Possibilities and Limitations of Spatially Explicit Site Index Modelling for Spruce Based on National Forest Inventory Data and Digital Maps of Soil and Climate in Bavaria (SE Germany)

Combining national forest inventory (NFI) data with digital site
maps of high resolution enables spatially explicit predictions of
site productivity. The aim of this study is to explore the
possibilities and limitations of this database to analyze the
environmental dependency of height-growth of Norway spruce and to
predict site index (SI) on a scale that is relevant for local
forest management. The study region is the German federal state of
Bavaria. The exploratory methods comprise significance tests and
hypervolume-analysis. SI is modeled with a Generalized Additive
Model (GAM). In a second step the residuals are modeled using
Boosted Regression Trees (BRT). The interaction between temperature
regime and water supply strongly determined height growth. At sites
with very similar temperature regime and water supply, greater
heights were reached if the depth gradient of base saturation was
favorable. Statistical model criteria (Double Penalty Selection,
AIC) preferred composite variables for water supply and the supply
of basic cations. The ability to predict SI on a local scale was
limited due to the difficulty to integrate soil variables into the
model.