Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

Abstract

Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point cloud for extracting total tree height and diameter at breast height (1.3 m; DBH). We compared two different methods to accurately estimate total tree heights: the first method was based on a modified version of the local maxima algorithm for treetop detection, “HTTD”, and for the second method we used the centers of stem cross-sections at stump height (30 cm), “HTSP”. DBH was estimated by a computationally robust algebraic circle-fitting algorithm through hierarchical cluster analysis (HCA). This study aimed to assess the accuracy of these descriptors for evaluating total stem volume by comparing the results with the reference tree measurements. The difference between the estimated total stem volume from HTTD and measured stems was 2.732 m3 for European oak and 2.971 m3 for Norway spruce; differences between the estimated volume from HTSP and measured stems was 1.228 m3 and 2.006 m3 for European oak and Norway spruce, respectively. The coefficient of determination indicated a strong relationship between the measured and estimated total stem volumes from both height estimation methods with an R2 = 0.89 for HTTD and R2 = 0.87 for HTSP for European oak, and R2 = 0.98 for both HTTD and HTSP for Norway spruce. Our study has demonstrated the feasibility of finer-resolution remote sensing data for semi-automatic stem volumetric modeling of small-scale studies with high accuracy as a potential advancement in precision forestry.

Reliable Estimates of Merchantable Timber Volume from Terrestrial Laser Scanning

“…Based on the paired t-test for plot A (deciduous), there were statistically significant differences between measured and estimated merchantable volumes. In contrast, there were no statistically significant differences for plot B (coniferous) based on the Wilcoxon signed-rank test (Figure 8), which is consistent with a previous study we conducted [8].…”

“…Merchantable volume is a function of two important tree variables: (a) diameter and (b) height [3,4]. Diameter is commonly measured using calipers, logging tapes [5], and relascopes [6] in higher portions of the stem, while tree height or length is commonly measured using clinometers, laser rangefinders, hypsometers based on ultrasonic technology, and logging tapes [7,8]. Increasingly there is a need for regular and reliable updates of forest inventories (e.g., timber assessment of annual growth rates and stocking percentages) for planning and marketing purposes.…”

“…Modern remote sensing technologies, such as light detection and ranging (LiDAR), offer the advantage of conducting inventory surveys automatically, with high quality and reliability. The applications of terrestrial laser scanning (TLS) have largely focused on modelling forest structural metrics, such as height [15][16][17][18], diameter [15][16][17][18], stem volume [8,19,20], above-ground biomass [21,22], and branch architecture [23], among others. These modern techniques have proven to be superior to conventional field surveys for a series of reasons.…”

Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

“…We estimated the variance, standard error, confidence interval, and relative error for the mean individual volume ( ŷ) obtained from the scaling dataset (Equations ( 11) to (14) and for the estimated total volume ( Ŷ) (Equations ( 15) to ( 18)) based on a previous study [38]. Where N is the number of trees in the stand, n is the number of scaled trees, t is Student's t-distribution, Rj = ratio estimator, x i = is the volume of the scaled log cylinders, y i = individual volume per scaled tree, and x = mean volume of scaled log cylinders.…”

“…Managing planted forests requires precise, reliable information to support the decisions made in operational planning [10][11][12]. Forest attributes are estimated through forest inventories, which usually apply sampling methods based on sampling units and statistical extrapolation techniques [10,11,13,14]. For this reason, a precise estimation of the forest area is fundamental, since the results of the sampling are multiplied by it to determine the final estimate of the whole forest [15].…”