Hyperspectral data were obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images. Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 and 19 classes) and species (14 and 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 and 19 classes) and species (14 and 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (https://www.mdpi.com/470202) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).

Cette donnée raster résulte d'une classification par méthode d'apprentissage profond à partir d'imagerie très haute résolution spatiale (1.5m) SPOT 6/7. Des post-traitements ont été effectués afin de mieux caractériser les classes relatives à l'artificialisation.

This dataset is the definitive set of locality boundaries for the state of Victoria as defined by Local Government and registered by the Registrar of Geographic Names. The boundaries are aligned to Vicmap Property. This dataset is part of the Vicmap Admin dataset series.

The opening of the dams should impact the functioning of Atlantic salmon populations migrating up the Sélune River and its tributaries. Restoring the river's ecological continuity will alter population flows by allowing amphihaline sea trout to migrate further upstream and native trout to move downstream and upstream of the dams. The trout population is monitored at a network of stations covering the entire Sélune basin using an abundance index, which is carried out every two years before 2021 and every year since then. Field campaigns comply with the fishing protocol developed by INRAE and OFB. The aim of this method is to estimate the abundance of juvenile Atlantic salmon (Salmo salar) at a station (or sector). This protocol targets juveniles of the year (aged 0+) whose abundance reflects the renewal of generations within the population (or recruitment) and survival after the embryonic development phases under gravel and the first months of life in the open environment. Field campaigns are carried out by INRAE and the Manche and Ille-et-Vilaine Federations for Fishing and the Protection of the Aquatic Environment (FDAAPPMA50 and FDAAPPMA35). This dataset provides abundance data for Atlantic salmon (0+, 1+, 2+ and cumulative) as well as the salmon abundance index (PAS) by station and fishing session.

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.

Full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.

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.