Open-Earth-Monitor Global Workshop 2026

Large-scale reforestation is central to climate mitigation and ecosystem restoration, yet monitoring when and where restoration activities occur remains a major challenge. Existing satellite based approaches typically detect forest recovery only after canopy development, limiting their usefulness for timely monitoring, reporting, and verification (MRV) of restoration efforts.
We present a scalable framework for detecting reforestation interventions within one year of planting using multi-sensor satellite time series. The approach integrates Sentinel-1 radar and Sentinel-2 optical data to learn a characteristic temporal signature of restoration, capturing transitions from stable pre-intervention conditions to disturbance and early vegetation recovery. Training leverages a global dataset of verified reforestation sites combined with a synthetic control strategy to generate spectrally matched non restoration samples.
Our results show that early stage reforestation can be identified at the pixel level across diverse climate zones, substantially improving the temporal resolution of forest monitoring. The poster will present global scale examples, model outputs, and temporal signatures illustrating how restoration signals emerge prior to canopy closure.
This work supports more timely and transparent monitoring of reforestation efforts and has direct relevance for carbon accounting, climate finance, and large-scale restoration initiatives.