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Rockfall detection from terrestrial LiDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution
is fundamental to understand and model the main factors that predispose
to failure. In our study we analysed LiDAR point clouds aiming to:
(1) detect and characterise single rockfalls; (2) investigate their
spatial distribution. To this end, different cluster algorithms were
applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination
with the Expectation?Maximization (EM) in order to separate feature
points from clutter; 1b) a density based algorithm (DBSCAN) was applied
to isolate the single clusters (i.e. the
rockfall events); 2) finally we computed the Ripley's K-function to
investigate the global spatial pattern of the extracted rockfalls.
The method allowed proper identification and characterization of
more than 600 rockfalls occurred on a cliff located in Puigcercos
(Catalonia, Spain) during a time span of six months. The spatial
distribution of these events proved that rockfall were clustered
distributed at a welldefined distance-range. Computations were carried
out using R free software for statistical computing and graphics.
The understanding of the spatial distribution of precursory rockfalls
may shed light on the forecasting of future failures.