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The forest map remains essential for investigating plant ecology and biodiversity patterns. This study proposed methods for mapping forest types based on ecological niche modeling and then used fuzzy error matrix for accuracy assessment. The upper Ping basin of northern Thailand was selected as study area. The modeled data included forest inventory, topographic, climatic, soil, and geological data. Ecological niche factor analysis was used to model and produce the best habitat suitability index of each forest type, which were then combined using hierarchically generated coding. As a result, eight classes of forest types were generated: dry dipterocarp forest (7,373.94 km 2, 32.81%), evergreen ecotone or transition area (3,666.97 km 2, 16.32%), mixed deciduous forest (3,440.79 km 2, 15.31%), deciduous ecotone or transition area (3,225.58 km 2, 14.35%), deciduous and evergreen forest (2,027.12 km 2, 9.02), coniferous forest (CF; 365.28 km 2, 1.63%), moist and dry evergreen forest (290.08 km 2, 1.29%), and hill evergreen forest (270.56 km 2, 1.21%). Four variables were found to be critical in forest type distribution: elevation, mean annual temperature, annual maximum temperatures and annual minimum temperatures. To assess map accuracy, fuzzy error matrix, which allows the recognition of ambiguous classes and does not ignore variation in the interpretation of the reference data at class boundaries, was used (75.89% of overall accuracy).