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

Flood mapping in vegetated areas using an unsupervised clustering approach on Sentinel-1 and-2 imagery

The European Space Agency's Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available

Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations

Reliable flood forecasting systems are the prerequisite for proper flood warning systems. Currently, satellite remote sensing (SRS) observations are widely used to improve model forecasts. Although they provide distributed information, they are sometimes unable to satisfy flood modellers' needs due to low overpass frequencies and high measuring uncertainties. This paper assesses the potential of sparsely distributed, in situ floodplain water level sensors to provide accurate, near-real time flood information as a means to enhance flood predictions. A synthetic twin experiment evaluates the assimilation of different sensor network configurations, designed through time series clustering and Voronoi spacing. With spatio-temporal RMSEs reaching up to 1 cm, the study demonstrates great potential. Adequate sensor placement proved crucial for improved performance. In practice, observation locations should be chosen such that they are located rather close to the river, to increase the likelihood of early flooding and thus acquiring valuable information at an early stage of flooding. Furthermore, high measuring frequencies benefit the simulations, though one should be careful not to overcorrect water levels as these may result in inconsistencies. Lastly, a network size of 5 to 7 observations yields good results, while an increasing number of observations generally diminishes the importance of extra observations. Our findings could greatly contribute to future flood observing systems to either compensate for ungauged areas, or complement current SRS practices

The potential of OBIA for SAR-based flood mapping

International audienceIn our changing world, floods are a threat of increasing importance causing major fatalities and economic losses. Within this perspective, flood extent mapping is of great importance for both damage assessment and improving flood forecasts. While flood mapping through optical imagery is often hampered by the presence of clouds, Synthetic Aperture Radar (SAR) sensors are capable of sensing in all weather conditions during both day and night. Moreover, recently launched missions such as the Sentinel-1 and COSMO-SkyMed constellations provide improved temporal and spatial resolutions, thus even further increasing the potential of SAR for systematic flood mapping and monitoring. Due to their specular reflectance properties, open water surfaces typically appear dark and homogeneous on SAR images. Classification of these images is generally performed using a pixel-based approach. Frequently used algorithms include histogram thresholding, active contour models and pixel-based change detection methods. The use of object-based approaches for SAR-based flood mapping remains rare, although a couple of studies have worked with a segmentation step. However, pixel-based approaches suffer from quite some drawbacks. Especially thresholding typically results in classification products that still include a large number of dispersed misclassified pixels, thus requiring a post-processing step. Although computationally more expensive, active contour models mostly lead to higher accuracies, which indicates the importance of spatial context. An object-based approach allows taking into account this spatial context, as well as some other relevant properties such as object shape, proximity and homogeneity. Moreover, it is possible to include some additional information sources such as elevation data, land cover data and optical imagery. This study aims at further investigating the potential of OBIA for SAR-based flood mapping applications. A range of established pixel-based approaches will serve as a benchmark. Preliminary results demonstrate the benefit of both segmenting the image into objects as well as incorporating additional information sources

Towards operational flood monitoring in Flanders using Sentinel-1

As floods pose an increasing threat to our society, insights into their occurrence and dynamics are of major importance for emergency relief, damage assessment, the optimization of predictive models, and spatial planning. Due to their capability of providing synoptic observations independent of cloud cover and daylight, synthetic-aperture radar (SAR) sensors are an invaluable tool for flood mapping and monitoring. In this study, the potential of SAR, and more specifically Sentinel-1, for automated flood monitoring in Flanders is assessed. Its capability to detect floods with varying characteristics is investigated, and an approach for automated monitoring is presented. This approach, combining thresholding and region growing, requires a SAR image pair and several ancillary data layers, including elevation, land cover, and flood risk, as input. The resulting map discriminates permanent water, open flooding, long-term flooding, possible flooding, flooded vegetation, and possibly flooded forests from dry land. Invisible forested areas are indicated as well. A quantitative and qualitative accuracy assessment, based on 17 and 138 flood maps, respectively, highlights the approach's robustness and improved accuracy compared to benchmark techniques. Furthermore, main sources of confusion are identified and suggestions for future improvements are listed

Flood mapping based on synthetic aperture radar : an assessment of established approaches

In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest

Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images

The occurrence of litter in natural areas is nowadays one of the major environmental challenges. The uncontrolled dumping of solid waste in nature not only threatens wildlife on land and in water, but also constitutes a serious threat to human health. The detection and monitoring of areas affected by litter pollution is thus of utmost importance, as it allows for the cleaning of these areas and guides public authorities in defining mitigation measures. Among the methods used to spot littered areas, aerial surveillance stands out as a valuable alternative as it allows for the detection of relatively small such regions while covering a relatively large area in a short timeframe. In this study, remotely piloted aircraft systems equipped with multispectral cameras are deployed over littered areas with the ultimate goal of obtaining classification maps based on spectral characteristics. Our approach employs classification algorithms based on random forest approaches in order to distinguish between four classes of natural land cover types and five litter classes. The obtained results show that the detection of various litter types is feasible in the proposed scenario and the employed machine learning algorithms achieve accuracies superior to 85% for all classes in test data. The study further explores sources of errors, the effect of spatial resolution on the retrieved maps and the applicability of the designed algorithm to floating litter detection

Distinct autophagy-apoptosis related pathways activated by Multi-walled (NM 400) and Single-walled carbon nanotubes (NIST-SRM2483) in human bronchial epithelial (16HBE14o-) cells

Given the recent development in the field of particle and fibre toxicology, parallels have been drawn between Carbon nanotubes (CNTs) and asbestos. It is now established that both multi-walled (MWCNTs) and single-walled (SWCNTs) carbon nanotubes might contribute to pulmonary disease. Although multiple mechanisms might be involved in CNT induced pathogenesis, systematic understanding of the relationship between different CNT exposure (MWCNT vs SWCNT) and autophagy/ apoptosis/ necrosis, in human lung epithelial cells remains limited. In this study, we demonstrate that exposure to MWCNT (NM-400), but not SWCNT (NIST-SRM2483), leads to an autophagic response after acute exposure (24 h). MWCNT exposure was characterized by an increase in anti-apoptotic BCL2, downregulation of executor Caspase-3/7 and increase in expression of genes from the autophagy machinery. For SWCNT exposure however, we observed an overexpression of executor Caspase-3/7 and upregulation of pro-apoptotic BAX; enrichment for processes like cornification, apoptotic process, cell differentiation from proteomic analysis. These results clearly indicate a major difference in the pathways initiated by the CNTs, in vitro. While the present study design provides mechanistic understanding after an acute exposure for the tested CNTs, we believe that the information obtained here would have relevance in better understanding of CNT toxicity and pathogenesis in general.status: publishe