Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

Abstract

The automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. In this study, we demonstrated that an under-canopy UAV laser scanning system utilizing a rotating laser scanner can alone provide accurate estimates of canopy height and stem volume for the majority of trees in a boreal forest. We mounted a rotating laser scanner based on a Velodyne VLP-16 sensor onboard a manually piloted UAV. The UAV was commanded with the help of a live video feed from the onboard camera. Since the system was based on a rotating laser scanner providing varying view angles, all important elements such as treetops, branches, trunks, and ground could be recorded with laser hits. In an experiment including two different forest structures, namely sparse and obstructed canopy, we showed that our system can measure the heights of individual trees with a bias of −20 cm and a standard error of 40 cm in the sparse forest and with a bias of −65 cm and a standard error of 1 m in the obstructed forest. The accuracy of the obtained tree height estimates was equivalent to airborne above-canopy UAV surveys conducted in similar forest conditions or even at the same sites. The higher underestimation and higher inaccuracy in the obstructed site can be attributed to three trees with a height exceeding 25 m and the reduced point density of these tree tops due to occlusion and the limited ranging capacity of the scanner. Additionally, we used our system to estimate the stem volumes of individual trees with a standard error at the level of 10%. This level of error is equivalent to the error obtained when merging above-canopy UAV laser scanner data with terrestrial point cloud data. The results show that we do not necessarily need a combination of terrestrial point clouds and point clouds collected using above-canopy UAV systems in order to accurately estimate the heights and the volumes of individual trees in reference data collection.

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

“…We retrieved vertical profiles of PAI and PAD by flying the UAS in canopy gaps. Although more stable in heavy winds, it is unlikely that larger UASs carrying heavier payloads (Brüllhardt et al ., 2020; Hyyppä et al ., 2021) can fly with a similar degree of safety in such areas. However, before we can reach a potential lower limit of gap size with our UAS in the upper canopy, the issue of UAS drift requires attention.…”

“…Laser scanning instrumentation can be mounted on UAS platforms to expand the horizons of ALS from piloted aircraft to smaller, more manoeuvrable systems (Wallace et al ., 2012; Brede et al ., 2019; Yin & Wang, 2019). A recent study demonstrated that within‐canopy flight is possible with a laser scanning UAS, although the UAS was relatively large and therefore potentially challenging and risky to fly in dense forest canopies (Hyyppä et al ., 2021). Additionally, recent studies have shown that above‐canopy ALS‐like canopy trait estimation algorithms can be applied to Structure from Motion photogrammetry data collected from UAS platforms (Brüllhardt et al ., 2020; Lin et al ., 2021).…”

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

“…These under-canopy MLS methods have been found to detect trees with a high success rate, with some studies showing the completeness of stem detection reaching above 90% [13,15,16,27,28]. Similarly high detection rates of over 90% have been found with under-canopy UAV laser scanning systems in sparse forest plots [8,29,30]. Studies have shown that stem detection rates decrease in more complex forest plots with a higher stem density and a lot of understory vegetation [8,[28][29][30][31].…”

Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data

“…Typically, these types of point clouds are collected at the plot-level, thereby limiting their spatial scale. Recent advancements in RPAS and MLS systems have allowed the two to be combined for beneath canopy flights [40], allowing MLS data to be collected across larger spatial scales.…”