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Remote Sensing
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Remote Sensing in Urban Flooding Monitoring
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Remote Sensing in Urban Flooding Monitoring

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 12472

Special Issue Editors

Dr. Duong Tran Anh

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Guest Editor
Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 70000, Vietnam
Interests: hydrodynamics; hydrological modelling; climate change; remote sensing; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Tran Dang An

E-Mail Website
Guest Editor
Department of Water Resources Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam
Interests: mapping natural hazards; groundwater flow dynamics; seawater intrusion; water quality
Dr. Dung Duc Tran

E-Mail Website
Guest Editor
Center of Water Management and Climate Change, Vietnam National University, Ho Chi Minh City 100000, Vietnam
Interests: water resources management; floodwater management; socio-hydrology; climate change
Special Issues, Collections and Topics in MDPI journals
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. Thanh Duc Dang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of South Florida, 4202 E Fowler Ave., Tampa, FL 33620, USA
Interests: remote sensing; hydrology; land-use/cover change; climate change; machine learning

Special Issue Information

Dear Colleagues,

Urban flooding occurs when the local capacity to drain rainwater is overwhelmed by heavy and prolonged rainfall and the deterioration of the drainage system. This type of flooding often causes great economic losses due to the concentration of people and properties in urban areas. The world’s mega cities are all facing this problem, and the situation is exacerbated by climate change and an aging urban infrastructure, as well as rapid urban development, reducing the drainage area and water storage for urban expansion. 

Monitoring urban flooding is a major challenge because flooding is a direct and rapid consequence of rainfall. Rainfall forecasting is still a very difficult task due to the complexity of large-scale meteorological factors and surface topographic influence. Fortunately, many Earth Observation satellites have been launched into orbit to enhance the human capacity to monitor and manage the planet. In this Special Issue, we invite all researchers and scientists to contribute to, among others, floodwater detection, flood susceptibility mapping, and flood forecasting methods in urban areas using remote sensing (RS), geographic information systems (GISs), machine learning (ML), and deep learning (DL). We also encourage research that applies numerical modeling, artificial intelligence (AI), as well as modern image analysis techniques and field surveys to be submitted to this Special Issue. The following topics are going to be considered in this Special Issue: 

  • Flood forecasting methods
  • Flood susceptibility mapping
  • Urban flood management
  • Flood in coastal cities
  • Urban flood under climate change

Dr. Duong Tran Anh
Dr. Tran Dang An
Dr. Dung Duc Tran
Dr. Quoc Bao Pham
Dr. Thanh Duc Dang
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

  • floods
  • remote sensing
  • artificial intelligence
  • urban drainage
  • geographic information system
  • numerical simulation
  • climate change

Published Papers (4 papers)

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Research

21 pages, 6617 KiB  
Article
Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia
by Ahmed M. Al-Areeq, S. I. Abba, Mohamed A. Yassin, Mohammed Benaafi, Mustafa Ghaleb and Isam H. Aljundi
Remote Sens. 2022, 14(21), 5515; https://doi.org/10.3390/rs14215515 - 02 Nov 2022
Cited by 18 | Viewed by 3274
Abstract
Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine [...] Read more.
Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89–97%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Flooding Monitoring)
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20 pages, 2731 KiB  
Article
Flood Hazard Analysis Based on Rainfall Fusion: A Case Study in Dazhou City, China
by Lingxue Liu, Li Zhou, Tianqi Ao, Xing Liu and Xiaolong Shu
Remote Sens. 2022, 14(19), 4843; https://doi.org/10.3390/rs14194843 - 28 Sep 2022
Cited by 3 | Viewed by 1326
Abstract
In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion [...] Read more.
In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion methods, to fuse the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the observations in Dazhou City, China. Then, the random forest was applied to obtain the weights of various factors to facilitate a comprehensive flood hazard analysis under four rainfall durations. The results show that (1) the linear regression performs best out of the four fusion methods, with a correlation coefficient of 0.56; (2) the Digital Elevation Model (DEM) is the most impact factor with a weight of more than 0.2; and (3) the proposed flood analysis system performs well, as 70% of historical flood points are distributed in high and sub-high hazard areas and more than 93% of historical flood points are distributed in medium hazard areas. This study identified the flood hazard grade and distribution in Dazhou City, which could provide a valuable methodology to contribute to flood hazard analysis and disaster management with satellite rainfall. Furthermore, the results of this paper are profound for future work on the high-resolution flood risk assessment and management in Dazhou City. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Flooding Monitoring)
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27 pages, 8215 KiB  
Article
Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan
by Mujeeb Rahman Atefi and Hiroyuki Miura
Remote Sens. 2022, 14(15), 3647; https://doi.org/10.3390/rs14153647 - 29 Jul 2022
Cited by 8 | Viewed by 2577
Abstract
On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we [...] Read more.
On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Flooding Monitoring)
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16 pages, 5735 KiB  
Article
Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms
by Ismail Elkhrachy
Remote Sens. 2022, 14(3), 440; https://doi.org/10.3390/rs14030440 - 18 Jan 2022
Cited by 21 | Viewed by 4055
Abstract
In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters are applied to remote [...] Read more.
In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters are applied to remote sensing images to extract features that are used as independent features by ML algorithms to estimate flood depths. Data of dependent variables were obtained from the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2D) simulation model, as applied to the New Cairo, Egypt, post-flash flood event from 24–26 April 2018. Gradient boosting regression (GBR), random forest regression (RFR), linear regression (LR), extreme gradient boosting regression (XGBR), multilayer perceptron neural network regression (MLPR), k-nearest neighbors regression (KNR), and support vector regression (SVR) were used to estimate floodwater depths; their outputs were compared and evaluated for accuracy using the root-mean-square error (RMSE). The RMSE accuracy for all ML algorithms was 0.18–0.22 m for depths less than 1 m (96% of all test data), indicating that ML models are relatively portable and capable of computing floodwater depths using remote sensing data as an input. Full article
(This article belongs to the Special Issue Remote Sensing in Urban Flooding Monitoring)
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