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Forests 2017, 8(8), 300; doi:10.3390/f8080300

Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands

1
Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
2
Unidade de Xestión Forestal Sostible (GI-1837-UXFS), Departamento de Producción Vexetal e Proxectos de Enxeñaría, Universidade de Santiago de Compostela, Escola Politéctica Superior, R/Benigno Ledo s/n, 27002 Lugo, Spain
3
Department of Forest Engineering, Resources and Management (FERM), 3200 SW Jefferson Way, Corvallis, OR 97331, USA
4
Laboratory of Applications of Remote Sensing in Ecology (LARSE), US Forest Service—Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USA
5
US Forest Service—Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USA
6
Unidade de Biodiversidade e Botánica Aplicada (GI-1809-BIOAPLIC), Departamento de Botánica, Universidade de Santiago de Compostela, Escuela Politécnica Superior, R/Benigno Ledo s/n, 27002 Lugo, Spain
*
Author to whom correspondence should be addressed.
Received: 7 July 2017 / Revised: 1 August 2017 / Accepted: 10 August 2017 / Published: 18 August 2017
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Abstract

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. View Full-Text
Keywords: unmanned aerial vehicles (UAV); forest inventory; biomass growth; canopy height model (CHM); object based image analysis (OBIA); Structure from Motion (SfM) unmanned aerial vehicles (UAV); forest inventory; biomass growth; canopy height model (CHM); object based image analysis (OBIA); Structure from Motion (SfM)
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Guerra-Hernández, J.; González-Ferreiro, E.; Monleón, V.J.; Faias, S.P.; Tomé, M.; Díaz-Varela, R.A. Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands. Forests 2017, 8, 300.

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