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Flood Mapping in Urban and Vegetated Areas

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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: closed (31 January 2021) | Viewed by 36995

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Dr. Marco Chini

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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
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Dr. Luca Pulvirenti

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CIMA Research Foundation, via Armando Magliotto, 2 17100 Savona, Italy
Interests: microwave remote sensing of the Earth’s surface; hydrological applications of remote sensing

Special Issue Information

Dear Colleagues,

With the increasing number of Earth Observation (EO) satellites in orbit, with various modalities and frequencies, there has been a large increase in the capability to extract information on the surface changes with high spatial and temporal resolution. For the same geographical area, synthetic aperture radar (SAR) and optical data are now easily and, in some cases, freely available. For instance, the Copernicus Sentinel-1/2 satellites systematically acquire microwave and optical images of Earth. Emergency response services are potentially benefitting most from this huge amount of available data, because they require timely and frequent information about areas affected by damages.

Floods are the most frequent natural disasters in the world and the costliest in terms of economic losses. Mapping flood extent is fundamental to ascertain the damage and for relief organizations. SAR and optical systems, with their peculiar characteristics, represent powerful tools to monitor floods thanks to the very high spatial resolution of the new generation of sensors and the short revisit time of the present and future satellite constellations. However, mapping flooded vegetated and urban areas still represents a challenging problem. The development of new efficient algorithms and methods to tackle this issue is needed.

This Special Issue will focus on newly developed methods for the identification of floodwater in urban areas and on the presence of vegetation using remote sensing data. In particular, the submission of articles exploring the synergy between the new EO data and hydrodynamic models are highly encouraged. With regard to SAR data, studies using polarimetric and/or interferometric data are also solicited for this Special Issue.

Dr. Marco Chini
Dr. Luca Pulvirenti
Guest Editors

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Keywords

flood mapping
flood hazard mapping
urban areas
vegetation
SAR and optical data
hydrodynamic modeling
remote sensing
DInSAR coherence

Published Papers (9 papers)

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29 pages, 17364 KiB

Open AccessArticle

A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data

by Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Luca Ferraris and Silvia Puca

Remote Sens. 2021, 13(7), 1342; https://doi.org/10.3390/rs13071342 - 01 Apr 2021

Cited by 8 | Viewed by 2565

Abstract

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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18 pages, 6049 KiB

Open AccessArticle

Measuring the Impact of Natural Hazards with Citizen Science: The Case of Flooded Area Estimation Using Twitter

by Pierrick Bruneau, Etienne Brangbour, Stéphane Marchand-Maillet, Renaud Hostache, Marco Chini, Ramona-Maria Pelich, Patrick Matgen and Thomas Tamisier

Remote Sens. 2021, 13(6), 1153; https://doi.org/10.3390/rs13061153 - 18 Mar 2021

Cited by 6 | Viewed by 2434

Abstract

Twitter has significant potential as a source of Volunteered Geographic Information (VGI), as its content is updated at high frequency, with high availability thanks to dedicated interfaces. However, the diversity of content types and the low average accuracy of geographic information attached to individual tweets remain obstacles in this context. The contributions in this paper relate to the general goal of extracting actionable information regarding the impact of natural hazards on a specific region from social platforms, such as Twitter. Specifically, our contributions describe the construction of a model classifying whether given spatio-temporal coordinates, materialized by raster cells in a remote sensing context, lie in a flooded area. For training, remotely sensed data are used as the target variable, and the input covariates are built on the sole basis of textual and spatial data extracted from a Twitter corpus. Our contributions enable the use of trained models for arbitrary new Twitter corpora collected for the same region, but at different times, allowing for the construction of a flooded area measurement proxy available at a higher temporal frequency. Experimental validation uses true data that were collected during Hurricane Harvey, which caused significant flooding in the Houston urban area between mid-August and mid-September 2017. Our experimental section compares several spatial information extraction methods, as well as various textual representation and aggregation techniques, which were applied to the collected Twitter data. The best configuration yields a F1 score of 0.425, boosted to 0.834 if restricted to the 10% most confident predictions. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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20 pages, 22381 KiB

Open AccessEditor’s ChoiceArticle

InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia

by Luca Pulvirenti, Marco Chini and Nazzareno Pierdicca

Remote Sens. 2021, 13(1), 37; https://doi.org/10.3390/rs13010037 - 24 Dec 2020

Cited by 13 | Viewed by 2781

Abstract

A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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20 pages, 25096 KiB

Open AccessArticle

Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery

by Lisa Landuyt, Niko E. C. Verhoest and Frieke M. B. Van Coillie

Remote Sens. 2020, 12(21), 3611; https://doi.org/10.3390/rs12213611 - 03 Nov 2020

Cited by 21 | Viewed by 4194

Abstract

The European Space Agency’s Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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27 pages, 7085 KiB

Open AccessArticle

Testing Urban Flood Mapping Approaches from Satellite and In-Situ Data Collected during 2017 and 2019 Events in Eastern Canada

by Ian Olthof and Nicolas Svacina

Remote Sens. 2020, 12(19), 3141; https://doi.org/10.3390/rs12193141 - 24 Sep 2020

Cited by 15 | Viewed by 4242

Abstract

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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22 pages, 10967 KiB

Open AccessArticle

Automated Processing for Flood Area Detection Using ALOS-2 and Hydrodynamic Simulation Data

by Masato Ohki, Kosuke Yamamoto, Takeo Tadono and Kei Yoshimura

Remote Sens. 2020, 12(17), 2709; https://doi.org/10.3390/rs12172709 - 21 Aug 2020

Cited by 13 | Viewed by 4676

Abstract

Rapid and frequent mapping of flood areas are essential for monitoring and mitigating flood disasters. The Advanced Land Observing Satellite-2 (ALOS-2) carries an L-band synthetic aperture radar (SAR) capable of rapid and frequent disaster observations. In this study, we developed a fully automatic, fast computation, and robust method for detecting flood areas using ALOS-2 and hydrodynamic flood simulation data. This study is the first attempt to combine flood simulation data from the Today’s Earth system (TE) with SAR-based disaster mapping. We used Bayesian inference to combine the amplitude/coherence data by ALOS-2 and the flood fraction data by TE. Our experimental results used 12 flood observation sets of data from Japan and had high accuracy and robustness for use under various ALOS-2 observation conditions. Flood simulation contributed to improving the accuracy of flood detection and reducing computation time. Based on these findings, we also assessed the operability of our method and found that the combination of ALOS-2 and TE data with our analysis method was capable of daily flood monitoring. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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25 pages, 9743 KiB

Open AccessArticle

High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data

by Fernando Aristizabal, Jasmeet Judge and Alejandro Monsivais-Huertero

Remote Sens. 2020, 12(6), 900; https://doi.org/10.3390/rs12060900 - 11 Mar 2020

Cited by 15 | Viewed by 3312

Abstract

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset Height Above Nearest Drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features dual-polarized SAR only and dual-polarized SAR along with HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+36.0%), critical success index (CSI) (+39.95%), true positive rate (TPR) (+42.02%), and negative predictive value (NPV) (+17.26%). A marginal change of +0.15% was seen for positive predictive value (PPV), but true negative rate (TNR) fell −14.4%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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22 pages, 11234 KiB

Open AccessArticle

Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion

by Yu Li, Sandro Martinis, Marc Wieland, Stefan Schlaffer and Ryo Natsuaki

Remote Sens. 2019, 11(19), 2231; https://doi.org/10.3390/rs11192231 - 25 Sep 2019

Cited by 71 | Viewed by 6564

Abstract

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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12 pages, 8799 KiB

Open AccessLetter

Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019

by Ryo Natsuaki and Hiroto Nagai

Remote Sens. 2020, 12(6), 903; https://doi.org/10.3390/rs12060903 - 11 Mar 2020

Cited by 13 | Viewed by 3980

Abstract

Flood detection using a spaceborne synthetic aperture radar (SAR) has become a powerful tool for organizing disaster responses. The detection accuracy is increased by accumulating pre-event observations, whereas applying multiple observation modes results in an inadequate number of observations with the same mode from the same orbit. Recent flood detection studies take advantage of the large number of pre-event observations taken from an identical orbit and observation mode. On the other hand, those studies do not take account of the use of multiple orbits and modes. In this study, we examined how the analysis results suffered when pre-event observations were only taken from a different orbit or mode to that of the post-event observation. Experimental results showed that inundation areas were overlooked under such non-ideal conditions. On the other hand, the detection accuracy could be recovered by combining analysis results from possible alternate datasets and became compatible with ideal cases. Full article

(This article belongs to the Special Issue Flood Mapping in Urban and Vegetated Areas)

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