An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR) dataset using two different techniques focusing on (i) 3-D canopy structural information by discrete (XYZ) LiDAR metrics (DR-metrics), and (ii) the detailed geometric and radiometric information of the returned waveform by full-waveform LiDAR metrics (FW-metrics), and to evaluate the capacity of these metrics in predicting biomass and its components in subtropical forest ecosystems. This study was undertaken in a mixed subtropical forest in Yushan Mountain National Park, Jiangsu, China. LiDAR metrics derived from DR and FW LiDAR data were used alone, and in combination, in stepwise regression models to estimate total as well as above-ground, root, foliage, branch and trunk biomass. Overall, the results indicated that three sets of predictive models performed well across the different subtropical forest types (Adj-R² = 0.42–0.93, excluding foliage biomass). Forest type-specific models (Adj-R² = 0.18–0.93) were generally more accurate than the general model (Adj-R² = 0.07–0.79) with the most accurate results obtained for coniferous stands (Adj-R² = 0.50–0.93). In addition, LiDAR metrics related to vegetation heights were the strongest predictors of total biomass and its components. This research also illustrates the potential for the synergistic use of DR and FW LiDAR metrics to accurately assess biomass stocks in subtropical forests, which suggest significant potential in research and decision support in sustainable forest management, such as timber harvesting, biofuel characterization and fire hazard analyses.

Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this scanner depends largely on the efficacy of point cloud data (PCD) analysis. In this paper, we present a novel method for quantifying total leaf area within each tree canopy from PCD. Firstly, the shape, normal vector distribution and structure tensor of PCD features were combined with the semi-supervised support vector machine (SVM) method to separate various tree organs, i.e., branches and leaves. In addition, the moving least squares (MLS) method was adopted to remove ghost points caused by the shaking of leaves in the wind during the scanning process. Secondly, each target tree was scanned using two patterns, i.e., one scan and three scans around the canopy, to reduce the occlusion effect. Specific layer subdivision strategies according to the acquisition ranges of the scanners were designed to separate the canopy into several layers. Thirdly, 10% of the PCD was randomly chosen as an analytic dataset (ADS). For the ADS, an innovative triangulation algorithm with an assembly threshold was designed to transform these discrete scanning points into leaf surfaces and estimate the fractions of each foliage surface covered by the laser pulses. Then, a novel ratio of the point number to leaf area in each layer was defined and combined with the total number of scanned points to retrieve the total area of the leaves in the canopy. The quantified total leaf area of each tree was validated using laborious measurements with a LAI-2200 Plant Canopy Analyser and an LI-3000C Portable Area Meter. The results showed that the individual tree leaf area was accurately reproduced using our method from three registered scans, with a relative deviation of less than 10%. Nevertheless, estimations from only one scan resulted in a deviation of >25% in the retrieved individual tree leaf area due to the occlusion effect. Indeed, this study provides a novel connection between leaf area estimates and scanning sensor configuration and supplies an interesting method for estimating leaf area based on PCD.

The accurate estimation of individual tree level aboveground biomass (AGB) is critical for understanding the carbon cycle, detecting potential biofuels and managing forest ecosystems. In this study, we assessed the capability of the metrics of point clouds, extracted from the full-waveform Airborne Laser Scanning (ALS) data, and of composite waveforms, calculated based on a voxel-based approach, for estimating tree level AGB individually and in combination, over a planted forest in the coastal region of east China. To do so, we investigated the importance of point cloud and waveform metrics for estimating tree-level AGB by all subsets models and relative weight indices. We also assessed the capability of the point cloud and waveform metrics based models and combo model (including the combination of both point cloud and waveform metrics) for tree-level AGB estimation and evaluated the accuracies of these models. The results demonstrated that most of the waveform metrics have relatively low correlation coefficients (<0.60) with other metrics. The combo models (Adjusted R² = 0.78–0.89), including both point cloud and waveform metrics, have a relatively higher performance than the models fitted by point cloud metrics-only (Adjusted R² = 0.74–0.86) and waveform metrics-only (Adjusted R² = 0.72–0.84), with the mostly selected metrics of the 95th percentile height (H₉₅), mean of height of median energy (HOME_μ) and mean of the height/median ratio (HTMR_μ). Based on the relative weights (i.e., the percentage of contribution for R²) of the mostly selected metrics for all subsets, the metric of 95th percentile height (H₉₅) has the highest relative importance for AGB estimation (19.23%), followed by 75th percentile height (H₇₅) (18.02%) and coefficient of variation of heights (H_cv) (15.18%) in the point cloud metrics based models. For the waveform metrics based models, the metric of mean of height of median energy (HOME_μ) has the highest relative importance for AGB estimation (17.86%), followed by mean of the height/median ratio (HTMR_μ) (16.23%) and standard deviation of height of median energy (HOME_σ) (14.78%). This study demonstrated benefits of using full-waveform ALS data for estimating biomass at tree level, for sustainable forest management and mitigating climate change by planted forest, as China has the largest area of planted forest in the world, and these forests contribute to a large amount of carbon sequestration in terrestrial ecosystems.

In order to better assess the spatial variability in subtropical forest biomass, the goal of our study was to use small-footprint, discrete-return Light Detection and Ranging (LiDAR) data to accurately estimate and map above- and below-ground biomass components of subtropical forests. Foliage, branch, trunk, root, above-ground and total biomass of 53 plots (30 × 30 m) were modeled using a range of LiDAR-derived metrics, with individual models built for each of the three dominant forest types using stepwise multi-regression analysis. A regular grid covered the entire study site with cell size 30 × 30 m corresponding to the same size of the plots; it was generated for mapping each biomass component. Overall, results indicate that biomass estimation was more accurate in coniferous forests, compared with the mixed and broadleaved plots. The coefficient of determination (R²) for individual models was significantly enhanced compared with an overall generic, or common, model. Using independent stand-level data from ground inventory, our results indicated that overall the model fit was significant for most of the biomass components, with relationships close to a 1:1 line, thereby indicating no significant bias. This research illustrates the potential for LiDAR as a technology to assess subtropical forest carbon accurately and to provide a better understanding of how forest ecosystems function in this region.