Long-term Structural Recovery of Wildfire-affected Forests in Greece using GEDI and Landsat

Mediterranean ecosystems are increasingly exposed to frequent and high-severity wildfires, driven by rising temperatures, prolonged droughts, and land-use change, making wildfire one of the dominant disturbance agents shaping forest structure and function. There is a concern that frequent and high-severity wildfires may threaten the resilience of forests, even in fire-prone forest ecosystems, and their ability to recover to pre-fire levels. This has implications for carbon storage, biodiversity conservation, water regulation, and the long-term provision of ecosystem services on which both local communities and broader society depend. The availability of long-term multispectral satellite time series has demonstrated the ability to estimate the instantaneous impact of fires on forests and the recovery trajectories. Yet, spectral recovery is two-dimensional and does not necessarily mean functional, structural or compositional recovery which may be slower than simply tracking the greenness index trajectories. GEDI lidar metric display a larger variety of fire responses that spectral metrics but are only available since 2019. This study combines structural GEDI metrics with a Landsat-based historical forest disturbance to estimate the structural recovery of forests post fire in Greece from the 1985. Overall, we find post-fire vegetation recovery in Greece, using GEDI biomass, height, canopy cover, and foliage height density, likely takes 50 or more years. Low-intensity and small spatial scale fires recover within the first 20-30 years, while high-intensity and large fires show forest recovery likely >50 years. There is also some evidence of a lack of recovery trajectory or a new ecosystem state within the first 40 years for some regions. This work demonstrates how integrating lidar with long-term spectral archives can provide regional scale post-fire structural recovery assessments, can provide critical information to constrain terrestrial biosphere models predicting fire impacts and forest recovery, and can begin providing more targeted data locally to regionally for fire management, restoration practices and climate mitigation.