Climate and soil are among the most important factors determining variation in tree communities, but their effects have not been thoroughly elucidated to date for many vegetation features. In this study, we evaluate how climate and soil gradients affect gradients of vegetation composition, species diversity and dominance, structure and functional traits (seed mass and wood density) using over 327 000 trees in 158 sites distributed along environmental gradients in the transitions among the Atlantic forest, Cerrado and Caatinga in Minas Gerais State (MG), Brazil (nearly 600 000 km²). Gradients in species, genus and family abundance in addition to basal area, stem density, species diversity (Fisher’s alpha), dominance percentage, seed mass and wood density were correlated using multiple regressions with environmental variables, as summarized in four principal component analysis axes (two climatic—precipitation seasonality and temperature range—and two edaphic—soil fertility and soil moisture). Additionally, ordinary kriging maps were used to better illustrate the gradients. Multiple regression models indicate that all variables but dominance percentage were affected by one or more of the environmental gradients, but the average R² was low (26.25%). Kriging maps reinforced the patterns observed in the regression models. Precipitation seasonality and soil moisture gradients were the most important gradients affecting vegetation features. This finding suggests that water availability is an important determinant of vegetation features in these vegetation transitions.

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

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.

In this study, we tested the effectiveness of stand age, multispectral optical imagery obtained from the Landsat 8 Operational Land Imager (OLI), synthetic aperture radar (SAR) data acquired by the Sentinel-1B satellite, and digital terrain attributes extracted from a digital elevation model (DEM), in estimating forest volume in 351 plots in a 1,498 ha Eucalyptus plantation in northern Minas Gerais state, Brazil. A Random Forest (RF) machine learning algorithm was used following the Principal Component Analysis (PCA) of various data combinations, including multispectr al and SAR texture variables and DEM-based geomorphometric derivatives. Using multispectral, SAR or DEM variables alone (i.e. Experiments (ii)–(iv)) did not provide accurate estimates of volume (RMSE (Root Mean Square Error) > 32.00 m³ ha⁻¹) compared to predictions based on age since planting of Eucalyptus stands (Experiment (i)). However, when these datasets were individually combined with stand age (i.e. Experiments (v)–(vii)), the RF models resulted in better volume estimates than those obtained when using the individual multispectral, SAR and DEM datasets (RMSE < 28.00 m³ ha⁻¹). Furthermore, a model that integrated the selected variables of these data with stand age (Experiment (viii)) improved volume estimation significantly (RMSE = 22.33 m³ ha⁻¹). The large and increasing area of Eucalyptus forest plantations in Brazil and elsewhere suggests that this new approach to volume estimation has the potential to support Eucalyptus plantation monitoring and forest management practices.

This work aimed to evaluate the effect of planting sites on the elemental chemical composition of Eucalyptus urophylla wood and the quality of its charcoal and to verify the statistical relations among the properties measured in wood and charcoal. It was used twelve 7-years old trees from E. urophylla clone from Curvelo, Itacambira and Turmalina counties located in Minas Gerais State, Brazil. The elemental chemical composition (C, H, N, S and O) of the wood and C/H and C/N relations were determined. The wood was carbonized in a laboratorial muffle furnace. It was determinate the gravimetric yields in charcoal, in pyroligneous extract and, by difference, the non-condensable gases. The apparent relative density, the higher heating value, the proximate chemical composition and the yield in fixed carbon of the charcoals were also determined. The caloric value of charcoal was influenced by its proximate chemical composition. In the wood, it was observed significant effect from site on hydrogen content and C/H relation. The location also influenced the ash content and gravimetric yields in charcoal and fixed carbon, so it is considered important to consider the effect of planting site for charcoal production from the Eucalyptus urophylla clone studied.Objetivou-se avaliar o efeito do local de plantio na composição química elementar da madeira e na produção e qualidade do carvão vegetal de Eucalyptus urophylla e verificar as relações estatísticas entre as variáveis mensuradas na madeira e no carvão. Foram utilizadas doze árvores de um clone de E. urophylla, aos sete anos de idade, provenientes dos municípios de Curvelo, Itacambira e Turmalina (MG). Determinou-se a composição elementar (C, H, N, S e O) e as relações C/H e C/N da madeira. A madeira foi carbonizada em forno laboratorial tipo mufla. Foram avaliados os rendimentos gravimétricos em carvão vegetal, líquido pirolenhoso e, por diferença, os gases não condensáveis. Determinou-se também a densidade relativa aparente, o poder calorífico superior, a composição química imediata e o rendimento em carbono fixo dos carvões. O valor calórico do carvão vegetal foi influenciado por sua composição química imediata.Na madeira observou-se efeito significativo do local para o teor de hidrogênio e para a relação C/H. O local também influenciou o teor de cinzas e os rendimentos gravimétricos em carvão e carbono fixo, sendo, portanto, importante considerar o efeito do local de cultivo na produção do carvão vegetal do clone de Eucalyptus urophylla estudado

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.