Accuracy assessment and error analysis for diameter at breast height measurement of trees obtained using a novel backpack LiDAR system

Abstract

Background The LiBackpack is a recently developed backpack light detection and ranging (LiDAR) system that combines the flexibility of human walking with the nearby measurement in all directions to provide a novel and efficient approach to LiDAR remote sensing, especially useful for forest structure inventory. However, the measurement accuracy and error sources have not been systematically explored for this system. Method In this study, we used the LiBackpack D-50 system to measure the diameter at breast height (DBH) for a Pinus sylvestris tree population in the Saihanba National Forest Park of China, and estimated the accuracy of LiBackpack measurements of DBH based on comparisons with manually measured DBH values in the field. We determined the optimal vertical slice thickness of the point cloud sample for achieving the most stable and accurate LiBackpack measurements of DBH for this tree species, and explored the effects of different factors on the measurement error. Result 1) A vertical thickness of 30 cm for the point cloud sample slice provided the highest fitting accuracy (adjusted R2 = 0.89, Root Mean Squared Error (RMSE) = 20.85 mm); 2) the point cloud density had a significant negative, logarithmic relationship with measurement error of DBH and it explained 35.1% of the measurement error; 3) the LiBackpack measurements of DBH were generally smaller than the manually measured values, and the corresponding measurement errors increased for larger trees; and 4) by considering the effect of the point cloud density correction, a transitional model can be fitted to approximate field measured DBH using LiBackpack- scanned value with satisfactory accuracy (adjusted R2 = 0.920; RMSE = 14.77 mm), and decrease the predicting error by 29.2%. Our study confirmed the reliability of the novel LiBackpack system in accurate forestry inventory, set up a useful transitional model between scanning data and the traditional manual-measured data specifically for P. sylvestris, and implied the applicable substitution of this new approach for more species, with necessary parameter calibration.

LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

“…Lu et al [98] used seed points generated from BLS data for individual tree detection from UAV-LS, which significantly improved the accuracy for individual tree detection and segmentation. Xie et al's [100] results indicate that a vertical thickness of 30 cm for point cloud slices was best for DBH estimation (i.e., R square was 0.89 when compared with manual measurements) at the single tree level from BLS point clouds and DBH extracted from BLS was smaller than that from manual measurements. Zhang et al [101] compared the potential of TLS and BLS for the estimation of apple tree branches and the results show that TLS is better in the estimation of branch length and BLS is better for the number counting of branches.…”

“…Yan et al [99] proposed a new method (i.e., a concave hull by slices method) for individual tree segmentation, which obtained better accuracy than five existing methods including manual measurement, 3D convex hull, 3D alpha shape, convex hull by slices and a voxelbased method. BLS arouses more and more interest, currently because it is superior to both TLS and VLS due to its flexibility to collect close-by measurements in all directions in a timely manner [100]. Lu et al [98] used seed points generated from BLS data for individual tree detection from UAV-LS, which significantly improved the accuracy for individual tree detection and segmentation.…”

“…Zhang et al [101] compared the potential of TLS and BLS for the estimation of apple tree branches and the results show that TLS is better in the estimation of branch length and BLS is better for the number counting of branches. However, the application of BLS is still in an early stage, and its measurement accuracy and error sources have not been systematically explored [100].…”

“…Combining UAV-LS and BLS might be a successful future direction for AGB estimation for single trees. BLS and UAV-LS are both emerging remote techniques with unprecedented detailed 3D information for reconstructing forest structure [98,100,115]. However, few studies have tried this.…”

Estimation of breast height diameter and trunk curvature with linear and single-photon LiDARs

“…Higher-density LiDAR data, with a UAV-LiDAR, for example, may increase the density of pulses/m 2 which can get between the branches, which would to some extent mitigate the problem of trunk obscuration, by producing enough data from the trunk surface. The accuracy of present portable LiDARs is limited to the level of several centimeters (Xie et al 2020;Ghimire et al 2017), whereas the Finnish wood measurement law requires accuracy of ± 4% for trunk diameter measured in the harvester head corresponding to ± 1 cm accuracy with nominal trunk diameter of 25 cm (Heikurainen et al 2018). It should be noted, however, that Saarinen et al (2020) have presented promising results of terrestrial laser scanning (TLS) with a ground-based geosystem LiDAR, placed on a tripod, which clearly limits the mobility and flexibility of measurements.…”