In fast-growing forests such as Eucalyptus plantations, the correct determination of stand productivity is essential to aid decision making processes and ensure the efficiency of the wood supply chain. In the past decade, advances in remote sensing and computational methods have yielded new tools, techniques, and technologies that have led to improvements in forest management and forest productivity assessments. Our aim was to estimate and map the basal area and volume of Eucalyptus stands through the integration of forest inventory, remote sensing, parametric, and nonparametric methods of spatial prediction. This study was conducted in 20 5-year-old clonal stands (362 ha) of Eucalyptus urophylla S.T.Blake x Eucalyptus camaldulensis Dehnh. The stands are located in the northwest region of Minas Gerais state, Brazil. Basal area and volume data were obtained from forest inventory operations carried out in the field. Spectral data were collected from a Landsat 5 TM satellite image, composed of spectral bands and vegetation indices. Multiple linear regression (MLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) methods were used for basal area and volume estimation. Using ordinary kriging, we spatialised the residuals generated by the spatial prediction methods for the correction of trends in the estimates and more detailing of the spatial behaviour of basal area and volume. The ND54 index was the spectral variable that had the best correlation values with basal area (r = − 0.91) and volume (r = − 0.52) and was also the variable that most contributed to basal area and volume estimates by the MLR and RF methods. The RF algorithm presented smaller basal area and volume errors when compared to other machine learning algorithms and MLR. The addition of residual kriging in spatial prediction methods did not necessarily result in relative improvements in the estimations of these methods. Random forest was the best method of spatial prediction and mapping of basal area and volume in the study area. The combination of spatial prediction methods with residual kriging did not result in relative improvement of spatial prediction accuracy of basal area and volume in all methods assessed in this study, and there is not always a spatial dependency structure in the residuals of a spatial prediction method. The approaches used in this study provide a framework for integrating field and multispectral data, highlighting methods that greatly improve spatial prediction of basal area and volume estimation in Eucalyptus stands. This has potential to support fast growth plantation monitoring, offering options for a robust analysis of high-dimensional data.

The objective of this study was to map the spatial distribution of aboveground carbon stock (using Regression-kriging) of arboreal plants in the Atlantic Forest, Semi-arid woodland, and Savanna Biomes in Minas Gerais State, southeastern Brazil. The database used in this study was obtained from 163 forest fragments, totaling 4,146 plots of 1,000 m² distributed in these Biomes. A geographical model for carbon stock estimation was parameterized as a function of Biome, latitude and altitude. This model was applied over the samples and the residuals generated were mapped based on geostatistical procedures, selecting the exponential semivariogram theoretical model for conducting ordinary Kriging. The aboveground carbon stock was found to have a greater concentration in the north of the State, where the largest contingent of native vegetation is located, mainly the Savanna Biome, with Wooded Savanna and Shrub Savanna phytophysiognomes. The largest weighted averages of carbon stock per hectare were found in the south-center region (48.6 Mg/ha) and in the southern part of the eastern region (48.4 Mg/ha) of Minas Gerais State, due to the greatest predominance of Atlantic Forest Biome forest fragments. The smallest weighted averages per hectare were found in the central (21.2 Mg/ha), northern (20.4 Mg/ha), and northwestern (20.7 Mg/ha) regions of Minas Gerais State, where Savanna Biome fragments are predominant, in the phytophysiognomes Wooded Savanna and Shrub Savanna.

O setor florestal brasileiro está em plena expansão e com um aumento gradativo de investidores florestais optando pelo cultivo de espécies de madeira nobre. O mogno africano (Khaya spp.) é uma espécie que vêm se destacando na preferência dos empresários como opção no investimento florestal. Porém, estudos e pesquisas sobre a espécie, principalmente no Brasil, são escassos. Assim, a presente revisão procurou reunir diversas fontes de publicação, nacionais e internacionais, abordando aspectos históricos do mogno, buscando aclarar as características da espécie e a experiência de outros países no manejo dessa cultura.

This study focuses on the effects of different thinning regimes on clonal Eucalyptus plantations growth. Four different trials, planted in 1999 and located in Bahia and Espírito Santo States, were used. Aside from thinning, initial planting density, and post thinning fertilization application were also evaluated. Before canopy closure, and therefore before excessive competition between trees took place, it was found that stands planted under low densities (667 trees per hectare) presented a lower mortality proportion when compared to stand planted under higher densities (1111 trees per hectare). However, diameter growth prior to thinning operations was not statistically different between these two densities, presenting an overall mean of 4.9 cm/year. After canopy closure and the application of the thinning treatments, it was found that thinning regimes beginning early in the life of the stand and leaving a low number of residual trees presented the highest diameter and height growth. Unthinned treatments and thinning regimes late in the life of the stand (after 5.5 years), leaving a large number of residual trees presented the highest values of basal area production. The choice of the best thinning regime for Eucalyptus clonal material will vary according to the plantation objective.

The use of height-diameter models have important applications in the management of eucalyptus plantations. The aim of this paper is to provide adequate height models based on the diameter and stand variables that could be easily used and applied to plantation’s management. The data was based on tests located in Espírito Santo and Bahia. A total of 312 plots, including 35 different treatments (different initial planting densities, number and severity of thinning operations, fertilization regimes, among others), and four different clones. This resulted in 2,407 conditions for 85,608 tree measurements. The constructed equations were based on mixed-effect models, in order to accommodate possible autocorrelations, to address variations between sites and to obtain locally calibrated estimates. Among the different model structures and combination of variables tested and presented, the best model showed a coefficient of determination of 0.953 and 0.988, for the fixed and fixed+random parts, respectively. The model included a random factor for measurements-within-tree and tree-within-plot. The bias of the fixed part was 0.208 cm (0.0659%). We believe the models in this study can have broad applications in management and inventory methods applied to plantations.