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Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

McGonigle, C., Brown, C. J., and Quinn, R. 2010. Insonification orientation and its relevance for image-based classification of multibeam backscatter. – ICES Journal of Marine Science, 67: 1010–1023.The use of multibeam echosounders (MBES) for mapping benthic habitat has gained widespread acceptability. Multibeam backscatter imagery provides an objective tool for scientists and managers to chronicle the extent and condition of the benthic resource. However, there are no standardized methods describing how best to process backscatter data to derive meaningful segmentations, although several acquisition parameters have been identified as having the capacity to affect the classification result. This research attempts to determine how the orientation at which a feature is insonified can affect classification outcome using commercially available software (QTC-Multiview), and to evaluate this significance related to vessel speed as a proxy for data density. A complex 2-km2 area of Stanton Banks, UK, was selected as the test site for the study. The area was insonified using a Kongsberg Simrad EM1002 MBES at perpendicularly opposing orientations, at two different vessel speeds within the same 24-h period. The classifications displayed 53% (k = 0.396) similarity at 4 m s−1 and 49% (k = 0.342) at 2 m s−1 from opposing orientations. Common orientations at different speeds were 68% (k = 0.583) similar (east–west) and 53% (k = 0.384; north–south). Most of the variation was in topographically complex areas, which coincided with shallow depths (<60 m). Meteorological and oceanographic conditions at the time the data were collected were evaluated as having had the potential to influence the outcome of the classifications. Interpretation of the results suggests that the orientation at which insonification occurs has a greater ability to influence the classification result than vessel speed using an image-based technique. [ABSTRACT FROM PUBLISHER]

Abstract: In recent years, efforts have increased to develop quantitative, computer-directed methods for segmentation of multibeam (MBES) backscatter data. This study utilises MBES backscatter data acquired at Stanton Banks (UK) and subsequently processed through the QTC-Multiview software environment in a bid to evaluate the program''s ability to perform unsupervised classification. Statistical comparison with ground-truth data (grab, stills and video) enabled cross validation of acoustic segmentation and biological assemblages observed at the site. 132 unspecified variables were extracted from user-specified rectangular patches of the backscatter image, reduced to three vectors by PCA, then clustered and classified by the software. Multivariate analyses of ground-truth data were conducted on 75 stills images and 51 grab samples. Video footage coincident with the stills was divided into 30s segments and coded by dominant substrate and species. Cross tabulation determined the interrelationship between software classifications, multivariate analysis of the biological assemblages and coded video segments. Multiview optimally identified 19 classes using the automated clustering engine. These were revised to 6 habitats a posteriori, using combined analysis of ground-truth data and Multiview data products. These habitats broadly correspond to major physiographic provinces within the region. Multivariate statistical analysis reveals low levels of assemblage similarity (<35%) for samples occurring within Multiview classes, irrespective of the mode of acquisition. Coded video data is more spatially appropriate than the other methods of ground-truthing investigated, although it is less well suited to the extraction of truly quantitative data. Multivariate analysis indicates assemblages within physiographically distinct Multiview classes have a low degree of biological similarity, supporting the notion that abiotic proxies may be contraindicative of benthic assemblage variations. QTC-Multiview performs well as a mechanism for computer-assisted segmentation of MBES backscatter imagery into acoustic provinces; however a degree of caution is required prior to ascribing ecological significance to these classifications. [Copyright &y& Elsevier]

Over the past two decades the application of acoustics to the measurement of small-scale sediment processes has been gaining increasing acceptance within the sedimentological community. This has arisen because acoustics has the potential to measure non-intrusively, with high temporal and spatial resolution, profiles of suspended sediment size and concentration, profiles of flow, and the bedform morphology. In the present article we review the capability of acoustics to deliver on its potentiality to make a valuable and unique contribution to the measurement of small-scale sediment processes. The article introduces the reasons for using acoustics, the physics underlying the approach, a series of examples illustrating collected data, a discussion on some of the difficulties encountered when applying acoustics and finally a look to the future and possible new developments. [Copyright &y& Elsevier]

The ocean floor, its species and habitats are under pressure from various human activities. Marine spatial planning and nature conservation aim to address these threats but require sufficiently detailed and accurate maps of the distribution of seabed substrates and habitats. Benthic habitat mapping has markedly evolved as a discipline over the last decade, but important challenges remain. To test the adequacy of current data products and classification approaches, we carried out a comparative study based on a common dataset of multibeam echosounder bathymetry and backscatter data, supplemented with groundtruth observations. The task was to predict the spatial distribution of five substrate classes (coarse sediments, mixed sediments, mud, sand, and rock) in a highly heterogeneous area of the south-western continental shelf of the United Kingdom. Five different supervised classification methods were employed, and their accuracy estimated with a set of samples that were withheld. We found that all methods achieved overall accuracies of around 50%. Errors of commission and omission were acceptable for rocky substrates, but high for all sediment types. We predominantly attribute the low map accuracy regardless of mapping approach to inadequacies of the selected classification system, which is required to fit gradually changing substrate types into a rigid scheme, low discriminatory power of the available predictors, and high spatial complexity of the site relative to the positioning accuracy of the groundtruth equipment. Some of these issues might be alleviated by creating an ensemble map that aggregates the individual outputs into one map showing the modal substrate class and its associated confidence or by adopting a quantitative approach that models the spatial distribution of sediment fractions. We conclude that further incremental improvements to the collection, processing and analysis of remote sensing and sample data are required to improve map accuracy. To assess the progress in benthic habitat mapping we propose the creation of benchmark datasets. [ABSTRACT FROM AUTHOR]