Uncertainty is associated with GIS- Multi Criteria Decision Analysis (GIS-MCDA) when applied to disaster modeling. Technically speaking, GIS-MCDA model outcomes are prone to multiple types of uncertainty and error. In order to minimize the inherent uncertainty, within this research we introduced a novel approach of spatial explicit uncertainty and sensitivity analysis for GIS-MCDA models. This novel approach is developed based on early works published by FEZIZADEH et al. 2014a, 2014b and makes use the capability of Fuzzy-Analytical Hierarchical Process (FAHP), Monte Carlo Simulation (MCS) and Variance based Global Sensitivity Analysis (GSA). This approach was examined on forest fire susceptibility mapping. The methodology contains of three different phases. Within the first step, weights were computed to express the relative importance of factors (criteria) for forest fire susceptibility through FAHP. In the second step, the uncertainty and sensitivity of Forest Fire Risk Mapping was analyzed as a function of weights using MSC and GSA. Finally, the results were validated against the forest fire inventory database. The results indicate that further improvement in the accuracy of GIS-based MCDA can be achieved by applying the proposed sensitivity uncertainty analysis approach.