title: Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery.
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.
authors.affiliation: School of Energy, Environment and Agrifood, Cranfield University, Cranfield MK430AL, UK. m.rivas-casado@cranfield.ac.uk.
authors.forename: Monica Rivas
authors.initials: MR
authors.lastname: Casado
authors.affiliation: Regional Centre of Water Research Centre (UCLM), Ctra. de las Peñas km 3.2, Albacete 02071, Spain. rocio.ballesteros@uclm.es.
authors.forename: Rocio Ballesteros
authors.initials: RB
authors.lastname: Gonzalez
authors.affiliation: School of Energy, Environment and Agrifood, Cranfield University, Cranfield MK430AL, UK. t.kriechbaumer@cranfield.ac.uk.
authors.forename: Thomas
authors.initials: T
authors.lastname: Kriechbaumer
authors.affiliation: Hydromorphological Team, Environment Agency, Manley House, Kestrel Way, Exeter, Devon EX27LQ, UK. amanda.veal@environment-agency.gov.uk.
authors.forename: Amanda
authors.initials: A
authors.lastname: Veal
keywords: Artificial Neural Network; Unmanned Aerial Vehicle; feature recognition; hydromorphology; photogrammetry