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Forest land managers and researchers are faced with a myriad of ecological, wildlife, management, and legal issues directly tied to old-growth vegetation structures. However, lack of adequate old-growth inventories has hindered decision making. Spectral remote sensing has previously proved inadequate in filling this gap. Presented is machine learning approach that leverages both spectral and topographic data to describe old-growth niches and accurately predict presence/absence. Two sources of forest inventory plot data were utilized; an operationally derived and a targeted sample. Results show that although the targeted sample provided more accurate results, a strong bias is evident making landscape inference erroneous. The operational sample contained insufficient information to adequately describe the range of old-growth across the study area. The best results were provided by a combination of the …