Neste estudo o objetivo foi avaliar modelos de taper para descrever o perfil do fuste de Corymbia citriodora. Os dados utilizados foram obtidos de um plantio comercial localizado na região Sul do estado do Tocantins. Foram utilizados dados de 24 árvores de Corymbia citriodora cubadas rigorosamente com medições de diâmetros nas posições do fuste: 0,2; 0,4; 0,7; 1,3; 2,70 m e, sucessivamente, de 2 m em 2 m até um diâmetro em torno de 3 cm com casca. Foram testados 44 modelos de taper, sendo 25 lineares e 19 não lineares. Na avaliação destes modelos, considerou-se a análise da distribuição de resíduos, e a estatísticas: erro padrão da estimativa, coeficiente de determinação, delineamento em blocos casualizados com arranjo de parcelas subdivididas, desvio absoluto médio, raiz quadrada do erro médio, soma de quadrados dos resíduos, desvio padrão das diferenças, critério de informação de Akaike. O modelo com melhor desempenho foi um do tipo expoente-forma ajustado na forma não linear. Após definido este melhor modelo, desenvolveu-se o método da altura relativa para simular uma cubagem rigorosa a fim de proceder ao seu ajuste e comparação com o ajuste obtido por meio de uma cubagem rigorosa real. Nesta comparação utilizaram-se os critérios estatísticos: desvio médio, bias, correlação linear, erro padrão da estimativa, entre valores observados e estimados, precisão, obtida pelo teste de qui-quadrado e raiz quadrada do erro, além de análise da distribuição dos resíduos. Concluiu-se que o uso de uma cubagem simulada pelo método da altura relativa estima bem uma equação de taper como a que se obtém pelo uso de dados reais. / In this study, evaluate the control models to describe the profile of the Corymbia citriodora. The data used are obtained from a commercial plant located in the southern region of the state of Tocantins. There are data from 24 Corymbia citriodora trees accurately cubed with diameter measurements in stem families: 0.2; 0.4; 0.7; 1,3; 2.70 m and, successively, from 2 m in 2 m to a diameter of around 3 cm in shell. 44 taper models were tested, being 25 linear and 19 nonlinear. In the evaluation of these models, we consider an analysis of the distribution of residues, and statistics: standard error of estimation, coefficient of determination, randomized block design with split plot arrangement, mean absolute deviation, square root of mean error, sum of squares of residuals, standard deviation of the differences, Akaike's information criterion. The bestperforming model for an exponent-shape fit type adjusted in nonlinear form. This version has a long list of corrections, changes, and new developments that significantly improve functioning. In this comparison, statistical criteria are used: mean deviation, bias, linear correlation, standard error of the estimate, between observed and estimated values, precision, obtained by square test and square root of error, as well as analysis of distribution and residues. It was concluded that the use of a cube simulated by the method of elevation of the estimate, as well as a conciliation equation as one obtains by the use of real data.

Made available in DSpace on 2015-04-13T12:17:11Z (GMT). No. of bitstreams: 1 anabel.pdf: 2154290 bytes, checksum: d824e56d44643507585a7c486d3329e5 (MD5) Previous issue date: 2012-11-30 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The productive potential of the forest is traditionally based on the volume of commercial bole. However, the waste produced during the cutting operation can be availed as byproducts of forest management, representing an economical alternative in timber production. Thus, the aim of this study was to evaluate the volume of waste produced during the cutting operation in a managed forest, in the city of Silves /AM. Data were derived from waste produced in a number of one hundred trees explored. In which, the residue of cutting operation was all timber located in the area of influence of the cut, including, stump with and without buttresses, base and canopy branches of the tree explored and other trees that have fallen due to the cut. The volume found for one hundred trees exploited averaged 5.49 m³, thereof 1.57 m³, compose the volume of waste produced by these trees. Concerning the total volume of sampled trees, the stump contributed 2%, the base of the crown 1.19%, and branches with 24.31%. Their average volumes were, stump 0.12 m³, base of the crown 0.42 m³ and branches, 1.33 m³. The average volumes of stump base of the crown and main branches are statistically different. The open area formed by the cutting operation was approximately 206 m², which in this area were found on average 0.59 m³ of waste clearing. The volume of generated waste by the cutting operation was 25.42 m³/ha, but considering what could be used in application of timber, like branches, canopy base and buttresses, these represented in terms of volume, 13.41 m³/ha. Moreover, the rate that relates the volume of waste for each m³ of roundwood extracted was 1.35 for waste of explored tree, and 1.76 for waste of the cutting operation. These rates were higher than that provided by the legislation which is 1:1 m³, indicating that the rate may vary for each managed area, specifically. / O potencial produtivo da floresta, tradicionalmente, é em função do volume do fuste comercial. No entanto, os resíduos produzidos durante a operação de corte, podem ser aproveitados como produtos secundários do manejo florestal, representando uma alternativa econômica na produção madeireira. Diante disso, o objetivo deste estudo foi avaliar o volume de resíduos produzidos durante a operação de corte, em uma floresta manejada, no município de Silves/AM. Os dados foram provenientes de resíduos produzidos em cem árvores exploradas. Na qual, resíduo da operação de corte constituiu todo material lenhoso localizado na área de influência do corte, compreendendo, toco com e sem sapopemas, base da copa e galhos da árvore explorada e outras árvores que caíram em função do corte. O volume encontrado para as cem árvores exploradas foi em média 5,49 m3, destes, 1,57 m3, constituiu o volume de resíduo produzido por estas árvores. Em relação ao volume total das árvores amostradas, o toco contribuiu com 2 %, a base da copa, 1,19 % e galhos com 24,31 %, e seus volumes médios foram, toco, 0,12 m³, base da copa, 0,42 m³ e galhos, 1,33 m³. Os volumes médios de toco, base da copa e galhos principais são diferentes estatisticamente. A área aberta formada pela operação de corte foi em média 206 m2, na qual nesta área, foram encontrados em média 0,59 m³ de resíduos de clareira. O volume de resíduos gerados pela operação de corte foi de 25,42 m³/ha, mas, considerando o que poderá ser utilizado no aproveitamento da madeira, como galhos, base da copa e sapopemas, estes representaram em termos de volume, 13,41 m3/ha. E o índice que relaciona o volume de resíduos para cada m³ de tora extraída, foi de 1,35 para resíduo da árvore explorada e 1,76 para resíduos da operação de corte. Estes índices foram maiores do que o estabelecido pela legislação vigente que é de 1:1 m3, indicando que esse índice pode variar para cada área manejada, em particular.

Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. Previous research has shown that nearest-neighbor imputation methods can accurately estimate forest volume across a landscape by relating variables of interest to ground data, satellite imagery, and light detection and ranging (LiDAR) data. Alternatively, parametric models, such as linear and non-linear regression and geographic weighted regression (GWR), have been used to estimate net primary production and tree diameter. The goal of this study was to compare various imputation methods to predict forest biomass, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. In this study I compared the predictive performance of, 1) linear regression, GWR, gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest and 2) MSN and k-nn when imputing forest biomass at spatial scales ranging from 5,000 to 50,000 acres. To test the imputation methods a combination of ground inventory plots, LiDAR data, satellite imagery, and climate data were analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k=5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k=3), followed by the GWR model, and the random forest imputation. The GNN method was the least accurate. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k=5), followed by k-nn (k=3), and the random forest method. The GNN method, again, was the least accurate. The accuracy of MSN, the current imputation method used by the Malheur Nation Forest, and k-nn (k=5), the most accurate imputation method from the second chapter, were then compared over 6 spatial scales: 5,000, 10,000, 20,000, 30,000, 40,000, and 50,000 acres. The root mean square difference (RMSD) and bias were calculated for each of the spatial scale samples to determine which was more accurate. MSN was found to be more accurate at the 5,000, 10,000, 20,000, 30,000, and 40,000 acre scales. K-nn (k=5) was determined to be more accurate at the 50,000 acre scale. / Graduation date: 2013