Flood Inundation Mapping

Disaster Advances, 2019

Floods are one of the most commonly occurring and destructive natural disasters throughout the globe. Microwave Synthetic Aperture Radar (SAR) satellite data is mostly used for mapping and monitoring flood extents due to its capability of acquiring data in day and night even in adverse weather conditions as it can penetrate through the haze, rainfall, clouds and dust which are mostly found during the floods. Since SAR data is complex and coherent in nature, it requires extensive data preparation before analysing the data. This study describes the basic steps for SAR data preparation namely orbit file application, thermal noise removal, calibration, terrain flattening, speckle filtering, terrain correction and linear to decibels conversion using SNAP tool. Further, automation of this process is also discussed so that the final product can be used for near real-time applications.

Remote Sensing Applications: Society and Environment, 2019

The presence of speckle in visual images makes the automated digital image classification a challenging task. Therefore, reduction of speckles is an important pre-processing step. The choice of speckle filter depends on the requirements of an application and the characteristics of the dataset. In this study, some most preferred speckle filters are assessed for the data from Sentinel-1 to map flood extent. The Sentinel-1 (VV-vertical transmit, vertical receive and VH-vertical transmit, horizontal receive) polarizing filter data were used to evaluate machine learning algorithms, namely, random forest (RF) and support vector machine (SVM), to classify an inundated area. The accuracies of the classifications were assessed by kappa coefficient, overall accuracies, and producer's and user's accuracies. The present study suggests an approach to monitor damage and provide basic information to help local communities manage water-related risk, land planning, water management, and flood control programs.

International Journal of Environment and Climate Change

Floods are one of the disasters that cause many human lives and property. In Albania, most floods are associated with periods of heavy rainfall. In recent years, Synthetic Aperture Radar (SAR) sensors, which provide reliable data in all weather conditions and day and night, have been favored because they eliminate the limitations of optical images. In this study, a flood occurred in the Buna River region in March 2018, was mapped using SAR Sentinel-1 data. The aim of this study is to investigate the potential of flood mapping using SAR images using different methodologies. Sentinel-1A / B SAR images of the study area were obtained from the European Space Agency (ESA). Preprocessing steps, which include trajectory correction, calibration, speckle filtering, and terrain correction, have been applied to the images. RGB composition and the calibrated threshold technique have been applied to SAR images to detect flooded areas and the results are discussed here.

2021

Synthetic Aperture Radars (SAR) are the active remote sensing sensor and used for terrestrial remote sensing from the space platforms. In this paper the SAR imageries are used for the application of flood mapping and monitoring during the monsoon season when the optical remote sensing sensor fails to provide the cloud free clear imageries of parts of flood ridden Assam, India. Using pre-flood SAR imagery, permanent water bodies are delineated and cross validated using Sentinel 2B optical imagery with overall classification accuracy of 94.06%. SAR imagery during flood is processed to derive inundation map, cross validation is done using ISRO BHUVAN portal flood data. Radar backscatter variation in pre-flood and during flood period is studied for croplands and grasslands. Finally village level inundation map is prepared for the study area using village data available in District Census Handbook.

International Journal of Applied Earth Observation and Geoinformation, 2015

Operational flood mitigation and flood modeling activities benefit from a rapid and automated flood mapping procedure. A valuable information source for such a flood mapping procedure can be remote sensing synthetic aperture radar (SAR) data. In order to be reliable, an objective characterization of the uncertainty associated with the flood maps is required. This work focuses on speckle uncertainty associated with the SAR data and introduces the use of a non-parametric bootstrap method to take into account this uncertainty on the resulting flood maps. From several synthetic images, constructed through bootstrapping the original image, flood maps are delineated. The accuracy of these flood maps is also evaluated w.r.t. an independent validation data set, obtaining, in the two test cases analyzed in this paper, F-values (i.e. values of the Jaccard coefficient) comprised between 0.50 and 0.65. This method is further compared to an image segmentation method for speckle analysis, with which similar results are obtained. The uncertainty analysis of the ensemble of bootstrapped synthetic images was found to be representative of image speckle, with the advantage that no segmentation and speckle estimations are required. Furthermore, this work assesses to what extent the bootstrap ensemble size can be reduced while remaining representative of the original ensemble, as operational applications would clearly benefit from such reduced ensemble sizes.

Geomatics, Natural Hazards and Risk, 2014

ISPRS, 2019

Disasters including flash floods, earthquakes, and landslides have huge economic and social losses besides their impact on environmental disruption. Studying environmental changes due to climate change can improve public and expert sector's awareness and response towards future disastrous events. Synthetic Aperture Radar (SAR) data and Interferometric Synthetic Aperture Radar (InSAR) technologies are valuable tools for flood modeling and surface deformation modeling. This paper proposes an efficient approach to detect the flooded area changes using Sentinel-1A over Ramsar flood on 5th October 2018. For detection of the flooded area due to flash flood SARPROZ in MATLAB programming language is used and discussed. Flooded areas in Ramsar are detected based on the change detection modeling using normalized difference values of amplitude belonging to the master image (on 28th September 2018) and the slave image (on 10th October 2018).

Water, 2019

Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 SAR (Synthetic Aperture Radar) images as a powerful tool for flood mapping applied in the event of extraordinary floods that occurred during the month of April 2018 in the Ebro (Spain). More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation. Evaluating the different Sentinel-1 parameters, our analysis shows that the best results on the final flood mapping for this study area were obtained using VH (Vertical-Horizontal) polarization configuration and Lee filtering 7 × 7 window sizes. Two methods were applied to flood maps from Sentinel-1 SAR images: (1) RGB (Red, Green, Blue color model) composition based on the differences between the pre-and post-event images; and (2) the calibration threshold technique or binarization based on histogram backscatter values. When comparing our flood maps with the flood areas digitalized from vertical aerial photographs, done by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were coincident. The result of the flooding map obtained with the RADAR (Radio Detection and Ranging) image were compared with the layers with different return periods (10, 50, 100, and 500 years) for a selected zone of the study area of SNCZI (National Flood Zone Mapping System in Spain). It was found that the images are consistent and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.

AGU Fall Meeting 2021, 2021

Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.

Journal of the Indian Society of Remote Sensing , 2020

Floods are one of the most common natural disasters. In recent times, microwave synthetic aperture radar (SAR) satellite images have been used widely for mapping flood affected areas due to its all-weather capability and acquisition during day and night. Here, in this paper, an automated algorithm is proposed to delineate flood extent from SAR images without any human intervention. The algorithm consists of pre-processing steps like applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion. The water layer is delineated using multi-segmentation and Otsu's thresholding technique. Further, flood layer is extracted by postprocessing steps using majority filter, applying permanent water body mask and eliminating hill shadows. The algorithm is tested on a significant number of satellite images covering floods in India, which are having diverse terrain and flooding patterns. In this paper, the steps involved in delineating flood from SAR image of VH polarization from SENTINEL-1 satellite covering chronic flood-prone stretch of part of Ganga River in Bihar state is presented. Accuracy assessment is carried out with the flood layer derived from RADARSAT SAR HV polarized data acquired on the same day and an accuracy of about 96% is obtained. The total processing time taken for the extraction of the flood layer is 9 min. This automation process is beneficial for the generation of rapid flood inundation maps, with high accuracy, which is helpful for flood monitoring and relief management during a disaster.