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Application of Remote Sensing in Urban Climatology

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

Deadline for manuscript submissions: closed (15 March 2021) | Viewed by 42420

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Dr. Panagiotis Sismanidis

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Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Vas. Pavlou & I. Metaxa, Penteli, GR-15236 Athens, Greece
Interests: thermal remote sensing; surface urban heat islands; land surface temperatures; LST downscaling; LST Spatiotemporal fusion; LST annual cycle parameters

Prof. Dr. Benjamin Bechtel

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Department of Geography, Ruhr-University Bochum, DE-44801 Bochum, Germany
Interests: remote sensing of urban climates; crowdsourcing; urban health; thermal remote sensing
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Dr. Zina Mitraka

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Remote Sensing Lab, Foundation for Research and Technology Hellas (FORTH), N. Plastira 100, GR-70013 Heraklion, Greece
Interests: urban remote sensing; thermal remote sensing; urban climate; urban heat fluxes; urban surface cover and morphology; support to urban planning; urban resilience

Dr. Iphigenia Keramitsoglou

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Guest Editor

Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Vas. Pavlou & I. Metaxa, Penteli, GR-15236 Athens, Greece
Interests: thermal satellite remote sensing; surface urban heat islands; land surface temperature; LST downscaling; heatwaves; decision support systems; urban resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, more than half of the world population lives in cities. To accommodate the needs of the urban population, extensive areas have been converted to impervious surfaces and the local emission of heat, water vapor, and materials has changed profoundly. These surface and atmospheric modifications altered the local climate, making cities warmer than the surrounding non-urbanized areas. This relative warmth is widely-known as the urban heat island (UHI) effect and is a major problem for cities, since it increases the energy demand in summer, impacts the health and comfort of the urban population, and intensifies and prolongs heatwaves.

The successful study of climate effects in cities and the application of the acquired knowledge to the better planning and design of cities rely on the proper understanding, description, and modeling of urban form and urban function, which is a challenging and expensive task. Remote sensing is a prominent data source that can provide essential information, on a global level, about urban form (e.g. sprawl, land cover, 3D structure, sky view factor, etc.) and function (e.g. the land use, urban metabolism etc.). Moreover, it is used to study a relevant aspect of the UHI, the surface urban heat island (SUHI) directly via thermal infrared sensors.

This Special Issue invites authors to submit their work on innovative methods and applications that use remote sensing data to study the urban climate and inform better spatial planning and public health actions for mitigating UHI and heatwave effects. Potential topics include, but are not limited to:

The study of SUHIs using dense land surface temperature (LST) time series;
The relationship between surface and atmospheric UHI;
The generation of LST datasets that combine high spatial and temporal resolution;
Classifying cities into local climate zones (LCZ);
Understanding SUHIs in different LCZ and climates;
Monitoring urban energy exchanges from space;
Identification and extraction of SUHI hotspots and coldspots;
Evaluation of SUHI drivers, e.g., albedo, vegetation abundance, and sky-view factor;
Spatial and energy planning or public health actions that use remote sensing data for mitigating UHI and heatwave effects.

Dr. Panagiotis Sismanidis
Prof. Dr. Benjamin Bechtel
Dr. Zina Mitraka
Dr. Iphigenia Keramitsoglou
Guest Editors

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Keywords

Urban thermal remote sensing
land surface temperatures
LST downscaling
urban climatology
local climate zones
anthropogenic heat emissions
surface heat fluxes
heatwaves
LST hotspots
urban interventions

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Published Papers (9 papers)

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20 pages, 7270 KiB

Open AccessArticle

Mapping Land Surface Temperature Developments in Functional Urban Areas across Europe

by Anna Hellings and Andreas Rienow

Remote Sens. 2021, 13(11), 2111; https://doi.org/10.3390/rs13112111 - 27 May 2021

Cited by 15 | Viewed by 3185

Abstract

Unsustainable development paths have reached critical levels in Europe. In recent years, in cities, urbanization has been contributing to the intensification of urban heat islands. To analyze the development of surface urban heat islands (SUHI) in Europe in the last few years, the present study combines the land surface temperature (LST) from MODIS with the urban classes of the CORINE land cover data within 617 functional urban areas (FUAs). Urban and industrial uses have significantly higher LST than green urban areas across all years (about 4 to 6 °C), as do agricultural areas within cities. Besides land cover, location also influences LST differences. While, e.g., Bolzano (Italy) shows particularly large LST differences (>6 °C) between the core and the commuting zone, this effect is hardly visible in Porto (Portugal) and Madrid (Spain) (<2.5 °C). Cities of moderate climates show increasing differences between a city and its commuting zones with rising LST (r = 0.68), i.e., less cooling effects at night. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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22 pages, 16065 KiB

Open AccessFeature PaperArticle

Modeling Mean Radiant Temperature Distribution in Urban Landscapes Using DART

by Maria Angela Dissegna, Tiangang Yin, Hao Wu, Nicolas Lauret, Shanshan Wei, Jean-Philippe Gastellu-Etchegorry and Adrienne Grêt-Regamey

Remote Sens. 2021, 13(8), 1443; https://doi.org/10.3390/rs13081443 - 8 Apr 2021

Cited by 10 | Viewed by 4816

Abstract

The microclimatic conditions of the urban environment influence significantly the thermal comfort of human beings. One of the main human biometeorology parameters of thermal comfort is the Mean Radiant Temperature (Tmrt), which quantifies effective radiative flux reaching a human body. Simulation tools have proven useful to analyze the radiative behavior of an urban space and its impact on the inhabitants. We present a new method to produce detailed modeling of Tmrt spatial distribution using the 3-D Discrete Anisotropic Radiation Transfer model (DART). Our approach is capable to simulate Tmrt at different scales and under a range of parameters including the urban pattern, surface material of ground, walls, roofs, and properties of the vegetation (coverage, shape, spectral signature, Leaf Area Index and Leaf Area Density). The main advantages of our method are found in (1) the fine treatment of radiation in both short-wave and long-wave domains, (2) detailed specification of optical properties of urban surface materials and of vegetation, (3) precise representation of the vegetation component, and (4) capability to assimilate 3-D inputs derived from multisource remote sensing data. We illustrate and provide a first evaluation of the method in Singapore, a tropical city experiencing strong Urban Heat Island effect (UHI) and seeking to enhance the outdoor thermal comfort. The comparison between DART modelled and field estimated Tmrt shows good agreement in our study site under clear-sky condition over a time period from 10:00 to 19:00 (R² = 0.9697, RMSE = 3.3249). The use of a 3-D radiative transfer model shows promising capability to study urban microclimate and outdoor thermal comfort with increasing landscape details, and to build linkage to remote sensing data. Our methodology has the potential to contribute towards optimizing climate-sensitive urban design when combined with the appropriate tools. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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22 pages, 6797 KiB

Open AccessArticle

Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis

by Na Yao, Conghong Huang, Jun Yang, Cecil C. Konijnendijk van den Bosch, Lvyi Ma and Zhongkui Jia

Remote Sens. 2020, 12(23), 3906; https://doi.org/10.3390/rs12233906 - 28 Nov 2020

Cited by 24 | Viewed by 3595

Abstract

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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22 pages, 7079 KiB

Open AccessArticle

Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks

by Cheolhee Yoo, Yeonsu Lee, Dongjin Cho, Jungho Im and Daehyeon Han

Remote Sens. 2020, 12(21), 3552; https://doi.org/10.3390/rs12213552 - 30 Oct 2020

Cited by 33 | Viewed by 4717

Abstract

Recent studies have enhanced the mapping performance of the local climate zone (LCZ), a standard framework for evaluating urban form and function for urban heat island research, through remote sensing (RS) images and deep learning classifiers such as convolutional neural networks (CNNs). The accuracy in the urban-type LCZ (LCZ1-10), however, remains relatively low because RS data cannot provide vertical or horizontal building components in detail. Geographic information system (GIS)-based building datasets can be used as primary sources in LCZ classification, but there is a limit to using them as input data for CNN due to their incompleteness. This study proposes novel methods to classify LCZ using Sentinel 2 images and incomplete building data based on a CNN classifier. We designed three schemes (S1, S2, and a scheme fusion; SF) for mapping 50 m LCZs in two megacities: Berlin and Seoul. S1 used only RS images, and S2 used RS and building components such as area and height (or the number of stories). SF combined two schemes (S1 and S2) based on three conditions, mainly focusing on the confidence level of the CNN classifier. When compared to S1, the overall accuracies for all LCZ classes (OA) and the urban-type LCZ (OA_urb) of SF increased by about 4% and 7–9%, respectively, for the two study areas. This study shows that SF can compensate for the imperfections in the building data, which causes misclassifications in S2. The suggested approach can be excellent guidance to produce a high accuracy LCZ map for cities where building databases can be obtained, even if they are incomplete. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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28 pages, 15434 KiB

Open AccessArticle

Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones

by Nan Zhao, Ailong Ma, Yanfei Zhong, Ji Zhao and Liqin Cao

Remote Sens. 2019, 11(23), 2828; https://doi.org/10.3390/rs11232828 - 28 Nov 2019

Cited by 12 | Viewed by 4526

Abstract

Local climate zones (LCZ) have become a generic criterion for climate analysis among global cities, as they can describe not only the urban climate but also the morphology inside the city. LCZ mapping based on the remote sensing classification method is a fundamental task, and the protocol proposed by the World Urban Database and Access Portal Tools (WUDAPT) project, which consists of random forest classification and filter-based spatial smoothing, is the most common approach. However, the classification and spatial smoothing lack a unified framework, which causes the appearance of small, isolated areas in the LCZ maps. In this paper, a spatial-contextual information-based self-training classification framework (SCSF) is proposed to solve this LCZ classification problem. In SCSF, conditional random field (CRF) is used to integrate the classification and spatial smoothing processing into one model and a self-training method is adopted, considering that the lack of sufficient expert-labeled training samples is always a big issue, especially for the complex LCZ scheme. Moreover, in the unary potentials of CRF modeling, pseudo-label selection using a self-training process is used to train the classifier, which fuses the regional spatial information through segmentation and the local neighborhood information through moving windows to provide a more reliable probabilistic classification map. In the pairwise potential function, SCSF can effectively improve the classification accuracy by integrating the spatial-contextual information through CRF. The experimental results prove that the proposed framework is efficient when compared to the traditional mapping product of WUDAPT in LCZ classification. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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22 pages, 10544 KiB

Open AccessArticle

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

by Mohammad Karimi Firozjaei, Seyed Kazem Alavipanah, Hua Liu, Amir Sedighi, Naeim Mijani, Majid Kiavarz and Qihao Weng

Remote Sens. 2019, 11(18), 2094; https://doi.org/10.3390/rs11182094 - 8 Sep 2019

Cited by 39 | Viewed by 4928

Abstract

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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20 pages, 14803 KiB

Open AccessArticle

Revealing Kunming’s (China) Historical Urban Planning Policies Through Local Climate Zones

by Stéphanie Vandamme, Matthias Demuzere, Marie-Leen Verdonck, Zhiming Zhang and Frieke Van Coillie

Remote Sens. 2019, 11(14), 1731; https://doi.org/10.3390/rs11141731 - 22 Jul 2019

Cited by 24 | Viewed by 6642

Abstract

Over the last decade, Kunming has been subject to a strong urbanisation driven by rapid economic growth and socio-economic, topographical and proximity factors. As this urbanisation is expected to continue in the future, it is important to understand its environmental impacts and the role that spatial planning strategies and urbanisation regulations can play herein. This is addressed by (1) quantifying the cities’ expansion and intra-urban restructuring using Local Climate Zones (LCZs) for three periods in time (2005, 2011 and 2017) based on the World Urban Database and Access Portal Tool (WUDAPT) protocol, and (2) cross-referencing observed land-use and land-cover changes with existing planning regulations. The results of the surveys on urban development show that, between 2005 and 2011, the city showed spatial expansion, whereas between 2011 and 2017, densification mainly occurred within the existing urban extent. Between 2005 and 2017, the fraction of open LCZs increased, with the largest increase taking place between 2011 and 2017. The largest decrease was seen for low the plants (LCZ D) and agricultural greenhouse (LCZ H) categories. As the potential of LCZs as, for example, a heat stress assessment tool has been shown elsewhere, understanding the relation between policy strategies and LCZ changes is important to take rational urban planning strategies toward sustainable city development. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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19 pages, 4793 KiB

Open AccessArticle

Parameterization of Urban Sensible Heat Flux from Remotely Sensed Surface Temperature: Effects of Surface Structure

by Jinxin Yang, Massimo Menenti, E. Scott Krayenhoff, Zhifeng Wu, Qian Shi and Xiaoying Ouyang

Remote Sens. 2019, 11(11), 1347; https://doi.org/10.3390/rs11111347 - 4 Jun 2019

Cited by 9 | Viewed by 4251

Abstract

Sensible heat exchange has important consequences for urban meteorology and related applications. Directional radiometric surface temperatures of urban canopies observed by remote sensing platforms have the potential to inform estimations of urban sensible heat flux. An imaging radiometer viewing the surface from nadir cannot capture the complete urban surface temperature, which is defined as the mean surface temperature over all urban facets in three dimensions, which includes building wall surface temperatures and requires an estimation of urban sensible heat flux. In this study, a numerical microclimate model, Temperatures of Urban Facets in 3-D (TUF-3D), was used to model sensible heat flux as well as radiometric and complete surface temperatures. Model data were applied to parameterize an effective resistance for the calculation of urban sensible heat flux from the radiometric (nadir view) surface temperature. The results showed that sensible heat flux was overestimated during daytime when the radiometric surface temperature was used without the effective resistance that accounts for the impact of wall surface temperature on heat flux. Parameterization of this additional resistance enabled reasonably accurate estimates of urban sensible heat flux from the radiometric surface temperature. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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17 pages, 4778 KiB

Open AccessLetter

PLANHEAT’s Satellite-Derived Heating and Cooling Degrees Dataset for Energy Demand Mapping and Planning

by Panagiotis Sismanidis, Iphigenia Keramitsoglou, Stefano Barberis, Hrvoje Dorotić, Benjamin Bechtel and Chris T. Kiranoudis

Remote Sens. 2019, 11(17), 2048; https://doi.org/10.3390/rs11172048 - 30 Aug 2019

Cited by 2 | Viewed by 3743

Abstract

The urban heat island (UHI) effect influences the heating and cooling (H&C) energy demand of buildings and should be taken into account in H&C energy demand simulations. To provide information about this effect, the PLANHEAT integrated tool—which is a GIS-based, open-source software tool for selecting, simulating and comparing alternative low-carbon and economically sustainable H&C scenarios—includes a dataset of 1 × 1 km hourly heating and cooling degrees (HD and CD, respectively). HD and CD are energy demand proxies that are defined as the deviation of the outdoor surface air temperature from a base temperature, above or below which a building is assumed to need heating or cooling, respectively. PLANHEAT’s HD and CD are calculated from a dataset of gridded surface air temperatures that have been derived using satellite thermal data from Meteosat-10 Spinning Enhanced Visible and Near-Infrared Imager (SEVIRI). This article describes the method for producing this dataset and presents the results for Antwerp (Belgium), which is one of the three validation cities of PLANHEAT. The results demonstrate the spatial and temporal information of PLANHEAT’s HD and CD dataset, while the accuracy assessment reveals that they agree well with reference values retrieved from in situ surface air temperatures. This dataset is an example of application-oriented research that provides location-specific results with practical utility. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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