A series of tests were conducted under laboratory conditions to assess, first, the capacity of several fuel beds to be ignited by firebrands and to sustain a fire and, second, the capability of different types of firebrands to ignite fuel beds. Fuel beds and firebrands were selected among the most common in southern Europe. Regarding fuel bed flammability, results show that grasses are more flammable than litter and, among litters, Pinus species are the most flammable. The increase in bulk density and fuel moisture content involves an increase in the time to ignition, and a decrease in the other flammability parameters. The capability of firebrands to ignite fuel beds is higher when the firebrands drop in the flaming phase and with no air flow than in glowing phase with air flow. Logistic regression models to predict fuel bed ignition probability were developed. As a whole, results show a relationship between ignition probability of fuel bed and type or weight of firebrands. Pinus pinaster cone scale, P. halepensis cone scale, and Eucalyptus globulus leaf and bark can have ignition probabilities at least twice higher than pine bark when falling while in flaming combustion.

Fuel moisture and chemical content affecting live plant flammability can be measured through laboratory and field techniques, or remotely assessed. Standardization of methodologies and a better understanding of plant attributes and phenological status can improve models for fire management. Wildland fire management is subject to manifold sources of uncertainty. Beyond the difficulties of predicting accurately the fire behavior, uncertainty stems from incomplete understanding of ecological susceptibility to fire. We aimed at reviewing current knowledge of (i) plant attributes and flammability: fuel moisture and chemical content in leaves; (ii) experimental evaluation of flammability in the laboratory and in the field; and (iii) proxy evaluation of flammability: vegetation cover assessment at large scale, fuel seasonality, and biomass distribution using remote sensing and Light Detection and Ranging (LiDAR) techniques. We conducted a review of scientific literature from the last two decades on the three selected issues, with a specific focus on the Mediterranean region. We have evidenced important knowledge gaps: (i) developing standardized methodologies for laboratory- and field-scale assessment of vegetation flammability; (ii) introducing reliable approaches to test the impact of biogenic volatile organic compounds on fire spread; (iii) improving the analysis of spatiotemporal changes in vegetation dynamics, acknowledging distinctive vegetation phenological status as a relevant driver affecting leaf biomass and moisture contents; and (iv) further exploring the processes that shape fuel dynamics to understand how fuel characteristics change over time and space. We propose some improvements in the current knowledge of vegetation science and wildland fire ecology, aiming to generate more realistic models and effective planning in support of fire management in the Mediterranean basin.

Fuel bulk density and fuel moisture content effects on fire rate of spread were assessed in shrub fuels, comparing experimental data observed in outdoor wind tunnel burns and predictions from the physically-based model FIRETEC. Statistical models for the combined effects of bulk density and fuel moisture content were fitted to both the experimental and the simulated rate of spread values using non-linear regression techniques. Results confirmed a significant decreasing effect of bulk density on rate of spread in a power law in both laboratory burns and simulations. However, experimental data showed a lesser effect than simulations, suggesting a difference in the effective drag. Fuel moisture content effect was highly consistent, showing a similar exponential relationship with rate of spread in laboratory and in simulations. FIRETEC simulations showed similar orders of magnitude with predictions of two field-based empirical models, finding a significant correlation between rate of spread values. The study confirms the efficacy of the combined approach through experimental data and simulations to study fire behaviour.

Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field campaigns for LFMC monitoring, often focusing on indicator species to reduce sampling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS); MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent sample of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R² between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R² = 0.49) than the best empirical model fitted with MCD43A4 images (R² = 0.75). R² between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R² from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability (<80%). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems.