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Water
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Improving Flood Detection and Monitoring through Remote Sensing
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Improving Flood Detection and Monitoring through Remote Sensing

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 34234

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Alberto Refice

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Guest Editor
National Research Council—Institute for Electromagnetic Sensing of the Environment (CNR-IREA), 70126 Bari, Italy
Interests: remote sensing data processing applied to environmental monitoring; synthetic aperture radar interferometry; persistent scatterer interferometry; flood monitoring; geomorphological terrain analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Domenico Capolongo

E-Mail
Guest Editor
Earth and Environmental Sciences Dept.University of Bari, Bari, Italy
Interests: GIS; remote sensing; landslides; floods; geomorphology
Dr. Marco Chini

E-Mail Website
Guest Editor
Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
Interests: flood mapping; earthquakes damage detection; analysis of multitemporal data; classification; feature extraction; data fusion; segmentation; SAR and optical data; SAR interferometry
Special Issues, Collections and Topics in MDPI journals
Dr. Annarita D’Addabbo

E-Mail Website
Guest Editor
IREA, National Research Council, Bari, Italy
Interests: artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Measures to increase defenses against floods and reduce flood damages are more and more urgent, as floods are emerging as one of the most frequent and incisive disasters due to climate change. Gaining sufficient knowledge about the extent, duration, and more generally, time evolution of flood events is a necessary step toward this goal. This information can be used to draw maps of expected return periods for events of a certain magnitude.

Remote sensing is traditionally recognized as one of the most cost-effective technologies to gain detailed information about large areas of the earth surface. Its use in the monitoring of floods and inundations dates back to the first sensors and algorithms. In recent years, the wider availability of images from both radar and optical sensors, their low to null cost, and their tight and reliable acquisition schedules are opening the path to unprecedented levels of detail about inundation events, both on urbanized and remote areas.

Nevertheless, the new, higher monitoring capabilities of such imaging devices raise new challenges connected to image interpretation, in terms of discerning effectively the presence of floodwaters in different land-cover types and environmental conditions. In this sense, high expectations come from new methods that integrate the information obtained from multiple techniques, platforms, sensors, bands, and acquisition times. Moreover, the assessment of such techniques strongly benefits from the synergy with hydrological and/or hydraulic modeling of the evolution of flood events.

Much work is being done to devise effective methodologies to reach these ambitious goals, involving very heterogeneous knowledge bases and research fields, from advanced methods for image analysis and change detection, through data fusion (also recurring to the increasingly ubiquitous paradigms of artificial intelligence, deep learning, and big data analysis), to more applied fields such as geomorphological terrain analysis, hydrological and hydraulic modeling, and data assimilation methods.

The present Special Issue welcomes contributions to this varied body of research on, but not exclusively, the following topics:

 - flood monitoring through remote sensing data: experiences at all spatial scales and validations;

 - data fusion, change detection, scene understanding techniques applied to multi-source remote sensing, in situ/geographic data, models, etc., for flood mapping and/or monitoring;

 - assimilation of remotely sensed information within hydraulic or hydrological models;

 - remote sensing data integration in flood hazard, vulnerability, risk zonation procedures.

Dr. Marco Chini
Prof. Domenico Capolongo
Dr. Alberto Refice
Dr. Annarita D’Addabbo
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. Water 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 2600 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
  • Change detection
  • Remote sensing data fusion and/or integration
  • Data assimilation
  • Hydrological and hydraulic models
  • Multi-temporal, multi-sensor image analysis
  • Flood hazard zonation

Published Papers (8 papers)

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Editorial

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4 pages, 171 KiB  
Editorial
Improving Flood Detection and Monitoring through Remote Sensing
by Alberto Refice, Domenico Capolongo, Marco Chini and Annarita D’Addabbo
Water 2022, 14(3), 364; https://0-doi-org.brum.beds.ac.uk/10.3390/w14030364 - 26 Jan 2022
Cited by 7 | Viewed by 3611
Abstract
Floods are among the most threatening and impacting environmental hazards [...] Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)

Research

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23 pages, 8240 KiB  
Article
Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps
by David C. Mason, John Bevington, Sarah L. Dance, Beatriz Revilla-Romero, Richard Smith, Sanita Vetra-Carvalho and Hannah L. Cloke
Water 2021, 13(11), 1577; https://0-doi-org.brum.beds.ac.uk/10.3390/w13111577 - 02 Jun 2021
Cited by 14 | Viewed by 5153
Abstract
Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds [...] Read more.
Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method. Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
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27 pages, 16227 KiB  
Article
Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas
by Alberto Refice, Marina Zingaro, Annarita D’Addabbo and Marco Chini
Water 2020, 12(10), 2745; https://0-doi-org.brum.beds.ac.uk/10.3390/w12102745 - 30 Sep 2020
Cited by 21 | Viewed by 4755
Abstract
Flood detection and monitoring is increasingly important, especially on remote areas such as African tropical river basins, where ground investigations are difficult. We present an experiment aimed at integrating multi-temporal and multi-source data from the Sentinel-1 and ALOS 2 synthetic aperture radar (SAR) [...] Read more.
Flood detection and monitoring is increasingly important, especially on remote areas such as African tropical river basins, where ground investigations are difficult. We present an experiment aimed at integrating multi-temporal and multi-source data from the Sentinel-1 and ALOS 2 synthetic aperture radar (SAR) sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively. Information from the globally available CORINE land cover dataset, derived over Africa from the Proba V satellite, and available publicly at the resolution of 100 m, is also exploited. Integrated multi-frequency, multi-temporal, and multi-polarizations analysis allows highlighting different drying dynamics for floodwater over various land cover classes, such as herbaceous vegetation, wetlands, and forests. They also enable detection of different scattering mechanisms, such as double bounce interaction of vegetation stems and trunks with underlying floodwater, giving precious information about the distribution of flooded areas among the different ground cover types present on the site. The approach is validated through visual analysis from Google EarthTM imagery. This kind of integrated analysis, exploiting multi-source remote sensing to partially make up for the unavailability of reliable ground truth, is expected to assume increasing importance as constellations of satellites, observing the Earth in different electromagnetic radiation bands, will be available. Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
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16 pages, 11985 KiB  
Article
Evaluation of CYGNSS Observations for Flood Detection and Mapping during Sistan and Baluchestan Torrential Rain in 2020
by Mahmoud Rajabi, Hossein Nahavandchi and Mostafa Hoseini
Water 2020, 12(7), 2047; https://0-doi-org.brum.beds.ac.uk/10.3390/w12072047 - 18 Jul 2020
Cited by 18 | Viewed by 3464
Abstract
Flood detection and produced maps play essential roles in policymaking, planning, and implementing flood management options. Remote sensing is commonly accepted as a maximum cost-effective technology to obtain detailed information over large areas of lands and oceans. We used remote sensing observations from [...] Read more.
Flood detection and produced maps play essential roles in policymaking, planning, and implementing flood management options. Remote sensing is commonly accepted as a maximum cost-effective technology to obtain detailed information over large areas of lands and oceans. We used remote sensing observations from Global Navigation Satellite System-Reflectometry (GNSS-R) to study the potential of this technique for the retrieval of flood maps over the regions affected by the recent flood in the southeastern part of Iran. The evaluation was made using spaceborne GNSS-R measurements over the Sistan and Baluchestan provinces during torrential rain in January 2020. This area has been at a high risk of flood in recent years and needs to be continuously monitored by means of timely observations. The main dataset was acquired from the level-1 data product of the Cyclone Global Navigation Satellite System (CYGNSS) spaceborne mission. The mission consisted of a constellation of eight microsatellites with GNSS-R sensors onboard to receive forward-scattered GNSS signals from the ocean and land. We first focused on data preparation and eliminating the outliers. Afterward, the reflectivity of the surface was calculated using the bistatic radar equations formula. The flooded areas were then detected based on the analysis of the derived reflectivity. Images from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used for evaluation of the results. The analysis estimated the inundated area of approximately 19,644 km2 (including Jaz-Murian depression) to be affected by the flood in the south and middle parts of the Sistan and Baluchestan province. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we showed the capability of detecting floods in the study area. The sensitivity of the spaceborne GNSS-R observations, together with the relatively short revisit time, highlight the potential of this technique to be used in flood detection. Future GNSS-R missions capable of collecting the reflected signals from all available multi-GNSS constellations would offer even more detailed information from the flood-affected areas. Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
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20 pages, 9751 KiB  
Article
Geometry-Based Assessment of Levee Stability and Overtopping Using Airborne LiDAR Altimetry: A Case Study in the Pearl River Delta, Southern China
by Xianwei Wang, Lingzhi Wang and Tianqiao Zhang
Water 2020, 12(2), 403; https://0-doi-org.brum.beds.ac.uk/10.3390/w12020403 - 02 Feb 2020
Cited by 7 | Viewed by 3069
Abstract
Levees are normally the last barrier for defending flood water and storm surges in low-lying coastal cities. Levees in a large delta plain were usually constructed in different time and criteria and have been changing with age as well. Fast and quantitative assessment [...] Read more.
Levees are normally the last barrier for defending flood water and storm surges in low-lying coastal cities. Levees in a large delta plain were usually constructed in different time and criteria and have been changing with age as well. Fast and quantitative assessment of levee stability is critical but faces many challenges. This study designs a scoring approach to quickly assess levee stability and overtopping threats with geometric parameters from airborne Light Detection and Ranging (LiDAR). An automated procedure is developed to extract levees geometric parameters from 0.5 m grid LiDAR elevation, such as crown height, width and landside slope. The surveyed levee is seated in the Hengmen waterway in the Pearl River Delta, Southern China. Results show that the stability index using the assessment scores is higher than and superior to the common qualified rates adopted in previous studies. The qualified rate is defined as the count percentage that each parameter meets the designed criteria, while the assessment score proposed in this study assigns different credits to those below/above the designed criteria. The continuous crown heights provide detailed information on levee overtopping threats. The crown heights of levee A and B are above the designed elevation and the flood stage (4.5 m) in a 200-year return period. The crown heights of levee C, D and E are generally lower than 4.5 m and vary in a large range on different sections. The middle section of levee E for the harbor and dock area has front elevation slightly below the flood stage (3.54 m) in a 20-year return period. Moreover, the high precision LiDAR altimetry data reveal various morphological modifications in all levees, such as natural subsidence and artificial modifications, which greatly reduce levees safety and are severe threats to the community. The procedures and assessment approach developed in this study can be easily applied for levees fast assessment in the entire Pearl River Delta and somewhere else, thus offer a suitable mitigation suggestion ahead of levee failure or overtopping. Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
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23 pages, 9937 KiB  
Article
Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features
by Viktoriya Tsyganskaya, Sandro Martinis and Philip Marzahn
Water 2019, 11(9), 1938; https://0-doi-org.brum.beds.ac.uk/10.3390/w11091938 - 18 Sep 2019
Cited by 43 | Viewed by 4582
Abstract
Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities [...] Read more.
Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer’s/User’s accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use. Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
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16 pages, 15196 KiB  
Article
Application of Backpack-Mounted Mobile Mapping System and Rainfall–Runoff–Inundation Model for Flash Flood Analysis
by Takahiro Sayama, Koji Matsumoto, Yuji Kuwano and Kaoru Takara
Water 2019, 11(5), 963; https://0-doi-org.brum.beds.ac.uk/10.3390/w11050963 - 08 May 2019
Cited by 11 | Viewed by 3698
Abstract
Satellite remote sensing has been used effectively to estimate flood inundation extents in large river basins. In the case of flash floods in mountainous catchments, however, it is difficult to use remote sensing information. To compensate for this situation, detailed rainfall–runoff and flood [...] Read more.
Satellite remote sensing has been used effectively to estimate flood inundation extents in large river basins. In the case of flash floods in mountainous catchments, however, it is difficult to use remote sensing information. To compensate for this situation, detailed rainfall–runoff and flood inundation models have been utilized. Regardless of the recent technological advances in simulations, there has been a significant lack of data for validating such models, particularly with respect to local flood inundation depths. To estimate flood inundation depths, this study proposes using a backpack-mounted mobile mapping system (MMS) for post-flood surveys. Our case study in Northern Kyushu Island, which was affected by devastating flash floods in July 2017, suggests that the MMS can be used to estimate the inundation depth with an accuracy of 0.14 m. Furthermore, the landform change due to deposition of sediments could be estimated by the MMS survey. By taking into consideration the change of topography, the rainfall–runoff–inundation (RRI) model could reasonably reproduce the flood inundation compared with the MMS measurements. Overall, this study demonstrates the effective application of the MMS and RRI model for flash flood analysis in mountainous river catchments. Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
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Other

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17 pages, 8556 KiB  
Technical Note
Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery
by Giuseppe Ruzza, Luigi Guerriero, Gerardo Grelle, Francesco Maria Guadagno and Paola Revellino
Water 2019, 11(11), 2289; https://0-doi-org.brum.beds.ac.uk/10.3390/w11112289 - 31 Oct 2019
Cited by 19 | Viewed by 4053
Abstract
Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because [...] Read more.
Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time. Full article
(This article belongs to the Special Issue Improving Flood Detection and Monitoring through Remote Sensing)
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