Revision as of 13:53, 11 November 2020

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Note: this section provides a concise description of the algorithms deployed by GFMS partners.
For a more comprehensive documentation the reader is referred to the GFMS Product Description Documentation (PDD).

HASARD (LIST)

LIST’s HASARD mapping algorithm enables an automatic and reliable spaceborne Synthetic Aperture Radar-based (SAR) mapping of terrestrial water bodies (Matgen et al., 2011, Giustarini et al., 2015, Chini et al., 2017). The application was initially designed to enable an ‘on demand’ mapping of water bodies to support emergency response and disaster risk reduction at large scale. In addition, its latest evolution now allows for an ‘always on’ systematic monitoring of flood and water extent variations at high spatial and temporal resolution (Chini et al., 2020). The most recent version of the algorithm can be applied to process multi-temporal stacks of SAR images and used to update water and flood extent maps in NRT as new images are acquired, pre-processed and added to the data cube. The algorithm can also be used to re-process collections of SAR images to generate a record of historic flood and water extent maps (Chini et al., 2020). One characteristic of this approach is that it exploits at the same time contextual information through region growing and multi-temporal information through change detection. […]
Key points
HASARD is based on a scientifically validated and patented technology enabling a systematic, automatic and high-accuracy monitoring of water bodies using Sentinel-1 data. The algorithm uses a highly innovative sequence of hierarchical image splitting, statistical modelling and region growing to delineate and classify areas that changed their flooding-related backscatter response between two image acquisitions from the same orbits.
Uncertainty
The LIST algorithm for probabilistic flood mapping is based on Bayesian inference. Following the procedure introduced in Giustarini et al. (2016), the probability of each pixel in any newly acquired SAR image being flooded given its measured backscatter value is estimated in NRT. The approach is based [...]
References
(Matgen et al., 2011, Giustarini et al., 2015, Chini et al., 2017).

S-1FS (DLR)

This Sentinel-1 Flood Service (S-1FS) is part of DLR’s operational multi-sensor flood monitoring system, which consists in addition of automatic flood processing chains based on TerraSAR-X, Sentinel-2 and Landsat-8 data. Currently, DLR’s Sentinel-1-based flood mapping approach is integrated within a fully automatic processing chain which consists of automatic data ingestion, pre-processing of the Sentinel-1 data, computation and adaption of global auxiliary data (digital elevation models, topographic indices, low backscatter exclusion mask as well as reference water mask), classification of the flood extent, and dissemination of the crisis information, e.g. via a web-client. The processing chain can be easily transferred into the envisaged GFM system as described in the following. For the unsupervised initialization of the flood and water extent classification in pre-processed intensity Sentinel-1 data a parametric tile-based thresholding procedure is applied by labelling all pixels with a backscatter value lower than a threshold to the class “water”. This method has been originally developed for flood mapping using TerraSAR-X and TanDEM-X data by Martinis et al. (2009, 2015), and has been adapted to the systematic data stream of the Sentinel-1 mission by Twele et al. (2016).
Key points
The key strength of this algorithm is the automatic identification of flooded areas in the SAR data using hierarchical tile-based thresholding and the optimization of the classification by combining various information sources using fuzzy-logic theory and region growing.
Uncertainty
The fuzzy-logic based water class membership assignment is a part of a flood classification refinement step described in Martinis et al. (2015) and Twele et al. (2016). It aims to exclude water-lookalikes and to reduce underestimations from initial classification by constructing a fuzzy set that consists of (a) the backscatter level, (b) the elevation of an image element in comparison to the mean elevation of the initially derived water areas, (c) topographic slope information, and (d) the size of an individual flood object; degree of an element’s membership to the class water is determined by standard S and Z membership [...]
References
Martinis et al. (2009, 2015)

Sentinel-1 flood mapping (TU Wien)

The TU Wien Sentinel-1 flood mapping algorithm is a fully automatic, pixel-based flood extent mapping workflow which exploits time series of historical backscatter measurements. The backscatter time series are used twice: first to establish a pixel-wise backscatter distribution during non-flooded conditions and second to estimate a water backscatter distribution by collecting backscatter values from open water bodies at different locations. Based on these two distributions, it is possible to derive the posterior probability of corresponding to water or non-water by means of Bayesian Inference. Applying the Bayes Decision Rule finally leads to a class allocation for each pixel and implicitly allows establishing an uncertainty measure.
Key points
This algorithm fully exploits the entire Sentinel-1 signal history within the data cube, realized by a set of a-priori computed statistical parameters layers that provide a highly accurate characterization of Earth’s land surface at pixel level, as well as of the incidence angle dependency in respect to the Sentinel-1 mission. With those parameters as input, and with the mathematical legacy of Bayes, the water delineation procedure can be designed computationally relatively slim and is hence most suitable for global operations in NRT.
Uncertainty
TU Wien’s S-1 flood mapping algorithm exploits the time series of historical backscatter measurements to generate model parameters for permanent water bodies and for each individual location on land. More specifically, the model parameter database comprises of the backscatter distribution of each land pixel under unflooded conditions, as well as the backscatter distribution of permanent water bodies at various incidence angles. In order to classify a pixel of incoming pre-processed Sentinel-1 scene as flooded or non-flooded, the posterior probability of the actual backscatter value belonging to each class is computed. The maximum posterior probability is used to classify the pixel based on Bayes Decision Rule, where the […]
References
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Ensemble flood algorithm

The three algorithms will run in parallel and have access to the same pre-processed Sentinel-1 input data. Next, the three generated flood and water extent maps are systematically combined into a single product (i.e. “consensus map”). To generate the combined product, each pixel is attributed the ratio of the number of classifications as flooded to the number of algorithms that were applied. A number of 1 would mean that all three algorithms agreed on its classification as flooded. The final classification is straightforward and based on a majority decision, i.e. a pixel is accepted as flooded when at least two algorithms classify it as water. As a result, all other pixels of the Sentinel-1 footprint and not included in a pre-computed exclusion mask are classified as unflooded. In the unlikely scenario that one or two algorithms fail to produce a result, the results of a single algorithm will be used to compute the final map. In case of a split decision between two algorithms, preference will be given to the algorithm that produced the highest CSI score during the quality assurance procedure. Key points
The ensemble algorithm integrates the results of three leading edge classification algorithms into a single product. It combines in a single approach the most advanced processing steps currently used in the field of SAR-based flood and water extent mapping (i.e. parameter optimization based on time series analysis at local and regional scale, region growing, combination of SAR intensity and ancillary data such as topography, etc.). The consensus product substantially improves the robustness and accuracy of the final product, in addition to building a high degree of redundancy into the production service.
Uncertainty
The ensemble uncertainty algorithm expresses the level of trust in the final, harmonized classification, based on equally weighted contributions from all three flood extent detection algorithms. Given differences in the expression of uncertainty in each of the individual processes, the motivation behind the ensemble uncertainty algorithm is to generate a single, easily interpretable classification uncertainty value for end users as a practical measure of confidence in the flood map product. First, uncertainty information from each of the individual algorithms is expressed in the same numerical range [0, 100] to ensure comparability and to facilitate further harmonization. In particular, probabilistic values from the LIST algorithm and fuzzy membership values from the DLR algorithm are first converted into classification uncertainty and next multiplied by a factor of 100. For DLR algorithm, since fuzzy membership values are only assigned to water pixels, all unflooded pixels are assigned a value of 0. The conversion of these probabilistic (resp. fuzzy membership) values is carried out as U = Wi * (100 - Pi) + (1 - Wi) * Pi, where [...]
References
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