Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial ImageryReport as inadecuate




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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.





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Author to whom correspondence should be addressed.



Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos

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





Author: Monica Rivas Casado 1,†,* , Rocio Ballesteros Gonzalez 2,†, Thomas Kriechbaumer 1 and Amanda Veal 3

Source: http://mdpi.com/



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