ULISSE: Parameter-Efficient Adaptation of Earth Vision Models to Monitor Forest Disturbance in Sentinel-2 Timeseries

Vito Recchia, Giuseppina Andresini, Annalisa Appice, Dino Ienco, Giuseppe Fiameni, Donato Malerba, ULISSE: Parameter-efficient adaptation of earth vision models to monitor forest disturbance in sentinel-2 time series, Ecological Informatics, vol. 94, p. 103668, 2026. DOI: 10.1016/j.ecoinf.2026.103668

Europe is one of the most forest-rich regions in the world, with forestry mainly based on the management of coniferous trees. However, the spruce forest ecosystem is vulnerable to several disturbance agents. In particular, bark beetle outbreaks have been the scourge of spruce trees in the last decade, and they are expected to further intensify due to climate change, with significant adverse effects on forest ecosystems. Hence, the monitoring of forest disturbances caused by the rapidly escalating bark beetle outbreaks represents a significant ecological and forestry challenge. This monitoring is traditionally performed by foresters during field surveys. On the other hand, open Sentinel-2 images, available with the Copernicus mission and processed with sophisticated deep learning techniques, have been recently established as an alternative to field surveys performed by foresters to monitor various environmental phenomena such as bark beetle outbreaks. In particular, several deep learning approaches have been recently proposed to map bark beetle tree dieback using Sentinel-2 images of forests. However, the current effectiveness of deep learning approaches, as a means to monitor bark beetle outbreaks in Sentinel-2 data is often limited by the reduced availability of ground truth information to supervise semantic segmentation models for this specific downstream task. In this study, we propose ULISSE, a deep learning semantic segmentation methodology for mapping forest tree dieback caused by bark beetle outbreak disturbances using Sentinel-2 image time series. ULISSE leverages a U-Net-like architecture with a multi-temporal encoder specifically designed to handle Sentinel-2 image time series. The framework integrates vision encoders pretrained on a large volume of Sentinel-2 images for land-cover classification. To capitalize on the representational capabilities of these pretrained encoders, we employ a Parameter-Efficient Fine-Tuning (PEFT) mechanism that adapts the multi-temporal encoder to our downstream segmentation task. This approach enables ULISSE to achieve high accuracy even with a limited amount of labeled data available at training time. Experimental results demonstrate the effectiveness of the proposed methodology across two case studies on mapping bark beetle disturbances in the Czech Republic and Romania study areas.