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A spatial distributional regression model is presented to predict the foreststructural diversity in terms of the distributions of the stem diameter atbreast height (DBH) in the protection forests in Ebensee, Austria. In total36,338 sample trees were measured via a handheld mobile personal laser scanningsystem (PLS) on 273 sample plots each having a 20 m radius. Recent airbornelaser scanning (ALS) data was used to derive regression covariates from thenormalized digital vegetation height model (DVHM) and the digital terrain model(DTM). Candidate models were constructed that differed in their linearpredictors of the two gamma distribution parameters. Non-linear smoothingsplines outperformed linear parametric slope coefficients, and the bestimplementation of spatial structured effects was achieved by a Gaussian processsmooth. Model fitting and posterior parameter inference was achieved by usingfull Bayesian methodology and MCMC sampling algorithms implemented in theR-package BAMLSS. Spatial predictions of stem count proportions per DBH classesrevealed that regeneration of smaller trees was lacking in certain areas of theprotection forest landscape.

Spatial prediction of diameter distributions for the alpine protection  forests in Ebensee, Austria, using ALS/PLS and spatial distributional  regression models