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Remote Sensing
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Remote Sensing of Global Floods: Observing, Modelling, and Forecasting
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Remote Sensing of Global Floods: Observing, Modelling, and Forecasting

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 3872

Special Issue Editors

Dr. Seyed Vahid Razavi-Termeh

E-Mail Website
Guest Editor
Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul 05006, Republic of Korea
Interests: geospatial artificial intelligence (GeoAI); geosciences, natural hazard; machine/deep learning algorithms; geographic information system (GIS); remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Khabat Khosravi

E-Mail Website
Guest Editor
School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
Interests: watershed hydrology; flood modeling; river engineering and sediment transport; natural hazards; groundwater modeling and vulnerability assessment; GIS and machine learning in soil and water science
Special Issues, Collections and Topics in MDPI journals
Dr. Renato Frasson

E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109, USA
Interests: remote sensing of rivers; discharge estimation; inverse problems; hydrology; computer modeling; remote sensing of floods
Special Issues, Collections and Topics in MDPI journals
Dr. Guy Jean Pierre Schumann

E-Mail Website
Guest Editor
1. Research and Education Department, RSS-Hydro, L-3593 Dudelange, Luxembourg
2. School of Geographical Sciences, University of Bristol, Bristol BS8 1QU, UK
Interests: earth observation; SAR; floods; disaster response; flood modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Floods threaten urban and agricultural communities and ecosystems. The escalating humanitarian and financial damages underscore the imperative need for flood management, control, and mitigation strategies. Geospatial science is a pivotal player in these efforts, mainly using a non-structural approach. Flood monitoring enabled by spaceborne observations coupled with artificial intelligence algorithms has transformative potential in designing modern early detection, flood response, and management systems. Satellite-based flood monitoring enables rapid responses by swiftly identifying flood-affected areas. The integration of artificial intelligence algorithms enhances the accuracy and efficiency of flood detection, providing a timely understanding of the spatial extent and severity of the inundation. This synergy between geospatial technologies and artificial intelligence facilitates the creation of spatial models that elucidate the relationships between floods and various contributing factors through geographic information systems (GISs) analysis. GISs, combined with diverse methodologies, including artificial intelligence (machine/deep learning), statistical methods, and multi-criteria decision-making techniques, enable the development of comprehensive and more accurate models. These models facilitate the creation of maps depicting flood susceptibility, vulnerability, risk, hazard, and resilience. By integrating information from satellite observations and spatial models, flood management strategies can be informed by a nuanced understanding of the complex interplay between environmental factors and flood occurrence. Furthermore, satellite observation and spatial modeling insights allow future floods to be predicted. This predictive capability is instrumental in designing robust flood warning systems that can forewarn communities and authorities, offering valuable time for preparedness and response.

The primary aim of this Special Issue is to advance geospatial science’s role in flood management. With a specific focus on satellite observations and artificial intelligence algorithms, the goal is to showcase innovative research contributing to efficient flood monitoring. This Special Issue seeks to facilitate interdisciplinary discussions, highlighting novel methodologies and spatial modeling techniques utilizing GISs, artificial intelligence, statistical methods, and multi-criteria decision making. With this goal in mind, this Special Issue aims to significantly contribute to improving flood mitigation.

In this context, this Special Issue invites expert contributions from around the world exploring topics such as:

  • Remote sensing technologies for flood monitoring;
  • Machine/deep learning applications for flood detection;
  • The spatial modeling of floods using different techniques;
  • The integration of GIS with artificial intelligence for flood susceptibility mapping (FSM);
  • Flood vulnerability, risk, and hazard assessment;
  • Predictive modeling and future flood trends;
  • The application of digital technologies in flood risk management (virtual reality (VR), augmented reality (AR), and digital twin (DT));
  • The design of flood warning systems.

Dr. Seyed Vahid Razavi-Termeh
Dr. Khabat Khosravi
Dr. Renato Frasson
Dr. Guy Jean Pierre Schumann
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

  • flood detection
  • synthetic aperture radar (SAR) imagery
  • artificial intelligence
  • spatial modelling
  • urban flood risk
  • flood susceptibility mapping
  • flood vulnerability and resilience
  • uncertainties in flood risk assessments
  • digital technologies in flood risk management
  • flood warning systems

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

Published Papers (2 papers)

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Research

16 pages, 2868 KiB  
Article
Automatic Water Body Extraction from SAR Images Based on MADF-Net
by Jing Wang, Dongmei Jia, Jiaxing Xue, Zhongwu Wu and Wanying Song
Remote Sens. 2024, 16(18), 3419; https://doi.org/10.3390/rs16183419 - 14 Sep 2024
Cited by 2 | Viewed by 1127
Abstract
Water extraction from synthetic aperture radar (SAR) images has an important application value in wetland monitoring, flood monitoring, etc. However, it still faces the problems of low generalization, weak extraction ability of detailed information, and weak suppression of background noises. Therefore, a new [...] Read more.
Water extraction from synthetic aperture radar (SAR) images has an important application value in wetland monitoring, flood monitoring, etc. However, it still faces the problems of low generalization, weak extraction ability of detailed information, and weak suppression of background noises. Therefore, a new framework, Multi-scale Attention Detailed Feature fusion Network (MADF-Net), is proposed in this paper. It comprises an encoder and a decoder. In the encoder, ResNet101 is used as a solid backbone network to capture four feature levels at different depths, and then the proposed Deep Pyramid Pool (DAPP) module is used to perform multi-scale pooling operations, which ensure that key water features can be captured even in complex backgrounds. In the decoder, a Channel Spatial Attention Module (CSAM) is proposed, which focuses on feature areas that are critical for the identification of water edges by fusing attention weights in channel and spatial dimensions. Finally, the high-level semantic information is effectively fused with the low-level edge features to achieve the final water detection results. In the experiment, Sentinel-1 SAR images of three scenes with different characteristics and scales of water body are used. The PA and IoU of water extraction by MADF-Net can reach 92.77% and 89.03%, respectively, which obviously outperform several other networks. MADF-Net carries out water extraction with high precision from SAR images with different backgrounds, which could also be used for the segmentation and classification of other tasks from SAR images. Full article
(This article belongs to the Special Issue Remote Sensing of Global Floods: Observing, Modelling, and Forecasting)
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26 pages, 16035 KiB  
Article
Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm
by Ali Nouh Mabdeh, Rajendran Shobha Ajin, Seyed Vahid Razavi-Termeh, Mohammad Ahmadlou and A’kif Al-Fugara
Remote Sens. 2024, 16(14), 2595; https://doi.org/10.3390/rs16142595 - 16 Jul 2024
Cited by 3 | Viewed by 1915
Abstract
Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) [...] Read more.
Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) > XGBoost-GWO (AUC: 0.961) > SVR-GWO (AUC: 0.960) > RNN (AUC: 0.956) > XGBoost (AUC: 0.953) > SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors. Full article
(This article belongs to the Special Issue Remote Sensing of Global Floods: Observing, Modelling, and Forecasting)
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