7th Workshop on Forest Fire Management - EARSeL, European ...

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III - FIRE DETECTION AND FIRE MONITORING
time with the Early Warning System for Fires operated by CONABIO for
Mexico and Central America.
2 - Data processing and product generation
The operational fire monitoring system at CONABIO has continuously been
developed and enhanced since 1999. The main component is the near-real
time detection and characterization of fires, including fast information supply
to the user by a processing chain. CONABIO receives up to eight MODIS
satellite overpasses and up to ten satellite passes of NOAA-AVHRR on a
daily basis. All passes are processed and provided as Level-1B data in nearreal
time to the scientific community and any interested user.
2.1 - Early-warning products
Information about environmental variables, especially before fire occurrence,
is crucial to support fire-related decisions.
In order to evaluate if current vegetation conditions represent a threat, it
is necessary to include several variables related to fire ignition, fire propagation,
fire vulnerability (Chuvieco et al., 2003b). Fuel moisture content
(FMC), defined as the proportion of water over dry mass, is the most
extended measurement of fire propagation potential (Trowbridge and Feller,
1988; Viegas et al., 1992); since FMC has a strong correlation with the
Normalized Difference Vegetation Index (NDVI) for grasslands and shrublands
(Yebra, 2008), NDVI was used to derive a Fire Propagation Index (FPI)
based exclusively on vegetation conditions.
FPI is calculated for a 10-day period with a normalized difference of the
present NDVI and a synthetic NDVI created by a harmonic analysis of a 4year
time series (de Badts et al., 2005); therefore, if the present vegetation
condition is below historical conditions, it can be assumed that the vegetation
is under stress and the possibility of fire propagation is higher. The
result is a map showing the areas where a wildfire could propagate once it
has been started.
Since vegetation conditions are strongly correlated to meteorological phenomena,
it was necessary to include some variables related, for instance, to
precipitation; and since vegetation takes humidity from the surrounding
environment and loses this humidity due to temperature, these three variables
were taken into account from different sources to model dead litter
moisture content: 1) duration of precipitation from TRMM (Tropical Rainfall
Measuring Mission), and from MODIS standard products: 2) land surface
temperature (LST) from MOD11 and 3) relative humidity from MOD07.
The model used equations from the US Forest Service risk model to estimate
moisture flux between dead litter over forested areas.