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Flood Monitoring, Modelling, Forecasting and Analysis with Remote Sensing Tools

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5195

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Special Issue Editors

Dr. Xudong Zhou

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Guest Editor

Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: riverine floods; hydrodynamic models; flood hazard; flood impact; satellite remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Leila Hashemi Beni

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Guest Editor

Geomatics Program, Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA
Interests: 3D data modeling; UAV; satellite remote sensing; automatic matching; change detection; machine learning/deep learning
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Dr. Menaka Revel

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Guest Editor

Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: data assimilation; remote sensing; satellite altimetry; surface water dynamics

Dr. Qing Yang

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Guest Editor

School of Fresh Water Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
Interests: flood mapping, modeling, and impact assessment; GIS; generative model; deep learning

Special Issue Information

Dear Colleagues,

Remote sensing technology has become an important tool to monitor natural disasters due to its advantages of large space coverage, short revisit periods, and abundant observation variables (e.g., rainfall, soil moisture, water storage, altimetry, water surface area, etc.). Floods, a natural disaster that develops rapidly and affects a wide range of areas, can also be monitored using remote sensing. The increasing availability of multi-source satellite data improves the spatiotemporal resolution of remote-sensing-based flood monitoring and presents opportunities to overcome previously encountered challenges in flood mapping. Moreover, the recent progress of machine learning algorithms also provides new ways to improve the efficiency and accuracy of remote-sensing-based flood detection. In addition to directly observing floodwater bodies, remote sensing data can be combined with hydrological and hydrodynamic models to overcome the discontinuous nature of satellite observations, create better models and forecast the dynamics of flood inundation. Additional remote sensing products, such as building footprints, night-time lights, and crop types, also play vital roles in flood impact prediction and analysis. In summary, it is of great value to further investigate the information and expand the application of remote sensing data to promote research into flood monitoring, modelling, forecasting and analysis. Therefore, this Special Issue invites papers on the following topics:

Flood monitoring using multi-sensor remote sensing data;
Flood monitoring algorithms based on new technologies such as machine learning;
Flood monitoring in obstacle areas (urban, vegetated, and mountain areas, etc.) or special land cover regions (desert and snow regions, etc.) with remote sensing data;
Calibrating and validating flood models, and data assimilation, using remote sensing data;
Flood forecasting using remote sensing data (such as soil water, rainfall data, etc.);
Analysis of flood hazards, risk, damages, vulnerability, resilience, etc., using remote sensing data.

Dr. Xudong Zhou
Dr. Leila Hashemi Beni
Dr. Menaka Revel
Dr. Qing Yang
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 monitoring
flood modeling
flood forecasting
data assimilation
flood hazard and risk
flood impact assessment

Published Papers (3 papers)

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Research

21 pages, 3482 KiB

Open AccessArticle

Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data

by Weiwei Ren, Xin Li, Donghai Zheng, Ruijie Zeng, Jianbin Su, Tinghua Mu and Yingzheng Wang

Remote Sens. 2023, 15(18), 4527; https://doi.org/10.3390/rs15184527 - 14 Sep 2023

Cited by 3 | Viewed by 1093

Abstract

Due to the scarcity of observational data and the intricate precipitation–runoff relationship, individually applying physically based hydrological models and machine learning (ML) techniques presents challenges in accurately predicting floods within data-scarce glacial river basins. To address this challenge, this study introduces an innovative hybrid model that synergistically harnesses the strengths of multi-source remote sensing data, a physically based hydrological model (i.e., Spatial Processes in Hydrology (SPHY)), and ML techniques. This novel approach employs MODIS snow cover data and remote sensing-derived glacier mass balance data to calibrate the SPHY model. The SPHY model primarily generates baseflow, rain runoff, snowmelt runoff, and glacier melt runoff. These outputs are then utilized as extra inputs for the ML models, which consist of Random Forest (RF), Gradient Boosting (GDBT), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Support Vector Machine (SVM) and Transformer (TF). These ML models reconstruct the intricate relationship between inputs and streamflow. The performance of these six hybrid models and SPHY model is comprehensively explored in the Manas River basin in Central Asia. The findings underscore that the SPHY-RF model performs better in simulating and predicting daily streamflow and flood events than the SPHY model and the other five hybrid models. Compared to the SPHY model, SPHY-RF significantly reduces RMSE (55.6%) and PBIAS (62.5%) for streamflow, as well as reduces RMSE (65.8%) and PBIAS (73.51%) for floods. By utilizing bootstrap sampling, the 95% uncertainty interval for SPHY-RF is established, effectively covering 87.65% of flood events. Significantly, the SPHY-RF model substantially improves the simulation of streamflow and flood events that the SPHY model struggles to capture, indicating its potential to enhance the accuracy of flood prediction within data-scarce glacial river basins. This study offers a framework for robust flood simulation and forecasting within glacial river basins, offering opportunities to explore extreme hydrological events in a warming climate. Full article

(This article belongs to the Special Issue Flood Monitoring, Modelling, Forecasting and Analysis with Remote Sensing Tools)

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19 pages, 15864 KiB

Open AccessCommunication

Flood Extent and Volume Estimation Using Remote Sensing Data

by Georgii Popandopulo, Svetlana Illarionova, Dmitrii Shadrin, Ksenia Evteeva, Nazar Sotiriadi and Evgeny Burnaev

Remote Sens. 2023, 15(18), 4463; https://doi.org/10.3390/rs15184463 - 11 Sep 2023

Cited by 2 | Viewed by 1832

Abstract

Floods are natural events that can have a significant impacts on the economy and society of affected regions. To mitigate their effects, it is crucial to conduct a rapid and accurate assessment of the damage and take measures to restore critical infrastructure as quickly as possible. Remote sensing monitoring using artificial intelligence is a promising tool for estimating the extent of flooded areas. However, monitoring flood events still presents some challenges due to varying weather conditions and cloud cover that can limit the use of visible satellite data. Additionally, satellite observations may not always correspond to the flood peak, and it is essential to estimate both the extent and volume of the flood. To address these challenges, we propose a methodology that combines multispectral and radar data and utilizes a deep neural network pipeline to analyze the available remote sensing observations for different dates. This approach allows us to estimate the depth of the flood and calculate its volume. Our study uses Sentinel-1, Sentinel-2 data, and Digital Elevation Model (DEM) measurements to provide accurate and reliable flood monitoring results. To validate the developed approach, we consider a flood event occurred in 2021 in Ushmun. As a result, we succeeded to evaluate the volume of that flood event at 0.0087 km³. Overall, our proposed methodology offers a simple yet effective approach to monitoring flood events using satellite data and deep neural networks. It has the potential to improve the accuracy and speed of flood damage assessments, which can aid in the timely response and recovery efforts in affected regions. Full article

(This article belongs to the Special Issue Flood Monitoring, Modelling, Forecasting and Analysis with Remote Sensing Tools)

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20 pages, 9266 KiB

Open AccessArticle

River Bars and Vegetation Dynamics in Response to Upstream Damming: A Case Study of the Middle Yangtze River

by Yong Hu, Junxiong Zhou, Jinyun Deng, Yitian Li, Chunrui Yang and Dongfeng Li

Remote Sens. 2023, 15(9), 2324; https://doi.org/10.3390/rs15092324 - 28 Apr 2023

Cited by 1 | Viewed by 1546

Abstract

Investigating river bars and their vegetation dynamics in response to upstream damming is important for riverine flood management and ecological assessment. However, our mechanical understanding of the damming-induced changes in river bar and vegetation, such as bar area, morphology, and leaf area index (LAI), remains limited for large river systems. Leveraging satellite images and in situ observed hydrogeomorphic data from, we improve a machine learning-based LAI inversion model to quantify variations in river bar morphology, vegetation distribution, and LAI in the Middle Yangtze River (MYR) following the operation of the Three Gorges Dam (TGD). Then we analyze the mechanisms controlling the bar and vegetation dynamics based on high-resolution river cross-sectional profiles as well as daily discharge, water levels, and sediment in both the pre- and post-TGD periods. Our results indicate that the river bar area decreased by approximately 10% from 2003 to 2020, while the vegetation area and average LAI of these bars increased by >50% and >20%, respectively. Moreover, the plant community on most river bars tended to expand from the bar tail to the bar head and from the edge to the center. The main factor driving vegetation expansion in the MYR after the TGD’s operation was the reduction in bar submergence frequency (by 55%), along with a slight bar erosion. Further analysis revealed that the standard deviation of annual discharge decreased by approximately 37%, and the frequency of vegetation-erosive flow decreased by approximately 74%. Our data highlight the potential impact of large dams downstream flow regimes and vegetation encroachement. Such findings further the understanding of the biogeomorphological impacts of large dams on the river bar vegetation and have important implications for riverine plant flux estimatin, flood management and ecological restoration in dammed river systems. Full article

(This article belongs to the Special Issue Flood Monitoring, Modelling, Forecasting and Analysis with Remote Sensing Tools)

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