The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting.

Forest resource surveys are of vital importance for grasping the current status of forest resources, formulating management strategies, and evaluating ecosystem functions. Traditional manual measurement methods have numerous limitations in complex forest environments. The emergence of LiDAR technology has provided a new approach. Backpack LiDAR has been increasingly applied due to its portability and flexibility. However, there is a lack of comprehensive research on the influence of different scanning routes on data quality and analysis results. In this study, forest plots of four tree species, namely Carya cathayensis, Cinnamomum camphora, Koelreuteria bipinnata, and Quercus acutissima in Chuzhou City, Anhui Province, were selected as the research objects. Six scanning routes were designed to collect point cloud data using backpack LiDAR. After preprocessing, including denoising and ground point classification, diameter at breast height (DBH) fitting and accuracy evaluation were carried out. The results indicated that the individual tree recognition rates of C. cathayensis, C. camphora, and K. bipinnata reached 100%, while that of Q. acutissima was between 64.71% and 78.07% and was significantly affected by the scanning route. The DBH fitting accuracy of each tree species varied among different routes. For example, C. cathayensis had high accuracy in routes 1 and 6, and C. camphora had high accuracy in routes 1 and 3. Tree species characteristics, scanning routes, and data processing methods jointly affected the DBH fitting accuracy. This study provides a basis for the application of backpack LiDAR in forest resource surveys. Although backpack LiDAR has advantages, it is still necessary to optimize data acquisition schemes targeting tree species characteristics and improve point cloud data processing algorithms to promote its in-depth application in the forestry field.

The accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is critical for assessing their carbon sequestration potential and supporting sustainable management. Satellite-based approaches are often constrained by signal saturation and mixed-pixel effects, whereas Unmanned Aerial Vehicle (UAV) imagery enables precise individual tree detection, overcoming these limitations. In this study, we propose a stand density (SD)-driven AGB estimation framework using high-resolution UAV RGB imagery. Individual bamboo positions were extracted using the Revised Local Maximum (RLM) algorithm, which achieved an optimal accuracy at a 2.5 m sampling interval (OA = 82.20%). Using 85 ground-truth plots, we developed six SD-AGB models and evaluated them via 10-fold cross-validation and independent UAV validation (10 plots). The Artificial Neural Network (ANN) model outperformed others, with strong calibration (R² = 0.94, RMSE = 3.78 Mg/ha), robust cross-validation (R² = 0.84 ± 0.06, RMSE = 5.24 ± 0.67 Mg/ha), and reliable independent validation (R² = 0.87, RMSE = 4.56 Mg/ha). Spatial mapping revealed a total of 14,190 bamboo plants with an average AGB of 32.80 Mg/ha. This UAV-based SD-AGB framework provides a robust, scalable, and cost-effective tool for precise biomass estimation, supporting sustainable bamboo forest management and carbon sequestration strategies and progress towards SDG 15.

Individual tree segmentation and diameter at breast height (DBH) estimation are fundamental to precision forest inventory. Light Detection and Ranging (LiDAR) technology has become an essential tool for achieving these objectives at the single-tree level. Different LiDAR platforms—notably unmanned aerial vehicle (UAV) and Mobile Laser Scanning (MLS)—each offer distinct advantages in capturing forest structural information. Multi-source LiDAR fusion has been proposed as a strategy to combine these complementary strengths. However, how to effectively select segmentation algorithms across different LiDAR data sources remains insufficiently understood, particularly for subtropical coniferous plantations with heterogeneous canopy structure. This study systematically compared four individual tree segmentation algorithms (Donager2021, Dalponte2016, Silva2016, and marker-controlled watershed segmentation [MCWS]) across three LiDAR data sources (UAV-only, MLS-only, and fused UAV–MLS) in Pinus massoniana plantations in subtropical China. DBH estimation models were then developed based on the best-performing segmentation results to examine whether data fusion simultaneously improves both detection and DBH retrieval accuracy. The main findings are as follows: (1) the three canopy height model (CHM)-based algorithms achieved a mean overall accuracy (OA) for individual-tree detection of approximately 64% on UAV data but fell below 30% on MLS data, failing to support effective detection; (2) The Donager2021 algorithm, which directly exploits trunk structure from three-dimensional point clouds, achieved the highest OA of 93.15% with MLS data and further improved to 94.82% with fused data; (3) DBH estimation reached a mean R² of 0.96 for both MLS and fused datasets, yet MLS LiDAR alone produced a lower RMSE (2.31 cm; rRMSE = 5.91%) than fused LiDAR (RMSE = 2.40 cm; rRMSE = 6.24%); and (4) higher detection accuracy did not necessarily lead to better DBH estimation, revealing a trade-off between the two objectives. These findings indicate that fusion does not universally improve all downstream tasks, and that the choice of LiDAR configuration and segmentation algorithm should be guided by specific inventory objectives.

Accurate quantification of campus vegetation carbon stocks is essential for advancing carbon neutrality goals and refining urban carbon management strategies. This study pioneers the integration of drone and backpack LiDAR data to overcome limitations in conventional carbon estimation approaches. The Comparative Shortest-Path (CSP) algorithm was originally developed to segment tree crowns from point cloud data, with its design informed by metabolic ecology theory—specifically, that vascular plants tend to minimize the transport distance to their roots. In this study, we deployed the Comparative Shortest-Path (CSP) algorithm for individual tree recognition across 897 campus trees, achieving 88.52% recall, 72.45% precision, and 79.68% F-score—with 100% accuracy for eight dominant species. Diameter at breast height (DBH) was extracted via least-squares circle fitting, attaining >95% accuracy for key species such as Magnolia grandiflora and Triadica sebifera. Carbon storage was calculated through species-specific allometric models integrated with field inventory data, revealing a total stock of 163,601 kg (mean 182.4 kg/tree). Four dominant species—Cinnamomum camphora, Liriodendron chinense, Salix babylonica, and Metasequoia glyptostroboides—collectively contributed 84.3% of total storage. As the first integrated application of multi-platform LiDAR for campus-scale carbon mapping, this work establishes a replicable framework for precision urban carbon sink assessment, supporting data-driven campus greening strategies and climate action planning.