RESUMO -Este estudo apresenta um modelo de distribuição diamétrica baseado em um modelo de Autômatos celulares (AC) unidimensionais e redes neurais artificiais (RNA) para a simulação de desbaste. Cada célula do AC proposto representa uma classe de dap, sendo o estado futuro previsto em função do estado atual dessa célula, do estado de suas quatro células vizinhas e de sua idade atual e futura. Como regra de evolução, utilizou-se uma RNA. A exatidão foi avaliada empregando-se o procedimento estatístico L&O relação entre frequências observada e estimada e realismo biológico do modelo construído. Entre as redes treinadas, foram selecionadas as 10 que representavam a evolução da distribuição diamétrica com maior exatidão. Entre essas 10 RNA, sete apresentaram valores estimados estatisticamente iguais aos observados (p>0,01). O enfoque de modelagem proposto permite estimar distribuições diamétricas futuras com exatidão.Palavras-chave: Inteligência computacional, Redes Neurais Artificiais e L&O. MODELING THE DIAMETER DISTRIBUTION OF THINNED EUCALYPTUS STANDS USING A CELLULAR AUTOMATA ABSTRACT -This study presents a diametric distribution model based on a one-dimensional cellular automata (CA) model and artificial neural network (ANN

Assessment of the performance of forest fire hazard indices is important for prevention and management strategies, such as planning prescribed burnings, public notifications and firefighting resource allocation. The objective of this study was to evaluate the performance of fire hazard indices considering fire behavior variables and susceptibility expressed by the moisture of combustible material. Controlled burns were carried out at different times and information related to meteorological conditions, characteristics of combustible material and fire behavior variables were recorded. All variables analyzed (fire behavior and fuel moisture content) can be explained by the prediction indices. The Brazilian EVAP/P showed the best performance, both at predicting moisture content of the fuel material and fire behavior variables, and the Canadian system showed the best performance to predicting the rate of spread. The coherence of the correlations between the indices and the variables analyzed makes the methodology, which can be applied anywhere, important for decision-making in regions with no records or with only unreliable forest fire data.

The purpose of this study was to evaluate the efficiency of using total and partial analysis data of the trunk of Tectona grandis trees and of permanent plots, for the construction of site index curves and growth and production modeling, at the level of individual trees and of the settlement. Data were collected in settlements located in the State of Mato Grosso, with ages ranging from 2 to 10 years at the time of tree logging for trunk analysis. At that time, 40 permanent plots were installed, that were measured in the five subsequent years. Trunk analysis data obtained from those plots were used for validation of a variable density model. This model estimated the production with accuracy and consistency. Then, data from permanent plots were added to those of trunk analysis and the model was readjusted. It can be concluded that the trunk analysis of Tectona grandis trees is an efficient alternative of data collection for growth and production studies. The possibility of grouping past data, obtained by trunk analysis, with permanent plots, for growth modeling purposes, was also confirmed. Finally, the efficiency of trunk analysis data for growth modeling at a level of individual trees was evaluated and confirmed.

Eucalyptus stands growth depends on genotype, age, quality of the local soil and silvicultural treatment. Environmental factors, mainly the water availability to plants throughout the years, temperature and solar radiation are relevant to production capacity. The models used in Brazil to stimulate the future production of forestry stands are those that estimate growth and/or production according to age, basal area and local index. One of the possible approaches to do so is the use of procedural models (ecophysiological) such as the 3PG and the artificial neural network. The current study has the aim to construct, validate and apply an artificial neural model to predict the production and growth of eucalyptus stands in Minas Gerais, Brazil. The herein used data resulted from continuous forestall inventory plots conducted in eucalyptus stands in the North, Center and South of the state. The edaphic and climatic information added to the IFC data were used to train neural nets on predicting growth and production in the state. A neural network, lacking inventory variables, was also trained to extrapolate the mean productivity in the entire state of Minas Gerais due to the physiographic, edaphic and climatic conditions. The neural network efficiency was attested by the great accuracy of productivity forecasts. The generated productivity maps are indicated for studies on the expansion of eucalyptus cultivation in the state. The applied methodology is simple and efficiently inapplicable to different forestry cultures in other states or countries.

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