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Forests and woodlands cover one third of Earth's surface and play a critical role in carbon sequestration, water regulation, timber production, soil protection, and biodiversity conservation. Accelerated by climate change, the decline of these and other key forest ecosystem services is caused by various biotic and abiotic disturbances. Among them, insect infestations and disease outbreaks can induce massive tree dieback and significantly disrupt ecosystem dynamics.

Forest surveillance is crucial to monitor, quantify and possibly prevent such events. However, most common strategies primarily rely on laborious and time-consuming field surveys, restricting geographical coverage and preventing large-scale analysis across vast territories.

On the other hand, the large amounts of remote sensing information collected via Earth observation missions constitutes an unprecedented opportunity to scale up forest dieback assessment and surveillance over large areas.

In a study published in the Journal of Intelligent Information Systems, researchers from the University of Bary Aldo Moro, Italy, and collaborators explore the performance of a data-centric semantic segmentation approach to detect forest tree dieback events due to bark beetle infestation in satellite images.

Named DIAMANTE (Data-centrIc semAntic segMentation to mAp iNfestations in saTellite imagEs), their approach trains a U-Net-like model from a labeled remote-sensing dataset prepared using both Copernicus Sentinel-1's SAR data and Sentinel-2's multi-spectral optical data.

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