Global Flood Monitoring Webinar 2022: Products Outline

The Copernicus Emergency Management Service has been developing a new operational product providing a continuous global, systematic, and automated monitoring of all land surface areas possibly affected by flooding. This new global flood monitoring (GFM) product processes all incoming Sentinel-1 images and analyses them using an ensemble of 3 flood detection algorithms providing a high timeliness and quality of the product. The workshop, in the form of a webinar, will present the currently available data and product that have been developed as part of the GFM focusing on the high-resolution satellite-based products for flood monitoring at global scale, freely accessible in real-time through GloFAS

A likelihood analysis of the Global Flood Monitoring ensemble product

Flooding is a natural disaster that can have devastating impacts on communities and individuals, causing significant damage to infrastructure, loss of life, and economic disruption. The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service (CEMS) addresses these challenges and provides global, near-real time flood extent masks for each newly acquired Sentinel-1 Interferometric Wide Swath Synthetic Aperture Radar (SAR) image, as well as archive data from 2015 on, and therefore supports decision makers and disaster relief actions. The GFM flood extent is an ensemble product based on a combination of three independently developed flood mapping algorithms that individually derive the flood information from Sentinel-1 data. Each flood algorithm also provides classification uncertainty information as flood classification likelihood that is aggregated in the same ensemble process. All three algorithms utilize different methods both for flood detection and the derivation of uncertainty information. The first algorithm applies a threshold-based flood detection approach and provides uncertainty information through fuzzy memberships. The second algorithm applies a change detection approach where the classification uncertainty is expressed through classification probabilities. The third algorithm applies the Bayes decision theorem and derives uncertainty information through the posterior probability of the less probable class. The final GFM ensemble likelihood layer is computed with the mean likelihood on pixel level. As the flood detection algorithms derive uncertainty information with different methods, the value range of the three input likelihoods must be harmonized to a range from low [0] to high [100] flood likelihood. The ensemble likelihood is evaluated on two test sites in Myanmar and Somalia showcasing the performance during an actual flood event and an area with challenging conditions for SAR-based flood detection. The findings further elaborate on the statistical robustness when aggregating multiple likelihood layers. The final GFM ensemble likelihood layer serves as a simplified appraisal of trust in the ensemble flood extent detection approach. As an ensemble likelihood, it provides more robust and reliable uncertainty information for the flood detection compared to the usage of a single algorithm only. It can therefore help interpreting the satellite data and consequently to mitigate the effects of flooding and accompanied damages on communities and individuals

GFM Product User Manual

This Product User Manual (PUM) is the reference document for all end-users and stakeholders of the new Global Food Monitoring (GFM) product of the Copernicus Emergency Management Service (CEMS). The PUM provides all of the basic information to enable the proper and effective use of the GFM product and associated data output layers. This manual includes a description of the functions and capabilities of the GFM product, its applications and alternative modes of operation, and step-by-step guidance on the procedures for accessing and using the GFM product

Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events

Knowledge about the cause of differential structural damages following the occurrence of hazardous hydrometeorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain. In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community

SAR-based probabilistic water segmentation with adapted Bayesian convolutional neural networks

The occurrence of hazard events, such as floods, has recognized ecological and socioeconomic consequences for affected communities. Geospatial resources, including satellite-based synthetic aperture radar (SAR) and optical data, have been instrumental in providing time-sensitive information about the extent and impact of these events to support emergency response and hazard management efforts. In effect, finite resources can be better optimized to support the needs of often extensively affected areas. However, the derivation of SAR-based flood information is not without its challenges and inaccurate flood detection can result in non-trivial consequences. Consequently, in addition to segmentation maps, the inclusion of quantified uncertainties as easily interpretable probabilities can further support risk-based decision-making. This pilot study presents the first results of two probabilistic convolutional neural networks (CNNs) adapted for SAR-based water segmentation with freely available Sentinel-1 Interferometric Wide (IW) swath Ground Range Detected (GRD) data. In particular, the performance of a variational inference-based Bayesian convolutional neural network (BCNN) is evaluated against that of a Monte Carlo Dropout Network (MCDN). MCDN has been more commonly applied as an approximation of Bayesian deep learning. Here we highlight the differences in the uncertainties identified in both models, based on the evaluation of an extended set of performance metrics to diagnose data and model behaviours and to evaluate ensemble outputs at tile- and scene-levels. Since the understanding of uncertainty and subsequent derivation of uncertainty information can vary across applications, we demonstrate how uncertainties derived from ensemble outputs can be integrated into maps as a form of actionable information. Furthermore, map products are designed to reflect survey responses shared by end users from regional and international organizations, especially those working in emergency services and as operations coordinators. The findings of this study highlight how the consideration of both segmentation accuracy and probabilistic performance can build confidence in products used to make informed decisions to support emergency response within flood situations

Provision of an Automated, Global, Satellite-based Flood Monitoring Product for the Copernicus Emergency Management Service: Product Definition Document (PDD)

This Product Definition Document (PDD) provides detailed technical specifications for all of the product output layers of the new Global Food Monitoring (GFM) product of the Copernicus Emergency Management Service (CEMS). The PDD provides the reference information required to understand all elements of the various data processing chains, and explains the contents of the GFM product output layers, the main assumptions underlying their generation, and the limitations of the data