Abstract
Disrupted vegetation is often used as an indicator for landslide activity in forested regions. The extraction of irregular trees from airborne laser scanning data remains problematic because of low quality of observed data and paucity of field data validation. We obtained high density airborne LiDAR (HDAL) data with 180 points m−2 for characterizing tree growth anomalies caused by landslides in the Barcelonnette region, the Southern French Alps. HDAL allowed the mapping of a complex landslide and its three kinematic zones. The TreeVaW method detecting trees from the HDAL data and determined their position and height, while the SkelTre-skeletonization method extracted the tree inclination. The tree growth anomalies are parameterized by tree height dissimilarities and tree inclinations. These parameters were successfully extracted from the HDAL and compared with field data. We revealed that the distribution of LiDAR-derived tree growth anomalies was statistically different for landslide areas as compared to stable areas.
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Acknowledgments
This work was supported by the Malaysia Fellowship (Ministry of Higher Education and Universiti Teknologi Malaysia), ITC-University of Twente in collaboration with Department of Physical Geography, Utrecht University, the Netherlands. The authors are grateful to Jean Philippe Malet (University of Strasbourg) for managing the airborne LiDAR campaign under funding from the French Project ANR Risk-NatSISCA ‘Système Intégré de Surveilllance de Glissements de Terrain Argileux’ (2009–2021) and Restauration des Terrains de Montagne (RTM, Division of Barcelonnette). This research also contributes to the EU FP7 Safeland project.
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Razak, K.A., Bucksch, A., Damen, M., van Westen, C., Straatsma, M., de Jong, S. (2013). Characterizing Tree Growth Anomaly Induced by Landslides Using LiDAR. In: Margottini, C., Canuti, P., Sassa, K. (eds) Landslide Science and Practice. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31325-7_31
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DOI: https://doi.org/10.1007/978-3-642-31325-7_31
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