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A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging / Hannah Vickers in Earth and space science, vol 3 n° 11 (November 2016)  

Public [article]  inEarth and space science > vol 3 n° 11 (November 2016) . - pp 446 - 462 Titre : A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging Type de document : Article/Communication Auteurs : Hannah Vickers, Auteur ; M. Eckerstorfer, Auteur ; Eirik Malnes, Auteur ; Y. Larsen, Auteur ; H. Hindberg, Auteur Année de publication : 2016 Article en page(s) : pp 446 - 462 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] avalanche [Termes IGN] détection automatique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] Norvège [Termes IGN] Troms Résumé : (auteur) Avalanches are a natural hazard that occur in mountainous regions of Troms County in northern Norway during winter and can cause loss of human life and damage to infrastructure. Knowledge of when and where they occur especially in remote, high mountain areas is often lacking due to difficult access. However, complete, spatiotemporal avalanche activity data sets are important for accurate avalanche forecasting, as well as for deeper understanding of the link between avalanche occurrences and the triggering snowpack and meteorological factors. It is therefore desirable to develop a technique that enables active mapping and monitoring of avalanches over an entire winter. Avalanche debris can be observed remotely over large spatial areas, under all weather and light conditions by synthetic aperture radar (SAR) satellites. The recently launched Sentinel-1A satellite acquires SAR images covering the entire Troms County with frequent updates. By focusing on a case study from New Year 2015 we use Sentinel-1A images to develop an automated avalanche debris detection algorithm that utilizes change detection and unsupervised object classification methods. We compare our results with manually identified avalanche debris and field-based images to quantify the algorithm accuracy. Our results indicate that a correct detection rate of over 60% can be achieved, which is sensitive to several algorithm parameters that may need revising. With further development and refinement of the algorithm, we believe that this method could play an effective role in future operational monitoring of avalanches within Troms and has potential application in avalanche forecasting areas worldwide. Numéro de notice : A2016-966 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/2016EA000168 En ligne : http://dx.doi.org/10.1002/2016EA000168 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83624 [article]