RESUMOO laser scanner terrestre é uma alternativa para a coleta de dados dendrométricos em campo sem a necessidade da derrubada da árvore. Assim, este estudo teve como objetivo avaliar a influência da distância da varredura laser terrestre na determinação das variáveis dendrométricas. Foram analisadas duas árvores com altura total de 17,14 m e 16,00 m. A varredura laser foi realizada a 5, 10, 15 e 20 m de distância da árvore. Os diâmetros foram obtidos em alturas parciais até o topo da árvore. Os resultados obtidos com a varredura laser foram validados com as medidas obtidas tradicionalmente em campo com suta e trena. A melhor distância para a obtenção das variáveis dendrométricas foi de 15 m para ambas as árvores. As

O objetivo deste artigo é apresentar os princípios de funcionamento dos equipamentos laser scanner terrestre e as possibilidades de aplicação da tecnologia no setor florestal. O texto relata as tecnologias embarcadas nos equipamentos bem como suas vantagens e desvantagens do ponto de vista de aplicação em levantamentos de informações florestais. São descritos os modelos de varredura laser comumente aplicados e os tipos de dados que são gerados. É feita uma descrição do estado da arte no setor florestal para obtenção de informações de alturas, diâmetros, áreas transversais e volumes de árvores, compreendendo também as fases de pré-processamento como identificação e filtragem dos objetos de interesse. Por fim, apresenta-se uma breve abordagem de novas frentes de pesquisa florestal que vem usando esta tecnologia para modelagem de material combustível presente sobre o solo, estudos ecológicos, dendrológicos e qualitativos. Com o potencial dos dados obtidos pelos equipamentos laser scanner terrestre, evidenciado pelas atuais pesquisas, espera-se que, ocorrendo uma popularização dos equipamentos, tenhamos sua utilização em larga escala no setor florestal.

& Key message We found high accuracy classification (F measure = 95%, on cross-validation) of Araucaria angustifolia (Bertol.) Kuntze, an endangered native species, and Hovenia dulcis Thunb. an aggressive, invasive alien species in WorldView-2 multispectral images. In applying machine learning algorithms, the spectral attributes mainly related to the near-infrared band were the most important for the models. & Context It is difficult to classify tree species in tropical rainforests due to the high spectral response's diversity of existing species, as well as to adjust efficient machine learning techniques and orbital image resolution. & Aims To explore the spectral and textural response of an endangered species (A. angustifolia) and an invasive species (H. dulcis) in WorldView-2 multispectral images, testing its recognition capability by machine learning techniques. & Methods We used a WordView-2 (2016) image with 0.5-m spatial resolution. Then we manually clipped the canopy area of the two species in this image using two compositions: True color composition (R=660 nm, G=545 nm, B=480 nm) and near-infrared composition (NIR-2=950 nm, G=545 nm, B=480 nm). Thus, we applied spectral and textural descriptors (pyramid histogram of oriented gradients-PHOG and Edge Filter), which selects the most representative features of the dataset. Finally, we used artificial neural networks (ANN) and random forest (RF) for tree species classification. & Results The species classification was performed with high accuracy (F measure = 95%, on cross-validation), essentially for spectral attributes using the near-infrared composition. RF surpassed the ANN classification rates and also proved to be more stable and faster for training and testing.Handling Editor: Barry A. Gardiner Contribution of the co-authors Crisigiovanni E. L. designed the methods, performed the experiments, processed the data, analyzed the results, and wrote most of the manuscript. Figueiredo Filho A. idealized the article, provided the materials (satellite image), and formulated the research framework. Pesck V. A. contributed to the geoprocessing and remote sensing analysis and cooperated in the methodology's design. De Lima V. A. cooperated in the methodology design and performed the machine learning analyses and data processing.

This study aimed to compare the efficiency of the sampling methods: Fixed Area, Bitterlich, Prodan and Modified Prodan to estimate the commercial volume and other dendrometric estimators for a 34 years old of Pinus taeda L. stands located in Campo Belo do Sul, Santa Catarina, Brazil. It were distributed a total of 10 sample units of the following methods: Fixed Area with 200, 400 and 500 m² of area, Bitterlich, Prodan and Modified Prodan were distributed, both with 6, 7, 8, 9 and 10 trees. In addition to collecting dendrometric data, the installation time of the sample units was timed, whereby the relative efficiency for each method was calculated. The comparison between the harvest volumes and the volumes estimated by the methods was performed by the Skott Knott test, and the results that did not differ statistically were weighted by the parameters of relative error, relative efficiency and proximity to harvest. All variations of the Modified Prodan and Prodan methods had sample insufficiency. The number of trees per hectare presented higher values for the 200 m² Fixed Area method and lower values for Prodan with 10 trees. Prodan with 6 trees got the shortest time. The Bitterlich method obtained sample adequancy at 10% error and presented the best result. Among the alternative methods to Fixed Area, Modified Prodan with 7 trees can be indicated for pilot inventory. However, when more precise results are needed, the Bitterlich method is indicated.

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