Long-Term Assessment of Post-Mining Spectral Recovery Patterns: Integrating Disturbance Timing, Land-Surface Transitions, and Benchmark-Relative Spectral Closure

Single-index greening trends can misrepresent post-mining recovery because they do not show whether disturbed surfaces are converging toward the spectral conditions of nearby stable vegetation. Here, we present a 22-year (2003–2024) Landsat-based assessment of the Nannihu molybdenum mine (Henan, China) by combining LandTrendr-based disturbance and recovery timing from annual NBR series with a benchmark-relative spectral recovery index (RSRI) and five-epoch random forest land-surface classification used as contextual support. The classifier was trained on 2024 samples and transferred to earlier epochs without independent validation at each epoch. Historical class labels should therefore be treated as approximate contextual support. A five-type recovery pathway typology showed that only 41.8% of mine-affected pixels followed vegetated recovery pathways, while 28.2% stabilized as non-vegetated surfaces and 25.0% remained under persistent disturbance. Even the combined vegetation recovery type had a mean RSRI of only 0.309 (SD = 0.143), suggesting that greening alone does not imply close benchmark-relative spectral proximity to the local stable-vegetation reference. Disturbance magnitude was the feature most strongly associated with RSRI variation (XGBoost SHAP mean, |SHAP| = 0.075). The RSRI quantifies benchmark-relative spectral proximity using local stable-vegetation benchmarks, and it does not measure species composition, biomass, or ecosystem function. This site-specific case study indicates that benchmark-relative spectral assessment can complement conventional greening metrics in retrospective mine monitoring using open-access Landsat archives, with field validation the natural next step toward linking these spectral findings to ecological or functional recovery.