Abstract: This work aims to analyze the potential of the Photogrammetric Point Cloud (PPC) obtained from Remote Piloted Aircraft (RPA) optical images for detecting and obtaining tree heights in a loblolly pine plantation using a global maximum filter. The enhanced algorithm used in this study is then named STD (Single Tree Detection). Field surveys were conducted to count all the trees in the field (Forest Census) and measure the trees’ height with a vertex hypsometer. The results were faced to PCC outcomes. The detection rate (r) was equal to the precision rate (p), indicating that the algorithm reaches a high tree detection performance. In summary, the STD algorithm segmented 2,192 trees, representing 89% of trees recorded in the forest census. The retrieved tree height reached, on average, a height of 17.05 m, whereas slightly higher by the traditional forest inventory (17.42 m). The root-mean-square error (RMSE) and Bias were 47 cm (2.8%) and -37 cm (-2.2%), respectively. The Dunnett test showed that the tree height did not significantly differ between the results obtained by traditional forest inventory from those generated by the STD. It confirms the potential use of PPC for forest inventory procedures.

Resumo As funções de densidade de probabilidade têm várias aplicações florestais, entre as quais o tratamento de dados biométricos e uso em modelagem. O objetivo deste estudo foi verificar qual função densidade de probabilidade melhor se ajusta ao diâmetro de copa (dcopa) e ao diâmetro quadrático (dq), e também analisar o efeito dessas variáveis na estrutura da floresta. O estudo foi realizado com uma base de dados de Araucaria angustifolia provenientes de um fragmento florestal, e também com dados de Pinus taeda de um plantio florestal, ambos oriundos do estado do Paraná. Para as respectivas áreas de estudo foram delimitadas as copas das árvores com suporte de ortofotos digitais. As funções avaliadas foram: Normal, Log-normal 2P, Beta, Gama, Weibull 3P e Weber. A função que melhor se ajustou à variável dq para ambas as espécies foi a Normal. Já para a variável dcopa, a função de Weibull 3P foi a que apresentou melhor aderência para o fragmento de araucária e a função de Weber para o plantio florestal de pinus.

Abstract The probability density functions have several forest applications, among which biometric data processing and modeling use. The objective of this study was to verify which probability density function best fits the cup diameter (dcopa) and the quadratic diameter (dq), and also to analyze the effect of these variables on the forest structure. The study was carried out with a database of Araucaria angustifolia from a forest fragment, and also with Pinus taeda data from a forest plantation, both from the state of Paraná. For the respective study areas, the crowns of the trees with digital orthophoto support were delimited. The evaluated functions were: Normal, Log-normal 2P, Beta, Gama, Weibull 3P and Weber. The function that best fit the variable dq for both species was Normal. For the variable dcopa, the function of Weibull 3P was the one that presented better adhesion to the Araucaria fragment and the Weber function for the Pinus forest plantation.