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Advisor(s)
Abstract(s)
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
Description
Keywords
unmanned aerial vehicles (UAV) forest inventory volume canopy height model (CHM) object based image analysis (OBIA) structure from motion (SfM)
Pedagogical Context
Citation
Forests 2019, 10, 905
Publisher
MDPI