ResumoA influência do gradiente ambiental na estrutura e no estoque de carbono pode auxiliar na compreensão da estocagem de carbono na Mata Atlântica e na tomada de decisão sobre metodologias e espécies a serem usadas na restauração ecológica do bioma nos diferentes gradientes.A estrutura e o estoque de carbono em duas porções do terreno, borda e interior, foram avaliados. A ordenação das comunidades foi verificada pela análise de escalonamento multidimensional não métrica (NMDS) e a análise de espécies indicadoras (ISA) foi realizada. O efeito de borda influencia na diferenciação da composição florística e estrutura entre os dois estratos e provocou diferença significativa ANOVA (p-valor = 0,002) a 5% de significância no estoque de carbono. Na região da borda foi encontrada uma estimativa de 45,43 MgC ha -1 e no interior 63,71 MgC ha -1 . As espécies Aparisthmium cordatum (A.Juss.) Baill. e Cupania vernalis Cambess. são indicadoras do ambiente de borda no fragmento. Já na região do interior as espécies Apuleia leiocarpa (Vogel) J.F.Macbr., Coussarea verticillata Müll.Arg., Dalbergia nigra (Vell.) Allemao ex Benth e Melanoxylon brauna Schott foram significativas pelo teste de Monte Carlo. Conclui-se que o efeito de borda afeta a estrutura, diversidade e estoque de carbono no fragmento florestal.

Objetivou-se com este estudo avaliar o comportamento dos índices de distribuição espacial das espécies Anadenanthera peregrina e Apuleia leiocarpa, em diferentes tamanhos de parcelas. Realizou-se um censo na Mata da Silvicultura, localizada em Viçosa, M.G. por meio do qual todos indivíduos das duas espécies, com DAP ≥ 20 cm, foram medidos e georreferenciados. Os dados foram agrupados em unidades amostrais de (10x10) m², (10x30) m², (10x50) m², (20x10) m², (20x30) m² e (20x50) m². O padrão de distribuição espacial das espécies foi identificado por meio dos índices de Payandeh (Pi), MacGuinnes (IGAi), Fracker e Brischle (Ki) e Morisita (IMi). Posteriormente, para cada tamanho de unidade amostral, selecionou-se aleatoriamente 10% do número total de parcelas e os índices foram calculados. Foram realizadas 10 repetições desse procedimento e a análise da distribuição espacial foi feita com base na média dos valores encontrados. O IGAi, Pi, e Ki foram diretamente proporcionais a área das parcelas e o IMi, inversamente proporcional. O IGAi apresentou as menores diferenças entre as médias das amostragens e os valores reais aferidos pelo censo. Diante disso, o IGAi foi o índice mais adequado para se estudar a distribuição espacial das espécies.Palavra-chave: análise espacial de árvores, índice de Morisita, agregação. PLOT SIZE INFLUENCE IN THE SPATIAL DISTRIBUTION CALCULATION OF Anadenanthera peregrina (L.) Speng. AND Apuleia leiocarpa J. F. Macbr IN SEMIDECIDUOUS  SEASONAL FOREST ABSTRACT:The objective of this study was to evaluate the behavior of spatial distribution indexes of the species Anadenanthera peregrina and Apuleia leiocarpa, in different plot sizes. A forest census was carried out in the Mata da Silvicultura, located in Viçosa, M.G. through which all individuals of both species, with DBH ≥ 20 cm, were measured and georeferenced. The data were grouped into sampling units of (10x10) m², (10x30) m², (20x10) m², (20x10) m² and (20x50) m². The spatial distribution pattern of the species was identified through the Payandeh (Pi), MacGuinnes (IGAi), Fracker and Brischle (Ki) and Morisita (IMi) indexes. Subsequently, for each sampling unit size, 10% of the total number of plots were randomly selected and the indexes were calculated. Ten replicates of this procedure were perfomed and the spatial analysis was done based on the average of the values found. The IGAi, Pi, and Ki were directly proportional to the plot area and the IMi, inversely proportional. The IGAi presented the smallest differences between the sampling means and the real value measured by the census. Noted that, it was clear that the IGAi was the most adequate index to study a spatial distribution of the species.Keywords: tree spatial analysis, Morisita Index, aggregation. DOI:

Activated carbon (AC) is a carbonaceous material used to adsorb and remove pollutants. One of the requirements for the material to be considered a precursor in the AC production is its high levels of carbon. Therefore, a promising alternative for the use of forest biomass and its residues would be the application of these materials as precursors in the AC production. Owing to the productive capacity of activated carbon in Brazil and the considerable availability of forest biomass, the objective of the study was to approach the production and market of activated carbon in Brazil. This study is a bibliographic review focused on the use of forest resources for the AC production. AC production occurs by three methods: physical, chemical and physico-chemical activation. At the national level, the main production method is the physical one, since it has the least cost. However, the Sector still requires the researches and technologies aimed at the standardisation and improvements in the whole production chain.

In this study an economic analysis was conducted to evaluate problem associated with a geographical information system (GIS) using facility location models for the p-median in order to optimize the location of aerodromes for the aerial fertilization (coverage fertilization) of eucalyptus plantations. The location model was tested on a 9,095.65 ha farm located in the Três Lagoas municipality in the Mato Grosso do Sul State, in Brazil. The non-capacitated p-median location model, available in ArcGIS, was evaluated in the location-allocation module. Simulations were performed based on one to five aerodromes. The fertilization and setup costs were calculated for each scenario. The results showed that the p-median location model was efficient in determining the optimal location of aerodromes. The economic analysis of the location model found that the lowest costs are incurred when using three aerodromes.

Accurate estimation of the volume and above-ground biomass of exploitable trees by the practice of selective logging is essential for the elaboration of a sustainable management plan. The objective of this study is to develop machine learning models capable of estimating the volume and biomass of commercial trees in the Southwestern Amazon, based on dendrometric, climatic and topographic characteristics. The study was carried out in the municipality of Porto Acre, Acre state, Brazil. The volume and biomass of sample trees were determined using dendrometric, climatic and topographic variables. The Boruta algorithm was applied to select the best set of variables. Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests (RF) and the Generalized Linear Model (GLM) were the machine learning methods evaluated. In general, the evaluated methods showed a satisfactory generalization power. The results showed that the volume and biomass predictions of commercial trees in the Amazon rainforest differed between the techniques (p < 0.05). ANNs showed the best performance in predicting the volume and biomass of commercial trees, with the highest ryŷ and the lowest RSME and MAE. Thus, machine learning methods such as SVM, ANN, RF and GLM are shown to be useful and efficient tools for estimating the volume and biomass of commercial trees in the Amazon rainforest. These methods can be useful tools to improve the accuracy of estimates in forest management plans.