This article presents the performance of a vegetation fuel type (FT) classification based on
conditional rules according to the Prometheus system, including an analysis of the effect of
cell size and scan density on mapping vertical structural types, exemplified as FT, using exclusively
LiDAR data. Since the Prometheus system does not specify any criterion for the
minimum extension where those methodologies can be applied, we searched for the optimal
classification cell size by gridding the study area at 20 and 40 m cell sizes. We also included
a study of the effects of varying the scan density from 2 to 0.5 pulses·m-2. To validate the classification
method, we used a stratified random sampling without replacement of 15 cells per
FT and made an independent visual assessment of FTs. The best results in terms of precision
were obtained for the combination of 0.5 pulses·m-2 and 20 m-resolution dataset, with an
overall accuracy of 84.13%. It was also showed that an increase in scan density would not
improve the global accuracy of the classification, but it would be desirable for a better detection
of the shrub stratum
