Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

Abstract

UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.

Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees

“…The sensitivity analysis with increasing raster cell sizes as a proxy for decreasing point density suggests that our best models could perform similarly using ALS data instead of TLS, providing point density is greater than 16 pts/m 2 . While the point density of ALS and DLS (laser scanning from drones) point clouds is dependent on the flight altitude and speed, number of flight lines, and scan and pulse rates, the 16 pts/m 2 threshold is attainable by both ALS and DLS [85][86][87]. This threshold is also consistent with the one suggested in a recent study for height estimation of coniferous trees using drone-based LiDAR point clouds of different point densities, which found that height accuracy only worsens at below 17 pts/m 2 [73].…”

Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions

“…Meanwhile, previous research has found variable accuracies when using ALS to estimate other attributes of individual trees. Several studies have found RMSEs for DBH estimates in the range of 4-8 cm [14,18,[36][37][38]. Using high-density ALS point clouds, Hyyppä et al [9] improved the accuracy to 2-3 cm (6-10%) by automatically detecting stem arcs, i.e., horizontal clusters of laser hits to the tree stem obtained during a brief period of time.…”

A Unmanned Aerial Vehicle (UAV)/Unmanned Ground Vehicle (UGV) Dynamic Autonomous Docking Scheme in GPS-Denied Environments

“…On the one hand, several methods have been based on infrastructure, which derives the location from distance information to anchors, such as GPS [9], motion capture system (MCS) [10], ultra-wideband (UWB) [11], etc. On the other hand, some on-board sensors (e.g., electro-optical devices [12], vision sensors [13,14] and laser scanners [15]) have been used to calculate the distance between the UAV and docking target in order to locate the target. The common knowledge is that GPS is cheap and relatively mature.…”