| This project focuses on the need for a comprehensive approach for forest fuels mapping based on spatial interpolation techniques. Four different fuel maps (1-HR, 10-HR, 100-HR and Live Woody fuel classes) were generated, which allowed better definition of the spatial variations in fuels, even within an area classified into the same fuel model class. Five statistical techniques (spline, polygonal mapping, inverse distance weighting) and seven geostatisticals techniques (ordinary kriging, universal, cokriging, point kriging and block kriging) were compared, in order to get the continuous surfaces that more precisely represent the spatial distribution for each fuel classes. The ancillary data required in cokriging were gathered from a Digital Elevation Model, a Landsat 5 TM and a forest inventory. Field data were collected from the “ejido” El Largo y Anexos, Cd. Madera (Chihuahua, México). Based on the mean square error values, in general, geostatistical techniques performed better than the traditional alternatives. As a result, 1-HR, 100-HR, LW fuel classes were better modeled through cokriging, while inverse distance weighting was the best alternative for 10-HR fuel. Although, it was not possible to establish a unique “best” spatial interpolation method, IDW showed a more constant performance.; Since there was not a spatially explicit surface fire behavior model that could use the four fuel-type maps, a spatial simulation model (SSM) was developed under a raster approach. This model was compared with FARSITE, an existing spatially explicit fire behavior model that is based on the fuel-model concept. The required fuel-model map for FARSITE was developed under the “Conditional Fuels Loading Concept”, which considers that each fuel model has a characteristic fuels loading combination.; It is recommended that future fuels evaluation should be based on the integration of a multi-resource approach and geostatistical techniques. Furthermore, nonlinear kriging techniques should be tested in the interpolation of forest fuels. On the other hand, geostatistical techniques can be used to monitor and predict changes in the spatial distribution of forest fuels. Further research is needed to define the more adequate sample design when interpolating fuels' spatial distribution. |
