ABSTRACT. Variable height is commonly used as an input attribute to estimate other variables. Thus, to ensure less susceptibility to errors, it is necessary to obtain the variable height correctly. In addition to DBH, hypsometric relationships are influenced by several factors, such as site, age, genetic variation, and silvicultural practices. The inclusion of these factors in hypsometric models can lead to a gain in the quality of the estimates and in the biological realism. The objective of this study was to propose and evaluate the performance of a model extracted from artificial neural network training and of new models to estimate the total height of eucalyptus trees. The data used in this study originated from temporary forest inventories conducted in eucalyptus stands in Minas Gerais, Brazil. A multilayer perceptron artificial neural network was trained, and a nonlinear equation was extracted from the best-performing network to predict the total heights of trees. New linear and nonlinear hypsometric models were constructed and fit considering variables related to individual trees (DBH) and stands (plot basal area, age and site index). The new hypsometric models proposed in this study showed satisfactory performance and are effective for estimating the total heights of eucalyptus trees, particularly the model extracted from the artificial neural network and the nonlinear model. ABSTRACT Thus errors correctly DBH variation practices realism Gerais Brazil trained bestperforming best performing (DBH plot area index. index . index)

RESUMO A radiação é um dos aspectos que influenciam a composição de espécies, sendo assim, a luz é um fator limitante para o crescimento das árvores. O estudo objetivou avaliar as variações morfológicas foliares entre indivíduos de Myrcia splendens (Sw.) DC., sob diferentes condições de luminosidade, em uma floresta urbana. O fragmento caracteriza-se por possuir uma área sem formação de dossel e uma com regeneração mais avançada. Foram coletados ramos de cinco indivíduos, totalizando 100 folhas. Avaliou-se a área foliar, massa fresca, massa seca, conteúdo de água, área foliar específica e teor de matéria seca das folhas. A análise de variância mostrou que a maioria das características (massa seca, área foliar específica, teor de matéria seca das folhas) variaram significativamente. A massa seca, conteúdo de água e área foliar apresentaram alta correlação com a massa fresca em ambos os ambientes. A variação morfológica encontrada em M. splendens permite a criação de estratégias de crescimento e estabelecimento da espécie de acordo com as condições ambientais.

ABSTRACT Solar radiation is one of the aspects which most influence species composition, so light is a limiting factor to tree growth in forests. This study aimed to evaluate the leaf morphological variations between individuals of the Myrcia splendens (Sw.) DC. species under different light conditions in an urban forest. The fragment is characterized by having an open canopy area and an advanced regeneration stage in which branches of five individuals were collected, totaling 100 leaves. The traits evaluated were leaf area, fresh biomass, dry biomass, water content, specific leaf area and leaf dry matter content. An analysis of variance showed that most of the morphological traits (dry biomass, specific leaf area, leaf dry matter content) varied significantly. Furthermore, variables such as dry biomass, water content and leaf area showed a high correlation with fresh biomass in both areas. The morphological variation found in M. splendens allows for creating growth and establishment strategies according to the environmental conditions.

ABSTRACT The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation.