RESUMO -Objetivou-se propor neste estudo uma metodologia com a utilização de Redes Neurais Artificiais (RNA), para redução do número de árvores a serem cubadas durante o processo de geração de equações volumétricas. Os dados utilizados neste estudo foram provenientes de cubagens de 2.700 árvores de povoamentos clonais de eucalipto localizados no Sul da Bahia. O treinamento das RNA foi feito visando à obtenção de redes para a estimação do volume com e sem casca. Como variáveis de entrada, utilizaram-se o diâmetro à altura do peito -1,30 m (dap), a altura e os diâmetros nas posições de 0,0; 0,5; 1,0; 1,5; 2,0; e 4,0 m do solo e os volumes obtidos até 2 e 4 m. A precisão do método foi feita com a aplicação do teste L&O. Avaliaram-se também a dispersão dos erros percentuais, o histograma de frequência dos erros percentuais e a raiz do erro quadrático médio (RMSE). A metodologia proposta neste estudo mostrou-se eficiente para a estimação do volume de árvores, sendo indicada para a obtenção do volume total com e sem casca de povoamentos de eucalipto, possibilitando a redução dos custos para a construção de equações volumétricas.Palavras-chave: Cubagem rigorosa; Inventário florestal; Mensuração florestal. (ANN) to reduce the number of trees to be scaled during the process of generating volumetric equations. The data used in this study were originated from measurements of 2,700 trees of clonal eucalyptus plantations located in southern Bahia State, Brazil. The training of ANN was performed in order to obtain networks for estimating the volume with and without bark. As input variables, we used the diameter at breast height (dbh) -1. 30 m, height, and diameter at 0.0, 0.5, 1.0, 1.5, 2.0 and 4.0 m above the ground and the volumes obtained until 2 and 4 m. The accuracy of the method was carried out using the test L & O. We also evaluated the dispersion of percentage errors, frequency histogram of the percentage error and the root mean square error (RMSE). The methodology proposed in this study proved to be efficient for estimating the volume of trees, and is indicated to obtain the total volume with and without bark of eucalyptus, enabling the reduction of costs for the construction of volumetric equations. ABSTRACT -The objective of this study was to propose a methodology by using Artificial Neural Networks
Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( r y y ^ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( r y y ^ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance.
The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field data.
Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history.
Objetivou-se neste estudo desenvolver e avaliar a aplicação de redes neurais artificiais para a projeção de parâmetros da distribuição Weibull. Utilizaram-se dados de parcelas permanentes de eucaliptos, mensuradas em oito ocasiões. Ajustou-se a função Weibull com dois parâmetros para todas as parcelas e ocasiões, pelo método da máxima verossimilhança. A projeção da distribuição diamétrica foi feita através de redes neurais artificiais. Comparou-se o método proposto com o método tradicionalmente utilizado na modelagem da distribuição diamétrica. Os modelos utilizando RNA apresentaram melhorias na dispersão gráfica dos resíduos, bem como das estatísticas avaliadas. O método proposto mostrou-se superior ao método comumente usado.
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