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Sensors 2015, 15(11), 27969-27989; doi:10.3390/s151127969

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

1
School of Energy, Environment and Agrifood, Cranfield University, Cranfield MK430AL, UK
2
Regional Centre of Water Research Centre (UCLM), Ctra. de las Peñas km 3.2, Albacete 02071, Spain
3
Hydromorphological Team, Environment Agency, Manley House, Kestrel Way, Exeter, Devon EX27LQ, UK
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Received: 29 July 2015 / Revised: 24 October 2015 / Accepted: 28 October 2015 / Published: 4 November 2015
(This article belongs to the Special Issue UAV Sensors for Environmental Monitoring)
View Full-Text   |   Download PDF [8992 KB, uploaded 4 November 2015]   |  

Abstract

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management. View Full-Text
Keywords: Unmanned Aerial Vehicle; photogrammetry; Artificial Neural Network; feature recognition; hydromorphology Unmanned Aerial Vehicle; photogrammetry; Artificial Neural Network; feature recognition; hydromorphology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Casado, M.R.; Gonzalez, R.B.; Kriechbaumer, T.; Veal, A. Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery. Sensors 2015, 15, 27969-27989.

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