Forest fuel management is the basis of fire prevention of great intensity and, by extension, one of the key elements of land management. After all, fire feeds on the live and dead plant biomassAnd the way that biomass is distributed across the landscape determines how a fire behaves, how fast it spreads, and how much energy it releases.
With a more variable climate, with droughts and heat waves And with increasingly longer risk seasons, anticipation is no longer optional. That's why fuel model mapping, field inventories, photo identification keys, and simulation tools have become indispensable allies for the decision-making in prevention and extinction, from planning to on-site intervention.
What is forest fuel and why does it influence fire?
In operational terms, forest fuel is a spatial grouping of vegetation that serves as an energy source for fire. This grouping includes live and dead biomasswith different sizes, moisture contents, arrangements and continuity, and has a direct influence on the intensity and severity of the fire.
The biggest challenge is its heterogeneity: vegetation varies greatly between ecosystems and even within the same forest formation. Therefore, to be able to work in the field, model, and plan, a... fuel simplification through classifications that group sets with similar expected behavior.
These classifications give rise to what are known as fuel models: artificial sets that identify representative values (density, load, distribution, horizontal and vertical continuity, etc.) of a “fuel complex”. These values are the inputs used by fire behavior simulators, such as those developed from the work of Rothermel and Burgan in the years 80.
In addition to the fuel itself, weather and topography play a role in the spread of a fire. Factors such as wind and temperature These factors influence the drying rate, flame angle, and convection, while the slope and exposure of the terrain either favor or hinder the advance of the fire front. Therefore, reliable prediction requires integrating fuel, time and relief.
Fuel models: from Rothermel-ICONA to Scott & Burgan and UCO40
To estimate propagation speed, flame length, or intensity, managers use "families" of models. Historically, in Spain, the 13 Rothermel-based models have been used (adapted). ICONA, 1987), supported by guides such as the photographic identification key (Anderson, 1982). This foundation was a leap forward for technicians and brigades to communicate a common language.
Over time, the variability of the territory demanded a richer classification. This is where the classification of Scott and Burgan (2005), which expands to 40 models and allows for the representation of more diverse fuel structures, something especially useful in Mediterranean mosaics where pastures, scrubland and wooded areas coexist with different stages of development and continuity.
In parallel, regional initiatives have refined the characterization for their own realities. The classification, for example, stands out. UCO40This was used as a reference to develop a field guide with an identification key and model sheets adapted to Andalusia. This type of reference allows the inventory and modeling to be applied to the real fuels of each territory.
By groups, models are usually organized into categories such as: pasturescrubland, undergrowth (intermix), crop residues or waste, and other cover. From an operational point of view, this structure helps to estimate horizontal and vertical continuity, as well as the fuel load effective for the flame front.
- Pastures: fine fuels with high seasonal availability and rapid response to changes in humidity.
- scrub: shrubs of varying height and density; they modulate flame length and speed of advance.
- Under trees: layers of undergrowth and leaf litter that connect vertically with tree canopies.
- Remains: residues from cutting and treatments; can increase secondary fires and sparks.
Inventories and modeling in Andalusia: ZAR, UCO40 and the CILIFO project
Within the framework of the NET872330 project, focused on forest fuel modeling, a specific field inventory service was developed for the parameterization and validation of fuel in the High Risk Zones (HRZs) of Andalusia. The main objective was to have a field manual with identification keys and model data sheets based on the classification UCO40.
The study area encompassed the entire Autonomous Community of Andalusia, plus a 20 km buffer zone extending into Portugal and the neighboring communities of Extremadura, Castilla-La Mancha, and Murcia. However, field sampling was conducted only within the Andalusian region, with greater intensity in the ZAR.
High-risk zones occupy approximately 58% of the Andalusian territory, which is equivalent to about 50.550 km2These are mostly forested areas with relatively frequent fires. In order to avoid overlooking structures relevant to large fires, the fieldwork is expanded outside of Tsar to capture significant fuels that, even if not in areas of higher frequency, can contribute to extreme propagations.
The combination of on-the-ground inventories and local classification (UCO40) allows for refining the input parameters of the simulators, improving the reliability of the predictions and helping to prioritize. silvicultural practices where there is the greatest potential for change in fire behavior.
- Ambit: Complete Andalusia + 20 km of adjacent cross-border and regional environment.
- Focus: intensive inventories in ZAR, with selective expansion outside of them.
- Starting Line: field guide with identification key and model sheets based on UCO40.
- Use: parameterization/validation for simulation and treatment planning.
Mapping models in the Valencian Community: remote sensing, LiDAR and precision
The regional ministry responsible for the environment and land management in the Valencian Community oversees, among other tasks, the prevention of forest fires. In a scenario of climate changeThe focus is on anticipating: understanding the structure of the fuel and its spatial distribution It is fundamental for predicting spread and planning resources.
Since the late 80s, the Rothermel classification, adapted by ICONA (13 models), was used. With the arrival of PATFOR (2011), this classification was incorporated into maps through cross-referencing of vector layers. However, in 2011, a revised provincial map (Valencia) of fuel models was developed based on the classification of Scott and Burgan (40 models), better reflecting the true diversity of the Valencian landscape.
The decisive leap came in 2018, when the General Directorate for Forest Fire Prevention promoted a complete update, relying on new, accessible technologies: satellite images and airborne LiDAR. Being open sources and low cost, they allowed a very reasonable balance between accuracy and budget.
The satellite-LIDAR combination brings together the best of both worlds: the satellite offers multispectral and time-series data to detect changes and types of land cover; TO DEAL It provides vertical vegetation structure (heights, densities, continuity) and microrelief. Using appropriate methodology, a high-resolution (10 x 10 m) fuel model map and a production line that facilitates its regular update.
The result: 18 models mapped across the Valencian Community, of which 14 are forest fuels (flammable) and 4 are non-flammable, associated with urban areas, agricultural land, bodies of water, and bare soil. This mapping is used both in prevention (treatment planning) as in ordinary forest management, and improves real-time operational analysis.
Examples of models and photographic key in the Valencian Community
Rapid on-site identification relies on photographic model keys, which is especially useful for teams from different units to label fuel consistently. Reference models, as local examples, include: GR2 (grasslands less than 1 m high) and GR7 (wetlands and marshes), which reflect fine fuel structures with their own moisture and continuity dynamics.
In scrubland, models such as SH4 (scrubland) and SH9 (regenerated), which express different densities and heights, with clear implications for flame length. When the scrub develops under trees, the behavior changes due to vertical continuity; that's where the model comes in. TU2 (scrubland under trees), with a risk of transition to tree canopies if the undergrowth is not managed.
Los remains Forestry operations and management require specific models such as SB3These labels help assess the potential of secondary fires and embers. Each label summarizes a set of quantitative and qualitative parameters that, once integrated into the simulator, allow for better prediction of the fire's behavior. flame front.
The consistent use of these keys ensures that mapping and inventories remain consistent over time. In practice, this consistency translates into maps that are more useful for prioritizing treatments and for assessing, during an emergency, where resources are located. weak points from the landscape.
Simulation and operational analysis tools
To anticipate the spread, technical services use computer tools that model fire behavior based on three main input blocks: meteorology (wind, temperature, and humidity), topography (slope, aspect, roughness), and vegetation (fuel pattern, load, vertical and horizontal continuity). These tools are increasingly necessary given the anticipated increase in forest fires. will increase in the coming years.
These tools are used in prevention (planning scenarios, treatment effectiveness analysis) and also during incidents, in on-site analysis. In the latter case, they help predict behavior, assess windows of opportunity, and support the anticipation of operations of attack, increasing the effectiveness and security of resources.
The quality of the prediction depends directly on the quality of the data: the more up-to-date and accurate the model mapping and weather series, the better we will capture changes in fuel moisture and in the system response to the wind and the slope.
Strategic Management Points (SMPs): actions that make a difference
A practical example of firefighting-oriented fuel management is the vegetation fuel treatment project in Strategic Management Points (SMPs) in response to the risk of forest fires, developed in the Balearic Islands. It involved 54,18 hectares in 21 PEGs spread across Mallorca, Menorca and Ibiza, financed with €456.630 from the Sustainable Tourism Tax.
The PEGs are located at critical points defined in the IV General Plan for Defense against Forest Fires: these are places that, due to their topographical or structural configuration, can multiply the advance of the fire; that is, points that cause a change for the worse in the behavior. They are categorized, for example, as Ravine Knots, Ridge Knots or Mountain Passes.
The objective of silvicultural treatments in these areas is to reduce the aggressive behavior of the fire and lower the intensity and length of the flame until the fire is attackable with certaintyThe key is to create horizontal and vertical discontinuities in the vegetation cover, reducing the density and continuity of the fuel.
The actions carried out include pruning, thinning, and clearing, along with habitat maintenance work. These have been done without introducing new species, altering the landscape, or changing the Principal structure of the masses. In addition, the aim is to prevent access to the treetops and keep the intensity below the extinction threshold.
The operational effects are noticeable: spread to more dangerous areas is prevented, the biomass load is reduced, accessibility within the stand is improved, and the flame intensity within the PEG itself. Together, these create real attack opportunities for teams, with slower, more manageable fires.
How fire is transmitted and what role fuel plays
The spread of fire is a process of heat transfer: conduction, convection, and radiation heat nearby fuels until they reach their ignition temperature. The continuity of the fuel (the “road” along which the fire travels) and its humidity They determine whether the front accelerates or breaks up.
Topography acts as a catalyst: the slope inclines the flame forward and dries out the fuels located uphill, while exposure conditions the insolation and the microclimatesIn canyons or ravines (“ravine knots”), channeled winds can increase propagation speed and spotting activity.
In this context, fuel management aims to disrupt continuity chains, both horizontally (treatment strips, mosaics) and vertically (understory-canopy disconnection). Combined with good topographic anchoring, these actions improve the maneuverability of the attack and reduce the potential for extreme behavior.
From photo to map: keys, satellite and LIDAR
The photographic key for models is a valuable tool for field personnel: it standardizes fuel identification, reduces subjectivity, and provides consistent data. inventories and the maps. When combined with remote sensing, it provides spatial coverage and update capability.
Satellite imagery provides spectral signatures and historical data that help distinguish land uses and cover and detect changes, while TO DEAL Airborne data allows for highly detailed measurements of heights, densities, and strata. Combining both data sources provides high-resolution (e.g., 10 x 10 m) and repeatable fuel model maps.
With clear methodologies, these layers can be kept up to date by incorporating new satellite passes or LiDAR coverage, so that the mapping reflects the natural evolution of the fuel and the modifications due to silvicultural treatments, incidents or episodes of mortality and the capacity of the forests to regenerate after a fire.
With clear methodologies, these layers can be kept up to date by incorporating new satellite passes or LiDAR coverage, so that the mapping reflects the natural evolution of the fuel and modifications due to silvicultural treatments, incidents or mortality episodes.
Good fuel handling practices
From a planning perspective, it's advisable to prioritize treatments where changes in fire behavior are most likely. This includes PEG with a multiplier effect, urban-forest interface edges, wind corridors, and areas with dense undergrowth beneath trees. The idea is to maximize the operational benefit per hectare treated.
In practice, the most effective methods usually combine thinning to reduce density, vertical pruning to break up vertical continuity, and selective clearing of the vegetation. undergrowthAll of this is aligned with conservation objectives and the maintenance of habitats, avoiding unnecessary alterations to the landscape and without introducing species.
After treatment, maintenance is crucial: fine fuel is quickly replenished. Updating maps, remote sensing monitoring, and checking the condition of the courteous And girdles help to maintain over time the effectiveness achieved with the initial investment.
A basic glossary so you don't get lost
- Fuel model: typical representation of a vegetation complex with expected fire behavior.
- CZAR: High Risk Zones; areas with greater susceptibility to frequent or high-impact fires.
- UCO40: classification of 40 models adapted to Andalusia for field guides and inventory.
- LIDAR: airborne laser sensor that measures vertical vegetation structure and microrelief.
- PEG: Strategic Management Points where fuel treatment has a key operational impact.
Looking at the whole picture, forest fuel management relies on a robust technical foundation (Rothermel-ICONA, Scott and Burgan models and regional classifications such as UCO40), well-designed field inventories (such as those carried out in the ZAR of Andalusia), and modern mapping that combines satellite and TO DEALIf we add to this actions in Strategic Management Points, with pruning, thinning and clearing aimed at creating discontinuity and reducing intensity, real opportunities are enabled to attack safely and effectively, both in prevention and in the midst of an emergency.