Open AccessMapping wildfire danger at regional scale with an index model integrating coarse spatial resolution remote sensing data
Author(s) -
Chéret Véronique,
Denux Jean Philippe
Publication year - 2007
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2005jg000125
Subject(s) - normalized difference vegetation index , vegetation (pathology) , environmental science , phenology , scale (ratio) , arid , remote sensing , dryness , index (typography) , physical geography , spatial ecology , growing season , common spatial pattern , enhanced vegetation index , vegetation index , climate change , geography , cartography , statistics , computer science , ecology , mathematics , medicine , surgery , pathology , world wide web , biology
Wildfires are a prevalent natural hazard in the south of France. Planners need a permanent fire danger assessment valid for several years over a territory as large and heterogeneous as Midi‐Pyrénées region. To this end, we developed an expert knowledge‐based index model adapted to the specific features of the study area. The fire danger depends on two complementary elements: spatial occurrence and fire intensity. Among the GIS layers identified as input variables for modeling, vegetation fire susceptibility is one of the most influent. However, the main difficulty at this scale is the scarcity or the lack of exhaustiveness of the data. In this respect, remote sensing imagery is capable of providing relevant information. We proposed to calculate an annual relative greenness index (annual RGRE) that reflects vegetation dryness in summer. We processed times series of Normalized Difference Vegetation Index (NDVI) from SPOT‐VEGETATION images over the last six available years (1998 to 2003). The first step was to verify that these images characterize vegetation types and highlight intraannual and interannual response variability. It is then possible to identify phenological stages corresponding to the maximum NDVI (and therefore to maximum photosynthetic activity) during the growing season, the minimum NDVI at the end of the growing season and the minimum NDVI during winter period. These phenology metrics ground the annual RGRE calculation. Values obtained for each observation year show significant correlation (r 2 = 0.70) with the De Martonne aridity index calculated for the same period. A synthesis of yearly index was integrated in the model as a variable that expresses fire susceptibility.