Accuracy of tree geometric parameters depending on the LiDAR data density

[EN] The aim of this study was to compare geometric parameters of olive trees (tree height, crown base height, crown diameters, crown area), using LiDAR data of different densities: 0.5, 3.5 and 9 points m(-2). Two strategies were proposed and verified with a focus on raster and raw data analysis. Statistical tests have shown, that for the tree height and crown base height estimation, the choice of strategy was irrelevant, but denser LiDAR data provided more accurate results. The raster analysis strategy applied for sparse and dense LiDAR datasets allowed crown shape to be determined with a similar accuracy which means raster data are useful for estimating other indirect tree parameters. The quality of results was independent from the tree size.The authors appreciate the financial support provided by the Vice-Rectorate for Research of the Universitat Politecnica de Valencia [Grant PAID-06-12-3297; SP20120534].Hadás, E.; Estornell Cremades, J. (2016). Accuracy of tree geometric parameters depending on the LiDAR data density. European Journal of Remote Sensing. 49:73-92. doi:10.5721/EuJRS20164905S73924