Bååtha, H., Gällerspångb, A., Hallsbyc, G., Lundströma, A., Löfgrena, P., Nilssona, M. and Ståhla, G. (2002) Remote Sensing, Field Survey, and Long-Term Forecasting: An Efficient Combination for Local Assessments of Forest Fuels. Biomass and Bioenergy, 22, 145-157.
https://doi.org/10.1016/S0961-9534(01)00065-4

has been cited by the following article:

• TITLE: Spatial Distribution of Fuel Models Based on the Conditional-Fuel-Loading Concept

  AUTHORS: José Germán Flores Garnica

  KEYWORDS: Validation, Fuel Mapping, Geostatistics, Ordinary Kriging

  JOURNAL NAME: Journal of Environmental Protection, Vol.9 No.2, February 25, 2018

  ABSTRACT: 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.