Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

The diameter distributions of trees in 50 temporary sample plots (TSPs) established Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta gamma-2P functions. parameters first second moments (mean variance, respectively) parameter recovery models (PRM). Linear used to predict both data. In recovering functions, location predetermined as minimum inventoried, scale maximum diameters predicted metrics. Kolmogorov–Smirnov (KS) statistic (Dn), number acceptances KS test, Cramér von Misses (W2) statistic, bias mean square error (MSE) evaluate goodness fits. fits for compared with all measured data 58 TSPs (LiDAR could only be extracted plots). fitting phase, fixed at a suitable value determined according forestry literature (0.75·dmin). linear recover two accurate, R2 values 0.750, 0.724 0.873 dg, dmed dmax. Reasonable results obtained goodness-of-fit statistics indicated that function was most followed function. Weibull-3P provided poorest Weibull-2P SB also yielded poor