To Fill or Not to Fill: Sensitivity Analysis of the Influence of Resolution and Hole Filling on Point Cloud Surface Modeling and Individual Rockfall Event Detection

Abstract

Monitoring unstable slopes with terrestrial laser scanning (TLS) has been proven effective. However, end users still struggle immensely with the efficient processing, analysis, and interpretation of the massive and complex TLS datasets. Two recent advances described in this paper now improve the ability to work with TLS data acquired on steep slopes. The first is the improved processing of TLS data to model complex topography and fill holes. This processing step results in a continuous topographic surface model that seamlessly characterizes the rock and soil surface. The second is an advance in the automated interpretation of the surface model in such a way that a magnitude and frequency relationship of rockfall events can be quantified, which can be used to assess maintenance strategies and forecast costs. The approach is applied to OPEN ACCESSRemote Sens. 2015, 7 12104 unstable highway slopes in the state of Alaska, U.S.A. to evaluate its effectiveness. Further, the influence of the selected model resolution and degree of hole filling on the derived slope metrics were analyzed. In general, model resolution plays a pivotal role in the ability to detect smaller rockfall events when developing magnitude-frequency relationships. The total volume estimates are also influenced by model resolution, but were comparatively less sensitive. In contrast, hole filling had a noticeable effect on magnitudefrequency relationships but to a lesser extent than modeling resolution. However, hole filling yielded a modest increase in overall volumetric quantity estimates. Optimal analysis results occur when appropriately balancing high modeling resolution with an appropriate level of hole filling.

Surface Reconstruction for Three-Dimensional Rockfall Volumetric Analysis

“…Multi-temporal TLS scans have been used to identify and characterize patterns of precursor rockfall activity [1][2][3]; and to detect and analyze millimeter-scale pre-failure deformation in rock slopes [4,5]. TLS systems have also been used for rock bridge characterization [6], assessing cliff-talus interactions [7], and most commonly; the development of remotely sensed rockfall databases [8][9][10][11][12][13].…”

“…Recent work by Olsen et al [10] raises a key issue with many of these analyses. At many study sites, complex slope geometry results in occlusions (i.e., holes) in the point cloud captured by the TLS system.…”

“…For accurate volume calculations, the triangulated surface mesh must be watertight, free of intersecting triangles and have consistent normal vector orientation. As noted by Olsen et al [10], ensuring that each mesh is topologically correct can require significant manual editing which is time-consuming and subjective. Therefore there is a need to evaluate automated surface reconstruction algorithms and their influence on resulting volumetric analyses, in order to improve methods used to monitor and quantify geomorphological processes.…”

“Use of 3D Point Clouds in Geohazards” Special Issue: Current Challenges and Future Trends

“…The work presented in this Special Issue by Olsen et al [16] utilized a 2.5D connected component algorithm for isolating individual failure clusters and for calculating the volume of each individual rockfall. Compared to other processing techniques using the original 3D point clouds (e.g., noise reduction and feature extraction, clustering of individual failures, automatic volume calculation have already been proposed [32,37]), Olsen and colleagues proposed an original technique for filling data gaps due to topographic obstructions on the interpolated mesh.…”

“…Several submissions were related to the development of specific methods for data treatment, examples including the pre-processing of High Dynamic Range (HDR) imagery, the automatic extraction of rockfalls from multi-temporal LiDAR datasets and the treatment of massive amounts of geometrical data for carrying out efficient 4D monitoring. Several other contributions deal with the investigation of a given physical phenomena including slow-moving lava flows coming from recent volcanic eruptions [12], shear displacements due to historical earthquakes [13] and mass movements both on mountainous areas [14] and rocky cliffs [15,16]. A short review of each topic of investigation addressed in this Special Issue, including an overall context and main findings, is given below.…”