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TitlePerformance of a consumer-grade unmanned aerial vehicle for imaging, elevation modelling and ground displacement mapping in permafrost terrain, Rankin Inlet area, southern Nunavut
AuthorOldenborger, G AORCID logo; Bellehumeur-Génier, O; LeBlanc, A -MORCID logo
SourceCanada-Nunavut Geoscience Office, Summary of Activities 2018, 2018 p. 153-165 Open Access logo Open Access
LinksOnline - En ligne
Alt SeriesNatural Resources Canada, Contribution Series 20180275
PublisherCanada-Nunavut Geoscience Office
Mediapaper; on-line; digital
File formatpdf
AreaRankin Inlet; Iqalugaarjuup Nunanga Territorial Park
Lat/Long WENS -92.4333 -92.0500 62.9333 62.7833
Subjectssurficial geology/geomorphology; geophysics; engineering geology; Nature and Environment; permafrost; ground ice; periglacial features; solifluction; ice wedges; ice-wedge polygons; mud boils; frost heaving; glacial deposits; glacial landforms; eskers; sediments; boulders; displacement; remote sensing; photogrammetric techniques; photogrammetric surveys; radar methods; topography; hummocks; troughs; regional planning; land use; in-field instrumentation; digital terrain modelling; unmanned aerial vehicles; glaciofluvial sediments; glaciofluvial subaqueous outwash fan sediments; Infrastructures; permafrost thaw
Illustrationsgeoscientific sketch maps; location maps; photographs; tables; geophysical images; digital elevation models; profiles; satellite images; plots; models
ProgramClimate Change Geoscience Permafrost
Released2018 12 28
AbstractUnmanned aerial vehicles (UAV) can be used for landscape reconnaissance and mapping periglacial features in permafrost terrain. The UAV images can be processed using structure-from-motion (SfM) photogrammetry to create digital elevation models (DEM), and differential DEMs generated from repeated UAV surveys can be used for mapping ground surface displacement. This study examines the performance of a small and portable consumer-grade UAV for image acquisition, elevation modelling and ground displacement mapping as part of an existing permafrost study near Rankin Inlet, Nunavut. Imagery is interpreted in terms of permafrost landforms, DEMs are compared to commercial elevation products, and UAV differential DEM results are compared to results obtained from differential interferometric synthetic aperture radar (DInSAR). Acquired images are very effective for landscape reconnaissance, surficial geological mapping and permafrost interpretation. The DEMs created from SfM processing of UAV images have subdecimetre relative accuracy over small regions, and high absolute accuracy near ground control points (GCPs). Away from GCPs, DEM accuracy degrades significantly, but the DEM is still a valuable tool for studying relative microtopography, and for qualitative interpretation of periglacial features. Although results at GCPs suggest subdecimetre to decimetre landscape change detection, ground displacement mapping using UAV differential DEMs is limited by low-frequency spatially variable artefacts. Comparison of UAV differential DEM results to DInSAR shows little correspondence at the kilometre-scale, but in the immediate vicinity of GCPs, displacement patterns are similar and there is good agreement with an independent measurement of seasonal ground displacement.
Summary(Plain Language Summary, not published)
Unmanned aerial vehicles (UAV) can be useful for understanding permafrost terrain. A small and portable consumer-grade UAV is used to create high-resolution aerial imagery and digital elevation models (DEM) at existing remote permafrost study sites near Rankin Inlet, Nunavut. Ground displacement maps are generated from UAV differential DEM. Imagery is interpreted in terms of permafrost landforms, DEM are compared to existing elevation data, and UAV differential DEM are compared to ground displacement maps from satellite remote sensing. UAV images are very effective for landscape reconnaissance, surficial geology, and permafrost interpretation. DEM created from UAV have high relative accuracy, but absolute accuracy depends heavily on location of ground control points. Similarly, UAV differential DEM provide high-resolution detection of landscape change, but exhibit significant errors away from areas of ground control.

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