Mangrove forests fringe many tropical coastlines and provide important ecological and economic benefits, but they remain vulnerable to land conversion. This study analyzed the spatio-temporal dynamics of mangrove cover in Ketapang District, South Lampung Regency, Indonesia, between 2018 and 2026 using Sentinel-2 Level-2A imagery and a Random Forest (RF) classifier implemented in Google Earth Engine. Thirteen predictors were used: nine Sentinel-2 bands (B2, B3, B4, B5, B6, B7, B8, B11 and B12) and four spectral indices (NDVI, MNDWI, NDRE and NDTI). Five land cover classes (mangrove vegetation, non-mangrove vegetation, fish ponds, open land and water bodies) were mapped for 2018, 2020, 2022, 2024 and 2026. Against reference data interpreted from very-high-resolution imagery (2018–2024) and collected by field ground-truthing (2026), overall accuracy ranged from 91.79% to 96.34% and kappa from 0.89 to 0.95; error-adjusted areas with 95% confidence intervals were estimated from the error matrices. The SWIR-2 band (B12) had the highest Gini importance in every year. Mapped mangrove area increased from 196.64 ha in 2018 to 267.97 ha in 2026 (+71.33 ha), and the error-adjusted increase was 82.05 ± 13.42 ha. Decomposition of change showed that in 2018–2020 a small net change (+6.35 ha, within the classification uncertainty) was accompanied by a swap of 59.95 ha (30.48% of the 2018 mangrove area), whereas over the four intervals gross loss decreased from 29.97 to 18.88 ha and persistence increased from 84.76% to 92.70%. Overlay with the official rehabilitation sites (129.00 ha planted in 2020–2023) showed that the net increase occurred almost entirely within these sites (+80.83 ha, compared with −9.47 ha outside them) and that their share of the gross gain rose from 16.0% before the main planting to 58.3–77.5% in 2020–2024; this spatial association is consistent with a substantial contribution of rehabilitation planting, although causality was not tested. The results show how frequent Sentinel-2 mapping combined with transition analysis and uncertainty estimation can support the monitoring of mangrove rehabilitation.