Abstract
Trees located along railway corridors pose a significant risk to transport infrastructure, particularly in the context of increasingly frequent and intense storm events driven by climate change. Proactive vegetation management strategies are therefore essential for reducing the risk of disruptions caused by falling trees. This study applies an existing GIS-based assessment framework to analyse tree fall risk and hazard along railway infrastructure. The approach is demonstrated along a corridor in the Sachsenwald forest, Germany, using high-resolution individual tree crown (ITC) data derived from the Digital Twin Germany (DigiZ-DE) project. By integrating high-resolution airborne LiDAR data and openly available geospatial datasets, the methodology enables spatially explicit assessments at the level of individual trees. It combines two complementary analytical perspectives: a basic risk-oriented exposure analysis, which identifies trees that may directly affect railway infrastructure based on height and distance to the track, and an advanced hazard-oriented susceptibility analysis, which incorporates site- and tree-specific factors. This dual structure enables both rapid operational screening and more comprehensive, precautionary vegetation management. The results can inform targeted interventions, enabling resource-efficient and ecologically sensitive mitigation strategies. While the accuracy of the outputs depends on the timeliness and quality of the input data, the modularity of the approach supports its transferability across regions and infrastructure types. Although applied here to railway systems, the framework is readily adaptable to other transport or utility corridors and can contribute to climate-resilient infrastructure planning and natural hazard management.