Background: The traditional method used to estimate tree biomass is allometry. In this method, models are tested and equations fitted by regression usually applying ordinary least squares, though other analogous methods are also used for this purpose. Due to the nature of tree biomass data, the assumptions of regression are not always accomplished, bringing uncertainties to the inferences. This article demonstrates that the Data Mining (DM) technique can be used as an alternative to traditional regression approach to estimate tree biomass in the Atlantic Forest, providing better results than allometry, and demonstrating simplicity, versatility and flexibility to apply to a wide range of conditions. Results: Various DM approaches were examined regarding distance, number of neighbors and weighting, by using 180 trees coming from environmental restoration plantations in the Atlantic Forest biome. The best results were attained using the Chebishev distance, 1/d weighting and 5 neighbors. Increasing number of neighbors did not improve estimates. We also analyze the effect of the size of data set and number of variables in the results. The complete data set and the maximum number of predicting variables provided the best fitting. We compare DM to Schumacher-Hall model and the results showed a gain of up to 16.5 % in reduction of the standard error of estimate. Conclusion: It was concluded that Data Mining can provide accurate estimates of tree biomass and can be successfully used for this purpose in environmental restoration plantations in the Atlantic Forest. This technique provides lower standard error of estimate than the Schumacher-Hall model and has the advantage of not requiring some statistical assumptions as do the regression models. Flexibility, versatility and simplicity are attributes of DM that corroborates its great potential for similar applications.
Weibull distributions have been widely used to describe tree stem diameter distributions. However, there is a scarcity of studies suggesting the best Weibull formulation. The parameters of the Weibull distribution are usually predicted either by the parameter prediction method (PPM) or by the parameter recovery method (PRM), although other methods have been proposed. Thus, this study aimed to evaluate the performance of eight Weibull formulations and to compare parameter prediction methods to describe diameter distributions of clonal eucalypt stands in Brazil. Data originated from re-measurements of 56 plots at ages 3, 5, and 6 years. Weibull distributions were fitted using the maximum likelihood method and evaluated in a goodness-of-fit-indicators ranking. The right-truncated two-parameters formulation showed the best results and was used to evaluate the parameter prediction methods. Stand attributes showed a strong relationship with shape and scale parameters. Regression models were developed and resulted in accurate estimates using PPM. PRM used a growth and yield system to estimate the stand attributes followed by the moment-based method. The Modified CDFR approach was also evaluated and presented the poorest results. While the PPM showed excellent results, PRM is recommended in older stands with inventory since it implicitly promotes compatibility among stand attributes.
BackgroundWe analyzed the dynamics of carbon (C) stocks and CO2 removals by Brazilian forest plantations over the period 1990–2016. Data on the extent of forests compiled from various sources were used in the calculations. Productivities were simulated using species-specific growth and yield simulators for the main trees species planted in the country. Biomass expansion factors, root-to-shoot ratios, wood densities, and carbon fractions compiled from literature were applied. C stocks in necromass (deadwood and litter) and harvested wood products (HWP) were also included in the calculations.ResultsPlantation forests stocked 231 Mt C in 1990 increasing to 612 Mt C in 2016 due to an increase in plantation area and higher productivity of the stands during the 26-year period. Eucalyptus contributed 58% of the C stock in 1990 and 71% in 2016 due to a remarkable increase in plantation area and productivity. Pinus reduced its proportion of the carbon storage due to its low growth in area, while the other species shared less than 6% of the C stocks during the period of study. Aboveground biomass, belowground biomass and necromass shared 71, 12, and 5% of the total C stocked in plantations in 2016, respectively. HWP stocked 76 Mt C in the period, which represents 12% of the total C stocked. Carbon dioxide removals by Brazilian forest plantations during the 26-year period totaled 1669 Gt CO2-e.ConclusionsThe carbon dioxide removed by Brazilian forest plantations over the 26 years represent almost the totality of the country´s emissions from the waste sector within the same period, or from the agriculture, forestry and other land use sector in 2016. We concluded that forest plantations play an important role in mitigating GHG (greenhouse gases) emissions in Brazil. This study is helpful to improve national reporting on plantation forests and their GHG sequestration potential, and to achieve Brazil’s Nationally Determined Contribution and the Paris Agreement.
RESUMO ABSTRACTModelos hipsométricos e volumétricos são ferramentas úteis para monitorar o desenvolvimento de plantios de restauração florestal. Neste estudo, foram ajustadas equações de estimativa de altura total (h) e volume total com casca (v) em função do diâmetro à altura do peito. Height-diameter and volume models are useful tools for monitoring the development of forest restoration stands. In this study, equations of total height (h) and total volume with bark (v) were fitted as a function of the diameter at the breast height. The data were collected in 20 trees measured and scaled in mixed young forest stands of restoration located in the municipalities of Itapoã do Oeste and Cujubim, North of Rondônia State, in which five heightdiameter and volume regression models were tested. Also, the use of a mean form factor for volume estimation was examined. The models were judged by the coefficient of determination (R²), standard error of the estimate in percentage (Syx%) and graphic residual analysis. All the tested height-diameter models presented satisfactory results, with Syx% of 15%, R² between 0.5443 and 0.5946, being Henriksen's model was the best. The volume models presented Syx values of 11%, R² between 0.8497 and 0.9117, therefore, precise. The best volume model was the Kopezky & Gehrardt. It was concluded that there is a strong correlation between the mensuration variables (between 0.7573 e 0.9531), that the form factor is not accurate to estimate volume. In addition, the regression models, both height-diameter and volume, are accurate and can provide reliable estimates for monitoring young restoration stands in Amazon.PALAVRAS-CHAVE: Cubagem, Plantios jovens, Quantificação volumétrica.
Avaliou-se a dinâmica em área e em estoques de volume de madeira, biomassa e carbono nas florestas nativas do Brasil no período 1990 a 2015. A fonte utilizada para o trabalho foi o Relatório FRA2015 (Avaliação dos Recursos Florestais) submetido pelo Serviço Florestal Brasileiro à FAO (Organização das Nações Unidas para Alimentação e Agricultura). Os dados publicados foram analisados criticamente, deduções dos parâmetros empregados foram realizadas e alguns ajustes nos cálculos foram efetivados. A área de florestas nativas no Brasil decresceu no período de 25 anos, de 542 M ha para 486 M ha, o que corresponde a 10% da superfície inicial computada em 1990. As maiores perdas de áreas florestais ocorreram nos biomas Amazônia e Cerrado que representaram 85% do total e 56 M ha em todo o País, sendo equivalente à superfície do Estado da Bahia. O estoque volumétrico de madeira foi reduzido em 8,45% no período avaliado, de 103 G m3 para 95 G m3, com maior perda no bioma Amazônia (79%). A biomassa total seca estocada nas florestas também decresceu 8,44%, de 126 G t para 115 G t, com maior redução na Amazônia (79%). O estoque de carbono caiu de 63 G t para 58 G t, expressando uma perda de 8,40% que se deu em maior intensidade no bioma Amazônia (80%). Concluiu-se que as reduções em volume, biomassa e carbono são atribuídas à diminuição da cobertura florestal em todos os biomas e que tais reduções implicaram em emissões de gases de efeito estufa.
BackgroundBiomass models are useful for several purposes, especially for quantifying carbon stocks and dynamics in forests. Selecting appropriate equations from a fitted model is a process which can involves several criteria, some widely used and others used to a lesser extent. This study analyzes six selection criteria for models fitted to six sets of individual biomass collected from woody indigenous species of the Tropical Atlantic Rain Forest in Brazil. Six models were examined and the respective fitted equations evaluated by the residual sum of squares, adjusted coefficient of determination, absolute and relative estimates of the standard error of estimate, and Akaike and Schwartz (Bayesian) information criteria. The aim of this study was to analyze the numeric behavior of these model selection criteria and discuss the ease of interpretation of them. The importance of residual analysis in model selection is stressed.ResultsThe adjusted coefficient of determination () and the standard error of estimate in percentage (Syx%) are relative model selection criteria and are not affected by sample size and scale of the response variable. The sum of squared residuals (SSR), the absolute standard error of estimate (Syx), the Akaike information criterion and the Schwartz information criterion, in turn, depend on these quantities. The best fit model was always the same within a given data set regardless the model selection criteria considered (except for SSR in two cases), indicating they tend to converge to a common result. However, such criteria are not always closely related across different data sets. General model selection criteria are indicative of the average goodness of fit, but do not capture bias and outlier effects. Graphical residual analysis is a useful tool to this detection and must always be used in model selection.ConclusionsIt is concluded that the criteria for model selection tend to lead to a common result, regardless their mathematical formulation and statistical significance. Relative measures of goodness of fitting are easier to interpret than the absolute ones. Careful graphical residual analysis must always be used to confirm the performance of the models.
ResumoA teca (Tectona grandis L. f.) possui uma das madeiras mais valiosas do mundo, e por este motivo é alvo de muitos estudos. Em razão de o início de seu cultivo ser recente no Brasil, são poucos os estudos relativos à modelagem biométrica de teca no País. O presente trabalho teve como objetivo testar seis diferentes modelos hipsométricos para estimar a altura total em 400 indivíduos juvenis de teca na região de Redenção-PA, medidos com hipsômetro Vértex IV. Para tanto, foram utilizados quatro diferentes critérios de seleção: coeficiente de determinação ajustado (R 2 aj.), erro-padrão da estimativa (Syx%), critério de informação de Akaike (AIC) e critério de informação Bayesiano (BIC), sendo a análise da distribuição dos resíduos também empregada para esse fim. Trinta e cinco árvores foram derrubadas para medição real de suas alturas, as quais foram empregadas para validação da melhor equação. Os indivíduos utilizados neste estudo apresentaram diâmetro a altura do peito (DAP) médio de 11,07±0,42 cm e altura total média (ht) de 9,25±0,31 m. Os seis modelos mostraram indicadores gerais de ajuste satisfatórios, mas a análise de resíduos evidenciou que quatro deles apresentam vieses nas estimativas. O modelo de Trorey foi selecionado como o mais preciso e acurado, considerando os critérios gerais de ajuste e a análise de resíduos, com R 2 aj. de 0,89, Syx% de 11,37%, AIC de 46,16 e 50,04 de BIC. A equação gerada pelo modelo de Trorey foi validada com dados independentes, proporcionando estimativas confiáveis e sem viés, considerando as condições vigentes neste estudo.Palavras-chave adicionais: critérios de seleção; dendrometria; inventário florestal; manejo florestal; modelagem;Tectona grandis. AbstractTeak (Tectona grandis L. f.) is one of the most valuable timbers in the world and, therefore, has been subject of various studies. However, a few modeling studies on biometrical measures of this species have been carried out so far in Brazil because this cultivation is still very recent in the country. This study aimed to test six different hypsometric models to estimate the total height of 400 young teak individuals in the region of Redenção-PA, measured with the Vertex IV hypsometer. Four different selection criteria were used: adjusted coefficient of determination (R 2 aj.), standard error of estimate (Syx%), Akaike information criteria (AIC) and Bayesian information criteria (BIC), and the graphical analysis of residuals was also performed. Thirty-five trees were felled and their actual heights were directly measured using tape, which were used to validate the best fitted equation. The tress used in this study had a mean of diameter at breast height (DAP) of 11.07±0.42 cm and total height (ht) of 9.25±0.31 m. The six models showed overall satisfactory indicators of fitting, but the graphical analysis of residuals showed that four of them produce biased estimates. The Trorey model was elected as been the most precise and accurate, considering the general criteria of fit and the graphical residual analysis of was...
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