Special Issue "Application of Remote Sensing in Urban Climatology"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Panagiotis Sismanidis
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 remote sensing; surface urban heat islands; land surface temperatures; LST downscaling; LST Spatiotemporal fusion; LST annual cycle parameters
Prof. Dr. Benjamin Bechtel
E-Mail Website
Guest Editor
Department of Geography, Ruhr-University Bochum, DE-44801 Bochum, Germany
Interests: Remote sensing of urban climates; crowdsourcing; urban health; thermal remote sensing
Special Issues and Collections in MDPI journals
Dr. Zina Mitraka
E-Mail Website
Guest Editor
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
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

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

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 2000 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

  • 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

Published Papers (5 papers)

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Open AccessArticle
Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones
Remote Sens. 2019, 11(23), 2828; https://doi.org/10.3390/rs11232828 - 28 Nov 2019
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 [...] Read more.
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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Open AccessArticle
A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations
Remote Sens. 2019, 11(18), 2094; https://doi.org/10.3390/rs11182094 - 08 Sep 2019
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 [...] Read more.
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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Open AccessArticle
Revealing Kunming’s (China) Historical Urban Planning Policies Through Local Climate Zones
Remote Sens. 2019, 11(14), 1731; https://doi.org/10.3390/rs11141731 - 22 Jul 2019
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 [...] Read more.
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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Open AccessArticle
Parameterization of Urban Sensible Heat Flux from Remotely Sensed Surface Temperature: Effects of Surface Structure
Remote Sens. 2019, 11(11), 1347; https://doi.org/10.3390/rs11111347 - 04 Jun 2019
Cited by 1
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 [...] Read more.
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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Open AccessLetter
PLANHEAT’s Satellite-Derived Heating and Cooling Degrees Dataset for Energy Demand Mapping and Planning
Remote Sens. 2019, 11(17), 2048; https://doi.org/10.3390/rs11172048 - 30 Aug 2019
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 [...] Read more.
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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