Old-growth forests in Nova Scotia provide ecological functions including carbon sequestration, nutrient cycling, genetic diversity, and habitats for species at risk. The mandate to protect old-growth forests in Nova Scotia has long been recognized; however, identifying potential old growth stands in the field remains challenging. To guide current field work, an old-growth forest potential index has been created which relies on photo-interpreted forest inventory data to generate old forest potential values. Remotely sensed data has been shown to be effective in other jurisdictions for predicting forest maturity attributes, and can improve upon some of the uncertainty associated with photo-interpretation. The goal of this study is to compare the predictive ability of a combination of remotely sensed metrics for identifying potential old-growth forest in Nova Scotia. This poster showcases ongoing efforts to combine data from Sentinel-2 multispectral satellite imagery, LiDAR, elevation models, and tree species data to determine where we might find more old growth in the province. Multispectral imagery will be used to extract vegetation indices, textural values, and species information. LiDAR metrics will be derived to determine height distribution, canopy structure, vertical diversity, and coarse woody debris. Species identification may help to address the unique challenge of mapping old-growth forest on a provincial scale within a variety of distinct ecoregions. Improved classification can benefit conservation efforts and forest management by focusing field identification resources and sharpening our understanding of where we might find old forests in Nova Scotia.