ASSESSING POST-FIRE VEGETATION TRAJECTORIES USING MACHINE LEARNING AND REMOTE SENSING: EVIDENCE FROM A MEDITERRANEAN SITE

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Abstract
Fire has acted as a major eco-evolutionary force since the evolutionary appearance of plants, shaping plant-traits, diversity, vegetation assembly, and ecosystem functioning. Its ecological role depends on long-term fire regimes. Anthropogenic land-use change and climate warming are disrupting these regimes, particularly in densely populated regions such as the Mediterranean Basin. In the Italian peninsula (Mediterranean region) fire activity peaks during the dry summer months and is projected to intensify under climate change scenarios. Recent methodological developments – based on emerging satellite data, ground-based observations combined with Random Forest (RF) habitat classification, and spectral indices such as the NDVI provide a robust framework for monitoring post-fire land-cover dynamics over time. In this study, we applied RF modelling to classify vegetation cover using a 2017–2024 satellite imagery time series of the Monte Pisano area (central Italy) to assess pre- and post-fire vegetation trajectories. Evergreen shrubs and trees exhibited rapid post-fire regrowth, whereas coniferous stands showed slower recovery rates. NDVI trends revealed an expected sharp decline immediately after the fire, followed by gradual recovery of broadleaf forests and shrubland communities. Moreover, our results indicated a progressive increase in the cover of native deciduous and evergreen species of high conservation value (listed under the Habitat Directive). The framework delivers spatially explicit insights into post-fire recovery, supporting targeted management, restoration under European Nature Restoration Regulation, and long-term monitoring in Mediterranean ecosystems. Incorporating fine-scale environmental variables may further improve classification accuracy and enhance assessments of vegetation resilience and ecosystem recovery following fire events. Highlights: Recurring fires strongly affect ecosystem structure and function in Mediterranean landscapes. Integrating remote sensing with Random Forest models enables effective monitoring of post-fire vegetation recovery over time. NDVI time series provide reliable proxies for tracking vegetation vigor and land-cover change. Post-fire recovery trajectories are shaped by fire severity, vegetation physiognomy, plant functional types, and soil conditions. Targeted restoration and management interventions informed by spatial–temporal vegetation patterns are urgently needed. The proposed framework aligns with objectives of the EU Nature Restoration Regulation for ecosystem and habitat recovery.

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