Monitoring river morphology & bank erosion using UAV imagery - A case study of the river Buëch, Hautes-Alpes, France

Abstract River morphology dynamics and river bank erosion were mapped using multi-temporal images acquired in June 2014 and June 2015 by an Unmanned Airborne Vehicle (UAV). The selected study sites are located in two dynamic parts of the floodplain of the river Buech in the Hautes-Alpes province in south-eastern France. The images were processed using the Structure from Motion algorithm into high spatial resolution OrthoMosaics of 5 cm pixel size and DEMs (Digital Elevation Model) of 10 cm pixel size. The positional and vertical accuracy of the UAV products were evaluated using Real Time Kinematic GPS (RTK-GPS) points measurements of markers laid out in the floodplain during image acquisition. Obtained accuracies are centimeters to decimeters. River morphology such as channel displacements, gravel bank displacement and avulsions were evaluated using the OrthoMosaics. The Buech river shows mainly braided river properties but at locations some meandering river properties were observed. Bank erosion volume calculations were made by comparing the high spatial resolution DEMs of 2014 and 2015. Accuracy of bank erosion assessment was evaluated using RTK-GPS transects of known sites of bank erosion. Bank retreat could be mapped at centimeter to decimeter detail but sometimes hampered by overhanging vegetation, water glitter and shadows. Our conclusions are that time-series of high spatial resolution UAV images can be acquired in a flexible and easy way, the individual images can nowadays be processed in a straightforward way into suitable products i.e. OrthoMosaics and DEMs which are valuable products for land administrators having a responsibility to survey and monitor rivers and control them. Accuracy of the UAV products is high in XYZ direction and sufficient for river monitoring purposes and for designing management measures.

[1]  Maarten G. Kleinhans,et al.  Human‐induced changes in bed shear stress and bed grain size in the River Waal (The Netherlands) during the past 900 years , 2009 .

[2]  J. Gonçalves,et al.  UAV photogrammetry for topographic monitoring of coastal areas , 2015 .

[3]  E. Vannametee Hydrograph prediction in ungauged basins: Development of a closure relation for Hortonian runoff , 2014 .

[4]  Mike Kirkby,et al.  Reconstructing flash flood magnitudes using ‘Structure-from-Motion’: A rapid assessment tool , 2014 .

[5]  J. Dietrich Riverscape mapping with helicopter-based Structure-from-Motion photogrammetry , 2016 .

[6]  S. M. Jong,et al.  High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles , 2014 .

[7]  L. Descroix,et al.  Water erosion in the southern French alps: climatic and human mechanisms , 2002 .

[8]  Lammert Kooistra,et al.  Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR , 2017, Sensors.

[9]  Ryan A. McManamay,et al.  REGIONAL FRAMEWORKS APPLIED TO HYDROLOGY: CAN LANDSCAPE-BASED FRAMEWORKS CAPTURE THE HYDROLOGIC VARIABILITY? , 2011 .

[10]  W. Marcus,et al.  Making riverscapes real , 2012 .

[11]  Carl J. Legleiter,et al.  Mapping spatial patterns of stream power and channel change along a gravel-bed river in northern Yellowstone , 2016 .

[12]  F. Liébault,et al.  LONG PROFILE RESPONSES OF ALPINE BRAIDED RIVERS IN SE FRANCE , 2013 .

[13]  Joseph M. Shea,et al.  Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery , 2016, Annals of Glaciology.

[14]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[15]  Simon Bennertz,et al.  Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[17]  Mark A. Fonstad,et al.  Topographic structure from motion: a new development in photogrammetric measurement , 2013 .

[18]  S. Robson,et al.  Mitigating systematic error in topographic models derived from UAV and ground‐based image networks , 2014 .

[19]  Jakub Langhammer,et al.  Multitemporal Monitoring of the Morphodynamics of a Mid-Mountain Stream Using UAS Photogrammetry , 2015, Remote. Sens..

[20]  Holger Schüttrumpf,et al.  Today's sediment budget of the Rhine River channel, focusing on the Upper Rhine Graben and Rhenish Massif , 2014 .

[21]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[22]  H. Hyyppä,et al.  Modern empirical and modelling study approaches in fluvial geomorphology to elucidate sub-bend-scale meander dynamics , 2017 .

[23]  Patrice E. Carbonneau,et al.  Cost‐effective non‐metric photogrammetry from consumer‐grade sUAS: implications for direct georeferencing of structure from motion photogrammetry , 2017 .

[24]  A. Tamminga,et al.  Hyperspatial Remote Sensing of Channel Reach Morphology and Hydraulic Fish Habitat Using an Unmanned Aerial Vehicle (UAV): A First Assessment in the Context of River Research and Management , 2015 .

[25]  Carlos Castillo,et al.  Image-based surface reconstruction in geomorphometry - merits, limits and developments , 2016 .

[26]  K. Oost,et al.  Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms , 2016 .

[27]  J. Fryer,et al.  Metric capabilities of low‐cost digital cameras for close range surface measurement , 2005 .

[28]  Mark W. Smith,et al.  Structure from motion photogrammetry in physical geography , 2016 .

[29]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[30]  Arko Lucieer,et al.  Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) , 2015, Remote. Sens..

[31]  J. Travelletti,et al.  UAV-based remote sensing of the Super-Sauze landslide : evaluation and results. , 2012 .

[32]  L. Descroix,et al.  Sediment budget as evidence of land-use changes in mountainous areas : two stages of evolution , 2005 .

[33]  B. G. Ruessink,et al.  Coastal dune dynamics in response to excavated foredune notches , 2017 .

[34]  G. W. Geerling,et al.  Changing Rivers: Analysing fluvial landscape dynamics using remote sensing , 2008 .

[35]  S. M. Jong,et al.  Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography , 2014 .

[36]  S. M. Jong,et al.  Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier , 2016 .

[37]  Charles S. Melching,et al.  River Dynamics and Integrated River Management , 2015 .

[38]  J. R. Allen,et al.  Principles of physical sedimentology , 1985 .

[39]  Holger Schüttrumpf,et al.  Fluvial sediment budget of a modern, restrained river: The lower reach of the Rhine in Germany , 2014 .

[40]  M. Favalli,et al.  Multiview 3D reconstruction in geosciences , 2012, Comput. Geosci..

[41]  Naoya Takeishi,et al.  Recent Developments in Aerial Robotics: A Survey and Prototypes Overview , 2017, ArXiv.