A Photon Cloud Filtering Method in Forested Areas Considering the Density Difference Between Canopy Photons and Ground Photons

Abstract

Photon cloud data filtering is crucial when obtaining forest vertical structure parameters from photon-counting LiDAR data. The proposed method, for the first time, takes into account the influence of the density difference between canopy photons and ground photons. A moving overlapping window approach is introduced to reduce the impact of an uneven background noise environment first. In each window, a modified elevation histogram statistics vector in the elevation direction is proposed to increase the density difference between signal and noise photons while also reducing the density difference between canopy and ground photons. The filtering results show that the average overall accuracy (OA) and standard deviation of the proposed method reach almost 0.99 and 0.01, respectively, which are much better results than those of the other existing filtering methods. Specifically, with the increase in the ratio of canopy photons to ground photons, the F-measure value of the proposed method reaches almost 0.99, and is also stable, which demonstrates that the proposed approach can almost completely eliminate the influence of the density difference between canopy photons and ground photons on the filtering results. In addition, the forest canopy heights obtained based on the proposed filtering method achieve the lowest root-mean-square error (RMSE) value of 3.18 m, compared to the other filtering methods. In summary, the proposed photon cloud data filtering method can retrieve reliable forest canopy height information from photon cloud data, and outperforms the other compared filtering methods in the given test site.