Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

Abstract

The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R2 = 0.89), NB (RMSE% = 17.31% and R2 = 0.93) and BB (RMSE% = 24.09% and R2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method’s applicability in diverse forest structures and biomes is warranted.

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

“…With the central point of the sample plot as the center, we created square windows with different sizes, from 1 m × 1 m to 8 m × 8 m, with an interval of 1 m, and extracted height percentile variables and density variables from the point clouds for each window size. Both height percentile variables and density variables depict the vertical structure of vegetation [40]. The height percentiles indicate the height values of Lidar points below which a certain percentage of the points are accounted for.…”

Supporting Operational Tree Marking Activities through Airborne LiDAR Data in the Frame of Sustainable Forest Management

“…A set of allometric equations, developed by Georgopoulos et al [22,24], was employed to calculate total and tree components' biomass. The aforementioned equations were specifically developed for fir species, using each tree's DBH and height to estimate each individual's stem, bark, branches, and needles biomass.…”

“…Therefore, LiDAR technology enables the generation of a detailed tree registry over an entire forest area, providing accurate positional information on each individual tree. Except for the efficient cut-tree marking, the precise geographical location and height of ALS-detected trees constitute a fundamental source of information for the estimation of additional, vital for sustainable management, tree attributes, such as the diameter at breast height (DBH), the tree biomass (e.g., foliage and branches biomass), the tree sequestrated carbon and the potential surface fuel load (SFL, the dry weight of woody and non-woody organic material potentially created in case of logging a tree) [14,21,22]. As such, forest managers can use the additional estimated tree parameters to further assess the carbon-and SFL-related ecological impacts arising from each harvesting activity.…”

Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland

“…CHM models are used to perform analyses to extract information about a stand of trees. They are often used in forestry to determine the quantities that characterize a stand, which is important for describing tree growth [12], planning silvicultural activities [13], and inventorying timber volume and stand biomass [12,14,15]. The precision of a canopy model is affected by many factors, including the density of the point cloud, the interpolation algorithm used, and the resolution of the DTM and DSM models from which the CHM model is created.…”