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 objective of this work was to analyze the spatial distribution and the behavior of species richness and diversity in a shrub savanna fragment, in 2003 and 2014, using ordinary kriging, in the state of Minas Gerais, Brazil. In both evaluation years, the measurements were performed in a fragment with 236.85 hectares, in which individual trees were measured and identified across 40 plots (1,000 m²). Species richness was determined by the total number of species in each plot, and diversity by the Shannon diversity index. For the variogram study, spatial models were fitted and selected. Then, ordinary kriging was applied and the spatial distribution of the assessed variables was described. A strong spatial dependence was observed between species richness and diversity by the Shannon diversity index (<25% spatial dependence degree). Areas of low and high species diversity and richness were found in the shrub savanna fragment. Spatial distribution behavior shows relative stability regarding the number of species and the Shannon diversity index in the evaluated years.

Mid-rotation fertilization presents an opportunity to increase the economic return of plantation forests in the southeastern United States (SEUS). For this reason, the Forest Productivity Cooperative established a series of mid-rotation fertilization trials in Pinus taeda L. plantations across the SEUS between 1984 and 1987. These trials identified site-specific responses to nitrogen (N) and phosphorus (P) fertilizers, resulting in increased stand production for 6–10 years after fertilization. There are successful volume response models that allow users to quantify the gain in stand productivity resulting from fertilization. However, all the current models depend on empirical relationships that are not bounded by biological response, meaning that greater fertilizer additions continue to create more volume gains, regardless of physiological limits. To address this shortcoming, we developed a bounded response model that evaluates relative volume response gain to fertilizer addition. Site index and relative spacing are included as model parameters to help provide realistic estimates. The model is useful for evaluating productivity gain in Pinus taeda stands that are fertilized with N and P in mid-rotation.

Modeling the growth of Bertholletia excelsa Bonpl. (B. excelsa) trees in natural forests is important for understanding the species’ ecology and for better defining site-specific management. In this sense, this study aimed to model the diameter growth rate of B. excelsa trees in contrasting forest environments in the Brazilian Amazon. This study was conducted in the Extractive Reserve Rio Cajari (RESEX Cajari). Growth models were fitted at species level to predict diameter growth rate in the two Amazonian forest environments. Subsequently, the age at which the B. excelsa tree in each forest environment reaches the minimum diameter for seed production was calculated by integrating the growth models. In each forest environment, the negative exponential behavior of the diametric growth rate of the tree species was fitted by an appropriate model. The time required for B. excelsa trees to reach the minimum diameter was shorter in the secondary forest environment when compared with that of the old growth terra firme forest (47 and 78 years to reach the diameter of 25 cm in the secondary and old growth terra firme forest, respectively). While the average growth pattern indicated higher diameter growth rates of B. excelsa in the secondary forest environment, the high level of uncertainty in the model’s estimation makes this inference complex. In conclusion, the secondary forest seems a favorable forest environment for the growth of B. excelsa trees, which may be an indicator of the potential for secondary forest environments to produce B. excelsa seeds in the future.