Abstract
Accurate collection of dendrometric information is essential for improving decision confidence and supporting potential advances in forest management planning (FMP). Total stem volume is an important forest inventory parameter that requires high accuracy. Terrestrial laser scanning (TLS) has emerged as one of the most promising tools for automatically measuring total stem height and diameter at breast height (DBH) with very high detail. This study compares the accuracy of different methods for extracting the total stem height and DBH to estimate total stem volume from TLS data. Our results show that estimates of stem volume using the random sample consensus (RANSAC) and convex hull and HTSP methods are more accurate (bias = 0.004 for RANSAC and bias = 0.009 for convex hull and HTSP) than those using the circle fitting method (bias = 0.046). Furthermore, the RANSAC method had the best performance with the lowest bias and the highest percentage of accuracy (78.89%). The results of this study provide insight into the performance and accuracy of the tested methods for tree-level stem volume estimation, and allow for the further development of improved methods for point-cloud-based data collection with the goal of supporting potential advances in precision forestry.
Highlights
In most countries, forest inventories are based on statistical sampling with traditional field measurements, which are usually costly, time-consuming, and laborious
The use of Terrestrial laser scanning (TLS) in modern forestry is a technology with high potential, which allows reliable and accurate reconstruction of stem geometry as long as the surface is sufficiently covered by points
In this paper we presented a benchmark assessment to investigate the accuracy of total stem volume at tree level from close-range sensing data, using different methodological approaches
Summary
Forest inventories are based on statistical sampling with traditional field measurements, which are usually costly, time-consuming, and laborious. The increasing demand for resources for human welfare, environmental protection, and conservation requires even higher precision and faster processing of forest inventories. Stem volume is an important input for basic forest inventories that helps in economic forecasting, decision making, and sustainable timber resource planning [1,2]. Stem volume cannot be measured directly, but must be modeled and predicted from other variables that are more measured. Stem volume is a function of the two main tree variables: (a) diameter at breast height (DBH), and (b) tree height [3,4]. Diameter is measured using either calipers or a logging tape [5], while tree height is commonly measured using clinometer, laser rangefinder, or hypsometer based on ultrasonic technology [6,7]
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