TY  - JOUR
A1  - Anders, Katharina
A1  - Marx, Sabrina
A1  - Boike, Julia
A1  - Herfort, Benjamin
A1  - Wilcox, Evan James
A1  - Langer, Moritz
A1  - Marsh, Philip
A1  - Höfle, Bernhard
T1  - Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites
Y1  - 2020-02-21
VL  - 45
IS  - 7
SP  - 1589
EP  - 1600
JF  - Earth Surface Processes and Landforms
DO  - 10.1002/esp.4833
DO  - 10.23689/fidgeo-4166
N2  - 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
N2  - This paper investigates methods using terrestrial laser scanning point clouds for quantifying thaw subsidence in permafrost‐underlain tundra fully automatically and by including information collected from expert analysts. Results of the developed methods achieve higher accuracies compared to manual in‐situ measurements, which are found to vary from reference measurements in the magnitude of the actual ground surface displacement observed in a 1‐year period. A link of automated quantification and expert judgement can enable transparent long‐term monitoring of thaw subsidence.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8506
ER  -