Khamarrul A. Razak (1,2), Alexander Bucksch (3,4), Michiel Damen (1), Cees van Westen (1), Menno Straatsma (1), Steven de Jong (5)

نویسندگان

  • Khamarrul A. Razak
  • Alexander Bucksch
  • Michiel Damen
  • Cees van Westen
  • Menno Straatsma
  • Steven de Jong
چکیده

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. HDAL allowed the mapping of a complex landslide and its three kinematic zones. The TreeVaw method delineated trees from the HDAL data and determined their position and height, while the SkelTreskeletonization method extracted the tree inclination. Tree detection accuracies were measured up to 88.2% in the transport zone with tree height (r 2 =0.77). The coefficients of determination for tree inclination were 0.82, 0.60, and 0.83 for source, transport, and deposition zones, respectively. We found the distribution of LiDAR-derived tree growth anomalies was statistically different for landslide areas as compared to stable areas.

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تاریخ انتشار 2011