Abstract& Key message The selection of stable metrics can generate reliable models between different data sets. The height metrics provide the greatest stability, specifically the higher percentiles and the mode. Height metrics transfer more predictive power than density metrics. & Context In forestry, there is an increasing development of aerial laser scanning (ALS). The flight missions that permit to record ALS point clouds are not yet standardized. Therefore, there is a need to identify the metrics that permit to infer robust forest stand estimates from the different point cloud acquisitions.& Aims The aim of this study is to identify stable metrics derived from different ALS data sets to be used as independent variable in stand volume models. & Methods Three different ALS data sets were taken from the same Eucalyptus plantation on the same day, each differing from the others in terms of flight altitude, laser power, and pulse frequency rate. Two sets of best predictive models were obtained for each data set based on two approaches: a basic approach using noncollinear metrics and an exhaustive search, and a second approach that added a pairwise KolmogorovSmirnov test to select stable metrics. & Results Height metrics proved more stable, especially higher percentiles (>50 %) and the mode. Models developed with stable metrics had similar performance compared to the basic approach. & Conclusion Percentiles higher than 50 % and the mode proved stable for that 6-year-old Eucalyptus plantation with a very homogeneous vertical structure. Further research widening the scope in terms of age and heterogeneity of vertical profiles is needed.

This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R² = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj. R² = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively).

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IDENTIFICAÇÃO DE ÁRVORES INDIVIDUAIS A pARTIR DE lEVANTAmENTOS lASER AEROTRANSpORTADO pOR mEIO DE JANElA INVERSARESUmO: O ponto crucial do filtro de máximos locais é a definição do tamanho da janela. Este trabalho propõe que a janela de busca seja determinada por uma relação inversa à altura do modelo digital de alturas e testa a hipótese de que uma janela de busca, inversamente proporcional, terá melhor desempenho do que a janela de busca proporcional. A área de estudo está localizada na região sudeste do Estado de British Columbia, Canadá. A vegetação natural é do tipo boreal e caracteriza-se pela predominância de duas espécies Picea engelmannii Parry ex. Engelmann (pícea de Engelmann) e Abies lasiocarpa (Hook.) Nutt. (abeto subalpino). O relevo é do tipo ondulado a montanhoso com altitudes que variam de 650 a 2400 metros. Foram lançadas 62 parcelas de campo com 256 metros quadrados. Foi usado um levantamento laser aerotransportado de retornos discretos, com densidade de 2 pontos por metro quadrado e small-footprint. As estimativas foram comparadas aos valores observados em campo durante a medição das parcelas. O desempenho das janelas de busca foi avaliado quanto ao percentual de acertos e quanto ao desvio absoluto. O filtro de máximos locais subestimou o número de árvores por hectare, considerando tanto a janela de busca proporcional quanto a inversamente proporcional. A relação inversa para a determinação da dimensão da janela resultou em melhores resultados, especialmente para regiões com maior densidade de árvores. INDIVIDUAl TREE IDENTIFICATION IN AIRbORNE lASER DATA bY INVERSE SEARCH wINDOwAbSTRACT: The local maximum filtering performance is highly dependent of the window size definition. This paper proposes that the window size should be determined by an inverse relationship to the canopy height model, and test the hypothesis that a windowsize inversely proportional will have better performance than the window proportional to the canopy height model. The study area is located in the southeastern region of the State of British Columbia, Canada. The natural vegetation is the boreal type and is characterized by the dominance of two species Picea engelmannii Parry ex. Engelmann (Engelmann spruce) and Abies lasiocarpa (Hook.) Nutt. (sub-alpine fir). The relief is mountainous with altitudes ranging from 650-2400 meters. 62 plots with 256 square meters were measured in the field. The airborne LiDAR had discrete returns, 2 points per square meter density and small-footprint. The performance of the search windows was evaluated based on success percentage, absolute average error and also compared to the observed values of the field plots. The local maximum filter underestimated the number of trees per hectare for both window sizing methods. The use of the inverse proportional window size has resulted in superior results, particularly for regions with highest density of trees.

A busca de camadas verticais homogêneas no dossel de uma floresta pode facilitar estudos para que inferências mais precisas sobre a estrutura de complexos agrupamentos arbóreos possam ser obtidas. O objetivo deste trabalho foi caracterizar o perfil vertical do dossel florestal em uma área de floresta ombrófila densa (bioma Mata Atlântica) a partir de um levantamento laser aerotransportado (ALS-Airborne Laser Scanning). Com o apoio da análise de agrupamento hierárquico, foram concatenadas classes de altura de um metro que resultaram em oito estratos de mínima colinearidade entre classes. Para essas classes de altura, o método k-means indicou que células de grade regular que cobria toda a área de estudo poderiam ser reunidas em quatro grupos de perfil vertical: baixo (pontos ALS entre 7 e 11 metros), médio-baixo (pontos ALS entre 15 e 18 metros), médio-alto (pontos ALS entre 19 e 22 metros) e alto (pontos ALS entre 23 e 26 metros). Esses grupos refletem características do perfil vertical do dossel em escala de ecosítio, evidenciando diferenças estruturais da paisagem. Os grupos obtidos apresentaram distribuição espacial coerente com a topografia local, além de correspondência com levantamentos ecológicos anteriormente conduzidos em campo por outros autores. Palavras-chave: LiDAR, estratificação vertical, ALS, análise de agrupamento, nuvem de pontos.

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