Meteorological Classification of Litter Fuel Moisture in Paired Conifer and Broadleaf Stands: Implications for FFMC Applicability in Korea

Litter fuel moisture governs the ignition and early spread of forest fires, yet its meteorological controls in Korean conifer and broadleaf stands, and the correspondence of existing fire-weather index classes to locally observed moisture, remain poorly characterised. We directly measured litter fuel moisture in paired conifer and broadleaf stands at four regions in Korea during one spring fire season and classified moisture class (Dry/Intermediate/Moist) from 26 meteorological and soil predictors under time-blocked, nested cross-validation. Five classifiers performed moderately and indistinguishably (macro-F1 0.39–0.51; quadratic weighted kappa 0.20–0.47), all above naive baselines, and performance declined under leave-one-region-out transfer. A multiple-testing-corrected interaction test detected a forest-type-dependent response only for soil water content, and neither a forest-type indicator nor separate per-type models improved prediction over a single pooled model. The Fine Fuel Moisture Code (FFMC) was significantly correlated with measured litter moisture (r = −0.57 conifer, −0.48 broadleaf) and was more sensitive to Dry conditions than any classifier, but its ignition-probability-derived class boundary corresponded weakly to the observed classes, and a moisture-equivalent boundary produced a complementary rather than superior error pattern. Meteorological data provide site-calibrated discrimination of litter-moisture class; forest-type differences are present but narrow, and FFMC class boundaries require local calibration before operational use.