Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing in Support of Crisis Management: How Space-Based Information...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing in Support of Crisis Management: How Space-Based Information Can Support Operations and the Decision-Making Process

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2973

Special Issue Editors

Dr. Valerio Baiocchi

E-Mail Website
Guest Editor
Department of Civil Construction and Environmental Engineering, Sapienza University of Rome, 8, 00184 Rome, Italy
Interests: GIS; GNSS; remote sensing; photogrammetry; cultural heritage surveys
Special Issues, Collections and Topics in MDPI journals
Dr. Roberta Onori

E-Mail Website
Guest Editor
European Union’s SatCen, Madrid, Spain
Interests: photogrammetry; survey; remote sensing; GIS; post seismic survey
Special Issues, Collections and Topics in MDPI journals
Dr. Erica Nocerino

E-Mail Website
Guest Editor
Department of Humanities and Social Sciences, University of Sassari, 07100 Sassari, Italy
Interests: underwater photogrammetry; quality assessment; system calibration; 3D surveying and mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent emergencies, such as pandemics, wars, and droughts resulting from heat waves, and, more in general, the effects of the climate change, have forced many governments over the years to study, design, and develop recovery and prevention measures. These critical events have mixed and heavy impacts on the environment, population, and economic activities, affecting urban geographical areas but also open countryside, mountain peak, sea, and ocean environments, as well as the arctic regions.

Studying how social, economic, climate, and environmental factors are affected and how they may vary across different geographical areas is crucial, also in the view of implemented and planned counter measures. Decision-makers require tools to understand these crises and their impact and to identify the most suitable way to recover from and prevent them. Remote sensing can provide valuable support, enabling the development of monitoring and forecast models/indices in support of ground measurements and observations.

Remote sensing allows us to detect, measure, and model changes and events that occur on the Earth’s surface and in the atmosphere.

We would like to invite you to contribute papers focusing on the impact of climate change and critical events such as droughts, forest fires, ice melting, floods, Land Surface Temperature (LST), air pollution, and other manmade and natural risks and their consequences affecting stability and economic aspects.

We encourage the submission of papers which test and analyze different geospatial methods and methodologies, including big data analytics, space-time modelling and simulation, environmental modelling, data visualization, hot-spot analysis, and change detection, both with the help of open source and non-open source software.

Dr. Valerio Baiocchi
Dr. Roberta Onori
Dr. Erica Nocerino
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

  • LST
  • air pollutants
  • environment
  • climate
  • heat islands
  • lockdown periods
  • change detection
  • hot-spot analysis
  • seismic events
  • emergencies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 5155 KiB  
Article
Impact of Arable Land Abandonment on Crop Production Losses in Ukraine During the Armed Conflict
by Kaixuan Dai, Changxiu Cheng, Siyi Kan, Yaoming Li, Kunran Liu and Xudong Wu
Remote Sens. 2024, 16(22), 4207; https://doi.org/10.3390/rs16224207 - 12 Nov 2024
Cited by 1 | Viewed by 1346
Abstract
The outbreak of Russia-Ukraine conflict casted an impact on the global food market, which was believed to be attributed to that Ukraine has suffered significant production losses due to cropland abandonment. Nevertheless, recent outbreaks of farmer protests against Ukraine’s grain exports demonstrated that [...] Read more.
The outbreak of Russia-Ukraine conflict casted an impact on the global food market, which was believed to be attributed to that Ukraine has suffered significant production losses due to cropland abandonment. Nevertheless, recent outbreaks of farmer protests against Ukraine’s grain exports demonstrated that the production losses might not be as severe as previous estimates. By utilizing the adaptive threshold segmentation method to extract abandoned cropland from the Sentinel-2 high-resolution imagery and calibrating the spatial production allocation model’s gridded crop production data from Ukraine’s statistical data, this study explicitly evaluated Ukraine’s crop-specific production losses and the spatial heterogeneity. The results demonstrated that the estimated area of abandoned cropland in Ukraine ranges from 2.34 to 2.40 million hectares, constituting 7.14% to 7.30% of the total cropland. In Ukrainian-controlled zones, this area spans 1.44 to 1.48 million hectares, whereas in Russian-occupied areas, it varies from 0.90 to 0.92 million hectares. Additionally, the total production losses for wheat, maize, barley, and sunflower amount to 1.92, 1.67, 0.70, and 0.99 million tons, respectively, with corresponding loss ratios of 9.10%, 7.48%, 9.54%, and 8.67%. Furthermore, production losses of wheat, barley, and sunflower emerged in both the eastern and southern states adjacent to the conflict frontlines, while maize losses were concentrated in the western states. The findings imply that Ukraine ought to streamline the food transportation channels and maintain stable agricultural activities in regions with high crop production. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Crisis Management: How Space-Based Information Can Support Operations and the Decision-Making Process)
►▼ Show Figures

Figure 1

26 pages, 2707 KiB  
Article
Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)
by Andrea Masiero, Alberto Guarnieri, Valerio Baiocchi, Domenico Visintini and Francesco Pirotti
Remote Sens. 2024, 16(21), 3971; https://doi.org/10.3390/rs16213971 - 25 Oct 2024
Viewed by 1096
Abstract
The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect [...] Read more.
The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Crisis Management: How Space-Based Information Can Support Operations and the Decision-Making Process)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop