Nieblas A.E., Barde Julien, Bernard S., Imzilen Taha, Kerzerho V., Rouyer S., Bonhommeau S. (2018). Enrichment of trajectories with environmental data, and standardization of tagging data using NetCDF.
Victoria Mahé : CTOI, 12 p. multigr. (Documents de Réunion - CTOI ; IOTC–2018–WPDCS14–25_Rev1). Groupe de Travail sur la Collecte des Données et les Statistiques : GTCDS14, 14., Mahé (SEY), 2018/11/29 - 201812/02.
Titre du document
Enrichment of trajectories with environmental data, and standardization of tagging data using NetCDF
Année de publication
2018
Type de document
Colloque
Auteurs
Nieblas A.E., Barde Julien, Bernard S., Imzilen Taha, Kerzerho V., Rouyer S., Bonhommeau S.
Source
Victoria Mahé : CTOI, 2018,
12 p. multigr. (Documents de Réunion - CTOI ; IOTC–2018–WPDCS14–25_Rev1).
Colloque
Groupe de Travail sur la Collecte des Données et les Statistiques : GTCDS14, 14., Mahé (SEY), 2018/11/29 - 201812/02
Geolocalisation and trajectory analysis can aid in understanding the ecological processes driving an organism. By associating satellite-derived environmental data with individual trajectories of electronically-tagged organisms, it could be possible to define environmental characteristics of the tagged species' functional habitats (i.e., reproduction, nutrition). These data can also help identify biotic envelopes or predict the effects of climate change on marine species distributions. The objective of the present work, undertaken as a collaboration between IFREMER and IRD, is to standardize electronic tag data files into network common data format (NetCDF) format, following the standards defined within the POPSTAR project for tag data (doi http://dx.doi.org/10.13155/34980), and enrich the positional data with satellite-derived surface environment data (e.g., sea surface temperature, salinity, sea level) and model-derived environment data at observed depths (e.g., temperature, salinity, currents). We accounted for positional uncertainty using 95%, 75%, and 50% uncertainty polygons around the estimated positions of individuals. We summarised environmental conditions within these uncertainty polygons using the mean, minimum, maximum, quantiles, and standard deviation of the selected enrichment parameter. We generated generic codes to enable the automatic enrichment of position data from points and polygons. Furthermore, we developed algorithms to convert the enriched data into NetCDF format for subsequent visualisation and analysis.
Plan de classement
Ecologie, systèmes aquatiques [036]
;
Télédétection [126]
Localisation
Fonds IRD [F B010075958]
Identifiant IRD
fdi:010075958
