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Advisor(s)
Abstract(s)
Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are
economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation
attributes from lidar remote sensing data combined with statistical modeling approaches is a step
towards forest inventory operationalization and might improve industry e ciency in monitoring and
managing forest resources. In this study, we first developed and tested a framework for modeling
individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and
linear mixed-e ect models (LME) and assessed the gain in accuracy compared to a conventional
linear fixed-e ects model (LFE). Second, we evaluated the potential of using the tree-level estimates
for determining tree attribute uniformity across di erent stand ages. In the field, tree measurements,
such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh)
were collected through conventional forest inventory practices, and tree-level aboveground carbon
(AGC) was estimated using allometric equations. Individual trees were detected and delineated
from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume
and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived
crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying
intercept and slope model, setting species, genotype, and stand (alone and nested) as random e ects.
For comparison, we also modeled the same attributes using a conventional LFE model. The tree
attribute estimates derived from the best LME model were used for assessing forest uniformity at
the tree level using the Lorenz curves and Gini coe cient (GC).We successfully detected 96.6% of
the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was
composed of the stand as a random e ect variable, and canopy height, crown volume, and crown
projected area as fixed e ects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%,
12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME
model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations,
especially for the older stands. All stands showed a high level of tree uniformity with GC values
approximately 0.2. This study demonstrates that accurate detection of individual trees and their
associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity
in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further
underscores the high potential of our proposed approach to monitor standing stock and growth in
Eucalyptus—and similar forest plantations for carbon dynamics and forest product planning
Description
Keywords
lidar tree level modeling aboveground carbon remote sensing linear mixed-effect models
Pedagogical Context
Citation
Remote Sens. 2020, 12, 3599
Publisher
MDPI