remotesensing-logo

Journal Browser

Journal Browser

Remote Sensing, GIS and Numerical Models for Urban Flood Risk Assessment

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2572

Special Issue Editors


E-Mail Website
Guest Editor
Research and Education Department, RSS-Hydro, L-3593 Dudelange, Luxembourg
Interests: hydrometeorological hazards; fluvial hydraulics; hydraulic numerical modelling; early warning system

Special Issue Information

Dear Colleagues,

There is now global consensus that we are experiencing climate change impacts at an unprecedented rate. This is especially true for the most vulnerable places and communities. Hydroclimatic extremes are increasingly occurring at spatial and temporal scales that are exceeding past measurement records. Urban areas are increasingly exposed, especially due to the vast urbanization and uncontrolled anthropic alteration of the territory. In these densely inhabited urbanscapes, short-lived severe rainfall events can generate massive flooding causing uncountable economical losses and also frequently leading to numerous fatalities. Such phenomena are projected to increase in frequency under the ongoing changing climate further, exacerbating the hydrological cycle.

Recently, there has been much progress in research that allows fostering our understanding of urban flood risk, particularly due to great technological advances in remote sensing, including satellites and drones, cloud computing, online big data processing platforms, faster numerical model codes, and a general proliferation of open-access GIS datasets. Additionally, the last few years have seen substantial development in the field of Artificial Intelligence (AI), especially in novel Machine Learning (ML) models that are becoming ever more skillful in predicting flood risk at local scales and can greatly complement numerical flood models.

By leveraging these technologies, we can enhance our understanding of flood risk and inform decision-making related to flood management and disaster response. Furthermore, they can drive the planning of flood mitigation strategies and inform the development of real-time flood warning systems.

This special issue will collect papers that make a considerable contribution to the recent advances in the use of remote sensing, GIS, AI/ML and numerical models to improve our understanding of changing flood risks in urban areas.

We are particularly interested in article contributions that focus on the following topics:

  • The use of remote sensing, and/or GIS, numerical modeling, AI/ML to assess urban flood risk and support flood risk management strategies;
  • Measuring or modelling urban hydrology, including runoff, discharge, infiltration, drainage, etc.;
  • Integration of surface processes with underground drainage systems;
  • The use of drones and IoT technology in urban flood risk assessment.

Dr. Guy Jean Pierre Schumann
Dr. Paolo Tamagnone
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

  • urban flood risk
  • pluvial flooding
  • climate change impact
  • flood modelling
  • urban hydrology
  • remote sensing and GIS

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 7048 KiB  
Article
Flood Susceptibility Assessment with Random Sampling Strategy in Ensemble Learning (RF and XGBoost)
by Hancheng Ren, Bo Pang, Ping Bai, Gang Zhao, Shu Liu, Yuanyuan Liu and Min Li
Remote Sens. 2024, 16(2), 320; https://doi.org/10.3390/rs16020320 - 12 Jan 2024
Viewed by 1294
Abstract
Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, [...] Read more.
Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, utilizing remote sensing data and limited historical inundation records. In this study, two ensemble learning algorithms, Random Forest (RF) and XGBoost, were adopted to assess the flood susceptibility of Kunming, a typical mountainous urban area prone to severe flood disasters. A flood inventory was created using flood observations from 2018 to 2022. The spatial database included 10 explanatory factors, encompassing climatic, geomorphic, and anthropogenic factors. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were selected for model comparison. To minimize the influence of expert opinions on model training, this study employed a strategy of uniformly random sampling in historically non-flooded areas for negative sample selection. The results demonstrated that (1) ensemble learning algorithms offer higher accuracy than other machine learning methods, with RF achieving the highest accuracy, evidenced by an area under the curve (AUC) of 0.87, followed by XGBoost at 0.84, surpassing both ANN (0.83) and SVM (0.82); (2) the interpretability of ensemble learning highlighted the differences in the potential distribution of the training data’s positive and negative samples. Feature importance in ensemble learning can be utilized to minimize human bias in the collection of flooded-site samples, more targeted flood susceptibility maps of the study area’s road network were obtained; and (3) ensemble learning algorithms exhibited greater stability and robustness in datasets with varied negative samples, as evidenced by their performance in F1-Score, Kappa, and AUC metrics. This paper further substantiates the superiority of ensemble learning in flood susceptibility assessment tasks from the perspectives of accuracy, interpretability, and robustness, enhances the understanding of the impact of negative samples on such assessments, and optimizes the specific process for urban flood susceptibility assessment using data-driven methods. Full article
Show Figures

Figure 1

17 pages, 9187 KiB  
Article
Automated Surface Runoff Estimation with the Spectral Unmixing of Remotely Sensed Multispectral Imagery
by Chloe Campo, Paolo Tamagnone and Guy Schumann
Remote Sens. 2024, 16(1), 136; https://doi.org/10.3390/rs16010136 - 28 Dec 2023
Viewed by 605
Abstract
This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and [...] Read more.
This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and soil permeability data. This process generates a detailed vegetation classification and slope-corrected composite curve number (CN) map using information at the subpixel level, which is crucial for estimating excess runoff during intense precipitation events. The algorithm designed with this methodology is automated and utilizes freely accessible multispectral imagery. Leveraging the vegetation–impervious–soil (V-I-S) model, it is assumed that land cover comprises V-I-S components at each pixel. Automated Music and spectral Separability-based Endmember Selection is employed on a generic spectral library to obtain the most relevant V-I-S endmember spectra for a particular image, which is then employed in multiple endmember spectral mixture analysis to obtain V-I-S fraction maps. The derived fractions are utilized in combination with the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index to adapt the CN map to different seasons and climatic conditions. The methodology was applied to Esch-sur-Alzette, Luxembourg, over a four-year period to validate the methodology and quantify the increase in the impervious surface area in the commune and the relationship with the runoff dynamics. This approach provides valuable insights into infiltration and runoff dynamics across diverse temporal and geographic ranges. Full article
Show Figures

Figure 1

Back to TopTop