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Free keywords:
SHapley Additive exPlanation, Machine learning, Environmental drivers, Resistance, Remote sensing, Normalized Difference Moisture Index (NDMI)
Abstract:
Increases in the frequency and intensity of droughts and heat waves are threatening forests around the world. Climate-driven tree dieback and mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially fine-grained understanding of the site characteristics making forests more susceptible to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate meteorological indicators. To address this research gap, we investigated the drivers of drought-induced forest damage in a particularly drought-affected region of Central Europe using SHapley Additive exPlanations (SHAP) values, an explainable artificial intelligence (XAI) method which allows for the relevance of predictors to be quantified spatially. To develop a reproducible approach that facilitates transferability to other regions, open-source data was used to characterize the meteorological, vegetation, topographical, and soil drivers of tree vulnerability, representing 41 predictors in total. The forest drought response was characterized as a binary variable (“damaged” or “unchanged”) at a 30-m resolution based on the Normalized Difference Moisture Index (NDMI) anomaly (%) between a baseline period (2013–2017) and recent years (2018–2022). We revealed critical tipping points beyond which the forest ecosystem shifted towards a damaged state: <81 % tree cover density, <4 % of broadleaf trees, and < 24 m canopy height. Our study provides an enhanced understanding of trees’ response to drought, which can support forest managers aiming to make forests more climate-resilient, and serves as a prototype for interpretable early-warning systems.
