Butt rot (BR) damage of a tree results from a decay caused by a pathogenic fungus. 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, maps of BR damages are typically lacking in forest information systems. Timber volume damaged by BR was predicted 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). This study utilized Random Forests 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). Our findings showed 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 resulted in an RMSE of 11.4 m³ · ha⁻¹ (pseudo-R²: 0.66) whereas the mapping case resulted in a pseudo-R² of 0.60. When spatially distinct clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo-R² associated with the mapping case were 15.6 m³ · ha⁻¹ and 0.37, respectively. The findings associated with the different cross-validation schemes indicated 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.

树木的腐烂 (BR) 损坏是由病原真菌引起的腐烂引起的。与挪威云杉相关的 BR 损害（Picea abies [L.] Karst. ）造成整个北半球木材生产的巨大经济损失。虽然 BR 损害的信息对于森林管理的最佳决策至关重要，但森林信息系统中通常缺乏 BR 损害的地图。使用 186,026 根树干（明伐）的采伐机信息、遥感和环境数据（例如气候和地形特征）在挪威的林分级别预测了 BR 损坏的木材体积。本研究使用具有两组预测变量的随机森林模型：（1）收获后可用的预测变量（理论案例）和（2）收获前可用的预测变量（映射案例）。我们的研究结果表明，表征森林成熟度的森林属性，例如基于遥感的高度、采伐的木材体积和胸高的二次平均直径是最重要的预测变量之一。从机载激光扫描数据和 Sentinel-2 图像中获得的遥感预测变量比环境变量更重要。具有留出突出交叉验证的理论案例导致 RMSE 为 11.4 m³ · ha ^-1 (伪R ² : 0.66) 而映射情况导致伪R ²为0.60。当空间上不同的采伐林分簇被用作交叉验证的单位时，与映射案例相关的 RMSE 值和伪 R ²分别为 15.6 m ³ · ha ^-1和 0.37。与不同交叉验证方案相关的发现表明，了解空间上靠近林分的 BR 状态对于在 BR 损害映射中获得令人满意的错误率非常重要。