Mountain pine time series mapping with Sentinel-2 and assessing their ecological role in Alpine vegetation dynamics

Kurzfassung

Mountain pine (Pinus mugo) is a critical species in the krummholz zone of alpine ecosystems and vital to understanding treeline dynamics under ongoing climate change. Despite their various ecosystem functions, their spatial distribution remains poorly quantified across complex mountain terrain. In this study, Sentinel-2 multi-temporal optical imagery, along with topographic and textural features, is used to train a random forest model and map the species in southern Germany at 10-m resolution. The classification achieves an F1 score of 87.5–90.4% under spatially independent validation, and was applied to generate annual mountain pine distribution maps from 2017 to 2025. Applying the approach to higher-resolution UAS data acquired near Zugspitze in Summer 2025 highlights the sensor-scale limitation. This is evidenced by underdetection in the Sentinel-2 maps at higher elevations, particularly in landscapes with smaller, fragmented patches, due to mixed pixels at the Sentinel-2 scale. The annual mountain pine time-series maps highlight patch dynamics, distribution in relation to terrain, and a detectable upslope shift in the upper boundary of the distribution within the German part of the European Alps. To demonstrate the ecological relevance of these maps, an exploratory fixed-effects analysis suggests that greenness is influenced mainly by growing-season and early-winter temperature, pointing to an energy-limited system favoured by warming conditions. Our results highlight the potential of high-resolution species-level mapping using machine learning in data-limited regions. The approach provides scalable workflows for other species in alpine ecosystems, supporting robust monitoring amid ongoing environmental change.