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Estimating the Ecological Services of Urban Green Infrastructures Using Remote Sensing

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 2024 | Viewed by 2520

Special Issue Editors

The Environmental Sensing Laboratory, Department of Civil and Environmental Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
Interests: remote sensing; urban sustainability; greenhouse gases; air pollutants; precision agriculture
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Guest Editor
The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker 8499000, Israel
Interests: remote sensing; geographic information systems; climate change; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The urban population is rapidly increasing, with approximately 90% expected to live in cities by 2100. As urban environments are hot spots for greenhouse gas and pollutant emissions and are set to become even denser, sustainable urban living requires careful and well-informed planning to mitigate these emissions effectively. Urban green infrastructures are a common solution, typically considered a net sink for CO2. They also reduce the urban heat island effect through evapotranspiration and enhance the comfort of city dwellers through shading. However, due to the heterogeneous nature of urban land use, accurately isolating and quantifying its ecological services is challenging.

This Special Issue aims to showcase studies exploring various aspects of using remote sensing to better understand the contribution of green infrastructures to urban sustainability and to estimate their ecological service capacity. Urban green infrastructures (UGIs) are defined as urban green ecosystems that include vegetation, soil, and built elements and can range from a single tree in the street to an urban forest. Contributions utilizing all remote sensing platforms (e.g., satellites, planes, and drones) and sensors (e.g., spectral, thermal, and LiDAR) are welcome. Articles may address, but are not limited to, the following topics in combination with remote sensing and UGIs:

  • Preprocessing remote sensing imagery for the study of UGI, e.g., classification and downscaling;
  • Carbon cycle of UGIs;
  • UGIs evapotranspiration and urban heat island effect;
  • Shading of UGI and walkability;
  • UGI planning and management to increase urban sustainability;
  • Biodiversity of UGIs and its impact on their ecological services;
  • LULC change in vegetation and its impact on urban sustainability.

Dr. Oz Kira
Prof. Dr. Arnon Karnieli
Guest Editors

Mr. Julius Bamah
Guest Editor Assistant
Affiliation: The Department of Civil and Environmental Engineering, Faculty of Engineering Sciences, Ben Gurion University of the Negev, Beersheba, Israel
Email: /
Interests: environmental monitoring; solar induced chlorophyll fluorescence; GIS; remote sensing of environment; image processing

Mr. Alexander Takele Muleta
Guest Editor Assistant
Affiliation: The Department of Civil and Environmental Engineering, Faculty of Engineering Sciences, Ben Gurion University of the Negev, Beersheba, Israel
Email: /
Interests: environmental monitoring; solar induced chlorophyll fluorescence; GIS; remote sensing of environment; image processing

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

  • urban sustainability
  • urban green infrastructures
  • urban ecological services
  • vegetation classification
  • carbon cycle
  • evapotranspiration
  • urban heat islands
  • shading

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Published Papers (1 paper)

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Research

31 pages, 4586 KiB  
Article
A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights
by Fei Li, Tan Yigitcanlar, Madhav Nepal, Kien Nguyen Thanh and Fatih Dur
Remote Sens. 2024, 16(16), 3032; https://doi.org/10.3390/rs16163032 - 18 Aug 2024
Cited by 2 | Viewed by 2064
Abstract
Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, [...] Read more.
Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, and validating those models. Many existing approaches do not adequately meet urban planning needs due to insufficient spatial resolution, temporal coverage, and accuracy. To address this gap, this paper introduces the U-HEAT framework, a conceptual model for analyzing urban heat vulnerability. The primary objective is to outline the theoretical foundations and potential applications of U-HEAT, emphasizing its conceptual nature. This framework integrates machine learning (ML) with remote sensing (RS) to identify urban heat vulnerability at both long-term and detailed levels. It combines retrospective and forward-looking mapping for continuous monitoring and assessment, providing essential data for developing comprehensive strategies. With its active learning capacity, U-HEAT enables model refinement and the evaluation of policy impacts. The framework presented in this paper offers a standardized and sustainable approach, aiming to enhance practical analysis tools. It highlights the importance of interdisciplinary research in bolstering urban resilience and stresses the need for sustainable urban ecosystems capable of addressing the complex challenges posed by climate change and increased urban heat. This study provides valuable insights for researchers, urban administrators, and planners to effectively combat urban heat challenges. Full article
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