Key message: In this paper, we first introduced a novel method for directly measuring tree diameters from UAV-LiDAR point clouds utilizing the χ ²-filtering technique and a technique for measuring tree heights using pseudo-waveforms. Context: Eucalyptus plantation forests constitute the largest expanse of planted broad-leaved forests worldwide. Detailed and accurate individual tree attributes are essential for precision forestry. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) are frequently employed to acquire information on individual trees. However, both technologies suffer from low efficiency. Therefore, the challenge remains how to access this information efficiently. Aims: Consequently, this paper investigated a novel technical approach to automatically extract individual tree attributes using low-cost UAV-LiDAR technology. Methods: The framework consists of three independent yet interrelated approaches. Firstly, the tree trunks were detected using an approach based on the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm. It utilized 3D point clouds to achieve precise tree counts and their approximate locations. These locations then enabled cylindrical segmentation of the point clouds at the trunk level, facilitating diameter measurement. Secondly, stem diameters were directly measured using the probability density function of the chi-square distribution. This process produced precise stem diameters, trunk positions, and growth directions, which were subsequently used to determine the center of the crown top for tree height extraction. Lastly, the tree height was estimated based on the pseudo-waveforms. We validated this framework by acquiring ultra-high-density UAV-LiDAR data in an Eucalyptus plantation. Results: The result indicated a precision of 91.1% for individual tree detection, with an F-score of 0.916. The root mean square errors (RMSEs) for direct measurements of diameter at breast height (DBH) and tree height were 14.60% (2.18 cm) and 2.69% (0.31 m), respectively. Furthermore, this study suggested that the classical circle-fitting method might not be suitable for directly measuring tree diameter using low-cost UAV-LiDAR data. Conclusion: The proposed framework facilitates automated inventory and monitoring in Eucalyptus plantation forests. However, more trials are needed to verify the framework’s applicability in other planted and natural forests.

Key message: Sample size (number of plots) may significantly affect the accuracy of forest attribute estimations using airborne LiDAR data in large-scale subtropical areas. In general, the accuracy of all models improves with increasing sample size. However, the improvement in estimation accuracy varies across forest attributes and forest types. Overall, a larger sample size is required to estimate the stand volume (VOL), while a smaller sample size is required to estimate the mean diameter at breast height (DBH). Broad-leaved forests require a smaller sample size than Chinese fir forests. Context: Sample size is an essential factor affecting the cost of LiDAR-assisted forest resource inventory. Therefore, investigating the minimum sample size required to achieve acceptable accuracy for airborne LiDAR-based forest attribute estimation can help improve cost efficiency and optimize technical schemes. Aims: The aims were to assess the optimal sample size to estimate the VOL, basal area, mean height, and DBH in stands dominated by Cunninghamia lanceolate, Pinus massoniana, Eucalyptus spp., and other broad-leaved species in a large subtropical area using airborne LiDAR data. Methods: Statistical analyses were performed on the differences in LiDAR metrics between different sample sizes and the total number of plots, as well as on the field-measured attributes. The relative root mean square error (rRMSE) and the determination coefficient (R²) of multiplicative power models with different sample sizes were compared. The logistic regression between the coefficient of variation of the rRMSE and the sample size was established, and the minimum sample size was determined using a threshold of less than 10% for the coefficient of variation. Results: As the sample sizes increased, we found a decrease in the mean rRMSE and an increase in the mean R², as well as a decrease in the standard deviation of the LiDAR metrics and field-measured attributes. Sample sizes for Chinese fir, pine, eucalyptus, and broad-leaved forests should be over 110, 80, 85, and 60, respectively, in a practical airborne LiDAR-based forest inventory. Conclusion: The accuracy of all forest attribute estimations improved as the sample size increased across all forest types, which could be attributed to the decreasing variations of both LiDAR metrics and field-measured attributes.

Airborne LiDAR has been widely used to map forest inventory attributes at various scales. However, most of the developed models on airborne LiDAR-based forest attribute estimations are specific to a study site and forest type (or species), so it is essential to develop predictive models with excellent generalization capabilities across study sites and forest types for the consistent estimation of forest attributes. In this study, 13 LiDAR-derived metrics, which depicted the three-dimensional structural aspects of stand canopy and had clear forest mensuration and ecology significance, were categorized into three groups (height, density, and vertical structure). A rule-based exhaustive combination was then used to construct 86 multiplicative power formulations consisting of 2–5 predictors for estimating the stand volume and basal area. By calibrating and validating these formulations using data from four forest types in the three study regions, we obtained the 24 best local models. Based on these models we proposed a set of accuracy criteria to determine generalized formulations and models. By applying two selection methods (the mean and mixed data methods), we finally archived the eight best region-generalized models, which could be used for estimating the stand volume and basal area of four forest types across study sites on a province scale. This study highlights the accuracy criteria and procedures for developing generalized formulations and models for consistent estimations of forest inventory attributes using airborne LiDAR data.

Over the last two decades, airborne light detection and ranging (LiDAR) has been developed into an advanced tool for practical forest resource inventory monitoring over large areas. Nonetheless, improving the accuracy of forest inventory attribute estimations remains an ongoing challenge. This paper introduces a novel framework for estimating forest inventory attributes based on the stratification of vertical forest structures (VFS). According to the composition and spatial arrangement of the superior, middle, and inferior strata in the tree layer, the forest stand was classified into six distinct VFS classes. Subsequently, the multiplicative power models were established for the stratification-based estimations of the forest inventory attributes, including stand volume (VOL), basal area (BA), and above-ground biomass (AGB), by using a rule-based exhaustive combination approach, and their performances were comparatively analyzed. The result indicated that: compared to the accuracy (relative root mean squared error, rRMSE) of the species-based estimation, the weighted average rRMSE of stratum-based VOL, BA, and AGB estimations of four forest types (Chinese fir, Masson pine, eucalyptus, and broadleaf forests) decreased by 0.3%–7.3%, +3.6%–9.4%, and 0.7%–8.7%, respectively, and the accuracy was significantly improved after stratification. Even after clustering the VFS into two or three classes using cluster analysis, the accuracy of forest attribute estimations remained superior to that of the species-based estimations. Notably, the coefficients of variation for both forest attributes and LiDAR metrics experienced a substantial decrease, and their statistical relationship considerably strengthened within most strata post-VFS classification, which led to an improvement in the accuracy of the forest attribute estimations. The methodology presented in this paper provides a significant advance in improving the accuracy of forest inventory attributes for large areas using airborne LiDAR data.

Investigating the impact of field plot size on the performance of estimation models for forest inventory attributes could help optimize the technical schemes for an operational airborne LiDAR-assisted forest resource inventory. However, few studies on the topic have focused on subtropical forests. In this study, 104 rectangular plots of 900 m² (subdivided into nine quadrats with an area of 10 × 10 m) in subtropical planted forests (Chinese fir, pine, eucalyptus, and broad-leaved forest, 2–56 years old) were used to establish four datasets with six different plot sizes (100, 200, 300, 400, 600, and 900 m²) by combining quadrats. The differences in the LiDAR-derived metrics and forest attributes between plots of different sizes were statistically analyzed. Based on the multivariate power models with stable structures, the differences in estimation accuracies of the stand volume (VOL) and basal area (BA) using plot data of different sizes were compared. The results indicated that: (1) the mean differences in LiDAR-derived metrics of the plots of different sizes in all forest types were small, and most of them had no statistically significant differences (α = 0.05) between the plots of different sizes and the 900 m² plots; however, the standard deviation of the difference increased rapidly with decreasing plot size; (2) except for the maximal tree height of the plots, the other forest attributes, including the mean tree height, diameter at breast height, BA, and VOL of all forest types, showed no statistically significant differences between the plots of different sizes and the 900 m² plots; and (3) with increasing plot size, the accuracies of VOL and BA estimations improved markedly, and the effects of plot size on the estimation accuracies of the different forest attributes and different forest types were essentially the same. Spatial averaging resulted in the variations in the independent variables (LiDAR variables) and dependent variables (forest attributes) decreasing gradually with the increasing plot size, which was the main reason for the model’s accuracy improving. In applying airborne LiDAR to a large-scale subtropical planted forest inventory, the plot size should be at least 600 m² for all forest types.