Choosing the right close-range technology for measuring DBH in fast-growing trees plantations

Abstract

Recently, the cultivation of fast-growing tree (FGT) plantations has gained importance due to the growing energy and climate crisis. FGT plantations have the potential to reduce carbon footprints and lower greenhouse gas emissions by utilization of local renewable energy sources. Effective monitoring of above-ground biomass (AGB) is crucial for the successful management of these plantations. Standard methods for estimating AGB rely on easily measurable parameters, such as Diameter at Breast Height (DBH) and tree height, which are highly correlated with AGB. Traditional methods for measuring DBH include measuring tapes and calipers; however, these techniques can be labor-intensive, time-consuming, and limited when assessing large areas. Innovative approaches, such as photogrammetry, terrestrial laser scanning (TLS), mobile laser scanning (MLS), and iPhone LiDAR scanning, can complement these traditional methods by generating point clouds that can be used for extracting dendrometric parameters. This study evaluates the effectiveness of TLS (RIEGL VZ-1000), MLS (Stonex X120 GO), iPhone LiDAR (iPhone 13 Pro MAX), and terrestrial photogrammetry (iPhone 13 Pro MAX) for estimating DBH in a Paulownia plantation. Each technology has limitations: while TLS offers high accuracy, it is also expensive and time-consuming. Similarly, MLS is relatively costly. On the other hand, iPhone LiDAR and terrestrial photogrammetry are more affordable alternatives; however, the iPhone LiDAR has a limited scanning range, and photogrammetry requires considerable time and expertise for data collection and processing. The primary objective of this study was to evaluate these technologies based on their accuracy in DBH estimation, ease of use, data collection, processing time, and cost within the ideal conditions of a Paulownia plantation (characterized by the absence of understory, level ground, and uniform tree shape and spacing). The aim was to determine whether traditional methods could be replaced with more efficient, quicker, easier, and cost-effective alternatives. Results indicated that TLS, MLS, and photogrammetry provided similar DBH estimation accuracies, with root mean square error (RMSE) values between 0.7 and 0.72 cm and relative RMSE values between 2.87 % and 2.95 %. In contrast, the iPhone LiDAR was the least accurate, with an RMSE of 1.7 cm and an rRMSE of 6.96 %. This study demonstrates that all evaluated technologies offer sufficient accuracy for DBH estimation, although TLS and MLS capture additional parameters at a higher cost. Therefore, TLS is impractical for DBH estimation in plantation environments due to its high cost, time, and labor demands. While less expensive, terrestrial photogrammetry also requires significant time investment and operator expertise. Despite its cost, MLS achieved the best results among all the evaluated technologies and proved to be the fastest and relatively simple. If cost is a concern, the best solution for DBH estimation in an FGT plantation environment would be iPhone LiDAR scanning. It represents the most affordable option with satisfactory accuracy and ease of use.