  Butt rot (BR) damages associated with Norway spruce (Picea abies [L.] Karst.)
account for considerable economic losses in timber production across the
northern hemisphere. While information on BR damages is critical for optimal
decision-making in forest management, the maps of BR damages are typically
lacking in forest information systems. We predicted timber volume damaged by BR
at the stand-level in Norway using harvester information of 186,026 stems
(clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain
characteristics). We utilized random forest (RF) models with two sets of
predictor variables: (1) predictor variables available after harvest
(theoretical case) and (2) predictor variables available prior to harvest
(mapping case). We found that forest attributes characterizing the maturity of
forest, such as remote sensing-based height, harvested timber volume and
quadratic mean diameter at breast height, were among the most important
predictor variables. Remotely sensed predictor variables obtained from airborne
laser scanning data and Sentinel-2 imagery were more important than the
environmental variables. The theoretical case with a leave-stand-out
cross-validation achieved an RMSE of 11.4 $m^3ha^{-1}$ (pseudo $R^2$: 0.66)
whereas the mapping case resulted in a pseudo $R^2$ of 0.60. When the spatially
distinct k-means clusters of harvested forest stands were used as units in the
cross-validation, the RMSE value and pseudo $R^2$ associated with the mapping
case were 15.6 $m^3ha^{-1}$ and 0.37, respectively. This indicates that the
knowledge about the BR status of spatially close stands is of high importance
for obtaining satisfactory error rates in the mapping of BR damages.
