Multispectral Spaceborne Proxies of Predisposing Forest Structure Attributes to Storm Disturbance—A Case Study from Germany

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.