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The multiple protocols that have been developed to characterize river hydromorphology, partly in response to legislative drivers such as the European Union Water Framework Directive (EU WFD), make the comparison of results obtained in different countries challenging. Recent studies have analyzed the comparability of existing methods, with remote sensing based approaches being proposed as a potential means of harmonizing hydromorphological characterization protocols. However, the resolution achieved by remote sensing products may not be sufficient to assess some of the key hydromorphological features that are required to allow an accurate characterization. Methodologies based on high resolution aerial photography taken from Unmanned Aerial Vehicles (UAVs) have been proposed by several authors as potential approaches to overcome these limitations. Here, we explore the applicability of an existing UAV based framework for hydromorphological characterization to three different fluvial settings representing some of the distinct ecoregions defined by the WFD geographical intercalibration groups (GIGs). The framework is based on the automated recognition of hydromorphological features via tested and validated Artificial Neural Networks (ANNs). Results show that the framework is transferable to the Central-Baltic and Mediterranean GIGs with accuracies in feature identification above 70%. Accuracies of 50% are achieved when the framework is implemented in the Very Large Rivers GIG. The framework successfully identified vegetation, deep water, shallow water, riffles, side bars and shadows for the majority of the reaches. However, further algorithm development is required to ensure a wider range of features (e.g., chutes, structures and erosion) are accurately identified. This study also highlights the need to develop an objective and fit for purpose hydromorphological characterization framework to be adopted within all EU member states to facilitate comparison of results. [ABSTRACT FROM AUTHOR]

Macroalgae blooms (MABs) are a global natural hazard that are likely to increase in occurrence with climate change and increased agricultural runoff. MABs can cause major issues for indigenous species, fish farms, nuclear power stations, and tourism activities. This project focuses on the impacts of MABs on the operations of a British nuclear power station. However, the outputs and findings are also of relevance to other coastal operators with similar problems. Through the provision of an early-warning detection system for MABs, it should be possible to minimize the damaging effects and possibly avoid them altogether. Current methods based on satellite imagery cannot be used to detect low-density mobile vegetation at various water depths. This work is the first step towards providing a system that can warn a coastal operator 6–8 h prior to a marine ingress event. A fundamental component of such a warning system is the spectral reflectance properties of the problematic macroalgae species. This is necessary to optimize the detection capability for the problematic macroalgae in the marine environment. We measured the reflectance signatures of eight species of macroalgae that we sampled in the vicinity of the power station. Only wavelengths below 900 nm (700 nm for similarity percentage (SIMPER)) were analyzed, building on current methodologies. We then derived 1st derivative spectra of these eight sampled species. A multifaceted univariate and multivariate approach was used to visualize the spectral reflectance, and an analysis of similarities (ANOSIM) provided a species-level discrimination rate of 85% for all possible pairwise comparisons. A SIMPER analysis was used to detect wavebands that consistently contributed to the simultaneous discrimination of all eight sampled macroalgae species to both a group level (535–570 nm), and to a species level (570–590 nm). Sampling locations were confirmed using a fixed-wing unmanned aerial vehicle (UAV), with the collected imagery being used to produce a single orthographic image via standard photogrammetric processes. The waveband found to contribute consistently to group-level discrimination has previously been found to be associated with photosynthetic pigmentation, whereas the species-level discriminatory waveband did not share this association. This suggests that the photosynthetic pigments were not spectrally diverse enough to successfully distinguish all eight species. We suggest that future work should investigate a Charge-Coupled Device (CCD)-based sensor using the wavebands highlighted above. This should facilitate the development of a regional-scale early-warning MAB detection system using UAVs, and help inform optimum sensor filter selection. [ABSTRACT FROM AUTHOR]

Damage caused by flood events is expected to increase in the coming decades driven by increased land use pressures and climate change impacts. The insurance sector needs accurate and efficient loss adjustment methodologies for flood events. These can include remote sensing approaches that enable the rapid estimation of (i) damage caused to property as well as (ii) the number of affected properties. Approaches based on traditional remote sensing methods have limitations associated with low-cloud cover presence, oblique viewing angles, and the resolution of the geomatic products obtained. Unmanned aerial vehicles (UAVs) are emerging as a potential tool for post-event assessment and provide a means of overcoming the limitations listed above. This paper presents a UAV-based loss-adjustment framework for the estimation of direct tangible losses to residential properties affected by flooding. For that purpose, features indicating damage to property were mapped from UAV imagery collected after the Desmond storm (5 and 6 December 2015) over Cockermouth (Cumbria, UK). Results showed that the proposed framework provided an accuracy of 84% in the detection of direct tangible losses compared with on-the-ground household-by-household assessment approaches. Results also demonstrated the importance of pluvial and, from eye witness reports, lateral flow flooding, with a total of 168 properties identified as flooded falling outside the fluvial flood extent. The direct tangible losses associated with these additional properties amounted to as high as £3.6 million. The damage-reducing benefits of resistance measures were also calculated and amounted to around £4 million. Differences in direct tangible losses estimated using the proposed UAV approach and the more classic loss-adjustment methods relying on the fluvial flood extent was around £1 million—the UAV approach providing the higher estimate. Overall, the study showed that the proposed UAV approach could make a significant contribution to improving the estimation of the costs associated with urban flooding, and responses to flooding events, at national and international levels. [ABSTRACT FROM AUTHOR]