Development of an algorithm to generate a Lidar pit - free canopy height model

  • Khosravipour A
  • Skidmore A
  • Isenburg M
 et al. 
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Lidar-derived Canopy Height Models (CHMs) are commonly used for extracting relevant forest information. Often irregular height variations – also called data pits or simply pits – are present in the CHM. These pits typically appear when the first Lidar return is far below the canopy which tends to happen for two reasons. The first reason is that a laser beam deeply penetrates into the branches and the foliage before producing the first return (Persson et al., 2002). The second reason is multiple laser beams – possibly from different flight lines – produce their first return in close horizontal proximity but with a great height difference because they “see” the canopy or the ground from different angles (Leckie et al., 2003). These pits hamper the correct extraction of forestry metrics from the CHM. Previous studies recommend applying smoothing methods such as a median filter or a Gaussian filter to reduce the data pits. However, smoothing modifies the CHM leading to subsequent misinterpretation of the biophysical tree parameters (Solberg et al., 2006).

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  • A. Khosravipour

  • A.K. Skidmore

  • Martin Isenburg

  • T. Wang

  • Y.A. Hussin

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