Development of Estimation Models for Individual Tree Aboveground Biomass Based on TLS-Derived Parameters

Abstract

Forest biomass is a foundation for evaluating the contribution to the carbon cycle of forests, and improving biomass estimation accuracy is an urgent problem to be addressed. Terrestrial laser scanning (TLS) enables the accurate restoration of the real 3D structure of forests and provides valuable information about individual trees; therefore, using TLS to accurately estimate aboveground biomass (AGB) has become a vital technical approach. In this study, we developed individual tree AGB estimation models based on TLS-derived parameters, which are not available using traditional methods. The height parameters and crown parameters were extracted from the point cloud data of 1104 trees. Then, a stepwise regression method was used to select variables for developing the models. The results showed that the inclusion of height parameters and crown parameters in the model provided an additional 3.76% improvement in model estimation accuracy compared to a DBH-only model. The optimal linear model included the following variables: diameter at breast height (DBH), minimum contact height (Hcmin), standard deviation of height (Hstd), 1% height percentile (Hp1), crown volume above the minimum contact height (CVhcmin), and crown radius at the minimum contact height (CRhcmin). Comparing the performance of the models on the test set, the ranking is as follows: artificial neural network (ANN) model > random forest (RF) model > linear mixed-effects (LME) model > linear (LN) model. Our results suggest that TLS has substantial potential for enhancing the accuracy of individual-tree AGB estimation and can reduce the workload in the field and greatly improve the efficiency of estimation. In addition, the model developed in this paper is applicable to airborne laser scanning data and provides a novel approach for estimating forest biomass at large scales.

Natural vegetation biomass and the dimension of forest quality in tropical agricultural landscapes

“…We note that using forest degradation categories is not recommended when more direct approaches are available to quantify AGB, as we did with our reference land units, and as possible with other alternatives requiring specific aerial surveying (e.g., d'Oliveira et al, 2020; Wang et al, 2023). By focusing on categories clearly detectable by the human eye and easily trained when using machine learning, we introduce a step that treats a continuous variable in a discrete manner.…”

Evaluation of Accuracy in Estimating Diameter at Breast Height Based on the Scanning Conditions of Terrestrial Laser Scanning and Circular Fitting Algorithm

“…This study examined the variations in accuracy depending on the data construction methods centered around DBH. Recently, research using LiDAR systems to estimate various forest-related parameters such as tree height, biomass, forest structure, and 3D modeling was conducted [43][44][45][46][47]. Therefore, future research should not only explore DBH but also how data construction methods influence the estimation of various forest parameters.…”

A Novel Approach to Characterizing Crown Vertical Profile Shapes Using Terrestrial Laser Scanning (TLS)

“…Although trunk diameter extraction was not performed in this study, it was studied in the vertical direction using point cloud data; so, it can be compared with the results of Li's study for mutual verification. TLS applies a bottom-up scanning method [95,103], which gradually becomes less effective as the trees become taller. Due to the mutual occlusion of tree crowns, it further increases the noise of the top crown point cloud, which eventually leads to a decrease in the accuracy of the CRs extracted at the top of the tree crown.…”