Regression Analysis for Forest Inventory Data with Time and Space Dependencies

In this paper the data of a forest health inventory are analysed.
Since 1983 the degree of defoliation (damage), together with
various explanatory variables (covariates) concerning stand, site,
soil and weather, are recorded by the second of the two authors, in
the forest district Rothenbuch (Spessart, Bavaria). The focus is on
the space and time dependencies of the data. The mutual
relationship of space-time functions on the one side and the set of
covariates on the other is worked out. To this end we employ
generalized linear models (GLMs) for ordinal response variables and
employ semiparametric estimation approaches and appropriate
residual methods. It turns out that (i) the contribution of
space-time functions is quantitatively comparable with that of the
set of covariates, (ii) the data contain much more (timely and
spatially) sequential structure than smooth space-time structure,
(iii) a fine analysis of the individual sites in the area can be
carried out with respect to predictive power of the covariates.