Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data
MDPI, 2019
Online
academicJournal
Zugriff:
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 fromUnmannedAerialVehicles(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 E ciency (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 di erence 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
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Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data
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Autor/in / Beteiligte Person: | Guerra-Hernández, Juan ; Cozensa, Diogo N. ; Cardil, Adrian ; Silva, Carlos Alberto ; Botequim, Brigite ; Soares, Paula ; Silva, Margarida ; González-Ferreiro, Eduardo ; Diaz-Varela, Ramón ; Repositório da Universidade de Lisboa |
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Veröffentlichung: | MDPI, 2019 |
Medientyp: | academicJournal |
DOI: | 10.3390/f10100905 |
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