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Usage of Remote Sensing Data and Machine Learning Methods for Sustainable Urban Planning

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

Deadline for manuscript submissions: closed (7 October 2021) | Viewed by 5461

Special Issue Editor

Independent Scientist, Overijssel, The Netherlands
Interests: remote sensing; earth observation; machine learning; artificial intelligence; computer vision; feature engineering; big data visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cities were initially ignored by most climate change scientists. They were assumed to be highly developed and adaptable to environmental changes. In 2020, thanks to satellite- and IoT-based Earth-observation technologies, we know that this assumption is not true. Cities actually contribute to the acceleration of climate change, not only by directly increasing air and water pollution but also by creating heat islands, which damage the outside environment.

As you may be aware, more than half of the world’s population lives in cities. This number is expected to become much higher in Europe before 2028. Highly populated cities increase the demand for energy, transportation, water, and solutions for acquiring these supplies in a smart manner. This could be achieved by understanding current resource needs, elucidating the behaviours that lead to unsustainable resource use, and offering new intelligent ways to suggest new strategies for keeping our resources clean and available for the health of citizens and even the good functioning of economies.

I believe that researchers might bring more insights and understanding to this field using machine learning, artificial intelligence, and other mathematical techniques to extract meaningful indicators from Earth-observation and other IoT data. For instance, showing the correlations of different indicators, visualizing resource usage, and explaining the impacts of cities on the environment and public health might help to suggest better objectives to target for achieving smart cities that can adapt to climate change.

If possible, I would like to encourage researchers to use their novel methods on some real-life use cases and conduct experiments for a specific city that they know well.

I am looking forward to receiving manuscripts that are dedicated to helping our planet and taking the state of the art in this field one step further.

With my best regards,

Prof. Dr. Beril Sirmacek
Guest Editor

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

  • Remote sensing
  • Machine learning
  • Artificial intelligence
  • Sustainable development goals
  • Food security
  • Public health
  • Biodiversity protection
  • Green transportation
  • Air quality
  • Clean water
  • Smart cities
  • Big data
  • Geoscience and Earth observation

Published Papers (1 paper)

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Research

25 pages, 4238 KiB  
Article
Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran
by Faezeh Najafzadeh, Ali Mohammadzadeh, Arsalan Ghorbanian and Sadegh Jamali
Remote Sens. 2021, 13(21), 4469; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214469 - 07 Nov 2021
Cited by 21 | Viewed by 4389
Abstract
Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has [...] Read more.
Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development. Full article
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