Use of multi-temporal UAV-derived imagery for estimating individual tree growth in Pinus pinea stands
High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86–87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015–2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool ; We thank the Portuguese Science Foundation (SFRH/BD/52408/2013) for funding the research activities of Juan Guerra and the Galician Government and European Social Fund (Official Journal of Galicia—DOG No. 52, 17/03/2014 p. 11343, exp: POS-A/2013/049) for funding the postdoctoral research stays of Eduardo González-Ferreiro. This research was supported by SuFoRun project "Models and decision Support tools for integrated Forest policy development under global change and associated Risk and Uncertaint" funded by the European Union's H2020 research and innovation ...