Multilevel Adaptive Photon Cloud Noise Filtering Algorithm for Different Observation Time Scenes in Forest Environments

Advanced Topographic Laser Altimeter System (ATLAS) is a new micropulse photon-counting laser system that offers unprecedented options for the observation of forest ecosystems. However, the ATLAS system is sensitive to solar background noise, which poses a tremendous challenge to the photon cloud noise filtering for various observation time scenes in a forest environment. This article presents a multilevel adaptive photon cloud noise filtering algorithm (MLAPCNF) for different observation time scenes that integrate the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm and the improved localized statistics algorithm. The MLAPCNF algorithm was tested at different observation time scenarios, laser intensities, and forest coverage using the ATLAS dataset for forests located in nine study areas in the United States. The results showed that the MLAPCNF algorithm was effective in identifying noise photons and preserving signal photons in the raw ATLAS data with an
$R$
value of 0.99 and
$F$
value of 0.79 which produced marginally superior results than the other existing filtering methods. The
$F$
values of the MLAPCNF algorithm under daytime observation conditions were 0.01–0.03 higher than those under nighttime observation conditions, indicating that the algorithm performed better under daytime observation conditions. Results demonstrated that the proposed method can eliminate the impact of observation time differences in forest environments. Overall, the MLAPCNF algorithm outperforms the other existing filtering techniques at the given test site and is capable of delivering accurate data for estimating forest structural parameters.