Windstorms are among the primary drivers of forest disturbances. Although they are inherent part of the natural ecosystem processes, they severely impact managed forests. Modeling approaches serve as key tools for the evaluation of disturbance risk and different predisposing factors. However, data availability on relevant forest attributes can be problematic on a larger scale. While spaceborne remote sensing has already proven itself as a tool for disturbance detection, its use in relation to predisposing forest attributes remains underexploited. The present work explores multispectral object-based proxy predictors for statistical wind disturbance modeling based on the publicly available Sentinel-2 imagery and recorded damage polygons from the pan-European FORWIND database. Potential predictors were tested in logistic and random forests (RF) regression models for both disturbance occurrence and severity for a case study of a major storm event in Northern Germany from 2017. The results reveal a general potential of the derived spaceborne variables to be used as proxy variables to critical predisposing forest attributes. The presented proxy variables also outperformed a set of publicly available derived spatial data products for modeling both disturbance occurrence and severity. Model accuracies were moderate (reaching AUC = 0.76 for logistic regression fit and AUC = 0.69 for predictive accuracy of RF models), yet falling within the range of reported results in previous studies from the field. Limitations of the spectral satellite imagery as a single information source were acknowledged; however, the results indicate the further potential of spaceborne imagery applications in disturbance modeling, assessment and resulting mapping of disturbance susceptibility at different spatial scales. Considering the growing spatiotemporal availability of high-resolution spaceborne data, we propose that a model representation of post-disturbance forest patterns could improve the understanding of complex disturbance regimes and recurrent susceptibility.

Lowland forests benefiting from groundwater availability are important ecosystems in Central Europe, both from ecological and economic perspectives. Besides a great reduction in their extent in the historical times and further shifts in the land use and water management regimes intensified during the industrial era, continuing changes in the groundwater and overall hydro-climatic conditions can pose significant challenges to them. Although tree-ring analyses serve as widely used tools to assess the climatic impact on tree growth and vitality, few studies have attempted to investigate the effects of subsurface hydrology on interannual fluctuations in xylem production. In this study, we compared the tree-ring width series of pedunculate oak (Quercus robur L.) from a forested area in southwestern Hungary with the time series of monthly groundwater depth and climatic variables over the period of 1920–2017 with a specific focus on 1961–2017. The radial growth of the studied trees showed the strongest relationship with late winter and early spring groundwater and drought conditions preceding the growing season, differing from the commonly reported climatic signals marked by early summer meteorological conditions of the vegetation season. The results suggest that the groundwater recharge during the dormant period preceding the vegetation season and the groundwater levels in early spring were among the key limiting factors on tree growth in the study area. In the growing years starting with a sufficiently high groundwater table, even scarce summer precipitation did not seem to limit radial growth drastically. However, unfavorable shifts in climatic conditions during the past few decades and the associated uncertainties in the future groundwater regime imply that additional active measures aimed at maintaining and restoring groundwater conditions may well be highly beneficial for sustaining groundwater-dependent forest ecosystems and their productivity.