@article{fdi:010097057,
  title = {{H}ow climate, fire types and topography drive forest biomass vulnerability to fires assessed from high resolution space-for-time analysis},
  author = {{V}allet, {L}. and {M}ouillot, {F}lorent},
  editor = {},
  language = {{ENG}},
  abstract = {{C}limate change is driving the expansion of fire-prone areas and the intensification of wildfire events, threatening {E}uropean forests. {Y}et, how this shift in fire regime might be more impactful on ecosystems lacks data-driven assessments of forest ecosystem vulnerability to this disturbance. {W}e propose a comprehensive framework for assessing forest biomass vulnerability to wildfires across {F}rance, leveraging high-resolution tree height data and {L}andsat-based fire history reconstruction. {W}e developed a new mapping of forest aboveground biomass ({AGB}) at 10m resolution, providing detailed insights into biomass distribution across various forest types. {A}dditionally, we compiled a new database of fire polygons in {F}rance since 1984, encompassing 1762 fires, which serves as a critical resource for time since last fire and fire characteristics. {U}tilizing a space-for-time approach, we characterized post-fire drivers of biomass recovery, integrating species-specific fire response traits, fire characteristics, climatic conditions, and topographic variables. {O}ur findings reveal significant regional variations, with temperate forests exhibiting higher vulnerability due to greater biomass exposure, lower resistance, and recovery rates, with local effects of topo-climates and fire size. {T}he high-resolution mapping of quantitative vulnerability offers a robust tool for fire risk management, emphasizing the importance of considering post-fire recovery in carbon stock assessments. {T}his work aims to inform decision-makers and enhance fire prevention strategies, contributing to sustainable forest management under changing climate conditions.},
  keywords = {{V}ulnerability ; {A}boveground biomass ; postfire dynamics ; {S}pace-for-time ; remote sensing ; fire ecology ; {GEDI} ; landsat ; {F}orest ; {R}andom {F}orest ; {D}river ; {FRANCE}},
  booktitle = {},
  journal = {{E}cological {I}ndicators},
  volume = {186},
  numero = {},
  pages = {114850 [22 p.]},
  ISSN = {1470-160{X}},
  year = {2026},
  DOI = {10.1016/j.ecolind.2026.114850},
  URL = {https://www.documentation.ird.fr/hor/fdi:010097057},
}