Mobile Laser Scanning for Estimating Tree Structural Attributes in a Temperate Hardwood Forest

Abstract

The emergence of mobile laser scanning (MLS) systems that use simultaneous localization and mapping (SLAM) technology to map their environment opens up new opportunities for characterizing forest structure. The speed and accuracy of data acquisition makes them particularly adapted to operational inventories. MLS also shows great potential for estimating inventory attributes that are difficult to measure in the field, such as wood volume or crown dimensions. Hardwood species represent a significant challenge for wood volume estimation compared to softwoods because a substantial portion of the volume is included in the crown, making them more prone to allometric bias and more complex to model. This study assessed the potential of MLS data to estimate tree structural attributes in a temperate hardwood stand: height, crown dimensions, diameter at breast height (DBH), and merchantable wood volume. Merchantable wood volume estimates were evaluated to the third branching order using the quantitative structural modeling (QSM) approach. Destructive field measurements and terrestrial laser scanning (TLS) data of 26 hardwood trees were used as reference to quantify errors on wood volume and inventory attribute estimations from MLS data. Results reveal that SLAM-based MLS systems provided accurate estimates of tree height (RMSE = 0.42 m (1.78%), R2 = 0.93), crown projected area (RMSE = 3.23 m2 (5.75%), R2 = 0.99), crown volume (RMSE = 71.4 m3 (23.38%), R2 = 0.99), DBH (RMSE = 1.21 cm (3.07%), R2 = 0.99), and merchantable wood volume (RMSE = 0.39 m3 (18.57%), R2 = 0.95), when compared to TLS. They also estimated operational merchantable volume with good accuracy (RMSE = 0.42 m3 (21.82%), R2 = 0.94) compared to destructive measurements. Finally, the merchantable stem volume derived from MLS data was estimated with high accuracy compared to TLS (RMSE = 0.11 m3 (8.32%), R2 = 0.96) and regional stem taper models (RMSE = 0.16 m3 (14.7%), R2 = 0.93). We expect our results would provide a better understanding of the potential of SLAM-based MLS systems to support in-situ forest inventory.

Remarks on Geomatics Measurement Methods Focused on Forestry Inventory

“…One new method is to implement measurement devices into common instruments such as smartphones or tablets. Using a mobile phone with RGB-D SLAM for obtaining basic parameters such as DBH, the height of trees, and tree position was analyzed [42].…”

Prompt Mapping Tree Positions with Handheld Mobile Scanners Based on SLAM Technology

“…These advanced technologies have been applied for documenting intricate architecture [37]; additionally, HMLSs have gained attention for documenting cultural heritage in various urban environments [38]. Additionally, authors often compare HMLSs with various technologies, such as TLS, airborne laser scanning (ALS), photogrammetry, or other remote sensing outputs [39][40][41][42], but, to the best of our knowledge, no one has yet compared these two devices and their precision and limits in forests environment.…”

Optimization of unmanned aerial vehicle application for measuring in complex urban green space with rich landscape value

“…In recent years, point cloud products have provided the possibility to efficiently measure tree biometric variables such as DBH, tree height and crown diameter (Kwong and Fung, 2020;Novotny et al, 2021;Wang et al, 2019). UAV image (Fakhri and Latifi, 2021), mobile laser scanning (MLS) (Vandendaele et al, 2022), terrestrial laser scanning (TLS) (Liang et al, 2016), and airborne laser survey (ALS) (Koma et al, 2021) all showed high accuracy. In the field of urban forestry, there have been notable endeavors that have yielded success in the application of point cloud technology.…”