EMERALD improves how forest disturbances such as windthrow and bark beetle damage are detected and managed, by clearing the cloud and shadow that hide them in satellite imagery and bringing out the finer detail underneath.
Forest management works best when disturbances are caught early, whether a windthrow or damage from bark beetles. Current monitoring often misses them, because cloud cover and shadow in satellite imagery hide a disturbance or delay its detection. Late detection means late action, and the ecological and economic consequences deepen: forests lose habitat and biodiversity, and the forestry sector takes a financial toll.
EMERALD builds on the deep learning pipeline from SAFIR and adds new image refinement to it. Where SAFIR is developing modules that remove cloud and correct shadow, EMERALD adds super-resolution, which rebuilds a high-resolution image from a low-resolution one. With that sharper image, the models can examine forest structure and damage more closely, identify tree species and read tree health. A model also has to work outside the area it learned from, so EMERALD trains on newly collected data together with open datasets from across Europe, Austria and Portugal among them. That range keeps the models adaptable and effective across different conditions and forest types, and helps them read disturbances more accurately and efficiently.
Alongside SAFIR’s cloud and shadow removal, EMERALD brings super-resolution into ESA’s Green Transformation Information Factory (GTIF), handing over all three as validated models with the datasets and documentation others need to build on them. Clearer monitoring gives forest agencies and policymakers a better basis for their decisions, and supports more sustainable forestry and conservation. Because these Earth observation insights are high in quality and scale easily, EMERALD can feed into wider efforts on forest management and climate adaptation, helping keep forests resilient as the pressures on them grow.
