Mixed-effects models for estimating stand volume by means of small footprint airborne laser scanner data.

For this study, hierarchical datasets in that several sample plots are located within a stand were analysed for study sites in the USA and Germany. The German data had an additional hierarchy since the stands are located within four distinct public forests. Fixed-effects models and mixedeffects models with a random intercept on the stand level were fit to each dataset. The coefficients varied significantly also between adjacent study sites. The mixed-effects models significantly improved the estimates and especially reduced the bias which was present for numerous stands in the predictions of the fixed-effects models. The RMSE for the German study site was higher (22.5% to 31.3%) than for the US study site (16.7%).Unlike the American dataset, it was necessary to take the spatial correlation of the data into consideration for the German dataset. A mixed-effects model with random effects on the study site and stand level was fit to the complete German dataset. It provided comparable goodness-of-fit statistics for the local mixed-effects models. The study shows the potential of mixed-effects models in this context. It illustrates that use of mixed models could be a good alternative to the common practise of fitting different models for different groups of data.

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