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Urban Resilience with Remote Sensing—Observation, Measurement, Evaluation and Applications II

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 1432

Special Issue Editors


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Guest Editor
School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen Campus, Shenzhen 510055, China
Interests: urban remote sensing; digital image analysis; big remote sensing data analysis; nightlight remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geography, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91905, Israel
Interests: urban remote sensing; nightlight remote sensing; remote sensing image analysis; GIS; spatial analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Interests: urban remote sensing; urban sustainability; big earth data process and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching the second Special Issue of Remote Sensing to be released under the title “Urban Resilience with Remote Sensing—Observation, Measurement, Evaluation and Applications”.

In recent decades, the world has witnessed rapid urbanization, and the urban population is projected to rise to 80% by 2050. The high density of urban areas makes them especially vulnerable to both the impacts of acute disasters and the effects of the changing climate. It is thus critical that we address sustainability challenges facing cities by taking steps such as poverty reduction, disaster reduction and prevention, climate change mitigation, environmental sustainability maintenance, and social inclusion measures. These efforts towards urban resilience not only offer to help individuals, communities, and business cope with multiple stresses; they also allow for the exploitation of opportunities for transformational development, constituting the primary focus of many global agencies, such as the World Bank, UN, and GEO.

The urban resilience framework is multidimensional in nature, consisting of four core dimensions: leadership and strategy, health and well-being, economy and society, and infrastructure and environment. Remote sensing has been applied to the monitoring of urban infrastructure and environments in various ways. With recent advances in remote sensing in terms of spatial, temporal, and spectral resolutions and data processing algorithms, remote sensing is expected to provide important observations and tools for monitoring, evaluating, and modeling urban resilience. To help global cities persevere through future challenges, while also positively adapting and moving towards sustainability, this Special Issue invites for original research papers covering topics including, but not limited to:

  • Urban spatial structure and development;
  • Urban green space;
  • Implementation of new technologies toward resilient cities;
  • Urban transportation systems and development;
  • Urban infrastructure and building health monitoring;
  • Climate impacts on urban areas;
  • Urban flooding: prediction, monitoring, and mitigation;
  • Remote observations for resilient cities;
  • Urban heat island;
  • Urban carbon emissions;
  • Remote-sensing-based urban resilient index;
  • Urban resilience evaluation and modeling with remote sensing data as well as other data;
  • Case studies;
  • Dedicated hardware and software solutions.

Prof. Dr. Qingling Zhang
Dr. Hongsheng Zhang
Prof. Dr. Noam Levin
Dr. Zhongchang Sun
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

  • urban resilience
  • global change
  • change detection
  • multisource data fusion
  • signal processing and data mining
  • artificial intelligence
  • urban science
  • sustainability
  • resilient cities

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Published Papers (1 paper)

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Research

14 pages, 26108 KiB  
Article
A One-Dimensional Light Detection and Ranging Array Scanner for Mapping Turfgrass Quality
by Arthur Rosenfield, Alexandra Ficht, Eric M. Lyons and Bahram Gharabaghi
Remote Sens. 2024, 16(12), 2215; https://doi.org/10.3390/rs16122215 - 19 Jun 2024
Viewed by 956
Abstract
The turfgrass industry supports golf courses, sports fields, and the landscaping and lawn care industries worldwide. Identifying the problem spots in turfgrass is crucial for targeted remediation for turfgrass treatment. There have been attempts to create vehicle- or drone-based scanners to predict turfgrass [...] Read more.
The turfgrass industry supports golf courses, sports fields, and the landscaping and lawn care industries worldwide. Identifying the problem spots in turfgrass is crucial for targeted remediation for turfgrass treatment. There have been attempts to create vehicle- or drone-based scanners to predict turfgrass quality; however, these methods often have issues associated with high costs and/or a lack of accuracy due to using colour rather than grass height (R2 = 0.30 to 0.90). The new vehicle-mounted turfgrass scanner system developed in this study allows for faster data collection and a more accurate representation of turfgrass quality compared to currently available methods while being affordable and reliable. The Gryphon Turf Canopy Scanner (GTCS), a low-cost one-dimensional LiDAR array, was used to scan turfgrass and provide information about grass height, density, and homogeneity. Tests were carried out over three months in 2021, with ground-truthing taken during the same period. When utilizing non-linear regression, the system could predict the percent bare of a field (R2 = 0.47, root mean square error < 0.5 mm) with an increase in accuracy of 8% compared to the random forest metric. The potential environmental impact of this technology is vast, as a more targeted approach to remediation would reduce water, fertilizer, and herbicide usage. Full article
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