Rapid melting dynamics of an alpine glacier with repeated UAV photogrammetry

Abstract Glacial retreat is a major problem in the Alps, especially over the past 40 years. Unmanned aerial vehicles (UAVs) can provide an unparalleled opportunity to track the spatiotemporal variations in rapidly changing glacial morphological features related to glacial dynamics. The objective of this study is to evaluate the potential of commercial UAV platforms to detect the evolution of the surface topography and morphology of an alpine glacier over a short time scale through the repeated acquisition of high-resolution photogrammetric data. Two high-resolution UAV surveys were performed on the ablation region of the Morteratsch Glacier (Swiss Alps) in July and September 2016. First, structure-from-motion (SfM) techniques were applied to create orthophotos and digital surface models (DSMs) of the glacial surface from multi-view UAV acquisitions. The geometric accuracy of DSMs and orthophotos was checked using differential global navigation satellite system (dGNSS) ground measurements, and an accuracy of approximately 17 cm was achieved for both models. High-resolution orthophotos and DSMs made it possible to provide a detailed characterization of rapidly changing glacial environments. Comparing the data from the first and the second campaigns, the evolution of the lower part of the glacier in response to summer ablation was evaluated. Two distinct processes were revealed and accurately quantified: an average lowering of the surface, with a mean ice thinning of 4 m, and an average horizontal displacement of 3 m due to flowing ice. These data were validated through a comparison of different algorithms and approaches, which clearly showed the consistency of the results. The melt rate spatial patterns were then compared to the glacial brightness and roughness maps derived from the September UAV acquisition. The results showed that the DSM differences describing the glacial melt rates were inversely related to the glacial brightness. In contrast, a positive but weaker relationship existed between the DSM differences and glacial roughness. This research demonstrates that UAV photogrammetry allows the qualitative and quantitative appreciation of the complex evolution of retreating glaciers at a centimetre scale spatial resolution. Such performance allows the detection of seasonal changes in the surface topography, which are related to summer ablation and span from the processes affecting the entire glacier to those that are more local.

[1]  Benjamin T. Crosby,et al.  Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska , 2013, Remote. Sens..

[2]  Toby N. Tonkin,et al.  Ice-cored moraine degradation mapped and quantified using an unmanned aerial vehicle: A case study from a polythermal glacier in Svalbard , 2016 .

[3]  Laurence C. Smith,et al.  Derivation of High Spatial Resolution Albedo from UAV Digital Imagery: Application over the Greenland Ice Sheet , 2017, Front. Earth Sci..

[4]  Roger G. Barry,et al.  Glacier monitoring within the Global Climate Observing System* , 2000, Annals of Glaciology.

[5]  Geert Verhoeven,et al.  Taking computer vision aloft – archaeological three‐dimensional reconstructions from aerial photographs with photoscan , 2011 .

[6]  S. Mernild,et al.  A predictive model for the spectral “bioalbedo” of snow , 2017 .

[7]  L. Cathles,et al.  Modeling surface-roughness/solar-ablation feedback: application to small-scale surface channels and crevasses of the Greenland ice sheet , 2011, Annals of Glaciology.

[8]  Regine Hock,et al.  Temperature index melt modelling in mountain areas , 2003 .

[9]  Anshuman Bhardwaj,et al.  UAVs as remote sensing platform in glaciology: Present applications and future prospects , 2016 .

[10]  Bernhard Hofmann-Wellenhof,et al.  GNSS - Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more , 2007 .

[11]  M. Bierkens,et al.  Climate Change Will Affect the Asian Water Towers , 2010, Science.

[12]  J. Oerlemans,et al.  Dust from the dark region in the western ablation zone of the Greenland ice sheet , 2010 .

[13]  Thomas H. Painter,et al.  Detection and Quantification of Snow Algae with an Airborne Imaging Spectrometer , 2001, Applied and Environmental Microbiology.

[14]  K. Cook An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection , 2017 .

[15]  J. Brasington,et al.  Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport , 2003 .

[16]  T. R. Lauknes,et al.  The glaciers climate change initiative: Methods for creating glacier area, elevation change and velocity products , 2015 .

[17]  B. D. Mauro,et al.  Impact of impurities and cryoconite on the optical properties of the Morteratsch Glacier (Swiss Alps) , 2017 .

[18]  Christophe Kinnard,et al.  Albedo over rough snow and ice surfaces , 2014 .

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

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

[21]  N. Glasser,et al.  Formation of band ogives and associated structures at Bas Glacier d’Arolla, Valais, Switzerland , 2002, Journal of Glaciology.

[22]  Aslak Grinsted,et al.  Image georectification and feature tracking toolbox: ImGRAFT , 2014 .

[23]  S. Robson,et al.  Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment , 2016 .

[24]  J. Oerlemans,et al.  Reconstruction of the annual balance of Vadret da Morteratsch, Switzerland, since 1865 , 2009, Annals of Glaciology.

[25]  Jay Gao,et al.  Applications of remote sensing, GIS and GPS in glaciology: a review , 2001 .

[26]  O. Eisen,et al.  Calibration of a higher-order 3-D ice-flow model of the Morteratsch glacier complex, Engadin, Switzerland , 2013, Annals of Glaciology.

[27]  Izabela Karsznia,et al.  UAV-based detection and spatial analyses of periglacial landforms on Demay Point (King George Island, South Shetland Islands, Antarctica) , 2017 .

[28]  Roger G. Barry,et al.  The status of research on glaciers and global glacier recession: a review , 2006 .

[29]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[30]  B. D. Mauro,et al.  Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV, and satellite observations , 2015 .

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

[32]  T. Farr,et al.  The roughness of natural terrain: A planetary and remote sensing perspective , 2001 .

[33]  Sergio Cogliati,et al.  Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS , 2017, Remote. Sens..

[34]  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 .

[35]  Martin Funk,et al.  An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier d'Arolla, Switzerland , 2005 .

[36]  Andrew Pomfret,et al.  High resolution mapping of supra‐glacial drainage pathways reveals link between micro‐channel drainage density, surface roughness and surface reflectance , 2015 .

[37]  J. Ryan,et al.  UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet , 2015 .

[38]  J. Oerlemans Extracting a Climate Signal from 169 Glacier Records , 2005, Science.

[39]  Mevlut Yetkin,et al.  Comparison of accuracy of GPS techniques , 2012 .

[40]  M. Huss Density assumptions for converting geodetic glacier volume change to mass change , 2013 .

[41]  Takahiro Abe,et al.  Initiation of a major calving event on the Bowdoin Glacier captured by UAV photogrammetry , 2016 .

[42]  D. Lague,et al.  Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) , 2013, 1302.1183.

[43]  Claudio Smiraglia,et al.  High-resolution mapping of glacier surface features. The UAV survey of the Forni Glacier (Stelvio National Park, Italy) , 2015 .

[44]  S. Kohshima,et al.  Spatial distribution and abundance of red snow algae on the Harding Icefield, Alaska derived from a satellite image , 2006 .

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

[46]  Bernd Scheuchl,et al.  Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data , 2017, Remote. Sens..

[47]  S. Ullman The interpretation of structure from motion , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[48]  M. Becht,et al.  Feeding the hungry river: Fluvial morphodynamics and the entrainment of artificially inserted sediment at the dammed river Isar, Eastern Alps, Germany , 2017 .

[49]  Ruedi Boesch,et al.  Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles' Imagery on Glaciers , 2017, Remote. Sens..

[50]  A. Anesio,et al.  The biogeography of red snow microbiomes and their role in melting arctic glaciers , 2016, Nature Communications.

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

[52]  J. Oerlemans,et al.  Temporal and spatial variation of the surface albedo of Morteratschgletscher, Switzerland, as derived from 12 Landsat images , 2003, Journal of Glaciology.

[53]  J. Malet,et al.  Image-based mapping of surface fissures for the investigation of landslide dynamics , 2013 .

[54]  M. R. van den Broeke,et al.  Retreating alpine glaciers: increased melt rates due to accumulation of dust (Vadret da Morteratsch, Switzerland) , 2009, Journal of Glaciology.