Integrating Remote Sensing and in-situ observations of Biodiversity, towards a fully interoperable observation and data framework

Expected Outcome: Project results are expected to contribute to all of the following expected outcomes: advancing robust, policy-relevant ecosystem assessment, nature protection and restoration planning activities, and biodiversity trend prediction based on fit for purpose data, thus supporting EU biodiversity and climate objectives; strengthened capacity of researchers, practitioners and decision-makers to improve biodiversity monitoring practices and address knowledge gaps via the integration of data and observations across sensors and platforms, ranging from omics-based data (genomic, transcriptomic, metabolomic), various in-situ to satellite-based Earth observation data; enhanced usability of in-situ datasets as training and validation resources for statistical, machine learning and advanced AI-based approaches, in support of applications such as habitat classification, ecosystem mapping and biodiversity trend prediction. Scope: Achieving the goals of the EU biodiversity strategy for 2030, the Kunming-Montreal Global Biodiversity Framework, and assessing progress towards their defined targets, requires coherent, integrated and long-term monitoring approaches, underpinned by FAIR (Findable, Accessible, Interoperable and Re-usable) data systems. Many existing in-situ data collections (i.e. genomic assessments, ground sampling, drone and airborne observations) were not designed with Satellite Remote Sensing integration in mind (i.e. for validation and calibration,…