Supplementary data to article "From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey"

This repository provides regression models and annual prediction rasters for tree mortality in Germany.  

Regression models:Logistic regression models which predict tree mortality for the species (beech = Fagus sylvatica, oak = Quercus petraea and robur, pine = Pinus sylvestris, spruce = Picea abies) and species groups (OB = other broadleaves, OC = other conifers) based on observations of dead trees in theGerman Crown Condition Survey (Waldzustandserhebung) and a set of environmental predictor variables. The predictors come from the domains of climate (clim), site conditions (site, i.e. topography, soil, land cover, deposition), tree age (age) and some models contain pairwise interaction terms between predictors (inter). All models were fit in R and are represented as objects of the class glm and stored in files of the type rds.

Prediction rasters:Spatial predictions of the mortality rate across Germany for each tree species and species group and for each year from 1998 to 2022. The rasters have a spatial resolution of 100 m. Missing valuesmark areas where the species/group does not occur. The mortality values are given as integers between 0 (no mortality) and 10000 (100% mortality). The coordinate reference system is Lambert Azimuthal Equal Area (LAEA; EPSG:3035). The rasters are provided in the file format GeoTIFF (tif).

A detailed description of the data sources and analyses can be found in the following article.

Citation:Knapp, N., Wellbrock, N., Bielefeldt, J., Dühnelt, P., Hentschel, R., Bolte, A., 2024. From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey.

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