Flood Extent Mapping Research Articles

A Framework for Flood Extent Mapping using CNN Transfer Learning

A Framework for Flood Extent Mapping using CNN Transfer Learning

Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping

Synthetic Aperture Radar (SAR) intensity is used as an input to many flood-mapping algorithms. The appearance of floodwater tends to cause a substantial decrease of backscatter intensity over scarcely vegetated terrain. However, limitations exist in areas where the SAR backscatter is not sufficiently sensitive to surface changes, e.g. shadow areas due to topography or obstacles on the ground, densely forested areas, sand, etc. Thus, we argue that it is of paramount importance to complement any SAR-based flood extent map with an exclusion map (EX-map) indicating all areas where the presence of water cannot be derived from SAR intensity observations. In this study, we introduce a methodology for generating an EX-map based on the analysis of time-series of SAR backscatter data. In particular, the identification of the EX-map is based on the combined use of three temporal indicators based on backscatter statistics, i.e. temporal median, minimum and standard deviation. As a test case, EX-maps were derived from Sentinel-1 data acquired during the 2014–2019 time period from six representative study sites. Reference maps were generated using a global land cover map, Digital Elevation Model (DEM)-derived shadow/layover masks, global urban footprint (GUF) data and a Sand Exclusion Layer (SEL). The cross-comparison revealed that the EX-map was consistent with reference maps obtained from other data sources.

Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept

Abstract. Coupled hydrologic and hydraulic models represent powerful tools for simulating streamflow and water levels along the riverbed and in the floodplain. However, input data, model parameters, initial conditions, and model structure represent sources of uncertainty that affect the reliability and accuracy of flood forecasts. Assimilation of satellite-based synthetic aperture radar (SAR) observations into a flood forecasting model is generally used to reduce such uncertainties. In this context, we have evaluated how sequential assimilation of flood extent derived from SAR data can help improve flood forecasts. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. To this end, two assimilation methods are explored and compared: the sequential importance sampling method (standard method) and its enhanced method where a tempering coefficient is used to inflate the posterior probability (adapted method) and reduce degeneracy. The experimental results show that the assimilation of SAR probabilistic flood maps significantly improves the predictions of streamflow and water elevation, thereby confirming the effectiveness of the data assimilation framework. In addition, the assimilation method significantly reduces the spatially averaged root mean square error of water levels with respect to the case without assimilation. The critical success index of predicted flood extent maps is significantly increased by the assimilation. While the standard method proves to be more accurate in estimating the water levels and streamflow at the assimilation time step, the adapted method enables a more persistent improvement of the forecasts. However, although the use of a tempering coefficient reduces the degeneracy problem, the accuracy of model simulation is lower than that of the standard method at the assimilation time step.

Advanced analysis and integration of remote sensing and in situ data for flood monitoring

The increasingly occurrence of flood events claims our capacity to enhance risk reduction and damage mitigation. The current large availability of satellite data constitutes a fundamental resource for disciplines such as fluvial geomorphology and hydrology that exploit the new technologies and techniques to develop innovative approaches for improving flood phenomenon investigation. The present work, extracted from a PhD thesis, describes the application of advanced analyses and data integrations to detect and to monitor flood events. The new data fusion methodologies are tested in various areas, at different spatial and temporal scales, in various surface conditions. The results (flood extent maps, geomorphic index maps, flood inundation maps, etc.) demonstrate the advantage to use complementary information sources and the progress in addressing scientific research towards the production of operational systems.

Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods

Abstract. Flash floods observed in headwater catchments often cause catastrophic material and human damage worldwide. Considering the large number of small watercourses possibly affected, the use of automated methods for flood inundation mapping at a regional scale can be of great help for the identification of threatened areas and the prediction of potential impacts of these floods. An application of three mapping methods of increasing level of complexity is presented herein, including a digital terrain model (DTM) filling approach (height above nearest drainage/Manning–Strickler or HAND/MS) and two hydrodynamic methods (caRtino 1D and Floodos 2D). These methods are used to estimate the flooded areas of three major flash floods observed during the last 10 years in southeastern France, i.e., the 15 June 2010 flooding of the Argens river and its tributaries (585 km of river reaches), the 3 October 2015 flooding of small coastal rivers of the French Riviera (131 km of river reaches) and the 15 October 2018 flooding of the Aude river and its tributaries (561 km of river reaches). The common features of the three mapping approaches are their high level of automation, their application based on a high-resolution (5 m) DTM, and their reasonable computation times. Hydraulic simulations are run in steady-state regime, based on peak discharges estimated using a rainfall–runoff model preliminarily adjusted for each event. The simulation results are compared with the reported flood extent maps and the high water level marks. A clear grading of the tested methods is revealed, illustrating some limits of the HAND/MS approach and an overall better performance of hydraulic models which solve the shallow water equations. With these methods, a good retrieval of the inundated areas is illustrated by critical success index (CSI) median values close to 80 %, and the errors on water levels remain mostly below 80 cm for the 2D Floodos approach. The most important remaining errors are related to limits of the DTM, such as the lack of bathymetric information, uncertainties on embankment elevation, and possible bridge blockages not accounted for in the models.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

\u201cListening to the Sounds of the Water\u201d: Bringing Together Local Knowledge and Biophysical Data to Understand Climate-Related Hazard Dynamics

Integrating local knowledge and scientific information can aid in co-developing locally relevant approaches for climate change adaptation and disaster risk reduction. Communities along the Mekong River have adapted to variability in temperature, rainfall, and flooding patterns over time. Rapid environmental change in the Mekong Basin presents a new set of challenges related to drought, altered seasonal rainfall, more frequent high-flow flood events, and water withdrawals for hydropower and irrigation. We present a multi-method approach to understand how local knowledge of the spatial and temporal patterns of floods, droughts, and rainfall can be integrated with scientific information along a flood-prone section of the lower Mekong River in Kratie Province, Cambodia. Participatory hazard mapping of community members’ knowledge of the movement of floodwaters through the landscape enabled interpretation of flood extent mapping using Synthetic Aperture Radar images from the Sentinel-1A satellite. Seasonal calendars of weather patterns and livelihood activities, together with local indicators of flooding, rainfall, and drought were compared with trends in 35 years of rainfall data, and highlighted “pressure points” at the beginning and end of the rainy season where agriculture may be particularly impacted by climate change. We discuss potential applications of our findings for adaptation and hazard planning.

A large-scale 2005–2012 flood map record derived from ENVISAT-ASAR data: United Kingdom as a test case

Synthetic Aperture Radars (SAR) are adequate sensors for mapping water bodies from space as they can be used to acquire data of equal quality both day and night and practically regardless of weather conditions. Furthermore, the global coverage of SAR data provides an opportunity to generate global scale flood records that are essential for improving our understanding of flood risks worldwide and of how these risks are changing over time. In this study, we introduce an automatic change-detection based method that allows global-scale flood records to be generated using the readily and freely available ENVISAT-ASAR data collection. It consists of the following three steps: (i) flood image identification; (ii) reference image selection; (iii) floodwater detection. As a test case, this study uses all available ENVISAT-ASAR images from eight different orbital tracks that were acquired over the United Kingdom over the period 2005–2012. Due to a lack of large-scale ground truth data, the evaluation of the results is carried out using different data sources. First, subsets of the flood maps over the Severn River basin are evaluated using a flood extent map that was manually digitized from very high-resolution aerial imagery. According to our results, the overall accuracy of both flood maps' subsets is higher than 85% while the user accuracy of the flood class is above 88%. Next, for the regions and images without available ground truth data, a visual inspection is carried out using simulations generated by the hydraulic model LISFLOOD-FP, as well as LANDSAT 7 ETM+ images obtained with a 30 m spatial resolution. Meanwhile, by comparing the acquisition dates of identified flood SAR images, the LISFLOOD-FP model results and optical data, a good agreement has been found. The experimental results over the United Kingdom indicate that the proposed method has strong potential for the generation of a global flood data record from the ENVISAT-ASAR archive.

Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data

Flooding occurs frequently and causes loss of lives, and extensive damages to infrastructure and the environment. Accurate and timely mapping of flood extent to ascertain damages is critical and essential for relief activities. Recently, deep-learning-based approaches, including convolutional neural network (CNN) has shown promising results for flood extent mapping. However, these methods cannot extract floods underneath vegetation canopy using the optical imagery. This article attempts to address this problem by introducing an integrated CNN and region growing (RG) method for the mapping of both visible and underneath vegetation flooded areas. The CNN-based classifier is used to extract flooded areas from the optical images, whereas, the RG method is applied to estimate the extent of floods underneath vegetation that are not visible from imagery using the digital elevation model. A data augmentation technique is applied for training the CNN-based classifier to improve the classification results. The results show that the data augmentation can enhance the accuracy of image classification and the proposed integrated method efficiently detects floods in both the visible and the areas covered by vegetation, which is essential to supporting effective flood emergency response and recovery activities.

Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform

ABSTRACT The new era of cloud platform technologies opens up many opportunities for near real-time dissemination of disaster information to the end-users. The present study utilizes the European Space Agency (ESA) Research and Service Support (RSS) CloudToolbox platform to monitor the spatio-temporal dynamics of a flood event. A collective flood monitoring framework is formulated to rapidly assess cyclone-induced flood in the CloudToolbox platform. The outputs of the framework are spatio-temporal maps of flood extent, depth, and hot spot zones. The framework utilizes Earth Observation (EO) images such as optical and C-band Synthetic Aperture Radar (SAR) images and an automatic Kittler and Illingworth thresholding algorithm for rapid flood mapping. The temporal flood depth maps are created with the Floodwater Depth Estimation Tool (FwDET) which requires only two input parameters, viz. flood extent, and Digital Elevation Model (DEM). Subsequently, flood hotspot zones are also identified. We tested the flood monitoring framework on Amphan cyclone-induced flood event at both regional and local levels. The spatio-temporal flood extent, depth, and hot spot maps are generated for the Amphan cyclone event and a 97% overall accuracy is achieved at the local level. The entire process took less than one hour for regional and local level analysis.

A Hidden Markov Tree Model for Flood Extent Mapping in Heavily Vegetated Areas based on High Resolution Aerial Imagery and DEM: A Case Study on Hurricane Matthew Floods

ABSTRACT Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available with the deployment of numerous small satellites and drones. However, analysing such imagery data to extract flood extent poses unique challenges due to the rich noise and shadows, obstacles (e.g. tree canopies, clouds), and spectral confusion between pixel classes (flood, dry) due to spatial heterogeneity. Existing machine-learning techniques often focus on spectral and spatial features from raster images without fully incorporating the geographic terrain within classification models. In contrast, we recently proposed a novel machine-learning model called geographical hidden Markov tree (HMT) that integrates spectral features of pixels and topographic constraint from Digital Elevation Model (DEM) data (i.e. water flow directions) in a holistic manner. This paper evaluates the model through case studies on high-resolution aerial imagery from National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey (NGS) together with DEM. Three scenes are selected in heavily vegetated floodplains near the cities of Grimesland and Kinston in North Carolina during Hurricane Matthew floods in 2016. Results show that the proposed HMT model outperforms several state of the art machine-learning algorithms (e.g. random forests, gradient-boosted model) by an improvement of F-score (the harmonic mean of the user’s accuracy and producer’s accuracy) from around 70% to 80% to over 95% on our datasets.

A METHOD TO GENERATE FLOOD MAPS IN 3D USING DEM AND DEEP LEARNING

Abstract. High-resolution remote sensing imagery has been increasingly used for flood applications. Different methods have been proposed for flood extent mapping from creating water index to image classification from high-resolution data. Among these methods, deep learning methods have shown promising results for flood extent extraction; however, these two-dimensional (2D) image classification methods cannot directly provide water level measurements. This paper presents an integrated approach to extract the flood extent in three-dimensional (3D) from UAV data by integrating 2D deep learning-based flood map and 3D cloud point extracted from a Structure from Motion (SFM) method. We fine-tuned a pretrained Visual Geometry Group 16 (VGG-16) based fully convolutional model to create a 2D inundation map. The 2D classified map was overlaid on the SfM-based 3D point cloud to create a 3D flood map. The floodwater depth was estimated by subtracting a pre-flood Digital Elevation Model (DEM) from the SfM-based DEM. The results show that the proposed method is efficient in creating a 3D flood extent map to support emergency response and recovery activates during a flood event.

Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: A case study of Hurricane Harvey

With increasing urbanization, in recent years there has been a growing interest and need in monitoring and analyzing urban flood events. Social media, as a new data source, can provide real-time information for flood monitoring. The social media posts with locations are often referred to as Volunteered Geographic Information (VGI), which can reveal the spatial pattern of such events. Since more images are shared on social media than ever before, recent research focused on the extraction of flood-related posts by analyzing images in addition to texts. Apart from merely classifying posts as flood relevant or not, more detailed information, e.g. the flood severity, can also be extracted based on image interpretation. However, it has been less tackled and has not yet been applied for flood severity mapping.In this paper, we propose a novel three-step process to extract and map flood severity information. First, flood relevant images are retrieved with the help of pre-trained convolutional neural networks as feature extractors. Second, the images containing people are further classified into four severity levels by observing the relationship between body parts and their partial inundation, i.e. images are classified according to the water level with respect to different body parts, namely ankle, knee, hip, and chest. Lastly, locations of the Tweets are used for generating a map of estimated flood extent and severity. This process was applied to an image dataset collected during Hurricane Harvey in 2017, as a proof of concept. The results show that VGI can be used as a supplement to remote sensing observations for flood extent mapping and is beneficial, especially for urban areas, where the infrastructure is often occluding water. Based on the extracted water level information, an integrated overview of flood severity can be provided for the early stages of emergency response.

A FUSION APPROACH FOR FLOOD MAPPING USING SENTINEL-1 AND SENTINEL-2 DATASETS

Abstract. The frequency of flood events has increased in recent years most probably due to the climate change. Flood mapping is thus essential for flood modelling, hazard and risk analyses and can be performed by using the data of optical and microwave satellite sensors. Although optical imagery-based flood analysis methods have been often used for the flood assessments before, during and after the event; they have the limitation of cloud coverage. With the increasing temporal availability and spatial resolution of SAR (Synthetic Aperture Radar) satellite sensors, they became popular in data provision for flood detection. On the other hand, their processing may require high level of expertise and visual interpretation of the data is also difficult. In this study, a fusion approach for Sentinel-1 SAR and Sentinel-2 optical data for flood extent mapping was applied for the flood event occurred on August 8th, 2018, in Ordu Province of Turkey. The features obtained from Sentinel-1 and Sentinel-2 processing results were fused in random forest supervised classifier. The results show that Sentinel-2 optical data ease the training sample selection for the flooded areas. In addition, the settlement areas can be extracted from the optical data better. However, the Sentinel-2 data suffer from clouds which prevent from mapping of the full flood extent, which can be carried out with the Sentinel-1 data. Different feature combinations were evaluated and the results were assessed visually. The results are provided in this paper.

DERIVING EXCLUSION MAPS FROM C-BAND SAR TIME-SERIES: AN ADDITIONAL INFORMATION LAYER FOR SAR-BASED FLOOD EXTENT MAPPING

Abstract. Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images as floodwater often leads to a substantial decrease of backscatter. However, limitations still exist in many areas, such as shadow, layover, urban areas and densely vegetated areas, where the SAR backscatter is not sufficiently impacted by floodwater-related surface changes. This study focuses on these so-called exclusion areas, i.e. areas where SAR does not allow detecting water based on change detection. Our approach considers both pixel-based time series analyses and object-based spatial analyses using 20m Sentinel-1 Interferometric Wide Swath data, including 922 Sentinel-1 tiles covering the River Severn basin (UK) and the Lake Maggiore area (Italy). The results show that our exclusion map presents a good agreement (∼63%) with reference data derived from different data sources and indicate that it may complement SAR-derived flood extent maps. Allowing to accurately identify potential misclassifications in flood extent mapping, our exclusion map provides valuable information for flood management and, in particular, flood forecasting and prediction.

Statistical and Spatial Analyses of the Kelantan Big Yellow Flood 2014

Flooding is a yearly phenomenon which is mostly reflected in the Kelantan River basin during the northeast monsoon season due to the heavy rainfall. The December 2014 flood in Kelantan known as ‘Kelantan big yellow flood’ was unprecedented and the largest recorded flooding event in the century. Flood mitigation, especially in massive floods, should focus on minimizing the risk of severe flooding in one area. A reliable flood extent map is essential to plan proper flood mitigation measures by authorities prior to the extreme flood event (say 1 in 1000 years event). This study is an attempt to resolve the issue of severe flooding in a given area by preparing a map of zones prone to flooding. This was achieved mainly by site visits to the flood-prone area and gathering primary data from face to face interview with local people who were the victims of this flooding. This primary data is also crucial in determining flood vulnerability with respect to economic and social impacts. The integration of primary data with secondary data from several related agencies has resulted in preparing a reliable flood extent map which is slightly different from other such maps developed by Government, NGOs and active researchers in this field. The empirical findings using frequency analysis also helped in preparing a flood vulnerability index based on primary data gathered from the local people who were the flood victims.

A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach

Insights into flood dynamics, rather than solely flood extent, are critical for effective flood disaster management, in particular in the context of emergency relief and damage assessment. Although flood dynamics provide insight in the spatio-temporal behaviour of a flood event, to date operational visualization tools are scarce or even non-existent. In this letter, we distil a flood dynamics map from a radar satellite image time series (SITS). For this, we have upscaled and refined an existing design that was originally developed on a small area, describing flood dynamics using an object-based approach and a graph-based representation. Two case studies are used to demonstrate the operational value of this method by visualizing flood dynamics which are not visible on regular flood extent maps. Delineated water bodies are grouped into graphs according to their spatial overlap on consecutive timesteps. Differences in area and backscatter are used to quantify the amount of variation, resulting in a global variation map and a temporal profile for each water body, visually describing the evolution of the backscatter and number of polygons that make up the water body. The process of upscaling led us to applying a different water delineation approach, a different way of ensuring the minimal mapping unit and an increased code efficiency. The framework delivers a new way of visualizing floods, which is straightforward and efficient. Produced global variation maps can be applied in a context of data assimilation and disaster impact management.

Repeated Hurricanes Reveal Risks and Opportunities for Social-Ecological Resilience to Flooding and Water Quality Problems.

Hurricanes that damage lives and property can also impact pollutant sources and trigger poor water quality. Yet, these water quality impacts that affect both human and natural communities are difficult to quantify. We developed an operational remote sensing-based hurricane flood extent mapping method, examined potential water quality implications of two "500-year" hurricanes in 2016 and 2018, and identified options to increase social-ecological resilience in North Carolina. Flooding detected with synthetic aperture radar (>91% accuracy) extended beyond state-mapped hazard zones. Furthermore, the legal floodplain underestimated impacts for communities with higher proportions of older adults, disabilities, unemployment, and mobile homes, as well as for headwater streams with restricted elevation gradients. Pollution sources were repeatedly affected, including ∼55% of wastewater treatment plant capacity and swine operations that generate ∼500 M tons/y manure. We identified ∼4.8 million km2 for possible forest and wetland conservation and ∼1.7 million km2 for restoration or altered management opportunities. The results suggest that current hazard mapping is inadequate for resilience planning; increased storm frequency and intensity necessitate modification of design standards, land-use policies, and infrastructure operation. Implementation of interventions can be guided by a greater understanding of social-ecological vulnerabilities within hazard and exposure areas.

Automatic Detection of Flood Using Remote Sensing Images

Remote sensing imaging (RSI) technology has recently been identified as an effective photogrammetric data acquisition platform to rapidly provide high resolution images due to its profitability, its ability to fly at low altitude and the ability to analysis in dangerous areas. The various kinds of classification techniques are have been used for flood extent mapping for finding the flood affected region, but based on the color region based analysis the classified hazardous area has very complex. Due to over the above issues in this work there significant enhancements have appeared in the classification of remote sensing images using Contiguous Deep Convolutional Neural Network (CDCNN).In the flood detection system the four different kinds of process like preprocessing, segmentation, feature extraction and the Contiguous Deep Convolutional Neural Network (CDCNN) has been executed for identifying the flood defected region. This works also investigates and compare with the possible methods with the proposed CDCNN for accurately identified by the Classification details of the RSI

Applicability of HEC-RAS 2D and GFMS for flood extent mapping: a case study of Sangam area, Prayagraj, India

Catastrophic floods cause losses to millions of lives and property damages every year. The situation is more precarious particularly in countries lacking advanced tools and early warning systems for quick disaster management and mitigation. This article presents a study conducted to develop and understand methodology for delineation of flood extent and identification of various flood risk zones in Prayagraj, India at the confluence of River Ganga and Yamuna (sangam). In this study, a well-established hydraulic model (HEC-RAS 5.0.7 (2D)) and Global Flood Monitoring System (GFMS) tools were utilized. The methodology solves two glitches related to flood studies, i.e., capability of HEC-RAS 2D flood model and GFMS discharge for 1D and 2D flood modeling and secondly using the output of the model, development of a methodology for the identification of various flood risk zones. The modeled and observed data were in close agreement indicating a successful application of HEC-RAS and GFSM data/tools. Using the methodology of this study, various flood modeling and delineation of risk zones can be made for various flood-affected areas in India and worldwide. These tools can also be used for making guidelines for emergency response in flood-affected areas.