Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites

Abstract

This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long‐term monitoring of permafrost subsidence. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

Combined Use of Aerial Photogrammetry and Terrestrial Laser Scanning for Detecting Geomorphological Changes in Hornsund, Svalbard

“…The snow cover present in the aerial imagery taken in 2020 and data gaps in both point clouds led us to integrate both datasets [32,52]. Usually, within point-cloud-based strategies, the data acquired in at least two epochs are used to determine the geometric changes between them [30,43,44,52]. Here, apart from the accuracy assessment, we compared and combined the data acquired within one year.…”

“…M3C2 is an algorithm used for multitemporal point cloud distance calculations. It estimates local positions in two input point clouds by using the surface normal vectors to determine the median point within a cylinder of defined radius [42,43]. For the point-cloud-based strategy, the M3C2 algorithm is chosen as the bestestablished method, especially in earth sciences, when dealing with irregular surfaces [44].…”

Construction of “Space-Sky-Ground” Integrated Collaborative Monitoring Framework for Surface Deformation in Mining Area

“…Through the efficient cooperation of space monitoring components, sky monitoring components and ground monitoring components, the spatial-temporal dynamic evolution process of surface deformation and ground cracks development in the mining process is effectively monitored, and the deformation and failure law of surface subsidence in mining area is obtained, as is shown in Figure 3. The space monitoring component, the sky monitoring component and the ground monitoring component are described in detail as follows: 1-3 cm SBAS-InSAR [51] Enhancement of SAR data usage; Low cost <1 cm TLS [52][53][54][55] Long range; High accuracy 1 cm UAV [56,57] High speed and efficiency; Flexibility Space monitoring components can obtain large-scale, high-precision and high-spatialresolution surface deformation. Compared with traditional observation technology, InSAR has incomparable advantages for mine subsidence monitoring in a large area and complex terrain conditions.…”

On-Site Measurement on Surface Disturbance Law of Repeated Mining with High Relief Terrain

“…At present, many people have conducted research on the surface subsidence monitoring in the mining area using new measurement methods, including UAV [5], 3D laser scanning [6], InSAR [7][8][9][10][11], etc., but these methods have the disadvantage that the accuracy cannot meet the engineering requirements. Therefore, traditional field measurement methods, such as leveling, traverse, and GPS are still the main methods for subsidence monitoring in the mining area.…”