Poplars are essential to China's forestry, contributing to timber production, ecological restoration, and shelterbelt construction. Branch architecture critically influences tree growth, demanding scalable solutions beyond manual methods to assess phenotypic variation in large-scale poplar breeding programs. Unmanned aerial vehicle light detection and ranging (UAV LiDAR) provides an efficient alternative; however, existing methods focus on conifers, leaving a gap in approaches for the more complex morphology of poplar branches. This study proposes a poplar branch reconstruction algorithm utilizing material transport flux and object-level geometric features from low-cost UAV LiDAR data. First, a voxel-based near-centroid method is used to extract skeleton points from tree point clouds. Next, a material transport flux model identifies individual branches, and geometric features of transport paths, including path length and curvature, are used to reconstruct each branch. Finally, branch parameters are estimated based on reconstructed branches. Data from a 5-ha plot were collected using the DJI Zenmuse L1 UAV LiDAR at the Shishou National Poplar Breeding Station, Hubei Province, China. Results demonstrate the proposed algorithm achieves high accuracy in first-order branch identification (F1-score = 1), with second-order branches having an average F1-score of 0.69. Branch length estimation demonstrates an RMSE of 0.47 m, while branch angles show an RMSE of 7.06°. The study also reveals structural variability in branch traits, with the highest variability observed in the second-order branch length (coefficient of variation = 29.68%), and a moderate positive correlation between first- and second-order branch lengths (correlation coefficient = 0.34), providing insights into tree growth patterns. This approach offers a framework for high-throughput phenotyping, which provides an efficient solution towrads advanced tree breeding using UAV LiDAR.

Accurate estimations of biomass and its temporal dynamics are crucial for monitoring the carbon cycle in forest ecosystems and assessing forest carbon sequestration potentials. Recent studies have shown that integrating process-based models (PBMs) with remote sensing data can enhance simulations from stand to regional scales, significantly improving the ability to simulate forest growth and carbon stock dynamics. However, the utilization of PBMs for large-scale simulation of larch carbon storage distribution is still limited. In this study, we applied the parameterized 3-PG (Physiological Principles Predicting Growth) model across the Mengjiagang Forest Farm (MFF) to make broad-scale predictions of the biomass and carbon stocks of Larix olgensis plantation. The model was used to simulate average diameter at breast height (DBH) and total biomass, which were later validated with a wide range of observation data including sample plot data, forest management inventory data, and airborne laser scanning data. The results showed that the 3-PG model had relatively high accuracy for predicting both DBH and total biomass at stand and regional scale, with determination coefficients ranging from 0.78 to 0.88. Based on the estimation of total biomass, we successfully produced a carbon stock map of the Larix olgensis plantation in MFF with a spatial resolution of 20 ​m, which helps with relevant management advice. These findings indicate that the integration of 3-PG model and remote sensing data can well predict the biomass and carbon stock at regional and even larger scales. In addition, this integration facilitates the evaluation of forest carbon sequestration capacity and the development of forest management plans.

Identifying the spatiotemporal distributions and phenotypic characteristics of understory saplings is beneficial in exploring the internal mechanisms of plant regeneration and providing technical assistances for continues cover forest management. However, it is challenging to detect the understory saplings using 2-dimensional (2D) spectral information produced by conventional optical remotely sensed data. This study proposed an automatic method to detect the regenerated understory saplings based on the 3D structural information from aerial laser scanning (ALS) data. By delineating individual tree crown using the improved spectral clustering algorithm, we successfully removed the overstory canopy and associated trunk points. Then, individual understory saplings were segmented using an adaptive-mean-shift-based clustering algorithm. This method was tested in an experimental forest farm of North China. Our results showed that the detection rates of understory saplings ranged from 94.41% to 152.78%, and the matching rates increased from 62.59% to 95.65% as canopy closure went down. The ALS-based sapling heights well captured the variations of field measurements [R² = 0.71, N = 3,241, root mean square error (RMSE) = 0.26 m, P < 0.01] and terrestrial laser scanning (TLS)-based measurements (R² = 0.78, N =443, RMSE = 0.23 m, P < 0.01). The ALS-based sapling crown width was comparable with TLS-based measurements (R² = 0.64, N = 443, RMSE = 0.24 m). This study provides a solution for the quantification of understory saplings, which can be used to improve forest ecosystem resilence through regulating the dynamics of forest gaps to better utilize light resources.