Beyond Accuracy: Ecological Validity of Machine Learning Models for Assessing Vegetation Conservation Classes in South Korea

ABSTRACT The assessment of vegetation conservation class (VCC) has traditionally relied on field surveys, which are often limited by observer subjectivity and spatiotemporal inefficiencies. To address these challenges, this study proposes a machine learning approach utilizing 5779 vegetation relevés collected from the National Natural Environment Survey in South Korea. We developed a random forest model trained to predict VCC, specifically focusing on phytosociological structural variables such as physiognomic dominant species and vertical stratification richness. The model achieved an overall accuracy of 80.4% on an independent test set, demonstrating a substantial level of agreement with expert evaluations. Notably, it successfully identified the rare, high‐conservation value Class I forests despite class imbalance. Variable importance analysis revealed that structural features and indicator species associated with successional stages played decisive roles in classification, confirming that the model secured not only statistical accuracy but also ecological validity. These findings suggest that objective machine learning tools can effectively complement subjective conventional surveys, thereby significantly enhancing the efficiency and responsiveness of national‐scale ecosystem monitoring.