A global 30 m disturbance-recovery age dataset for young natural and planted forests (1985–2024)

Natural and planted forests differ substantially in ecological functions and economic values, with forest age serving as a key indicator of their developmental and carbon dynamics. However, existing global forest age datasets remain limited by coarse spatial resolution and insufficient representation of forest origin differences. In this study, we developed a 30 m global disturbance-recovery age dataset (1985–2024) by integrating Landsat time series with the Continuous Change Detection and Classification (CCDC) algorithm on Google Earth Engine. To account for differences in successional behavior between natural forests (NF) and planted forests (PF), we introduced a type-stratified processing framework. Specifically, a Global Natural and Planted Forest (GNPF) mask was used to guide forest-type-dependent refinement during the post-segmentation stage, where dual-threshold segment fusion rules were applied (> 0.03 for NF and > 0.04 for PF, respectively) to better align spectral recovery trajectories with expected recovery patterns. In addition, we generated a pixel-level Spatial Uncertainty Index (SUI) derived from model residuals to quantify mapping reliability. Validation using extensive reference samples demonstrates good agreement between estimated and observed forest age (R2 = 0.74, RMSE = 7.17 years). Globally, NF displayed contrasting age structures, with older stands prevalent in Europe (84.38 %), South America (82.61 %), and North America (80.62 %), while Australia showed a bimodal distribution reflecting both mature and regenerating forests. PF were generally younger, with young stands concentrated in Australia (Age 1–5: 65.77 %) and relatively older plantations in Europe (Age 36–40: 53.99 %). The dataset provides a spatially explicit characterization of forest age patterns and can support studies of forest carbon dynamics, ecosystem modeling, and global assessments of forest structural changes. The dataset is publicly available at Zenodo (https://doi.org/10.5281/zenodo.22030377, Wang et al., 2026).