Wildland fires continue to pose a threat to Ontario's boreal forest, which is vast and ecologically diverse. As climate change exacerbates these dangers, current and accurate fire-mapping tools are essential for effective land management to ensure recovery. In this work, we investigated how well BorealDB, a relatively new yearly forest disturbance mapping product, correlates with fire boundaries identified by slopes of two common remote sensing indices: NDVI (Normalized Difference Vegetation Index) and NBR (Normalized Burn Ratio). We examined BorealDB's two confidence levels (≥ 67% and ꞊ 100%) to see if higher ensemble confidence correlates with remotely sensed wildland fire disturbance boundaries. While Chi-square tests occasionally demonstrated no direct relationship between BorealDB classifications and NDVI/NBR slope categories, visual analysis with stacked bar charts of correspondence between BorealDB and remote sensing methods revealed consistent trends in agreement/disagreement across the years analyzed. BorealDB's 100% confidence criterion corresponded more closely to boundaries identified by remote sensing methods. This illustrates that when multiple wildland fire mapping products agree, the remote sensing-based vegetation index slopes correspond best. These findings highlight that when BorealDB is unavailable, vegetation index slopes can be used to identify wildland fire boundaries, and that only points with high confidence in BorealDB should be used to characterize disturbance locations and boundaries. Integrating BorealDB with NDVI and NBR slope data could assist land managers and scientists make better decisions regarding disturbance mapping and thus conservation planning, and post-fire recovery.