Stand-Level Model–Map Disagreement as a Forest Type Map Update Signal: A Sentinel-2 Deep-Learning Approach

Forest type maps require periodic revision, yet field inspection of every stand is impractical. This study tested whether stand-level disagreement between a Sentinel-2 U-Net classifier and the Republic of Korea’s forest type map could support screening for revision. A seven-class U-Net was trained using seasonal Sentinel-2 composites from 2025, and pixel predictions were aggregated to forest stands. Disagreeing stands passing dominance and confidence thresholds were flagged and assessed by blinded interpreters using high-resolution basemaps supplemented by Sentinel-2 composites. Stand-level agreement with the map was 0.69 by stand count and 0.76 when area-weighted. In the primary analysis, high-precision-class flags were mismatched in 41.3% of interpreted stands (29.7% when population-weighted), compared with 7.7% of controls. Test-set class precision, the pre-specified prioritization criterion, did not distinguish more reliable flags in this analysis. An exploratory partition by flag type yielded weighted rates of corresponding mismatches of 80.9% for stocking-status flags and 26.0% for species-dominance flags, representing 12.3- and 2.4-fold enrichment over their respective region-wide baselines. Approximately 35% of flagged stands were estimated to be mismatches, and the flags captured approximately 7.5% of estimated mismatches. These findings support model–map disagreement as a potential tool for prioritizing stand verification in the study region.