Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping

Abstract

Phenotyping has been a reality for aiding the selection of optimal crops for specific environments for decades in various horticultural industries. However, until recently, phenotyping was less accessible to tree breeders due to the size of the crop, the length of the rotation and the difficulty in acquiring detailed measurements. With the advent of affordable and non-destructive technologies, such as mobile laser scanners (MLS), phenotyping of mature forests is now becoming practical. Despite the potential of MLS technology, few studies included detailed assessments of its accuracy in mature plantations. In this study, we assessed a novel, high-density MLS operated below canopy for its ability to derive phenotypic measurements from mature Pinus radiata. MLS data were co-registered with above-canopy UAV laser scanner (ULS) data and imported to a pipeline that segments individual trees from the point cloud before extracting tree-level metrics. The metrics studied include tree height, diameter at breast height (DBH), stem volume and whorl characteristics. MLS-derived tree metrics were compared to field measurements and metrics derived from ULS alone. Our pipeline was able to segment individual trees with a success rate of 90.3%. We also observed strong agreement between field measurements and MLS-derived DBH (R2 = 0.99, RMSE = 5.4%) and stem volume (R2 = 0.99, RMSE = 10.16%). Additionally, we proposed a new variable height method for deriving DBH to avoid swelling, with an overall accuracy of 52% for identifying the correct method for where to take the diameter measurement. A key finding of this study was that MLS data acquired from below the canopy was able to derive canopy heights with a level of accuracy comparable to a high-end ULS scanner (R2 = 0.94, RMSE = 3.02%), negating the need for capturing above-canopy data to obtain accurate canopy height models. Overall, the findings of this study demonstrate that even in mature forests, MLS technology holds strong potential for advancing forest phenotyping and tree measurement.

Automatic tree detection and attribute characterization using portable terrestrial lidar

“…Xie et al (2022) stated that when trees are big, the shapes of their stems' cross-sections tend to be irregular, thus, increasing deviations between different measuring methods. Some other authors, such as Ryding et al (2015), Bauwens et al (2016), andHartley et al (2022), also noted that irregularities (bark roughness and textures, or non-regular cross-section shapes) have an influence on the measurement of diameters. In fact, the greatest deviation coincides with the largest trees (for example, trees number 7 and 12).…”

What matters most? Assessment of within-canopy factors influencing the needle microbiome of the model conifer, Pinus radiata

“…Scans followed an approximate path that took in the study tree and the neighbouring trees within the trial area. Using simultaneous localisation and mapping (SLAM), as opposed to global-navigational-satellite system (GNSS), the Hovermap is much more suited to the often GNSS-denied environments in forest stands [ 35 ]. Point clouds were generated within the Emesent software package version 1.5 (Emesent, Milton, QLD, Australia) and colourised using the Emesent colourisation kit.…”

Use of a Consumer-Grade UAV Laser Scanner to Identify Trees and Estimate Key Tree Attributes across a Point Density Range

“…Under these circumstances, it was necessary to fuse ULS data with LiDAR collected from ground-based sensors to estimate DBH with high precision [65]. However, we have tested LiDAR from a mobile laser scanner within a range of unpruned radiata pine stands and found that the dense needle cover makes it impossible to reconstruct the stem in unpruned stands [68] which constitute the majority of New Zealand plantation forests [3]. In contrast, the models employed here, use canopy metrics, that can be more robustly characterized from ULS.…”