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A compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)  

Public [article]  inRemote sensing > vol 13 n°10 (May-2 2021) . - n° 2002 Titre : A compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study Type de document : Article/Communication Auteurs : Hannah Vickers, Auteur ; Eirik Malnes, Auteur ; Ward van Pelt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2002 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] données multicapteurs [Termes IGN] image à haute résolution [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] manteau neigeux [Termes IGN] modélisation [Termes IGN] Normalized Difference Snow Index [Termes IGN] série temporelle [Termes IGN] surveillance hydrologique [Termes IGN] Svalbard Résumé : (auteur) Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. Numéro de notice : A2021-438 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102002 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97822 [article]

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]