Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data
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Título: | Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data |
Autor/a: | Guerra Hernández, Juan Cosenza, Diogo N. Cardil, Adrián Silva, Carlos Alberto Botequim, Brigite Soares, Paula Silva, Margarida González Ferreiro, Eduardo Manuel Díaz Varela, Ramón Alberto |
Centro/Departamento: | Universidade de Santiago de Compostela. Departamento de Botánica |
Palabras chave: | Unmanned aerial vehicles (UAV) | Forest inventory | Volume | Canopy height model (CHM) | Object based image analysis (OBIA | Structure from motion (SfM) | |
Data: | 2019 |
Editor: | MDPI |
Cita bibliográfica: | Guerra-Hernández, Cosenza, Cardil, Silva, Botequim, Soares, Silva, et al. (2019). Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data. Forests, 10(10), 905. MDPI AG. Retrieved from http://dx.doi.org/10.3390/f10100905 |
Resumo: | 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. |
Versión do editor: | https://doi.org/10.3390/f10100905 |
URI: | http://hdl.handle.net/10347/21590 |
DOI: | 10.3390/f10100905 |
E-ISSN: | 1999-4907 |
Dereitos: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) |
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