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dataset

131 record(s)
 
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  • The resource provides two land cover maps of Réunion Island for the years 1950 and 2022 derived from the analysis of ortho-photographs at the island scale (Source IGN). The produced typology uses five cover classes: forest, low vegetation, agriculture, urban, and shadow (related to topography). The method used is based on encoding the two aligned rasters, converted into a single band of grayscale for 2022, using a vision-transformer deep learning model. From the features calculated for each pixel, a random forest classification model is trained separately for each year using a set of ROIs (Regions Of Interest), target polygons delineated within each of the selected classes through photo-interpretation of the original images. Model validation is performed on independent sets of polygons also defined by photo-interpretation. The maps provided in the resource are derived from the prediction of cover classes for both years using the trained and validated models. These are raw predictions, meaning that no post-processing has been applied to reduce potential noise due to classification errors. The shared resource is part of the results from the FRAG'ILE research and development program (FRAGmentation en milieu InsuLairRE, UR / CBNM/ IRD, funded by OFB, https://fragile.frama.io).

  • WW3 model reanalysis on SWIO (south-western indian ocean) area at 0.5 degree of resolution

  • Le projet a pour objectif d'améliorer le signal de la pluie détectée par les géophones en comparant les données météorologiques d'un disdromètre, d'un pluviomètre et de 3 géophones afin d'extraire des données des sismographes pour mieux comprendre le transport sédimentaire issu du réseau sismologique installé dans la rivière des pluies et la rivière du Mat. Les objectifs sont : 1) déterminer les caractéristiques sismiques de la pluie sur le site de mesures 2) A terme, comprendre le déclenchement des éboulements et glissements liés aux pluies A court terme, ce projet devrait aussi permettre de : 1) comprendre pour un même type de pluie l'influence de sols de rugosités différentes sur les signaux enregistrés par les sismomètres 2) intégrer/contraindre pour un même type de sol l'influence de types de pluies différentes sur les enregistrements des sismomètres 3) déterminuer l'influence des tailles des gouttes et du nombre de gouttes (indications données par le disdromètre) sur le signal sismique

  • The ressource describes the dataset obtained by deploying the GAMIC GMWR-25-DP RADAR in the South of Reunion Island, in Saint Joseph.

  • This ressource is part of the action 1 of the ESPOIRS Project. Multiple GNSS Stations have been installed or updated in the SWIO and the data are available here. "TO ADD : Different datasets, list of stations, etc ...."

  • The overall objective of the ESPOIRS project is to obtain a better understanding of the variability, statistical properties and formation mechanisms of intense tropical precipitation at regional and local scales. ESPOIRS is thus interested in the entire life cycle of precipitation at several space-time scales. * Through the analysis of the distribution of the large-scale humidity field which drives the formation of precipitation at the regional scale using a GNSS network. * Through the characterization of internal (dynamics, microphysics) and external (interactions with the relief) processes, which drive the formation and life cycle of extreme weather events at the local scale => transportable Polarized Doppler X-band precipitation radar.

  • Le radar mini-BASTA est un radar nuage (95GHz) dédié à l’étude des nuages et du brouillard. Le radar mesure l’énergie rétrodiffusée par les hydrométéores, cette énergie peut donc être reliée à la quantité d’eau contenue dans le nuage (liquide et glace). Il fonctionne en routine quotidiennement sur le site de l’observatoire du Maïdo, sur l'Ile de La Réunion. Ce jeu de données est au niveau L0, et les données sont non calibrées. Paramètre principal: Profil vertical de réflectivité radar, mesure du décalage Doppler. Contexte de la mesure: observation routine.

  • The station is managed by the Observatoire de la Zone Critique de la Réunion (OZC-R) from Observatoire des Sciences de l'Univers de La Réunion (OSU-Réunion, Université de La Réunion). This is a forest station located at 1285m asl in the Reunion National Park. Rainwater is monthly collected (PALMEX rain collector) for δ18O and δ2H water isotopes analysis from 2016. Analyses are carried out at the Institut de Physique du Globe in Paris (PARI analytical platform) and within the IR-OZCAR network. The major ions rainfall composition is also studied. The station also measures several meteorological variables outside the canopy (precipitation, atmospheric pressure, temperature, relative humidity, global and photosynthetically active radiation) but also humidity linked to clouds and fog, as well as ground temperature.

  • Le radar BASTA est un radar nuage (95GHz) dédié à l’étude des nuages et du brouillard. Le radar mesure l’énergie rétrodiffusée par les hydrométéores, cette énergie peut donc être reliée à la quantité d’eau contenue dans le nuage (liquide et glace). Il fonctionne en routine quotidiennement sur le site de l’observatoire du Maïdo, sur l'Ile de La Réunion. Le radar BASTA Réunion a été calibré au LATMOS avant son installation à la Réunion. Ce jeu de données est au format niveau L0. Paramètre principal: Profil vertical de réflectivité radar, mesure du décalage Doppler. Contexte de la mesure: observation routine.

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    The dataset of the first observatory from the French network of critical zone observatories (OZCAR) located in an insular tropical and volcanic context, integrating a “Tropical Montane Cloud Forest”: The ERORUN-STAFOR observatory. This collaborative observatory is located in the northern part of La Réunion island (Indian Ocean) within the watershed of Rivière des Pluies (45.0 km²) which hosts the TMCF of Plaines des Fougères, one of the best preserved natural habitats in La Réunion Island. Since 2015, the ERORUN-STAFOR monitoring in collaboration with local partners collected a multidisciplinary dataset with a constant improvement of the instrumentation over time. At the watershed scale and in its vicinity, the ERORUN-STAFOR observatory includes 10 measurement stations covering the upstream, midstream and downstream part of the watershed. The stations record a total of 48 different variables through continuous (sensors) or periodic (sampling) monitoring. The dataset consists of continuous time series variables related to (i) meteorology, including precipitation, air temperature, relative humidity, wind speed and direction, net radiation, atmospheric pressure, cloud water flux, irradiance, leaf wetness and soil temperature (ii) hydrology, including water level and temperature, discharge and electrical conductivity (EC) of stream, (iii) hydrogeology, including groundwater level, water temperature and EC in two piezometers and one groundwater gallery completed by soil moisture measurements under the canopy. The dataset is completed by periodic time series variables related to (iv) hydrogeochemistry, including field parameters and water analysis results. The periodic sampling survey provides chemical and isotopic compositions of rainfall, groundwater, and stream water at different locations of this watershed. The ERORUN-STAFOR monitoring dataset extends from 2014 to 2022 with an acquisition frequency from 10 min to hourly for the sensor variables and from weekly to monthly frequency for the sampling. Despite the frequent maintenance of the monitoring sites, several data gaps exist due to the remote location of some sites and instrument destruction by cyclones. This observatory is a unique research site in an insular volcanic tropical environment offering three windows of observation for the study of critical zone processes through upstream-midstream-downstream measurements sites. This high-resolution dataset is valuable to assess the response of volcanic tropical watersheds and aquifers at both event and long-term scales (i.e. global change). It will also allow various progress in understanding the significant role of the TMCFs in the recharge processes, the hydrogeological conceptual model of volcanic islands, the watershed hydrosedimentary responses to extreme climatic events and their respective evolution under changing climatic conditions.