Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing Applications in Flood Forecasting and Monitoring
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 Applications in Flood Forecasting and Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 9952

Special Issue Editors

Dr. Sk Ajim Ali

E-Mail Website
Guest Editor
Department of Geography, Aligarh Muslim University, Aligarh, India
Interests: machine learning; disaster management; natural hazards; geographic information system; multiple-criteria decision analysis; waste management; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Bahram Choubin

E-Mail Website
Guest Editor
Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran
Interests: hydrology; natural hazards; climate change; spatial modeling; hydrologic forecasting
Dr. Farhana Parvin

E-Mail Website
Guest Editor
Department of Geography, Aligarh Muslim University, Aligarh, India
Interests: landslides; debris flow; susceptibility
Dr. Quoc Bao Pham

E-Mail Website
Guest Editor
Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland
Interests: hydrology; groundwater; machine learning; water resources management; climate change; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Meriame Mohajane

E-Mail Website
Guest Editor
Construction Technologies Institute (ITC), National Research Council (CNR), 70124 Bari, Italy
Interests: land use/land cover modelling; vegetation; forest fire; climate change; prediction; geostatistical analysis; ecological monitoring and assessment; geoinformatics (GIS); multi-/hyperspectral remote sensing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The journal Remote Sensing (ISSN 2072-4292, IF 5.349) is currently running a Special Issue entitled, “Remote Sensing Applications in Flood Forecasting and Monitoring.” Dr. Sk Ajim Ali, Dr. Bahram Choubin, Dr. Farhana Parvin, Dr. Quoc Bao Pham, and Dr. Meriame Mohajane are serving as Guest Editors for this issue. We think you could make an exceptional contribution based on your expertise in this particular field.

The characteristics of a flood region are extracted using remote sensing (RS) technology, which also provides information on potential hazards and challenges. Flood risk maps can be created using the image and remote sensing data that has been collected. The technology is frequently used for making post-flood damage assessments and comprehensive mapping of flood extents. By employing high-resolution imagery of the area before and after the disaster, it can be utilized to assess the impact caused by flooding events. Remote sensing is crucial for disaster-related assessments because quick and accurate information about the location, area, and severity of a disaster's damage is required to support response and recovery efforts. One of the recent developments in the application of remote sensing to flood-related problems is the use of LIDAR (light detecting and ranging) sensors. This technology has become very popular for creating DEMs for flood-prone areas.

Considering all these advantages of remote sensing, the main objective of this Special Issue is to provide a scientific forum for advancing the successful application of remote sensing (RS) technologies and geographic information system (GIS)-based methods toward flood forecasting and monitoring in various flood-prone terrains on Earth, as well as to foster informed discussions among scientists and stakeholders on this pressing issue.

This Special Issue aims to provide an outlet for high-quality peer-reviewed publications that implement state-of-the-art methods and techniques incorporating geoinformatics-based methods to map, evaluate, and model flood forecasting, its monitoring, and their implications, together with the framing of newer hypotheses that can further understandings of the operative processes.

The Special Issue may include (without being limited to) the following themes:

  • The role of remote sensing in flood assessment and risk management;
  • Flood disaster studies with a remote sensing perspective;
  • Flood preparedness and remote sensing: flood forecasting;
  • Remote sensing in flood emergency mapping (FEM) for disaster response;
  • Climate change and floods;
  • Estimation of future floods based on urban land use and land cover;
  • Mapping, assessing, and monitoring the floods in urban and coastal areas;
  • Remote sensing and glacial lake outburst floods (GLOFs);
  • Remote sensing and the flood early warning system (FEWS);
  • Urbanization and urban flood prediction.

Given your competence in this area, we invite you to contribute a paper on the aforementioned subjects or any relevant issues.

With best regards,

Dr. Sk Ajim Ali
Dr. Bahram Choubin
Dr. Farhana Parvin
Dr. Quoc Bao Pham
Dr. Meriame Mohajane
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

  • remote sensing
  • multi-hazard classification
  • flood forecasting
  • flood monitoring
  • risk assessment
  • climate change
  • flood early warning system
  • machine learning
  • geographic information system

Published Papers (4 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

Jump to: Other

31 pages, 21224 KiB  
Article
Integrated Approach for the Study of Urban Expansion and River Floods Aimed at Hydrogeomorphic Risk Reduction
by Andrea Mandarino, Francesco Faccini, Fabio Luino, Barbara Bono and Laura Turconi
Remote Sens. 2023, 15(17), 4158; https://doi.org/10.3390/rs15174158 - 24 Aug 2023
Cited by 1 | Viewed by 2322
Abstract
Urbanization in flood-prone areas is a critical issue worldwide. The historical floods, the urban expansion in terms of building footprint, the extent and construction period of inundated buildings with reference to two representative floods (5–6 November 1994 and 24–25 November 2016), and the [...] Read more.
Urbanization in flood-prone areas is a critical issue worldwide. The historical floods, the urban expansion in terms of building footprint, the extent and construction period of inundated buildings with reference to two representative floods (5–6 November 1994 and 24–25 November 2016), and the ground effects and dynamics of these events were investigated in the cities of Garessio, Ceva, and Clavesana, along the Tanaro River (NW Italy). An integrated approach based on historical data analysis, photograph interpretation, field surveys, and GIS investigations was adopted, and novel metrics for quantitative analysis of urbanization and flood exposure at the individual-building scale were introduced. The considered cities were hit by damaging floods several times over the last centuries and experienced an increase in built-up surface after the mid-19th century, especially between the 1930s and 1994. The 1994 and 2016 high-magnitude floods highlighted that urban expansion largely occurred in flood-prone areas, and anthropogenic structures conditioned flood propagation. One of the rare Italian cases of the relocation of elements exposed to floods is documented. This research aims to emphasize the relevance of information on past floods and urbanization processes for land planning and land management and the need for land use planning for flood control to forbid new urban expansion in potentially floodable areas. The outcomes represent an essential knowledge base to define effective and sustainable management measures to mitigate hydrogeomorphic risk. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Flood Forecasting and Monitoring)
►▼ Show Figures

Figure 1

18 pages, 6063 KiB  
Article
Evaluation of Satellite-Based Precipitation Products over Complex Topography in Mountainous Southwestern China
by Xuan Tang, Hongxia Li, Guanghua Qin, Yuanyuan Huang and Yongliang Qi
Remote Sens. 2023, 15(2), 473; https://doi.org/10.3390/rs15020473 - 13 Jan 2023
Cited by 4 | Viewed by 1197
Abstract
Satellite-based precipitation products (SBPPs) are essential for rainfall quantification in areas where ground-based observation is scarce. However, the accuracy of SBPPs is greatly influenced by complex topography. This study evaluates the performance of Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping [...] Read more.
Satellite-based precipitation products (SBPPs) are essential for rainfall quantification in areas where ground-based observation is scarce. However, the accuracy of SBPPs is greatly influenced by complex topography. This study evaluates the performance of Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) in characterizing rainfall in a mountainous catchment of southwestern China, with an emphasis on the effect of three topographic variables (elevation, slope, aspect). The SBPPs are evaluated by comparing rain gauge observations at eight ground stations from May to October in 2014–2018. Results show that IMERG and GSMaP have good rainfall detection capability for the entire region, with POD = 0.75 and 0.93, respectively. In addition, IMERG overestimates rainfall (BIAS = −48.8%), while GSMaP is consistent with gauge rainfall (BIAS = −0.4%). Comprehensive analysis shows that IMERG and GSMaP are more impacted by elevation, and then slope, whereas aspect has little impact. The independent evaluations suggest that variability of elevation and slope negatively correlate with the accuracy of SBPPs. The accuracy of GSMaP presents weaker dependence on topography than that of IMERG in the study area. Our findings demonstrate the applicability of IMERG and GSMaP in mountainous catchments of Southwest China. We confirm that complex topography impacts the performance of SBPPs, especially for complex topography in mountainous areas. It is suggested that taking topographical factors into account is needed for hydrometeorological applications such as flood forecasting, and SBPP evaluations and retrieval technology require further improvement in the future for better applications. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Flood Forecasting and Monitoring)
►▼ Show Figures

Figure 1

25 pages, 28288 KiB  
Article
Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping
by Seyd Teymoor Seydi, Yousef Kanani-Sadat, Mahdi Hasanlou, Roya Sahraei, Jocelyn Chanussot and Meisam Amani
Remote Sens. 2023, 15(1), 192; https://doi.org/10.3390/rs15010192 - 29 Dec 2022
Cited by 21 | Viewed by 4086
Abstract
Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model [...] Read more.
Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model based on the Cascade Forest Model (CFM) was developed for FSM. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. The performance of the proposed CFM was evaluated over two study areas, and the results were compared with those of other six machine learning methods, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The result showed CFM produced the highest accuracy compared to other models over both study areas. The Overall Accuracy (AC), Kappa Coefficient (KC), and Area Under the Receiver Operating Characteristic Curve (AUC) of the proposed model were more than 95%, 0.8, 0.95, respectively. Most of these models recognized the southwestern part of the Karun basin, northern and northwestern regions of the Gorganrud basin as susceptible areas. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Flood Forecasting and Monitoring)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 9380 KiB  
Technical Note
Automatic Identification of Earth Rock Embankment Piping Hazards in Small and Medium Rivers Based on UAV Thermal Infrared and Visible Images
by Renzhi Li, Zhonggen Wang, Hongquan Sun, Shugui Zhou, Yong Liu and Jinping Liu
Remote Sens. 2023, 15(18), 4492; https://doi.org/10.3390/rs15184492 - 12 Sep 2023
Viewed by 826
Abstract
Piping is a major factor contributing to river embankment breaches, particularly during flood season in small and medium rivers. To reduce the costs of earth rock embankment inspections, avoid the need for human inspectors and enable the quick and widespread detection of piping [...] Read more.
Piping is a major factor contributing to river embankment breaches, particularly during flood season in small and medium rivers. To reduce the costs of earth rock embankment inspections, avoid the need for human inspectors and enable the quick and widespread detection of piping hazards, a UAV image-acquisition function was introduced in this study. Through the collection and analysis of thermal infrared and visible (TIR & V) images from several piping field simulation experiments, temperature increases, and diffusion centered on the piping point were discovered, so an automatic algorithm for piping identification was developed to capture this phenomenon. To verify the identification capabilities, the automatic identification algorithm was applied to detect potential piping hazards during the 2022 flooding of the Dingjialiu River, Liaoning, China. The algorithm successfully identified all five piping hazard locations, demonstrating its potential for detecting embankment piping. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Flood Forecasting and Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop