Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

Abstract& Key message In this paper it is shown that a simultaneous adjustment provides more efficient estimates of total tree biomass than with independent modelling for biomass estimates by compartments (canopy, bole and roots).

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Aim of study: In this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands.Area of study: Two commercial forest stands were selected, with two plots each. The forest species studied were Eucalyptus spp. and Pinus taeda L. and the trees had an age of 1.5 years.Material and methods: The individual trees were detected based on watershed segmentation and local maxima, using the spectral values stored in the point cloud. After the tree detection, the heights were calculated using two approaches, in the first one using the Digital Surface Model (DSM) and a Digital Terrain Model, and in the second using only the DSM. We used the UAV-derived heights to estimate an uniformity index.Main results: The trees were detected with a maximum 6% of error. However, the height was underestimated in all cases, in an average of 1 and 0.7 m for Pinus and Eucalyptus stands. We proposed to use the models built herein to estimate tree height, but the regression models did not explain the variably within the data satisfactorily. Therefore, the uniformity index calculated using the direct UAV-height values presented results close to the field inventory, reaching better results when using the second height approach (error ranging 2.8-7.8%).Research highlights: The uniformity index using the UAV-derived height from the proposed methods was close to the values obtained in field. We noted the potential for using UAV imagery in forest monitoring.

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<p><span>As redes neurais artificiais (RNA) possuem grande potencial como alternativa à análise de regressão convencional, dada a capacidade de aprendizado de informações de um conjunto de dados e a generalização desse aprendizado para dados desconhecidos. Nesse sentido, o objetivo do presente trabalho foi utilizar RNAs para a estimativa do diâmetro relativo, volume total e comercial, bem como a comparação do desempenho em relação a funções de afilamento convencionais. Dados provenientes de 47 árvores de <em>Eucalyptus</em> sp. foram utilizados no treinamento e validação das RNAs e no ajuste das funções de afilamento de Hradetzky e Garay. O desempenho das RNAs foi muito semelhante ao das funções de afilamento na estimava do diâmetro relativo. As estimativas de volume total e comercial com RNAs se mostraram mais precisas e com menor dispersão dos resíduos que Hradetzky e Garay. RNAs se mostraram acuradas e adequadas para a estimativa de diâmetro relativo e volume.</span></p>

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.

O presente estudo teve como objetivo avaliar anualmente a dinâmica da fixação do carbono na biomassa arbórea em Floresta Ombrófila Mista Montana ao longo de uma década. Foram utilizados dados de inventário florestal contínuo advindos de quatro parcelas permanentes de um hectare cada, localizadas em General Carneiro, PR. O período de coleta dos dados iniciou em 1999 e foi concluído em 2009. A estimativa de biomassa arbórea acima do solo no ano de 1999 foi de 178,5 t.ha-1, atingindo valores de 186,75 t.ha-1 e obtendo um IPA da ordem de 0,75 t.ha-1.ano-1. Dessa forma, as estimativas de carbono estocado no fragmento avaliado foram de 74,07 t.ha-1, atingindo, após uma década, 77,53 t.ha-1. Conclui-se que a floresta mostrou, nas condições vigentes, baixa capacidade em fixar carbono, com uma taxa de incremento anual da ordem de 0,31 t.ha-1.ano-1. No entanto, embora a floresta apresente um baixo incremento anual de carbono, pode ser caracterizada como um sumidouro do mesmo, devido ao estoque de carbono existente em sua biomassa florestal. Dessa forma, mesmo uma floresta próxima da estagnação em termos de fixação de carbono pode proporcionar benefícios socioambientais, entre outros, na forma de sumidouros de carbono.Palavras-chave: Biomassa florestal; estoque de carbono; dinâmica de comunidade.AbstractA decade of carbon sink dynamics in biomass in mixed rain forest in the south of Paraná. This research aims to  evaluate, annually, dynamics of carbon sink in Montana Ombrophilous Mixed Forest along a decade. The data is from four permanent continuous inventory plots of one hectare each, located in General Carneiro - PR. The data collection began in 1999 and was completed in 2009. The above-ground tree biomass estimated in 1999 was 178.5 t ha-1, reaching values of 186.75 t.ha-1, with an annual biomass growing 0.75 t ha-1. year-1. Therefore the stored carbon in the focused fragment was estimated at 74.07 t.ha-1; a decade after it reached 77.53 t ha-1 of stored carbon. As result, the forest revealed, under the prevailing conditions, low capacity to fix carbon, with an annual increase rate of 0.31 t ha-1.year-1. However, although the forest presents low annual carbon increase it can be characterized as a carbon sink, due to the existing carbon stock in the forest biomass. Therefore, even a forest near to stagnation in relation to carbon sink can still perform its environmental service.Keywords: Forest biomass; carbon stock; community dynamics.