Accuracy of Rockfall Volume Reconstruction from Point Cloud Data—Evaluating the Influences of Data Quality and Filtering

Rockfall processes are now commonly studied through monitoring campaigns using repeat lidar scanning. Accordingly, several recent studies have evaluated how the temporal resolution of data collection and various data-processing decisions can influence apparent rockfall volumes estimated typical database creation workflows. However, there is a lack that consider quality associated volume estimation. In this work, we perform series tests based on an existing reference from Front Range Colorado, USA, to isolate influences (point spacing), individual point precision, filter threshold applied change results, estimates obtained for rockfalls. While effects precision were found be limited levels gaussian noise (standard deviation per coordinate direction ≤ 0.02 m), systematic impacts estimates, with smallest rockfalls decreasing substantially increases in spacing threshold. Because these factors disproportionately impact smaller rockfalls, when change, slope power law describes relative frequency-volume distribution changes. Evidence presented suggests phenomenon explain discrepancies between slopes literature focused different scales activity. Overall, study demonstrates raw attributes estimation presents additional effect tends bias frequency–magnitude relationships towards underestimation prevalence small