Integration of Landsat–Sentinel Time Series and Flowering Phenology for Mapping Planted Forests and Distinguishing Tree Crops

Planted forests are increasingly promoted to meet rising demand for forest products and restore degraded lands, but their extent and ecological implications are often misrepresented because tree crops (e.g., orchards, plantation agriculture) exhibit similar spectral and spatial signatures to planted forests. This study aims to improve differentiation between planted forests and tree crops within national-scale restoration programs. We combined Landsat-derived NDVI time series targeting disturbance-related phenological windows with the LandTrendr algorithm to map planting/clearcutting events and fused in situ spectral measurements with Sentinel-2 to develop a modified orchard flowering index (MOFI). Random forest models evaluated classification performance using combinations of spatiotemporal spectral features, biomass accumulation proxies, and the MOFI. Incorporating the MOFI improved discrimination of tree crops versus planted forests, raising the planted forest F1 from 0.751 to 0.843. The combination of the MOFI and spatiotemporal spectral features achieved the highest accuracy (F1 = 0.843). The results show tree crops are concentrated on plains and gentle mountain slopes, while plantations occur mostly on slopes > 15°, with tree crops comprising 27.1% of mapped planted tree area. These findings imply that many national planted forest map estimates may be biased without phenology- and biomass-informed methods and that integrating Landsat and Sentinel phenology metrics supports more accurate monitoring for management and policy.