Temporal aggregation of point clouds improves permanent laser scanning of landslides in forested areas [Source Code]

Abstract: Permanent laser scanning has recently developed as a technology to monitor landslides by repeatedly acquiring point clouds at short intervals (e.g., sub-hourly). In such areas, forests can hinder the capture of ground points and reduce the quantity and thus the spatial coverage of direct surface change information. The objective of this study is to evaluate the effectiveness of aggregating sequential point clouds for improved point cloud analysis. A forested landslide that primarily consists of Pinus cembra and Picea abies is investigated using permanent laser scanning data comprising 600 scans with an acquisition frequency of three hours. With our application of tree trunk tracking, we demonstrate how temporal aggregation improves tree trunk representation, thereby enabling quantification of landslide displacement. Corresponding trunks are matched and tracked throughout the full time series to compute the 3D displacements of the trunks. The effects of temporal aggregation are analysed by applying it to a digital replica of the study site, which is generated by virtual laser scanning. This targeted temporal aggregation approach increased the number of detectable trunks in forested areas by a factor of 5-6. Similarly, the number of trunk matches across the time series increased by a factor of 5. Performance gains plateaued after three aggregated scans. By using a permanently installed terrestrial laser scanner that repeatedly scans through the canopy, our method allows direct quantification of 3D landslide displacements in densely forested terrain. Our findings demonstrate that temporal aggregation of point clouds significantly increases the applicability and performance of continuous, long-range laser scanning-based monitoring of forested environments.