Flood Extent Mapping Research Articles

River flood mapping in urban areas combining Radarsat-2 data and flood return period data

Near-real-time flood maps are essential to organize and coordinate emergency services' response actions during flooding events. Thanks to its capacity to acquire synoptic and detailed data during day and night, and in all weather conditions, Synthetic Aperture Radar (SAR) satellite remote sensing is considered one of the best tools for the acquisition of flood mapping information. However, specific factors contributing to SAR backscatter in urban environments, such as shadow and layover effects, and the presence of water surface–like radar response areas, complicate the detection of flood water pixels. This paper describes an approach for near-real-time flood mapping in urban and rural areas. The innovative aspect of the approach is its reliance on the combined use of very-high-resolution SAR satellite imagery (C-band, HH polarization) and hydraulic data, specifically flood return period data estimated for each point of the floodplain. This approach was tested and evaluated using two case studies of the 2011 Richelieu River flood (Canada) observed by the very-high-resolution RADARSAT-2 sensor. In both case studies, the algorithm proved capable of detecting flooding in urban areas with good accuracy, identifying approximately 87% of flooded pixels correctly. The associated false negative and false positive rates are approximately 14%. In rural areas, 97% of flooded pixels were correctly identified, with false negative rates close to 3% and false positive rates between 3% and 35%. These results highlight the capacity of flood return period data to overcome limitations associated with SAR-based flood detection in urban environments, and the relevance of their use in combination with SAR C-band imagery for precise flood extent mapping in urban and rural environments in a crisis management context.

Use of remote sensing data in comprehending an extremely unusual flooding event over southwest Bangladesh

Flooding is one of the natural disasters that affect the livelihood of the people living in the floodplains, like Bangladesh. Here, we proposed to employ SAR satellite images in assessing the flood extent and crop damage with the hydrometeorological observations in the southwestern region of Bangladesh. We observed that the unusual flood of the year 2000 was the combined effect of the huge amount of rainfall in the local areas as well as oncoming water flows from West Bengal in India. During late monsoon of 2000, we experienced that the amount of rainfall was in several magnitudes (250–450%) than the expected over the region. Bangladesh, one of the largest deltas in the world, in general experienced recurrent flood events from the spill of the three mighty rivers in every year. However, we observed that during 2000 the river situation was slightly above the average year conditions and below the moderate level of flood warning conditions, indicating non-riverine flooding. Therefore, we used the SAR images in delineating the flood extent and its damages for the standing aman crops. We observed that the flood extent mapping was having more than 95% agreements with the ground data and crop damage information was about 75% in agreement with the government estimates. The flood extent and crop damage map was found to be useful during the unprecedented flood in the southwestern region of Bangladesh. The use of near real-time SAR imageries thus would be helpful in developing strategies for flood management and disaster mitigation activities and could be utilized on a regular basis.

Do Remote Sensing Mapping Practices Adequately Address Localized Flooding? A Critical Overview

Local-scale flooding (LSF) is usually characterized by much less severe damage compared to extreme flood events; however, it does have marked local environmental influence, especially when it is characterized by regular and frequent occurrence and long duration. Knowledge about the spatial extent of flood-prone areas is essential for flood risk and land management purposes, spatial planning, or emergency response. Flood mapping procedures have been supported by remote sensing for several decades, and progress in remote sensing technology and image processing over the last two decades has made flood extent analysis possible at an unprecedented level of detail. Here we provide an overview of applications of remote sensing technologies for analyzing the extent of flood events and discuss their applicability for LSF. We report on applications of data from the optical visible and reflective infrared spectrum, active microwave spectrum, and airborne laser scanning technology. Additionally, applications of elevation data supporting flood extent mapping are reviewed. The review reveals that in general remote sensing techniques and data types are likely to have similar capabilities and limitations for analyzing LSF as they have for extreme floods. However, data from many current remote sensing sensors are inadequate for LSF analysis, since very high spatial resolution data are required for mapping localized flooding. Finally, airborne laser scanning is found to be an emerging and promising technology in flood-related water surface analysis.

Automatic Ex-post Flood Assessment Using Long Time Series of Optical Earth Observation Images

Our study uses a dense temporal stack of 78 Landsat 8 images for surface water extraction using automatic Earth Observation (EO) image pre-processing, coupled with analyses over time for flood detection. The analysis is conducted with our IQ (ImageQuerying) system developed in-house, which allows ad-hoc executing of spatio-temporal queries against semantically enriched EO images. To facilitate high performance analyses, the data are stored as a spatio-temporal data cube in an array database. The analyses are automatically-translated database queries, which increase reproducibility, readability and comprehensibility for a human operator and can be conducted within just a few minutes. The specific analysis for this contribution is based on flood-extent mapping over different user-definable time spans. The results indicate areas that have been flooded at least once in the selected time span and are therefore prone to being flooded in future events. Additional spatial queries (e.g., for the indication of cloud cover) support the quality assessment of the flood analyses. We compared our result with a flood mask derived from a SAR (synthetic aperture radar) image of a single event in Somalia (Hiran province). Larger flooded areas overlap in both analyses, despite the non-synchronous acquisition times of the images. The results can be used as input for improved risk assessment and management of floods.

Probabilistic mapping of flood-induced backscatter changes in SAR time series

The information content of flood extent maps can be increased considerably by including information on the uncertainty of the flood area delineation. This additional information can be of benefit in flood forecasting and monitoring. Furthermore, flood probability maps can be converted to binary maps showing flooded and non-flooded areas by applying a threshold probability value pF=0.5. In this study, a probabilistic change detection approach for flood mapping based on synthetic aperture radar (SAR) time series is proposed. For this purpose, conditional probability density functions (PDFs) for land and open water surfaces were estimated from ENVISAT ASAR Wide Swath (WS) time series containing >600 images using a reference mask of permanent water bodies. A pixel-wise harmonic model was used to account for seasonality in backscatter from land areas caused by soil moisture and vegetation dynamics. The approach was evaluated for a large-scale flood event along the River Severn, United Kingdom. The retrieved flood probability maps were compared to a reference flood mask derived from high-resolution aerial imagery by means of reliability diagrams. The obtained performance measures indicate both high reliability and confidence although there was a slight under-estimation of the flood extent, which may in part be attributed to topographically induced radar shadows along the edges of the floodplain. Furthermore, the results highlight the importance of local incidence angle for the separability between flooded and non-flooded areas as specular reflection properties of open water surfaces increase with a more oblique viewing geometry.

Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium-resolution SAR data and identifiability techniques

Abstract. Single satellite synthetic aperture radar (SAR) data are now regularly used to estimate hydraulic model parameters such as channel roughness, depth and water slope. However, despite channel geometry being critical to the application of hydraulic models and poorly known a priori, it is not frequently the object of calibration. This paper presents a unique method to simultaneously calibrate the bankfull channel depth and channel roughness parameters within a 2-D LISFLOOD-FP hydraulic model using an archive of moderate-resolution (150 m) ENVISAT satellite SAR-derived flood extent maps and a binary performance measure for a 30 × 50 km domain covering the confluence of the rivers Severn and Avon in the UK. The unknown channel parameters are located by a novel technique utilising the information content and dynamic identifiability analysis (DYNIA) (Wagener et al., 2003) of single and combinations of SAR flood extent maps to find the optimum satellite images for model calibration. Highest information content is found in those SAR flood maps acquired near the peak of the flood hydrograph, and improves when more images are combined. We found that model sensitivity to variation in channel depth is greater than for channel roughness and a successful calibration for depth could only be obtained when channel roughness values were confined to a plausible range. The calibrated reach-average channel depth was within 0.9 m (16 % error) of the equivalent value determined from river cross-section survey data, demonstrating that a series of moderate-resolution SAR data can be used to successfully calibrate the depth parameters of a 2-D hydraulic model.

Analyzing the sensitivity of a flood risk assessment model towards its input data

Abstract. The Small Island Developing States are characterized by an unstable economy and low-lying, densely populated cities, resulting in a high vulnerability to natural hazards. Flooding affects more people than any other hazard. To limit the consequences of these hazards, adequate risk assessments are indispensable. Satisfactory input data for these assessments are hard to acquire, especially in developing countries. Therefore, in this study, a methodology was developed and evaluated to test the sensitivity of a flood model towards its input data in order to determine a minimum set of indispensable data. In a first step, a flood damage assessment model was created for the case study of Annotto Bay, Jamaica. This model generates a damage map for the region based on the flood extent map of the 2001 inundations caused by Tropical Storm Michelle. Three damages were taken into account: building, road and crop damage. Twelve scenarios were generated, each with a different combination of input data, testing one of the three damage calculations for its sensitivity. One main conclusion was that population density, in combination with an average number of people per household, is a good parameter in determining the building damage when exact building locations are unknown. Furthermore, the importance of roads for an accurate visual result was demonstrated.

37 Fusion of SAR images for flood extent mapping in northern peninsula Malaysia

37 Fusion of SAR images for flood extent mapping in northern peninsula Malaysia

VULNERABILITY ASSESSMENT USING LIDAR DATA IN SILANG-STA. ROSA SUBWATERSHED, PHILIPPINES

Abstract. Silang-Sta. Rosa Subwatershed is experiencing rapid urbanization. Its downstream area is already urbanized and the development is moving fast upstream. With the rapid land conversion of pervious to impervious areas and increase frequency of intense rainfall events, the downstream of the watershed is at risk of flood hazard. The widely used freeware HEC-RAS (Hydrologic Engineering Center- River Analysis System) model was used to implement the 2D unsteady flow analysis to develop a flood hazard map. The LiDAR derived digital elevation model (DEM) with 1m resolution provided detailed terrain that is vital for producing reliable flood extent map that can be used for early warning system. With the detailed information from the simulation like areas to be flooded, the predicted depth and duration, we can now provide specific flood forecasting and mitigation plan even at community level. The methodology of using 2D unsteady flow modelling and high resolution DEM in a watershed can be replicated to other neighbouring watersheds specially those areas that are not yet urbanized so that their development will be guided to be flood hazard resilient. LGUs all over the country will benefit from having a high resolution flood hazard map.

Event-driven flood management: design and computational modules

Flood management is a set of activities that have to be carried out in collaboration with multiple agencies. Advanced flood information with early warning generated using remote sensing satellite technologies can help the agencies to effectively manage the situation on ground. Various environmental parameters and forecasts provided by different agencies can be analyzed and compared with historical flood events for generating probable flood event alerts. The information (environmental parameters) provided by the agencies are heterogeneous and noncompliant to standards and distributed in nature. Synchronization of data from distributed resources and automation of data analysis process for flood management is a primary prerequisite for faster and efficient decision-making. Web 2.0-based web services enable data creation, sharing, communication, and collaboration on web. Spatial data sharing on web 2.0 for making quality of service using open-source software for efficient flood management is a challenge. Available software architectures proposed for risk and environmental crisis management are too generic in nature and needs lot of modification for flood management. An event-driven model coupled with data standardization procedures using service-oriented architecture provides an effective framework for flood management. In this paper, a framework capable of collecting heterogeneous distributed flood-related information for analyzing and alerting probable flood events is proposed. The framework has been implemented to generate automatic flood extent maps, by analyzing the distributed satellite data (as service). The automation of flood delineation process reduces the overall flood product generation time. Open-source web tools have been utilized in development of spatial information system to visualize and analyze the actual situation on ground facilitating overall decision-making process.

Development of a downstream emergency response service for flood and related risks in Romania based on satellite data

Recently, the National Meteorological Administration initiated and started to implement user-driven services, based on satellite remote sensing and geo-information capacities. The paper presents this downstream emergency response service whose aim is to provide updated and accurate cartographic information in river flood prevention and post-crisis phase. The service is targeted to develop an interoperable framework for the management of the available geo-information using cutting-edge techniques and satellite data in order to provide high quality and accurate spatial products. An appropriate methodology was developed and tested, in order to process the optical or radar satellite imagery, with medium and high range spatial resolution, to rapid mapping the flood extent, to integrate the information in a GIS environment and finally to obtain standardized, cartographic products. The service is able to provide customized flood geospatial products (updated reference maps for the area affected, near real-time flood delineation maps, maximum flood extent maps, flooded area classification, flood evolution maps, damage assessment maps and reports) tailored to specific users and featuring near-real time delivery. A dedicated geo-portal, was developed to display, query, analyse and retrieve the spatial products. The end-users are able to access the system using a simple web browser to view and query the flood related product archive or download the selected products.

Comparing four operational SAR-based water and flood detection approaches

In recent years, the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) has gained a lot of experience in water surface extraction from synthetic aperture radar (SAR) data for various application domains. In this context, four approaches have been developed, which jointly form the so-called DFD Water Suite: The Water Mask Processor (WaMaPro) is based on a simple and high-performance algorithm that processes multi-sensor SAR data in order to provide decision-makers with information about the location of water surfaces. The Rapid Mapping of Flooding tool (RaMaFlood) has been developed for flood extent mapping using an interactive object-based classification algorithm. The TerraSAR-X Flood Service (TFS) is used for rapid mapping activities and provides satellite-derived information about the extent of floods in order to support emergency management authorities and decision-makers. It is based on a fully automated processing chain. The last approach is the TanDEM-X Water Indication Mask processor (TDX WAM). It is part of the processing chain for the generation of the seamless, accurate, and high-resolution global digital elevation model (DEM) produced based on data of the TanDEM-X mission. Its purpose is to support the subsequent DEM editing process by the generation of a global reference water mask. In this study, the design of the four approaches and their methodological backgrounds are explained in detail, while simultaneously elaborating on the preferred application domains for the different algorithms. The advantages and disadvantages of the four approaches are identified by qualitatively as well as quantitatively evaluating the water masks derived from data of the TanDEM-X mission for five test sites located in Vietnam, China, Germany, Mali, and the Netherlands.

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation simulation (extent and depth of flood) was conducted using hydrodynamic program HEC-RAS for 50- and 100-year interval floods. The validation of the flood hazard prediction map was conducted based on flood extent and depth maps. The results showed that the AHP technique is promising of making accurate and reliable prediction for flood extent. Therefore, the AHP and geographic information system (GIS) techniques are suggested for assessment of the flood hazard potential, specifically in no-data regions.

How presentation format affects the interpretation of probabilistic flood risk information

AbstractIn many European countries, flood awareness information is communicated through flood extent maps and probabilistic language. However, research suggests that probabilistic information is difficult to understand and that presentation format affects understanding and risk perception. In this study, thirty participants took part in focus groups to explore responses to a flood extent map, and completed a questionnaire to measure the effect of expressing the probability of flooding using different language formats on understanding and risk perception. Responses to the flood extent map reveal participants experienced difficulties interpreting the probabilistic information. In the questionnaire, understanding was high across statements for both self‐report and objective measures. However, the standard way of communicating risks, in terms of years per flooding event, evoked the lowest levels of understanding. For risk perception, there were differences in mean risk ratings across statements, and in general, people perceived the risk as greater when presented in terms of cumulative probability over a number of years than in terms of the probability of a single event. This suggests that by making alterations to the standard format used to communicate probabilistic flood risk information, it may be possible to increase understanding and awareness of the risks posed by flooding.

Flood Disaster Forecasting: A GIS-Based Group Analytic Hierarchy Process Approach

Geographic Information System (GIS) was integrated with Group Analytic Hierarchy Process (GAHP) to facilitate the simulation of flood likely areas based on a total number of five set of criteria/factors believed to be triggering flood generation in the study area. Two categories of experts namely hydrologists and geologists were considered. Saaty’s 1-9 scale of preference was employed in rating each factor’s influence in flood generation and the ratings from the experts were aggregated using a Geometric Mean method. Having done with the aggregation, priory weights of the factors were calculated; weights were further normalized through the Analytic Hierarchy Process (AHP). The result was further integrated into GIS system for spatial simulation of the likely flood areas. The result forecasted 39.1% of the total area to be very highly susceptible to flooding. Validation was carried out by superimposing the known flood extent map from radar satellite data over the flood forecasting model developed herein.

Road assessment after flood events using non-authoritative data

Abstract. This research proposes a methodology that leverages non-authoritative data to augment flood extent mapping and the evaluation of transportation infrastructure. The novelty of this approach is the application of freely available, non-authoritative data and its integration with established data and methods. Crowdsourced photos and volunteered geographic data are fused together using a geostatistical interpolation to create an estimation of flood damage in New York City following Hurricane Sandy. This damage assessment is utilized to augment an authoritative storm surge map as well as to create a road damage map for the affected region.

Flood extent mapping for Namibia using change detection and thresholding with SAR

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision-based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km2, 720 km2, and 673 km2 respectively. Pixels determined to be flooded in vegetation were typically <0.5% of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes.

Maximising the usefulness of flood risk assessment for the River Vistula in Warsaw

Abstract. The derivation of the flood risk maps requires an estimation of maximum inundation extent for a flood with a given return period, e.g. 100 or 500 yr. The results of numerical simulations of flood wave propagation are used to overcome the lack of relevant observations. In practice, deterministic 1-D models are used for that purpose. The solution of a 1-D model depends on the initial and boundary conditions and estimates of model parameters based on the available noisy observations. Therefore, there is a large uncertainty involved in the derivation of flood risk maps using a single realisation of a flow model. Bayesian conditioning based on multiple model simulations can be used to quantify this uncertainty; however, it is too computer-time demanding to be applied in flood risk assessment in practice, without further flow routing model simplifications. We propose robust and feasible methodology for estimating flood risk. In order to decrease the computation times the assumption of a gradually varied flow and the application of a steady state flow routing model is introduced. The aim of this work is an analysis of the influence of those simplifying assumptions and uncertainty of observations and modelling errors on flood inundation mapping and a quantitative comparison with deterministic flood extent maps. Apart from the uncertainty related to the model structure and its parameters, the uncertainty of the estimated flood wave with a specified probability of return period (so-called 1-in-10 yr, or 1-in-100 yr flood) is also taken into account. In order to derive the uncertainty of inundation extent conditioned on the design flood, the probabilities related to the design wave and flow model uncertainties are integrated. In the present paper that integration is done whilst taking into account the dependence of roughness coefficients on discharge. The roughness is parameterised based on maximum annual discharges. This approach allows for the relationship between flood extent and flow values to be derived, thus giving a cumulative assessment of flood risk. The methods are illustrated using the Warsaw reach of the River Vistula as a case study. The results indicate that deterministic and stochastic flood inundation maps cannot be quantitatively compared. We show that the proposed simplified approach to flood risk assessment can be applied even when breaching of the embankment occurs, with the condition that the flooded area is small enough to be filled rapidly.

Initial validation of non-authoritative data for road assessment

This research proposes a methodology that leverages non-authoritative data to augment flood extent mapping and the evaluation of transportation infrastructure. The novelty of this approach is the application of freely available, non-authoritative data and its integration with established data and methods. Crowdsourced photos and volunteered geographic data are fused together using a geostatistical interpolation to create an estimation of flood damage in New York City following Hurricane Sandy. This damage assessment is utilized to augment an authoritative storm surge map as well as to create a road damage map for the affected region.

Application of Hydrological Model, RS and GIS for Flood Mapping of Citarum Watershed, West Java Province, Indonesia

Citarum River Basin is considered as one of the vital and strategic river basins for the country’s development, particularly for the West Java Province and Metropolitan City of Jakarta. Flooding is almost occurred annually in Upper Citarum in Bandung area, the Capital City of the West Java Province and in Lower Citarum. This article presents the flood map of lower Citarum watershed delineated using hydrodynamic model and satellite image analysis. HEC-RAS was selected to model the flood. The ArcView program and the HEC-GeoRAS module were used to extract the geometric data and export to the RAS GIS Import File in the HEC-RAS import file format. This file was then imported in HECRAS model. The correction of geometric data such as adjusting the location of river banks, filtering the number of cross section point, etc. was then performed in HEC-RAS model. After the correction, the maximum discharge was used as input to simulate the flood event. Two images of ALOS PALSAR, one taken on a dry date (January 19, 2009) and the other on a wet date (March 26, 2010), were selected to be analysed using ENVI platform. One Dimension Hydrodynamic model HEC RAS has been used for flood inundation mapping using two different DEMs for the lower Citarum watershed. Flooded area for year 2007 (From existing map) is 105.11 km and simulated area from HEC RAS is 95.63 and 25.68 km with DEM-1 (extracted from spot height and contour data) and DEM-2 (extracted from ASTER data) respectively. ALOS PALSAR (year 2010) has been utilized for the flood extent mapping of year 2010. A threshold of -3 dB gives an area of 57.27 km of flooded areas. Keywords-Hydrological Model; Flood Inundation Model; ALOS-PALSAR; Citarum Watershed