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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 19; p. 3184
• Main Authors: Camarretta, Nicolò, A. Harrison, Peter, Lucieer, Arko, M. Potts, Brad, Davidson, Neil, Hunt, Mark
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: Architecture; Climate change; Climate effects; Design; Divergence; Drone aircraft; Environmental restoration; Eucalyptus; Genetic diversity; genetic variation; individual tree; LiDAR; Natural environment; Natural resource management; Natural resources; Phenotypes; Phenotyping; Photogrammetry; Productivity; Quantitative genetics; Remote sensing; Species; Species diversity; structural traits; tree architecture; Trees; UAV; Unmanned aerial vehicles; Australia; Tasmania Australia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12193184

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.