Filling invalid values in a lidar-derived canopy height model with morphological crown control

Abstract

The light detection and ranging (lidar) technique has rapidly developed worldwide in numerous fields. The canopy height model (CHM), which can be generated from lidar data, is a useful model in forestry research. The CHM shows the canopy height above ground, and it indicates vertical elevation changes and the horizontal distribution of the canopy’s upper surface. Many vegetation parameters, which are important in forest inventory, can be extracted from the CHM. However, some abnormal or sudden changes of the height values (i.e. invalid values), which appear as unnatural holes in an image, exist in CHMs. This article proposes an approach to fill the invalid values in lidar-derived CHMs with morphological crown control. First, the Laplacian operator is applied to an original CHM to determine possible invalid values. Then, the morphological closing operator is applied to recover the crown coverage. By combining the two results, the possible invalid values in the CHM can be confirmed and replaced by corresponding values in the median-filtered CHM. The filling results from this new method are compared with those from other methods and with charge-coupled device images for evaluation. Finally, a CHM with random noise is used to test the filling correctness of the algorithm. The experiments show that this approach can fill the most invalid values well while refraining from overfilling.