Improving lidar-based forest structure mapping with crown-level pit removal

Abstract

Lidar canopy height models (CHMs) often appear noisy usually due to large height variations within crowns called pitted pixels (or intra-canopy gaps). Forest structure mapping derived from these data may be erroneous since the presence of pitted pixels can distort the different statistical attributes calculated from the noisy CHM. In this paper, a robust algorithm called adaptive mean filter (AMF) has been developed to deal with the pitted pixels over CHMs. The algorithm adaptively detects pitted pixels within a 3 × 3 window by calculating a similarity index (SI) and comparing this value with the normalised value of the central pixel. Then a preset window size is used to fill the pitted pixels. The results of this filter using four preset window sizes were compared with other published filters for both individual tree and plot-level data in a pine plantation. The results show AMF can perform more efficiently than other filters for pitted pixel removal since it can fill up to 97 percent of pitted pixels while simultaneously preserving the maximum height values. Moreover, applying this pit filling method on the pitted CHM can significantly decrease the error of estimation of basal area and stand volume estimates by 10 percent. Finally, the overall accuracy of pine plantation structure estimates can be improved up to 17.7 percent using the CHM filtered by the AMF method compared to that derived from a pitted CHM.