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Remote Sensing
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Remote Sensing Based Urban Development and Climate Change Research
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Remote Sensing Based Urban Development and Climate Change Research

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

Deadline for manuscript submissions: 30 May 2024 | Viewed by 4037

Special Issue Editors

Dr. Ifigeneia Theodoridou

E-Mail Website
Guest Editor
Architects, Them Sofouli 57, GR-55131 Kalamaria, Greece
Interests: green roofs; urban environment; GEOBIA; remote sensing; large-scale energy consumption; carbon sequestration
Dr. Giorgos Mallinis

E-Mail Website
Guest Editor
Laboratory of Photogrammetry and Remote Sensing, The Polytechnical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: remote sensing; land use/land cover (LULC) mapping; biodiversity; ecosystem services; classification development and comparison; geographic object based image analysis; natural disasters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Remote Sensing Based Urban Development and Climate Change Research”, deals with the multifaceted subject of the analysis and growth of urban sustainability. Thus, as climate change is becoming cities’ number one threat globally, new technologies are being developed in order to overcome barriers and help researchers as well as stakeholders promote solutions and policies towards effective climate change mitigation and adaptation techniques in urban environments.

Remote sensing technologies have been used in numerous cases in order to identify and analyse urban built environments, identify typological patterns, analyse the urban building stock, understand the cities’ structure, calculate available roof areas for RES implementation and assess urban vegetation, as well as many more applications.

Numerous researchers have developed 3D data models for urban energy simulation and urban building energy modeling (UBEM), which can greatly contribute towards the identification and development of integrated policies of deep retrofitting actions. In addition, GIS and EO technologies are incorporated in a range of tools, used to create novel approaches, regarding the analysis of archetype buildings, DSS methodologies for building retrofitting, large-scale green roof application, UHI real-time measurements, bioclimatic upgrade of open public-spaces and many more areas.

This Special Issue aims at studies covering the whole spectrum of solutions and methodologies based on remote sensing technologies that deal with the subject of climate change mitigation and adaptation solutions. Topics may cover anything from the basic analysis of built to non-built areas in urban terrain, the development of 3D buildings and providing solutions to overcome barriers related to large-scale RES urban integration, as well as more comprehensive aims and scales, and complex data-driven analysis approaches. Thus, single-source and multi-source analysis models and methodologies are welcome. Articles may cover the following topics (although they are not limited to them):

  • UHI analysis technologies;
  • Novel GIS-based DSS tools;
  • Big data analysis using GIS and EO technologies;
  • Archetype large-scale approaches;
  • Tools for the development of three-dimensional geometry models;
  • Large-scale analysis tools for the integration of green-roofs and other vegetation-related measures towards improved urban environments;
  • Tools that ensure safe and strategic analysis of mitigation actions;
  • Climate change adaptation tools—promoting readiness and resilience measures;
  • Technologies for the identification of climate change vulnerable urban areas;
  • Other related topics promoting urban sustainability.

Dr. Ifigeneia Theodoridou
Dr. Giorgos Mallinis
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 submissions that pass pre-check are 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 2700 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

  • GIS technology
  • sustainable cities&nbsp
  • large-scale urban analysis
  • DSS tools
  • climate change

Published Papers (2 papers)

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Research

22 pages, 6379 KiB  
Article
Remote Sensing Analysis of the Surface Urban Heat Island Effect in Vitoria-Gasteiz, 1985 to 2021
by Cristina Laurenti Errea, Cátia Rodrigues de Almeida, Artur Gonçalves and Ana Cláudia Teodoro
Remote Sens. 2023, 15(12), 3110; https://doi.org/10.3390/rs15123110 - 14 Jun 2023
Cited by 6 | Viewed by 1429
Abstract
Vitoria-Gasteiz has taken several urban greening actions such as the introduction of a ring of parks that connect the city’s surroundings, a sustainable mobility plan, and urban green structure strategies. Previous studies establish a connection to the importance of greening to mitigate the [...] Read more.
Vitoria-Gasteiz has taken several urban greening actions such as the introduction of a ring of parks that connect the city’s surroundings, a sustainable mobility plan, and urban green structure strategies. Previous studies establish a connection to the importance of greening to mitigate the surface urban heat island (SUHI) and evaluate the effectiveness of these measures on urban climate. In this study, land surface temperature (LST), a remote sensing (RS) parameter, recorded by Landsat satellites (5, 7, and 8) was used to evaluate the effect of SUHI in Vitoria-Gasteiz between 1985–2021. The aim was to evaluate whether the urban greening actions influenced the local thermal conditions and, consequently, helped minimize the SUHI. Thirty sampling locations were identified, corresponding to different local climate zones (LCZ), at which LST data were extracted. A total of 218 images were processed and separated into summer and winter. Four of the 30 locations had, since 2003, on-site meteorological stations with regular air temperature (Tair) measurements which were used to validate the LST data. The results showed that Spearman’s correlation between Tair and LST was higher than 0.88 in all locations. An amount of 21 points maintained the same LCZ classification throughout the analysed period and nine underwent a LCZ transformation. The highest average temperature was identified in the city centre (urbanized area), and the lowest average was in a forest on the outskirts of the city. SUHI was more intense during the summer. A significant increase in SUHI intensity was identified in areas transformed from natural to urban LCZs. However, SUHI during satellite data acquisition periods has shown a minimal change in areas where sustainable practices have been implemented. RS was valuable for analysing the thermal behaviour of the LCZs, despite the limitation inherent in the satellite’s time of passage, in which the SUHI effect is not as evident. Full article
(This article belongs to the Special Issue Remote Sensing Based Urban Development and Climate Change Research)
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18 pages, 4945 KiB  
Article
Predicting Dust-Storm Transport Pathways Using a Convolutional Neural Network and Geographic Context for Impact Adaptation and Mitigation in Urban Areas
by Mahdis Yarmohamadi, Ali Asghar Alesheikh, Mohammad Sharif and Hossein Vahidi
Remote Sens. 2023, 15(9), 2468; https://doi.org/10.3390/rs15092468 - 08 May 2023
Cited by 4 | Viewed by 1943
Abstract
Dust storms are natural disasters that have a serious impact on various aspects of human life and physical infrastructure, particularly in urban areas causing health risks, reducing visibility, impairing the transportation sector, and interfering with communication systems. The ability to predict the movement [...] Read more.
Dust storms are natural disasters that have a serious impact on various aspects of human life and physical infrastructure, particularly in urban areas causing health risks, reducing visibility, impairing the transportation sector, and interfering with communication systems. The ability to predict the movement patterns of dust storms is crucial for effective disaster prevention and management. By understanding how these phenomena travel, it is possible to identify the areas that are most at risk and take appropriate measures to mitigate their impact on urban environments. Deep learning methods have been demonstrated to be efficient tools for predicting moving processes while considering multiple geographic information sources. By developing a convolutional neural network (CNN) method, this study aimed to predict the pathway of dust storms that occur in arid regions in central and southern Asia. A total of 54 dust-storm events were extracted from the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) product to train the CNN model and evaluate the prediction results. In addition to dust-storm data (aerosol optical depth (AOD) data), geographic context information including relative humidity, surface air temperature, surface wind direction, surface skin temperature, and surface wind speed was considered. These features were chosen using the random forest feature importance method and had feature importance values of 0.2, 0.1, 0.06, 0.03, and 0.02, respectively. The results show that the CNN model can promisingly predict the dust-transport pathway, such that for the 6, 12, 18, and 24-h time steps, the overall accuracy values were 0.9746, 0.975, 0.9751, and 0.9699, respectively; the F1 score values were 0.7497, 0.7525, 0.7476, and 0.6769, respectively; and the values of the kappa coefficient were 0.7369, 0.74, 0.7351, and 0.6625, respectively. Full article
(This article belongs to the Special Issue Remote Sensing Based Urban Development and Climate Change Research)
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