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The opening of the dams will have a significant impact on fish populations in the Sélune. Restoring the ecological continuity of the river will alter population flows by allowing certain amphihaline species to migrate further upstream and other species to move downstream and upstream of the dams. For many years now, as part of the ORE DiaPFC (Observatory for Research into the Environment of Diadromous Fish in Coastal Rivers), the SOERE OLA (Observation and Experimentation System for Environmental Research in Alpine Lakes) and the OFB-INRAE-Institut Agro-UPPA cluster, various INRAE units (U3E, UMR ECOBIOP and UMR CARRTEL) and OFB (DRAS) have been taking samples from numerous aquatic specimens. These samples, usually otoliths, scales, and/or fins, are then used by the scientific community to carry out various analyses and research. These samples are also collected as part of the Sélune observatory. Certified as a Biological Resource Center (BRC) by GIS IBISA, Colisa is part of the BRC4Env environmental pillar (network of Biological Resource Centers for the Environment) of the RARe infrastructure. Our catalog references these different hard tissue samples and offers a module that allows you to query our database and select the types of data that may be of interest to you. You can then export this data. This dataset does not contain any specific data, but provides a link to the COLISA (COLlection of Ichtyological SAmple) application, which allows the storage of collected samples. The COLISA application requires the creation of a user account to access the entire collection.
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This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50 cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NVDI) images at 0.5 m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time.
Catalogue GéoSAS