Classification of Rockfall Patterns using Remote Sensing Data for Hazard Assessment in Canadian Rail Corridors
M. van Veen
In the proceedings of: GeoVancouver 2016: 69th Canadian Geotechnical ConferenceSession: GEOHAZARDS - II Floods & Landslides
ABSTRACT: The White Canyon, located along the CN Ashcroft Subdivision in southwestern BC is subject to frequent rockfall activity. We are able to quantify this activity using terrestrial LiDAR scans to extract rockfall information including source zone locations and magnitudes. The addition of photogrammetry to our dataset allows us to classify the slope in terms of lithology and apply this classification to our rockfall data. During a period of 15 months, we identified 1618 rockfalls ranging in magnitude from 0.01 to 53 m3. We show that by separating the rockfalls into their different lithologies, we are able to better relate the data to triggering factors and to understand the failure processes operating on the slope.
RÉSUMÉ: Le White Canyon, situé le long de la subdivision Ashcroft du CN dans le sud-ouest de la Colombie-Britannique est soumis à des chutes de roches fréquentes. Nous sommes en mesure de quantifier ces activités en utilisant des analyses LiDAR terrestres pour extraire des informations tel que la magnitude et la location de la source des chutes de roche. L'ajout de la photogrammétrie à notre ensemble de données nous permet de classifier la pente selon sa lithologie classification à notre inventaire de chute de roches. Au cours d'une période de 15 mois, nous avons identifié 1618 chutes de roches 0.01 à 53 m3. Nous démontrons ainsi que la classification des chutes de roches selon leur lithologie permet de relier chaque évènement à des facteurs de déclenchement et de comprendre le processus de
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M. van Veen (2016) Classification of Rockfall Patterns using Remote Sensing Data for Hazard Assessment in Canadian Rail Corridors in GEO2016. Ottawa, Ontario: Canadian Geotechnical Society.
@article{3803_0712134342,author = M. van Veen,title = Classification of Rockfall Patterns using Remote Sensing Data for Hazard Assessment in Canadian Rail Corridors,year = 2016}