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.
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.
The aim of this study is to identify stable metrics derived from different ALS datasets to be used as independent variable in stand volume models.
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 non collinear metrics and an exhaustive search, and a second approach that added a pairwise Kolmogorov- Smirnov test to select stable metrics.
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.
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.
Eric Bastos Görgens, Petteri Packalen, André Gracioso Peres da Silva, Clayton Alcarde Alvares, Otavio Camargo Campoe, José Luiz Stape and Luiz Carlos Estraviz Rodriguez (2015) Stand volume models based on stable metrics as from multiple ALS acquisitions in Eucalyptus plantations Ann For Sci 72: 489-498 doi:10.1007/s13595-015-0457-x