Northern Canadian boreal forest is characterised by accumulation of a thick organic soil layer (paludification). Two types of paludification are recognised on the basis of topography and time since the last fire, viz., permanent paludification that dominates in natural depressions within the landscape, and reversible paludification that occurs on flat or sloping terrain over time following fire or mechanical site preparation. Accurate information about the occurrence of permanent or reversible paludification is required for land resource management. Such information is useful for the identification of locations of existing paludified areas where investment after harvesting should help to achieve greater productivity. This study investigated the potential for using a semi-automated method that was based on geomorphological analysis to map and differentiate between the two paludification types at the landscape scale within the Canadian Clay Belt region. For the purposes of this study, slope, topographic position index (TPI), and topographic wetness index (TWI) were generated from a LiDAR digital terrain model. TPI and TWI are, respectively, predictors of surface morphology (i.e., depressions vs flat areas) and moisture conditions (i.e., wet vs dry), and were used to explain paludification occurrence. A semi-automated classification method based on TPI and slope was firstly used to create six initial topographic position classes: deep-depressions, lower-slope depressions, flat surfaces, mid-slopes, upper-slopes, and hilltops. Each of these six classes was then combined with TWI classes (representing moisture conditions: wet, moderately wet, and dry) and this combination assisted in assigning each resulting class to one of the two paludification types. Slope and TWI values were used in sub-dividing the lower slope depression class, based on slope, into significantly different sub-classes, namely open and closed depressions (Tukey's HSD, P < 0.001). The distribution of field data (e.g., tree basal area, organic layer and fibric horizon thicknesses) within each position class provided additional information for corroborating the assignment of each class to a defined paludification type. The proposed semi-automated classification provided a relatively simple and practical tool for distinguishing and mapping permanent and reversible paludification types with an overall accuracy of 74%. The tool would be particularly useful for implementing strategies of sustainable management in remote boreal areas where field survey information is limited.