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Sensors 2017, 17(10), 2210; https://doi.org/10.3390/s17102210

Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization

1
School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK430AL, UK
2
Regional Centre of Water Research, Universidad de Castilla-La Mancha, Carretera de las Peñas km 3.2, 02071 Albacete, Spain
3
National Fisheries Services, Environment Agency, Threshelfords Business Park, Inworth Road, Feering, Essex CO61UD, UK
*
Author to whom correspondence should be addressed.
Received: 23 June 2017 / Revised: 18 September 2017 / Accepted: 21 September 2017 / Published: 26 September 2017
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Abstract

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. View Full-Text
Keywords: hydromorphology; intercalibration; unmanned aerial vehicle; photogrammetry; artificial neural network; water framework directive hydromorphology; intercalibration; unmanned aerial vehicle; photogrammetry; artificial neural network; water framework directive
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Rivas Casado, M.; González, R.B.; Ortega, J.F.; Leinster, P.; Wright, R. Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization. Sensors 2017, 17, 2210.

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