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Open AccessArticle

Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

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Forest Research Centre, School of Agriculture, University of Lisbon, Instituto Superior de Agronomia (ISA), Tapada da Ajuda, 1349-017 Lisboa, Portugal
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3edata. Centro de iniciativas empresariais. Fundación CEL. O Palomar s/n, 27004 Lugo, Spain
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Tecnosylva. Parque Tecnológico de León. 24009 León, Spain
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Biosciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20707, USA
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Department of Geographical Sciences, University of Maryland, College Park, Maryland, MD 20740, USA
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RAIZ (Forest and Paper Research Institute), The Navigator Company, Rua José Estevão, 3800-783 Eixo, Portugal
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Departamento de Tecnología Minera, Topografía y de Estructuras, Grupo de Investigación en Geomática e Ingeniería Cartográfica GI-202-GEOINCA, Escuela Superior y Técnica de Ingenieros de Minas, Universidad de León, Av. de Astorga s/n, Campus de Ponferrada, 24401 Ponferrada, Spain
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Biodiversity and Applied Botany Unit (GI BIOAPLIC 1809), Department of Botany, University of Santiago de Compostela, Escuela Politécnica Superior, R/Benigno Ledo, Campus Universitario, 27002 Lugo, Spain
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Author to whom correspondence should be addressed.
Forests 2019, 10(10), 905; https://doi.org/10.3390/f10100905
Received: 9 September 2019 / Revised: 23 September 2019 / Accepted: 8 October 2019 / Published: 15 October 2019
Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations. View Full-Text
Keywords: unmanned aerial vehicles (UAV); forest inventory; volume; canopy height model (CHM); object based image analysis (OBIA); structure from motion (SfM) unmanned aerial vehicles (UAV); forest inventory; volume; canopy height model (CHM); object based image analysis (OBIA); structure from motion (SfM)
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Guerra-Hernández, J.; Cosenza, D.N.; Cardil, A.; Silva, C.A.; Botequim, B.; Soares, P.; Silva, M.; González-Ferreiro, E.; Díaz-Varela, R.A. Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data. Forests 2019, 10, 905.

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