Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska

Abstract

Terrestrial laser scanners (TLS) allow large and complex landforms to be rapidly surveyed at previously unattainable point densities. Many change detection methods have been employed to make use of these rich data sets, including cloud to mesh (C2M) comparisons and Multiscale Model to Model Cloud Comparison (M3C2). Rather than use simulated point cloud data, we utilized a 58 scan TLS survey data set of the Selawik retrogressive thaw slump (RTS) to compare C2M and M3C2. The Selawik RTS is a rapidly evolving permafrost degradation feature in northwest Alaska that presents challenging survey conditions and a unique opportunity to compare change detection methods in a difficult surveying environment. Additionally, this study considers several error analysis techniques, investigates the spatial variability of topographic change across the feature and explores visualization techniques that enable the analysis of this spatiotemporal data set. C2M reports a higher magnitude of topographic change over short periods of time (∼12 h) and reports a lower magnitude of topographic change over long periods of time (∼four weeks) when compared to M3C2. We found that M3C2 provides a better accounting of the sources of uncertainty in TLS change detection than C2M, because it considers the uncertainty due to surface roughness and scan registration. We also found that localized areas of the RTS do not always approximate the overall retreat of the feature and show considerable spatial variability during inclement weather; however, when averaged together, the spatial subsets approximate the retreat of the entire feature. New data visualization techniques are explored to leverage temporally and spatially continuous data sets. Spatially binning the data into vertical strips Remote Sens. 2013, 5 2814 along the headwall reduced the spatial complexity of the data and revealed spatiotemporal patterns of change.

Error in target-based georeferencing and registration in terrestrial laser scanning

“…This approach has also widely been adopted by researchers (e.g. Barnhart andCrosby, 2013, Lague et al, 2013;Montreuil et al, 2013;Day, et al, 2013). They are single and spatial uniform statistics per point cloud.…”

“…Gallay et al, 2013), monitoring natural hazards (e.g. Jaboyedoff et al, 2012;Barnhart and Crosby, 2013) and investigating Earth surface processes (e.g. Schürch et al, 2011;Montreuil et al, 2013;Day et al, 2013).…”

Terrestrial laser scanning for quantifying small-scale vertical movements of the ground surface in Artic permafrost regions

“…Main advantage of this method is that it accounts for position uncertainties, i.e. random errors due to averaging of point positions (Barnhart and Crosby, 2013). The M3C2 algorithm consist of two parts: Firstly, point normal estimation is performed and secondly, differences are calculated.…”

“…Jones et al, 2015). Such ALS data typically has 25 decimeter resolution (Barnhart and Crosby, 2013), and are not sufficiently accurate to study seasonal vertical movements of the ground caused by thawing and freezing processes of the permafrost which is usually in the order of a few mm to cm. TLS monitoring is required to study such changes.…”

“…In recent years, 20 calculating the differences directly within the point cloud has been gaining more and more attention. Barnhart and Crosby (2013) Lague et al (2013) to detect surface changes on an evolving thermokarst in Alaska. They found that the M3C2 method is better suited to detect small-scale changes (down to 2-7 cm) as the distance calculation algorithms accounts for position uncertainties, i.e.…”