Possibilistic and probabilistic uncertainty are encountered in a variety of decision-making settings. A typical example is land classification analysis: the probabilistic uncertainty stems from various soil and vegetation data collected from the field and/or generated through simulation whereas the possibilistic uncertainty is due to the description of land condition on which a decision is to be made. The ma in objective of this study is to illustrate how incorporating fuzzy membership function to represent possibilistic uncertainty enhances the reliability of the analysis. A land classification analysis based on a previously developed methodology is used as a case study. The methodology enables handling both types of uncertainty: probabilistic uncertainty from the spatial simulation data and possibilistic uncertainty due to vagueness in land condition descriptions.