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Remote Sensing
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Remote Sensing Data for Modeling and Managing Natural Disasters
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Remote Sensing Data for Modeling and Managing Natural Disasters

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 November 2025 | Viewed by 2196

Special Issue Editors

Dr. Andrey Krasovskiy

E-Mail Website
Guest Editor
Agriculture Forestry and Ecosystem Services (AFE) Group, Biodiversity and Natural Resources (BNR) Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Interests: wildfire modeling; climate change impacts; natural disturbances
Special Issues, Collections and Topics in MDPI journals
Dr. Frédéric Berger

E-Mail Website
Guest Editor
INRAE, Paris, France
Interests: forestry; 3D rockfall modeling
Dr. Florian Kraxner

E-Mail Website
Guest Editor
Agriculture Forestry and Ecosystem Services (AFE) Group, Biodiversity and Natural Resources (BNR) Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Interests: forestry; mountain risk engineering; natural disturbances; policy impacts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural disasters are a growing threat globally, leading to multiple damage to ecosystems and the human population. The availability of remote sensing (RS) data is growing steadily, and RS is increasingly used for monitoring and modeling natural disasters. Monitoring is crucial to identify hotspots of natural disasters and integrate these data and information into early warning systems. At the same time, RS data are pivotal for model calibration and validation, which enables us to take a look into the future through modeling dedicated scenarios under a changing climate. In this way, by combining the power of RS and modeling,  important information for policy and decision makers can be produced, which, in turn, is key for reducing the risks posed by many kinds of natural disasters.

This Special Issue will collect research papers on the use of remote sensing data to monitor and model natural disasters at various spatial and temporal scales. This includes understanding the impacts of multiple/compound disasters and analyzing cascading effects. Our end goal is to gain an understanding of historical, current, and future hotspots and propose adaptation and mitigation options.

We encourage the submission of research articles on the following themes: wildfires, windstorms, bark beetles, floods, droughts, rockfalls, and landslides; we also invite papers that discuss their compound effects. Additionally, we welcome various assessments of climate change impacts and potential adaptation and mitigation options. These may include inter-alia suitability maps for agriculture and forestry, resilience studies, the development of multi-hazard hotspot maps, permanence discussions for land-based carbon dioxide reduction (CDR) methods, and early warning systems.

Dr. Andrey Krasovskiy
Dr. Frédéric Berger
Dr. Florian Kraxner
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

  • disaster modeling
  • natural disasters monitoring
  • resilience to natural disasters
  • multi-hazard hotspot maps
  • climate change impacts
  • early warning systems

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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

25 pages, 7974 KiB  
Article
Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge
by Jialou Wang, Jacob Sanderson, Sadaf Iqbal and Wai Lok Woo
Remote Sens. 2025, 17(9), 1540; https://doi.org/10.3390/rs17091540 - 26 Apr 2025
Viewed by 338
Abstract
Flooding is one of the most devastating natural disasters worldwide, with increasing frequency due to climate change. Traditional hydrological models require extensive data and computational resources, while machine learning (ML) models struggle to capture spatial dependencies. To address this, we propose a modified [...] Read more.
Flooding is one of the most devastating natural disasters worldwide, with increasing frequency due to climate change. Traditional hydrological models require extensive data and computational resources, while machine learning (ML) models struggle to capture spatial dependencies. To address this, we propose a modified U-Net architecture that integrates prior hydrological knowledge of permanent water bodies to improve flood susceptibility mapping in Northumberland County, UK. By embedding domain-specific insights, our model achieves a higher area under the curve (AUC) (0.97) compared to the standard U-Net (0.93), while also reducing training time by converging three times faster. Additionally, we integrate a Grad-CAM module to provide visualisations explaining the areas of attention from the model, enabling interpretation of its decision-making, thus reducing barriers to its practical implementation. Full article
(This article belongs to the Special Issue Remote Sensing Data for Modeling and Managing Natural Disasters)
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17 pages, 20963 KiB  
Article
Landslide Susceptibility Mapping Based on Ensemble Learning in the Jiuzhaigou Region, Sichuan, China
by Bangsheng An, Zhijie Zhang, Shenqing Xiong, Wanchang Zhang, Yaning Yi, Zhixin Liu and Chuanqi Liu
Remote Sens. 2024, 16(22), 4218; https://doi.org/10.3390/rs16224218 - 12 Nov 2024
Viewed by 1447
Abstract
Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou [...] Read more.
Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou County, Sichuan Province, as a case study, we developed an evaluation index system incorporating 14 factors. We employed three base models—logistic regression, support vector machine, and Gaussian Naive Bayes—assessed through four ensemble methods: Stacking, Voting, Bagging, and Boosting. The decision mechanisms of these models were explained via a SHAP (SHapley Additive exPlanations) analysis. Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. This approach effectively addresses the challenges of unreliable landslide susceptibility mapping in complex environments. Full article
(This article belongs to the Special Issue Remote Sensing Data for Modeling and Managing Natural Disasters)
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