摘要
Fuel model mapping has followed in general two trends: 1) indirect inferences, where some factors, presumably associated with fuel production, are related to a given fuel model;and 2) experts consulting, which has been used to classify and to validate other people classifications. However, reliance on expert judgment implies a subjective approach. Thus, I propone the integration of geostatistic techniques and the Conditional-Fuels-Loading concept (CFL) to define a more objective perspective in the fuel-model mapping. The information used in this study was collected in a forest of Chihuahua, Mexico, where fuels were inventoried in 554 (1000 m2) sample plots. These sample plots were classified using the CFL;and ordinary kriging (Gaussian, spherical and exponential) was used to interpolate the fuel-model values. Using the Akaike’s Information Criterion the spherical model performed best. The methodology allowed a finer definition of spatial distribution of fuel models. Some advantages of the CFL are: 1) it is based on actual fuel loads, and not only on vegetation structure and composition;2) it is objective and avoids the bias of different classifiers (experts);and 3) it avoids the need of the advice of experts.
Fuel model mapping has followed in general two trends: 1) indirect inferences, where some factors, presumably associated with fuel production, are related to a given fuel model;and 2) experts consulting, which has been used to classify and to validate other people classifications. However, reliance on expert judgment implies a subjective approach. Thus, I propone the integration of geostatistic techniques and the Conditional-Fuels-Loading concept (CFL) to define a more objective perspective in the fuel-model mapping. The information used in this study was collected in a forest of Chihuahua, Mexico, where fuels were inventoried in 554 (1000 m2) sample plots. These sample plots were classified using the CFL;and ordinary kriging (Gaussian, spherical and exponential) was used to interpolate the fuel-model values. Using the Akaike’s Information Criterion the spherical model performed best. The methodology allowed a finer definition of spatial distribution of fuel models. Some advantages of the CFL are: 1) it is based on actual fuel loads, and not only on vegetation structure and composition;2) it is objective and avoids the bias of different classifiers (experts);and 3) it avoids the need of the advice of experts.
