Next Article in Journal
Doping Ag in ZnO Nanorods to Improve the Performance of Related Enzymatic Glucose Sensors
Next Article in Special Issue
Adaptation of Dubins Paths for UAV Ground Obstacle Avoidance When Using a Low Cost On-Board GNSS Sensor
Previous Article in Journal
Energy Neutral Wireless Bolt for Safety Critical Fastening
Previous Article in Special Issue
Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(10), 2210;

Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization

School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK430AL, UK
Regional Centre of Water Research, Universidad de Castilla-La Mancha, Carretera de las Peñas km 3.2, 02071 Albacete, Spain
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)
Full-Text   |   PDF [3773 KB, uploaded 29 September 2017]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top