Synergy of GIS and Remote Sensing Data in Forest Fire Danger Modeling

Forest fires constitute an important problem for the environment degradation. In this paper, we propose a Dynamic Fire Risk Index (DFRI) that takes into account different static and dynamic factors of risk for fire occurrence. Variables like insolation hours, vegetation cover, altitude, slope, proximity to main roads, and fire statistics have been used to develop a Static Fire Risk Index (SFRI) using a logistic regression model. Using satellite data to derive water stress of forest, a new dynamic index is defined weighting the static index with the actual value of water stress indicators. This methodology has been previously tested for some fires in the Canary Islands (Spain), and, in this case, we prove its usefulness using both NOAA-AVHRR and Terra-MODIS sensors data. As test sites, two different fires that took place in September 2005 on La Palma Island and August 2007 on Tenerife Island (Canary Islands, Spain) have been considered in order to validate the suitability of these tools for a regional scale application, in an area where multiple microclimates are present mainly due to its steep orography and the trade winds.