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 efficiency 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-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different 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 effects. 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 coefficient (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 effect variable, and canopy height, crown volume, and crown projected area as fixed effects. 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.

The wood of different species may present macroscopic similarities, which hinders a reliable differentiation between the specie. This can be a problem, especially in the tropics, given the high species diversity in this region. To overcome this problem of wood identification, colorimetric criteria can be applied, aiming at a better and more objective classification, as opposed to the traditional anatomic criteria. Therefore, the aim of this research was to analyze the variability of the wood color considering the cutting planes (tangential and radial) and to determine its correlation with the wood basic density of ten Amazonian tree species. The following species were studied: Apuleia leiocarpa (amarelão), Hymenolobium modestum (angelim-pedra), Anadenanthera columbrina (angico), Dipteryx odorata (cumaru), Pouteria pachycarpa (goiabão), Mezilaurus itauba (itaubá), Hymenaea courbaril (jatobá), Simarouba amara (marupá), Astronium leicontei (muiracatiara) and, Bowdichia nitida (sucupira). The CIEL*a*b* system was used to determine the colorimetric parameters (L*, a*, b*, C*, and h*), which were obtained through a portable colorimeter. The species were classified in six color specific groups: light yellow, olive brown, yellowish olive, olive light, grayish white and reddish brown. We observed statistical effect of the cut planes only for luminosity, with higher mean for radial face. The correlation analyses between color and basic density showed that the denser woods are darker and have more red pigment (a*). There is negative correlation between luminosity (L*) and wood basic density (-0.78). The colorimetric parameter a* presented positive correlation with the basic density (r = 0.80).