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Remote Sensing
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Remote Sensing in Natural Hazard Exploration and Impact Assessment
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Remote Sensing in Natural Hazard Exploration and Impact Assessment

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1431

Special Issue Editors

Dr. Aggelos Pallikarakis

E-Mail Website
Guest Editor
Department of Earth and Atmospheric Sciences, Agricultural University of Athens, 75 Iera Odos, GR11855 Athens, Greece
Interests: active tectonic; earthquake; natural hazards; geology; paleoenvironment; seismic hazards
Special Issues, Collections and Topics in MDPI journals
Dr. Emmanouil Psomiadis

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Interests: earth observation; GIS; agriculture; geomorphology; natural resources; disasters
Special Issues, Collections and Topics in MDPI journals
Dr. Georgios Deligiannakis

E-Mail Website
Guest Editor
Department of Earth and Atmospheric Sciences, Agricultural University of Athens, 75 IeraOdos, GR11855 Athens, Greece
Interests: seismic hazard assessment; earthquake geology; remote sensing; structure from motion; tectonic geomorphology; earthquake catastrophe modeling; paleoseismology; UAV
Special Issues, Collections and Topics in MDPI journals
Dr. Michalis Diakakis

E-Mail Website
Guest Editor
Department of Geography and Climatology, Faculty of Geology and Geoenvironment, School of Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece
Interests: extreme events; flash flooding; flood mortality; risk perception; hydrogeomorphological disasters; landslides
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The uncontrollable expansion of urban areas has led to the constant growth of urban populations, resulting in increased exposure to numerous natural hazards. The natural disaster-related economic and human losses during the last century have been enormous. Enhancing the prevention efforts can lead to the reduction in losses both in terms of property and infrastructure losses and human losses. Especially in urban areas, there is an urgent need to increase prevention initiatives through constant monitoring and study of natural phenomena that can influence urban areas and their population.

Currently, advances in remote sensing techniques encompass state-of-the-art tools and applications, involving Geographic Information Systems (GIS), Unmanned Aerial Vehicles, and LiDAR (airborne and terrestrial), and have the potential to provide significant solutions in the field of natural hazard prevention. Advanced remote sensing offers a range of beneficial data to study natural disasters with higher spectral, temporal, and spatial resolution, as well as high accuracy and reliability information in a number of aspects included in natural risk management. This Special Issue aims to collect studies covering natural hazard management, prevention, and understanding in urban areas. More specifically, the main goal of this Special Issue is monitoring phenomena and natural hazards that can influence urban areas and threaten their population and their infrastructures.

Topics may cover anything from the conventional assessment and estimation of geological, geoenvironmental, and climate-related hazards to pandemic situations and health management issues. Hence, multiscale approaches or studies and interdisciplinary original research articles focused on natural hazards in urban environment monitoring are welcome. Articles may address, but are not limited, to the following topics:

  • Remote sensing in flood exploration and impact assessment;
  • UAV and LiDAR in natural hazard exploration;
  • UAV in geomorphological mapping;
  • Remote sensing and GIS applications in coastal area threats;
  • Remote sensing and GIS applications in natural disasters and extreme events;
  • Remote sensing and GIS applications in pandemic situations.

Dr. Aggelos Pallikarakis
Dr. Emmanouil Psomiadis
Dr. Georgios Deligiannakis
Dr. Michalis Diakakis
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

  • forest fires and erosion
  • pollution
  • climate changes
  • floods
  • earthquakes and active faults
  • liquefaction
  • pandemic situations
  • urban and coastal areas

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

Published Papers (2 papers)

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Research

27 pages, 24687 KiB  
Article
Harnessing InSAR and Machine Learning for Geotectonic Unit-Specific Landslide Susceptibility Mapping: The Case of Western Greece
by Stavroula Alatza, Alexis Apostolakis, Constantinos Loupasakis, Charalampos Kontoes, Martha Kokkalidou, Nikolaos S. Bartsotas and Georgios Christopoulos
Remote Sens. 2025, 17(7), 1161; https://doi.org/10.3390/rs17071161 - 25 Mar 2025
Viewed by 321
Abstract
Landslides are one of the most severe geohazards globally, causing extreme financial and social losses. While InSAR time-series analyses provide valuable insights into landslide detection, mapping, and monitoring, AI is also implemented in a variety of geohazards, including landslides. In the present study, [...] Read more.
Landslides are one of the most severe geohazards globally, causing extreme financial and social losses. While InSAR time-series analyses provide valuable insights into landslide detection, mapping, and monitoring, AI is also implemented in a variety of geohazards, including landslides. In the present study, a machine learning (ML) landslide susceptibility map is proposed that integrates the geotectonic units of Greece and incorporates various sources of landslide data. Satellite data from Persistent Scatterer Interferometry analysis, validated by geotechnical experts, resulted in an extremely large dataset of more than 3000 landslides in an area of interest, including the most landslide-prone area in Greece. The gradient-boosted decision tree was employed in the landslide susceptibility mapping. The model was trained on three geotectonic units and five prefectures of Western Greece and performed well in predicting landslide events. Finally, a SHAP (SHapley Additive exPlanations) analysis verified that precipitation and geology, which are the main landslide-triggering and preparatory factors, respectively, in Greece, positively affected landslide characterization. The innovation of the proposed research lies in the uniqueness of this newly created dataset, comprising a remarkably large number of landslide and non-landslide locations in Western Greece. By adopting a strict machine learning methodology, the spatial autocorrelation effect, which is overlooked in similar studies, was reduced. Also, leveraging the unique features of the geological formations, the model was trained to incorporate differences in the landslide susceptibility of formations located in different geotectonic units with variant geotechnical characteristics. The proposed approach facilitates the generalization of the model and sets a strong base for the creation of a national-scale landslide susceptibility mapping and forecasting system. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Hazard Exploration and Impact Assessment)
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27 pages, 7651 KiB  
Article
Flood Mud Index (FMI): A Rapid and Effective Tool for Mapping Muddy Areas After Floods—The Valencia Case
by Emanuele Alcaras
Remote Sens. 2025, 17(5), 770; https://doi.org/10.3390/rs17050770 - 23 Feb 2025
Viewed by 731
Abstract
Mapping flooded areas immediately after heavy rainfall is particularly challenging when sediment-laden floodwaters dominate the landscape. Traditional indices, such as the Normalized Difference Water Index (NDWI), are designed to detect water-covered areas but fail to identify muddy zones with high turbidity, which are [...] Read more.
Mapping flooded areas immediately after heavy rainfall is particularly challenging when sediment-laden floodwaters dominate the landscape. Traditional indices, such as the Normalized Difference Water Index (NDWI), are designed to detect water-covered areas but fail to identify muddy zones with high turbidity, which are common during extreme flood events. These muddy floodwaters often blend spectrally with surrounding land, leading to significant misclassifications. This study introduces the Flood Mud Index (FMI), a novel spectral index specifically developed to detect debris-laden flooded areas using only the red and blue bands. Landsat 8 imagery was utilized to validate the FMI, and its performance was evaluated through confusion matrices. The index achieved an overall accuracy of 97.86%, outperforming existing indices and demonstrating exceptional precision in delineating muddy floodplains. By relying solely on red and blue bands, the FMI is applicable to any platform equipped with RGB sensors, offering versatility for flood monitoring. Its compatibility with low-cost drones makes it especially valuable for rapid post-flood assessments, enabling immediate data collection even in scenarios with persistent cloud cover. The FMI addresses a critical gap in flood mapping, providing an effective tool for emergency response and management in sediment-rich environments. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Hazard Exploration and Impact Assessment)
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