In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations.

Point cloud data acquired by scanning with Light Detection and Ranging (LiDAR) devices typically contain irregular objects, such as trees, which lead to low classification accuracy in existing point cloud classification methods. Consequently, this paper proposes a deep point feature aggregation network (DPFANet) that integrates adaptive graph convolution and space-filling curve sampling modules to effectively address the feature extraction problem for irregular object point clouds. To refine the feature representation, we utilize the affinity matrix to quantify inter-channel relationships and adjust the input feature matrix accordingly, thereby improving the classification accuracy of the object point cloud. To validate the effectiveness of the proposed approach, a TreeNet dataset was created, comprising four categories of tree point clouds derived from publicly available UAV point cloud data. The experimental findings illustrate that the model attains a mean accuracy of 91.4% on the ModelNet40 dataset, comparable to prevailing state-of-the-art techniques. When applied to the more challenging TreeNet dataset, the model achieves a mean accuracy of 88.0%, surpassing existing state-of-the-art methods in all classification metrics. These results underscore the high potential of the model for point cloud classification of irregular objects.