Fire risk maps are fundamental tools for the management and prevention of forest fires, allowing the identification of the most vulnerable areas and thus facilitating the planning and optimization of firefighting resources. To obtain risk maps, it is necessary to consider propagation risks (slope, orientations, NDVI and NDWI index from Sentinel-2 and backscatter coefficient from Sentinel-1) and ignition risks, where human factors come into play, such as proximity to power lines, picnic areas, and agricultural plots. The combination of these variables allows for a precise analysis of fire risk and the generation of a detailed map of the study area using machine learning.

Quantifying carbon loss after a fire is crucial to assess the environmental impact. In this work, forest mass stocks have been evaluated using LiDAR data and biomass volume-based models to estimate the carbon loss in the Zamora's large fires in 2022 (Spain). The main species affected were forest stands of Quercus ilex, Quercus pyrenaica, Pinus sylvestris and Pinus pinaster. The carbon estimate has been calculated from the severity of the fire, making a final map of carbon stock losses. In this area, a total of approximately 0,5 megatons of carbon were lost, 106.304,10 tons of biomass of Quercus pyrenaica, 340.694,25 tons of Pinus sylvestris and 457.611,79 tons of Pinus pinaster.

The main objective of this study is to develop a clear and accurate methodology to create detailed and updated forest fire risk maps and forest fire carbon loss estimation maps, using remote sensing data in one of the most devastating wildfires that Spain has suffered in recent years. The application of these methodologies and the resulting maps contribute to the design of forest restoration strategies and climate change mitigation policies, promoting a more sustainable management of natural resources.