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Time-lapse photogrammetry of distributed snow depth during snowmelt / Simon Filhol in Water resources research, vol 55 n° 9 (September 2019)  

Public [article]  inWater resources research > vol 55 n° 9 (September 2019) Titre : Time-lapse photogrammetry of distributed snow depth during snowmelt Type de document : Article/Communication Auteurs : Simon Filhol, Auteur ; Alexis Perret , Auteur ; Luc Girod , Auteur ; Guillaume Sutter, Auteur ; Thomas V. Schuler, Auteur ; John F. Burkhart, Auteur Année de publication : 2019 Projets : 3-projet - voir note / Note générale : bibliographie This work was supported by the Norwegian Research Council—Enhancing Snow Competency of Models and Operators(ESCYMO) project (NFR 244024), the University of Oslo eInfrastructure Competence Hub Geohive, and the European Research Council under the European Union's Seventh FrameworkProgram (FP/2007-2013)/ERC GrantAgreement 320816. This work forms a contribution to LATICE, which is a strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo. The source code of the software is available at this site (https://github.com/ArcticSnow/photo4D). Data are freely available from Zenodo.org (Filhol et al., 2018).FILHOL ET AL.7925 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] chaîne de traitement [Termes IGN] code source libre [Termes IGN] eau de fonte [Termes IGN] manteau neigeux [Termes IGN] Norvège [Termes IGN] profondeur [Termes IGN] semis de points Résumé : (auteur) Characterizing snowmelt both spatially and temporally from in situ observation remains a challenge. Available sensors (i.e., sonic ranger, lidar, airborne photogrammetry) provide either time series of local point measurements or sporadic surveys covering larger areas. We propose a methodology to recover from a minimum of three synchronized time-lapse cameras changes in snow depth and snow cover extent over area smaller or equivalent to 0.12 km2. Our method uses photogrammetry to compute point clouds from a set of three or more images and automatically repeat this task for the entire time series. The challenges were (1) finding an optimal experimental setup deployable in the field, (2) estimating the error associated with this technique, and (3) being able to minimize the input of manual work in the data processing pipeline. Developed and tested in the field in Finse, Norway, over 1 month during the 2018 melt season, we estimated a median melt of 2.12 ± 0.48 m derived from three cameras 1.2 km away from the region of interest. The closest weather station recorded 1.94 m of melt. Other parameters like snow cover extent and duration could be estimated over a 300 × 400m region. The software is open source and applicable to a broader range of geomorphologic processes like glacier dynamic, snow accumulation, or any other processes of surface deformation, with the conditions of (1) having fixed visible points within the area of interest and (2) resolving sufficient surface textures in the photographs. Numéro de notice : A2019-663 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2018WR024530 Date de publication en ligne : 19/08/2019 En ligne : https://doi.org/10.1029/2018WR024530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99691 [article]