TrendEVI had a significant positive effect (p = 0.023) on TrendLST only when EVI had an increasing trend. Our study underscores the importance of quantifying and comparing the changes in LST in both urban and nonurban areas when investigating changes in SUHII using time-series trend analysis. Such analysis can provide insights into promoting city-based urban heat mitigation strategies which focused on both urban and nonurban areas. Full article
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Open AccessArticle
Determining the Boundary and Probability of Surface Urban Heat Island Footprint Based on a Logistic Model
Remote Sens. 2019, 11(11), 1368; https://doi.org/10.3390/rs11111368 - 06 Jun 2019
Cited by 14
Abstract
Studies of the spatial extent of surface urban heat island (SUHI or UHISurf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical [...] Read more.
Studies of the spatial extent of surface urban heat island (SUHI or UHISurf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical limitations on the ability of their algorithms to accurately determine the impacts of the shape, topography, and landscape heterogeneity of the city. The key to determining the FP boundary is identifying background temperatures in reference rural regions. Due to the instability of remote sensing data, these background temperatures should be determined automatically rather than manually, to eliminate artificial bias. To address this need, we developed an algorithm that adequately represents the decay of land surface temperature (LST) from the urban center to surrounding rural regions, and automatically calculates thresholds for reference rural LSTs in all directions based on a logistic curve. In this study, we applied this algorithm with data from the Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS) 8-day level 3 (L3) LST global grid product to delineate precise SUHI FPs for the Beijing metropolitan area during the summers of 2004–2018 and determine the interannual and diurnal variations in FP boundaries and their relationship with SUHI intensity. Full article
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Open AccessArticle
Four-band Thermal Mosaicking: A New Method to Process Infrared Thermal Imagery of Urban Landscapes from UAV Flights
Remote Sens. 2019, 11(11), 1365; https://doi.org/10.3390/rs11111365 - 06 Jun 2019
Cited by 1
Abstract
Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form [...] Read more.
Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form a single image mosaic and to construct a 3-D digital model of the measurement target. However, the mosaicking of thermal images is challenging due to low lens resolution and weak contrast in the single thermal band. In this study, a novel method, referred to as four-band thermal mosaicking (FTM), was developed in order to process thermal images. The method stacks the thermal band obtained by a thermal camera onto the RGB bands acquired on the same flight by an RGB camera and mosaics the four bands simultaneously. An object-based calibration method is then used to eliminate inter-band positional errors. A UAV flight over a natural park was carried out in order to test the method. The results demonstrated that with the assistance of the high-resolution RGB bands, the method enabled successful and efficient thermal mosaicking. Transect analysis revealed an inter-band accuracy of 0.39 m or 0.68 times the ground pixel size of the thermal camera. A cluster analysis validated that the thermal mosaic captured the expected contrast of thermal properties between different surfaces within the scene. Full article
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Open AccessArticle
Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003–2016
Remote Sens. 2019, 11(10), 1212; https://doi.org/10.3390/rs11101212 - 22 May 2019
Cited by 4
Abstract
Surface urban heat islands (SUHIs) have been investigated in many regions around the world, but little attention has been given with regard to SUHIs in South America. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data was used to [...] Read more.
Surface urban heat islands (SUHIs) have been investigated in many regions around the world, but little attention has been given with regard to SUHIs in South America. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data was used to investigate the diurnal, seasonal, and interannual variations in the SUHI intensity (SUHII, the urban LST minus the rural LST) in 44 South American cities in different climate zones and types of rural land. To examine the effects of factors that may influence the SUHII, correlations between the SUHII and the enhanced vegetation index (EVI), urban area, population, altitude, and anthropogenic heat emissions were performed. The results showed that the SUHI effect was obvious in South America. The mean daytime SUHII was higher than the mean night-time SUHII in all areas except for the arid climate zone. In the daytime, the summer displayed a stronger SUHII in the warm temperate climate zone than the other seasons. The night-time SUHII showed less obvious seasonal variations. In addition, the surrounding land cover influenced the SUHII. During the day, the SUHII was therefore stronger in rural areas that were covered by forests than in other types of rural land. Interannually, most cities showed an insignificant temporal trend in the SUHII from 2003 to 2016. The daytime SUHII was significantly and negatively correlated with the ∆EVI (the urban EVI minus the rural EVI) across the 44 cities, but a poor relationship was observed at night. In addition, anthropogenic heat emissions were positively correlated with the night-time SUHII. Urban area, population, and altitude were weakly correlated with the SUHII, which suggested that these factors may not have a significant impact on the spatial variations in the SUHII in South America. Full article
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Open AccessArticle
Study of the Seasonal Effect of Building Shadows on Urban Land Surface Temperatures Based on Remote Sensing Data
Remote Sens. 2019, 11(5), 497; https://doi.org/10.3390/rs11050497 - 01 Mar 2019
Cited by 3
Abstract
Building shadows (BSs) frequently occur in urban areas, and their area and distribution display strong seasonal variations that significantly influence the urban land surface temperature (LST). However, it remains unclear how BSs affect the LST at the city scale because it is difficult [...] Read more.
Building shadows (BSs) frequently occur in urban areas, and their area and distribution display strong seasonal variations that significantly influence the urban land surface temperature (LST). However, it remains unclear how BSs affect the LST at the city scale because it is difficult to extract the shaded area at the subpixel scale and to connect such areas with the LST at the pixel scale. In this study, we combined the sun angle, building height, building footprint and building occlusion to extract the seasonal spatial distribution of BSs in the central area of Beijing. The effect of BSs on the LST was analyzed using LST retrieved from Landsat-8 thermal infrared sensor data. First, the relationship between the LST patch fragmentation and proportion of BSs in the sample areas was modeled without vegetation. Then, we quantitatively studied the mitigated intensity of the LST in pure impervious surfaces (IS) and vegetation pixels covered by BSs; next, we analyzed the LST sensitivity of these pixels to BSs. The results showed that the existence of BSs influences the fragmentation of the low LST patches strongly from summer to winter. On the other hand, pure IS pixels totally covered by BSs experienced a greater cooling effect, with 3.16 K on 10 July, and the lowest cooling occurred between 14 and 25 December, with a mean of 1.24 K. Without considering the relationship in winter, the LST is nonlinearly correlated to the building shadows ratio (BSR) in pixels, and an approximate 10% increase in the BSR resulted in decreases in the LST of approximately 0.33 K (mean of 16 April and 10 May), 0.37 K (10 July) and 0.24 K (28 September) for pure IS pixels, and 0.18 K, 0.20 K and 0.15 K, respectively, for pure vegetation pixels. Further analysis indicates that the LST of pure IS pixels is more sensitive to BSs than that of vegetation because the self-regulation mechanism of vegetation reduces the cooling effect of BSs. These findings can help urban planners understand the cooling characteristics of BSs and design suitable urban forms to resist urban heat islands (UHIs). Full article
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