From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure

Abstract

The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype individual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. Individual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each individual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of divergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based diversity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities.

Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

“…The validation results achieved for the four structural traits measured in the field were in strong agreement with the results previously obtained on a larger subset of the same restoration experiment using LiDAR mounted on a unmanned aerial vehicle (UAV) [11] and, more generally, with the broader ZEB1 literature [17,18,29]. Consistent with previous observations [11], crown insertion height was difficult to validate. This was largely attributed to the nature of these restoration species, with crowns extending to the bottom of the stem in many cases, making the separation from the underlying grassy layer particularly difficult.…”

“…Two additional measures related to crown properties were also ground-truthed in this study: field-measured crown width was highly correlated with crown width at the widest cross-section (R 2 = 0.84, RMSE = 0.53 m (2018); R 2 = 0.85, RMSE = 0.52 m (2019)), while field-measurements of crown insertion height were poorly correlated with a LiDAR composite value (R 2 = 0.18, RMSE = 0.29 m (2018); R 2 = 0.09, RMSE = 0.30 m (2019)). This composite value derived from LiDAR was obtained after running a random forest model following Camarretta et al [11], fitting the 99th height percentile, diameter, rumple index, and height to the widest cross-section as predictors of the field measured insertion height.…”

“…Once individual tree crowns were segmented, the extraction and calculation of structural traits at the individual treelevel (Table 2) was carried out using the statistical language R [23]. These structural traits were chosen as they have been previously shown to significantly vary between and within species [11] and are presented as potential proxies for characterizing the return of structural complexity in ecological restoration plantings. To estimate above-ground biomass (AGB) and diameter at breast height (DBH) from the LiDAR point clouds, we applied the following allometric equations by Jucker et al [24]:…”

“…Unfortunately, the use of traditional field inventories for the assessment of spatial and temporal changes in forest structural complexity and forest structural traits can be expensive and time demanding [4]. Consequently, interest is growing in the restoration community towards using remote sensing applications to provide a timely and cost-effective alternative [9][10][11].…”

Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

“…For example, Torresan et al [24] applied a dense UAV-LS point cloud collected in a dense mixed forest in Italy, and showed the under-segmentation of small understorey trees. However, Camarretta et al [25] used UAV-LS data to model DBH across all size-classes in a restored Eucalypt forest in Tasmania, Australia. Using crown diameter and height as predictor variables, they observed an R 2 of 0.8 between measured and modelled DBH, demonstrating the potential of UAV-LS to produce accurate individual tree level DBH predictions.…”