COMBUS-IA CYL
CLASSIFICATION AND QUANTIFICATION OF FOREST FUELS IN CASTILLA Y LEÓN
Agresta S. Coop.
Castile and León Regional Government
Dynamic Classification and Quantification of Forest Fuels
What is it and what is it used for?
It is a platform based on artificial intelligence (AI), LiDAR data, and satellite information that generates high-resolution dynamic maps of forest fuels in Castilla y León. It is used to classify surface fuel models, quantify broken-down loads of deforested scrub (Erica australis) updated according to age, and generate canopy fuel parameters (CBH, CBD, and CFL). The results are made available through an open-source Spatial Data Infrastructure (SDI) (https://retechfor.agrestaweb.org/), which facilitates their download for use in preventive planning and firefighting.
The problem it solves
It addresses the limitation of rapid obsolescence in traditional, static fuel maps. In the face of ecosystems undergoing constant territorial evolution and forest fire disturbances exacerbated by the increased risk posed by climate change, it addresses the need for continuous, up-to-date mapping that reflects the dynamic structure of vegetation—a key factor in predicting fire behavior during extreme, large-scale forest fires.
“Artificial intelligenceand remote sensing for dynamic, continuous, and up-to-date management of forest fuel maps in Castile and León, to aid in fire prevention and suppression.”
What makes it innovative?
Change Detection Algorithm (EIKOS)
The automated annual update of maps using change-detection algorithms (EIKOS).
Dynamic models
Dynamic shrub growth models for heathlands, maintaining the current system without the need for continuous LiDAR flights.
Expected impact
From an environmental perspective, it represents a qualitative leap in the region’s resilience and in preventive planning to address the effects of climate change and wildfires. From a socioeconomic perspective, it enhances safety and contributes to protection at the urban-forest interface, providing government agencies and emergency services with advanced technological tools to anticipate fire behavior and optimize decision-making.
