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Advances in Earth Observation to Improve Flood Disaster Monitoring and Management

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 30866

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Dr. Guy Jean Pierre Schumann

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Guest Editor

1. Research and Education Department, RSS-Hydro, L-3593 Dudelange, Luxembourg
2. School of Geographical Sciences, University of Bristol, Bristol BS8 1QU, UK
Interests: earth observation; SAR; floods; disaster response; flood modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Laura Giustarini

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Guest Editor

Research and Education Department, RSS-Hydro, L-3593 Dudelange, Luxembourg
Interests: earth observation; SAR; optical; floods; hydrodynamic modeling; crop forecast

Dr. Ramona-Maria Pelich

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Guest Editor

Luxembourg Institute of Science and Technology, 41, rue du Brill, L-4422 Belvaux, Luxembourg
Interests: change detection; image classification; information fusion; parameter estimation; SAR imagery; target detection; disaster monitoring; maritime surveillance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The impacts of recent flood events are clearly covering spatial scales much larger than those typically covered by traditional ground-based measurement stations. Climate change is now being linked to impactful recent meteorological events with a high degree of probability; consequently, flood disasters now and in the future are likely to increase in terms of both magnitude and frequency. Parallel to this, Earth Observation (EO) and remote sensing technologies in general (sensors on the ground, on planes, and on drones) have considerably advanced. Moreover, online cloud computing services, as well as big data analytics, now allow large amounts of EO data to be processed in a timely manner through advanced algorithms, meaning that the resulting geospatial information can efficiently assist in flood disaster responses, often in near real-time. This Special Issue will publish research and review articles on topics focusing on EO data processing algorithms, products, and services to improve disaster management.

Dr. Guy Schumann
Dr. Laura Giustarini
Dr. Ramona-Maria Pelich
Guest Editors

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Keywords

Earth Observation
flood disasters
flood management
mapping
cloud-computing
modeling

Related Special Issue

Advances in Earth Observation to Improve Flood Disaster Monitoring and Management (Second Edition) in Remote Sensing

Published Papers (9 papers)

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Research

20 pages, 11363 KiB

Open AccessArticle

An Earth Observation Task Representation Model Supporting Dynamic Demand for Flood Disaster Monitoring and Management

by Zhongguo Zhao, Chuli Hu, Ke Wang, Yixiao Zhang, Zhangyan Xu and Xuan Ding

Remote Sens. 2023, 15(8), 2193; https://doi.org/10.3390/rs15082193 - 21 Apr 2023

Cited by 1 | Viewed by 1707

Abstract

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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26 pages, 4937 KiB

Open AccessArticle

Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

by Chiranjit Singha, Kishore Chandra Swain, Modeste Meliho, Hazem Ghassan Abdo, Hussein Almohamad and Motirh Al-Mutiry

Remote Sens. 2022, 14(24), 6229; https://doi.org/10.3390/rs14246229 - 08 Dec 2022

Cited by 12 | Viewed by 2583

Abstract

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (<5.0) and Boruta feature ranking (<10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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21 pages, 6108 KiB

Open AccessArticle

Urban Flood Detection Using TerraSAR-X and SAR Simulated Reflectivity Maps

by Shadi Sadat Baghermanesh, Shabnam Jabari and Heather McGrath

Remote Sens. 2022, 14(23), 6154; https://doi.org/10.3390/rs14236154 - 05 Dec 2022

Cited by 3 | Viewed by 2211

Abstract

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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30 pages, 7838 KiB

Open AccessArticle

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

by Khuong H. Tran, Massimo Menenti and Li Jia

Remote Sens. 2022, 14(22), 5721; https://doi.org/10.3390/rs14225721 - 12 Nov 2022

Cited by 15 | Viewed by 4599

Abstract

The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R² = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R² = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time

t

is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time

t - 1

to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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28 pages, 81095 KiB

Open AccessArticle

Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube

by Bernhard Bauer-Marschallinger, Senmao Cao, Mark Edwin Tupas, Florian Roth, Claudio Navacchi, Thomas Melzer, Vahid Freeman and Wolfgang Wagner

Remote Sens. 2022, 14(15), 3673; https://doi.org/10.3390/rs14153673 - 31 Jul 2022

Cited by 22 | Viewed by 4060

Abstract

Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS). Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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18 pages, 3758 KiB

Open AccessArticle

Atmospheric Effects and Precursors of Rainfall over the Swiss Plateau

by Wenyue Wang and Klemens Hocke

Remote Sens. 2022, 14(12), 2938; https://doi.org/10.3390/rs14122938 - 20 Jun 2022

Cited by 3 | Viewed by 1512

Abstract

In this study, we investigate the characteristics of atmospheric parameters before, during, and after rain events in Bern, Switzerland. Ground-based microwave radiometer data of the TROpospheric WAter RAdiometer (TROWARA) with a time resolution of 7 s, observations of a weather station, and the composite analysis method are used to derive the temporal evolution of rain events and to identify possible rainfall precursors during a 10-year period (1199 available rain events). A rainfall climatology is developed using parameters integrated water vapor (IWV), integrated liquid water (ILW), rain rate, infrared brightness temperature (T

_{IR}

), temperature, pressure, relative humidity, wind speed, and air density. It was found that the IWV is reduced by about 2.2 mm at the end of rain compared to the beginning. IWV and T

_{IR}

rapidly increase to a peak at the onset of the rainfall. Precursors of rainfall are that the temperature reaches its maximum around 30 to 60 min before rain, while the pressure and relative humidity are minimal. IWV fluctuates the most before rain (obtained with a 10 min bandpass). In 60% of rain events, the air density decreases 2 to 6 h before the onset of rain. The seasonality and the duration of rain events as well as the diurnal cycle of atmospheric parameters are also considered. Thus, a prediction of rainfall is possible with a true detection rate of 60% by using the air density as a precursor. Further improvements in the nowcasting of rainfall are possible by using a combination of various atmospheric parameters which are monitored by a weather station and a ground-based microwave radiometer. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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20 pages, 6529 KiB

Open AccessArticle

Identifying the Potential Dam Sites to Avert the Risk of Catastrophic Floods in the Jhelum Basin, Kashmir, NW Himalaya, India

by Muzamil Ahmad Rather, Gowhar Meraj, Majid Farooq, Bashir Ahmad Shiekh, Pankaj Kumar, Shruti Kanga, Suraj Kumar Singh, Netrananda Sahu and Surya Prakash Tiwari

Remote Sens. 2022, 14(7), 1538; https://doi.org/10.3390/rs14071538 - 22 Mar 2022

Cited by 19 | Viewed by 3117

Abstract

In September 2014, Kashmir witnessed a catastrophic flood resulting in a significant loss of lives and property. Such massive losses could have been avoided if any structural support such as dams were constructed in the Jhelum basin, which has a history of devastating floods. The GIS-based multicriteria analysis (MCA) model provided three suitability zones for dam locations. The final suitable dam sites were identified within the highest suitability zone based on topography (cross-sections), stream order, high suitable zone, minimum dam site interval, distance from roads, and protected area distance to the dam site. It was discovered that 10.98% of the total 4347.74 km² area evaluated falls in the high suitability zone, 28.88% of the area falls in the medium suitability zone, and 60.14% of the area falls in the low suitability zone. Within the study area, four viable reservoir sites with a holding capacity of 4,489,367.55 m³ were revealed. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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19 pages, 9641 KiB

Open AccessArticle

An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery

by Shujie Chen, Wenli Huang, Yumin Chen and Mei Feng

Remote Sens. 2021, 13(23), 4899; https://doi.org/10.3390/rs13234899 - 02 Dec 2021

Cited by 13 | Viewed by 3207

Abstract

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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22 pages, 7190 KiB

Open AccessArticle

Flood Hazard Mapping Using Fuzzy Logic, Analytical Hierarchy Process, and Multi-Source Geospatial Datasets

by Saeid Parsian, Meisam Amani, Armin Moghimi, Arsalan Ghorbanian and Sahel Mahdavi

Remote Sens. 2021, 13(23), 4761; https://doi.org/10.3390/rs13234761 - 24 Nov 2021

Cited by 37 | Viewed by 6065

Abstract

Iran is among the driest countries in the world, where many natural hazards, such as floods, frequently occur. This study introduces a straightforward flood hazard assessment approach using remote sensing datasets and Geographic Information Systems (GIS) environment in an area located in the western part of Iran. Multiple GIS and remote sensing datasets, including Digital Elevation Model (DEM), slope, rainfall, distance from the main rivers, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC) maps, soil type map, Normalized Difference Vegetation Index (NDVI), and erosion rate were initially produced. Then, all datasets were converted into fuzzy values using a linear fuzzy membership function. Subsequently, the Analytical Hierarchy Process (AHP) technique was applied to determine the weight of each dataset, and the relevant weight values were then multiplied to fuzzy values. Finally, all the processed parameters were integrated using a fuzzy analysis to produce the flood hazard map with five classes of susceptible zones. The bi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) images, acquired before and on the day of the flood event, were used to evaluate the accuracy of the produced flood hazard map. The results indicated that 95.16% of the actual flooded areas were classified as very high and high flood hazard classes, demonstrating the high potential of this approach for flood hazard mapping. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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