No abstract

Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper, which makes extensive use of geographic information technologies to offer a spatially explicit evaluation of fire risk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approaches to merge those variables into synthetic risk indices and finally the validation of the outputs. The model has been applied at a national level for the whole Spanish Iberian territory at 1-km2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P < 0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.

Biomass burning has critical ecological and social impacts. Recent changes in climate patterns and land use have involved alterations of traditional fire regimes, which have increased the negative impacts of fire. Live Fuel Moisture Content (LFMC) has proven to be one of the main factors related to fire risk, as it affects fire ignition and fire behavior, and therefore it is an essential indicator for fire risk assessment. The aim of our research was to explore several methods to convert LFMC into Ignition Probability (IP) at a national scale, considering climate and vegetation functional types. The project covers the Iberian Peninsula territory of Spain (492 175 km2), for a ten year period. The LFMC data was estimated from NOAA-AVHRR imagery, whereas fire occurrence was based on the standard MODIS Thermal Anomalies product (MOD14). Non-parametric significance tests, histograms and percentiles, classification trees, and logistic regression models were used for estimating the IP from five variables based on LFMC. These modelling approaches were compared and Logistic Regression (LR) analysis was found to be most advantageous, since it uses several predictor variables to compute a continuous probability of IP. The area under the ROC curve of the LR models for the Iberian Peninsula was 0.65 for the Mediterranean region and >0.8 for the Eurosiberian region. The LFMC from one week before the fire detection was the most influential variable in the statistical analysis and it was the main variable in the Mediterranean models. In the Eurosiberian models, the LFMC decrement since spring was also important. The LFMC one week before the fire detection and the difference between the LFMC one week and two weeks before the fire detection were included in the grassland model. Shrubland is less susceptible to rapid moisture changes than grassland, so the LFMC from two weeks before the fire and the LFMC decrement since spring were more influential.

We inverted the PROSPECT and GEOSAIL Radiative Transfer Models (RTM) using Moderate Resolution Imaging Spectrometer (MODIS) data to retrieve Live Fuel Moisture Content (LFMC) in woodlands located in the peninsular territory of Spain. Ecological rules were used to parameterize the RTM. This approach reduces the probability of an ill-posed problem in the inversion of the selected RTMs, by rejecting unrealistic combinations of input parameters. Three species representatives of each region were used to derive the ecological rules: Quercus ilex L., Quercus faginea L., and Pinus halepensis Mill. for the Mediterranean region, and Fagus sylvatica L., Quercus robur L. and Eucalyptus globulus Labill for the Eurosiberian region. Equivalent Water Thickness, Dry Matter and Chlorophyll content were taken from several data sources to separately parameterize both the Mediterranean (water-limited) and Eurosiberian (energy-limited) ecoregions of Spain. GEOSAIL was parameterized using a restricted range of Leaf Area Index (LAI) and specific canopy cover values, keeping other parameters fixed. The inversion was based on the Look Up Table technique using the minimum spectral angle as merit function. Several models were tested by using different inputs from standard MODIS products, as well as the fractional cover product developed by Guerschman et al. (2009). The model based on the reflectance bands and the Normalized Difference Infrared Index computed from the Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance product (MCD43A4) provided the most accurate results, with a LFMC's Root Mean Square Error (RMSE) of 27.7% (RMSE =27.3% for the Mediterranean and 28.7% for the Eurosiberian woodland). The estimation of LFMC was performed within the framework of a fire risk assessment system.