Landform mapping, elevation modelling, and thaw subsidence estimation for permafrost terrain using a consumer-grade remotely-piloted aircraft

Abstract

We assess performance of a small consumer-grade remotely-piloted aircraft (RPA) for landform mapping, elevation modelling, and thaw subsidence estimation in continuous permafrost terrain. We acquired RPA imagery near Rankin Inlet, Nunavut to construct orthomosaics and digital elevation models (DEMs) that we use to interpret geomorphology and surficial geology. We estimate seasonal thaw subsidence using DEM differences. To quantify accuracy, RPA DEMs are compared to a satellite-based reference elevation. Subsidence estimates are compared to measurements from differential interferometric synthetic aperture radar (DInSAR). We find that RPA images are very effective for mapping periglacial landforms and surficial geology with the chosen flight specifications. The DEMs exhibit vertical mean absolute error of approximately 1 cm at ground control points. Away from control points, relative vertical accuracy is approximately 3 cm. Comparison to the reference elevation results in survey-wide vertical mean absolute errors of 33–66 cm with high variability and spatial autocorrelation of elevation discrepancy. There is local agreement between DEM differences, DInSAR, and on-the-ground measurements of seasonal subsidence. Results suggest that small RPA may be applicable for mapping thaw subsidence on the order of a few centimetres near control points. However, DEM differences are influenced by vegetation and are contaminated by spatially-variable artefacts, preventing reliable survey-wide RPA estimation of seasonal thaw subsidence.

Modelled ground ice conditions in the Kivalliq region, Nunavut, Canada

“…Well-developed polygons are observed in marine sediments overlain with organic material near Arviat (Forbes et al 2014). Ice wedge polygons are also mapped in various deposits near Rankin Inlet (McMartin 2002;Oldenborger et al 2022a). However, there are no known volumetric estimates of wedge ice in the region.…”

“…Fluvial sands and silts had moderate ice contents (10-20%), whereas coarser grained till and glaciomarine deposits had low ice contents (5-10%). Elevated segregated ice content is also inferable from the presence of solifluction lobes over some fine-grained deposits (e.g., Oldenborger et al 2022a). Polygonal ground, interpreted as ice wedge polygons, are mapped extensively along the proposed Kivalliq Hydro-Fibre Link in coarse-grained glaciofluvial, beach ridge, and nearshore marine sediments, and in fine-grained fluvial, offshore marine, and organic deposits (McQuaig et al 2022).…”

“…Wedge ice abundance is modelled as either negligible or low in the study area due to the relatively short period since emergence, which is < 5000 years near the modern Hudson Bay coastline, and because modelled wedge ice accumulation is slower in the coarse-grained sediments that cover much of the study area inland. Near the coast, polygonal ground occurs in a variety of deposits and trough networks appear most developed in marine sediments (Figure 7a), which may be overlain by Holocene peat deposits between 0.4 and 1.0 m thick (Arsenault et al 1981;McMartin 2002;Forbes et al 2014;McQuaig et al 2022;Oldenborger et al 2022a).…”

Towards precise drone-based measurement of elevation change in permafrost terrain experiencing thaw and thermokarst

“…Point-based measurement techniques with sub-centimeter accuracy, such as from a tilt-arm instrument (Gruber 2020), could be used to validate drone-derived changes and to provide higher temporal resolution measurements. Drone-based elevation changes could also be compared to those derived from D-InSAR (Oldenborger et al 2022), which would provide larger-area monitoring and an indication if the finer drone-mapped changes are consistent over larger regions. High-resolution drone measurements could also be used to quantify and interpret sub-pixel variability in InSARmeasured elevation changes (Antonova et al 2018).…”

“…In the last decade, aerial drone-based surveying, combined with Structure-from-Motion (SfM) photogrammetry, has become a popular means to generate dense 3D point clouds and DEMs for elevation monitoring in a range of natural and anthropogenic settings, including permafrost. In several studies, repeat photographic drone surveys have been used to measure permafrost terrain elevation changes caused by coastal erosion, ground heave-subsidence, and slumping (van der Sluijs et al 2018;Cunliffe et al 2019;Clark et al 2021;Turner et al 2021;Oldenborger et al 2022). In addition, the cost of drone LiDAR instruments has been dropping, now making this a more practical alternative to drone photogrammetry.…”