Wildfires are the major reason for biomass loss and ecosystem change in Mediterranean forest landscapes, significantly impacting carbon dynamics and affecting post-fire recovery. Therefore, accurate assessment of wildfire carbon emissions is essential for understanding wildfire impacts and improving forest monitoring strategies in fire-prone regions.

The study investigates the use of multitemporal LiDAR-based aboveground biomass consumption (understory and overstory) from a suite of bitemporal point cloud structural change metrics. The final objective of the study is developing models to estimate combustion completeness using spectral relationships from Sentinel-2 indices and multitemporal LiDAR-based fuel consumption by forest type and fire severity levels. The study also addresses several methodological challenges, including differences in sensor characteristics and point density between Airborne Laser Scanning (ALS) and Unmanned Laser Scanning (ULS) datasets and the complex topography of the study area.

The study was conducted in the municipality of Lousã, central Portugal, using the August 2025 wildfire as a case study across different forest types, shrublands and fire severity levels. Pre-fire biomass conditions were estimated using countrywide ALS data acquired in July 2024 by “Direção Geral do Territorio” (DGT), while the estimation of post-fire biomass was derived from ULS data collected in October-November 2025 over an area of approximately 1,500 ha. In both cases, pre-existing ALS-based biomass models were applied to quantify biomass consumption before and after the fire. Fire severity indices were computed from pre- and post-fire Sentinel-2 Level 2A imagery. Biomass losses were analyzed in relation to fire severity indices, vegetation structure, and forest composition.

This study is expected to contribute to improving combustion completeness factors currently used in the Portuguese National Inventory Reports on greenhouse gases, providing more accurate estimates of biomass consumption and wildfire-related emissions across different forest types and fire severity levels.