Bamboo forest is a special forest type, and its aboveground biomass (AGB) is a key indicator of its carbon sequestration capacity and ecosystem productivity. Due to its complex canopy structure and particular growth pattern, the AGBs of individual bamboos that were estimated using traditional remotely sensed data are of relatively low accuracy. In recent years, the point cloud data scanned by terrestrial laser scanners (TLS) offer the possibility for more accurate estimations of bamboo AGB. However, bamboo culms tend to have various bending degrees during the growth process, which causes the AGB estimated on culm height (H) to be generally less than the true value. In this paper, taking one sample plot of the Moso bamboo forest in Hutou Village, Chongqing, China as the study site, we employed a TLS to acquire the point cloud data. The layer-wise distance discrimination method was first developed to accurately segment individual bamboos from the dense stand. Next, the diameter at breast height (DBH) and culm length (L) of an individual bamboo were precisely extracted by fitting the cross-section circle and constructing the longitudinal axis of the bamboo culm, respectively. Lastly, the AGBs of the Moso bamboos in the study site were separately calculated using the allometric equations with the DBH and L as predictor variables. As results, the precision of the complete bamboo segmentation was 90.4%; the absolute error ($AE$) of the extracted DBHs ranged from −1.22 cm to 0.88 cm ($R^2$ = 0.93, $RMSE$ = 0.40 cm); the $AE$ of the extracted Hs varied from –0.77 m to 1.02 m ($R^2$ = 0.91, $RMSE$ = 0.45 m); and the $AE$ of the extracted Ls varied from −1.08 m to 0.77 m ($R^2$ = 0.95, $RMSE$ = 0.23 m). The total estimated AGB of the Moso bamboos in the sample plot increased by 2.85%, from 680.40 kg on H to 696.36 kg on L. These measurements demonstrated the unique benefits of the TLS-acquired point cloud in characterizing the structural parameters of Moso bamboos and estimating their AGBs with high accuracy.

Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of large-area forests. Although individual tree heights extracted from point cloud data are of high accuracy, they are still affected by some weather and environment factors. In this study, taking a planted M. glyptostroboides (Metasequoia glyptostroboides Hu & W.C. Cheng) stand as the study object, we preliminarily assessed the effects of various illumination conditions (solar altitude angle and cloud cover) on tree height extraction using UAV LiDAR. The eight point clouds of the target stand were scanned at four time points (sunrise, noon, sunset, and night) in two consecutive days (sunny and overcast), respectively. The point clouds were first classified into ground points and aboveground vegetation points, which accordingly produced digital elevation model (DEM) and digital surface model (DSM). Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. Subsequently, individual trees were segmented based on the seed points identified by local maxima filtering. Finally, the individual tree heights of sample trees were separately extracted and assessed against the in situ measured values. As results, the R² and RMSEs of tree heights obtained in the overcast daytime were commonly better than those in the sunny daytime; the R² and RMSEs at night were superior among all time points, while those at noon were poorest. These indicated that the accuracy of individual tree height extraction had an inverse correlation with the intensity of illumination. To obtain more accurate tree heights for forestry applications, it is best to acquire point cloud data using UAV LiDAR at night, or at least not at noon when the illumination is generally strongest.