Using canopy heights from digital aerial photogrammetry to enable spatial transfer of forest attribute models: a case study in central Europe

This paper describes a workflow utilizing detailed canopy height information derived from digital airphotos combined with ground inventory information gathered in state-owned forests and regression modelling techniques to quantify forest-growing stocks in private woodlands, for which little information is generally available. Random forest models were trained to predict three different variables at the plot level: quadratic mean diameter of the 100 largest trees (d₁₀₀), basal area weighted mean height of the 100 largest trees (h₁₀₀), and gross volume (V). Two separate models were created–one for a spruce- and one for a beech-dominated test site. We examined the spatial portability of the models by using them to predict the aforementioned variables at actual inventory plots in nearby forests, in which simultaneous ground sampling took place. When data from the full set of available plots were used for training, the predictions for d₁₀₀, h₁₀₀, and V achieved out-of-bag model accuracies (scaled RMSEs) of 15.1%, 10.1%, and 35.3% for the spruce- and 15.9%, 9.7%, and 32.1% for the beech-dominated forest, respectively. The corresponding independent RMSEs for the nearby forests were 15.2%, 10.5%, and 33.6% for the spruce- and 15.5%, 8.9%, and 33.7% for the beech-dominated test site, respectively.