Using multiresolution and multitemporal satellite data for post-disaster landslide inventory in the Republic of Serbia View Full Text


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Article Info

DATE

2017-06-21

AUTHORS

Dragana Đurić, Ana Mladenović, Milica Pešić-Georgiadis, Miloš Marjanović, Biljana Abolmasov

ABSTRACT

This paper focuses on a specific event-based landslide inventory compiled after the May 2014 heavy rainfall episode in Serbia as a part of the post-disaster recovery actions. The inventory was completed for a total of 23 affected municipalities, and the municipality of Krupanj was selected as the location for a more detailed study. Three sources of data collection and analysis were used: a visual analysis of the post-event very high and high (VHR-HR) resolution images (Pléiades, WorldView-2 and SPOT 6), semi-automatic landslide recognition in pre- and post-event coarse resolution images (Landsat 8) and a landslide mapping field campaign. The results suggest that the visual and semi-automated analyses significantly contributed to the quality of the final inventory, including the associated planning strategies for conducting future field campaigns (as a final stage of the inventorying process), all the more so because the field-based and image-based inventories were focused on different types of landslides. In the most affected municipalities that had very high resolution satellite image coverage (19.52% of the whole study area), the density of the recognized landslides was approximately three times higher than that in those municipalities without satellite image coverage (where only field data were available). The total number of field-mapped landslides for the 23 municipalities was 1785, while image-based inventories, which were available only for the municipalities with satellite image coverage (77.43% of the study area), showed 1298 landslide records. The semi-automated landslide inventory in the test area (Krupanj municipality), which was based on coarse resolution multitemporal images (Landsat 8), counted 490 landslide instances and was in agreement with the visual analysis of the higher resolution images, with an overlap of approximately 40%. These results justify the use of preliminary inventorying via satellite image analysis and suggest a considerable potential use for preliminary visual and semi-automated landslide inventorying as an important supplement to field mapping. More... »

PAGES

1467-1482

References to SciGraph publications

  • 2015-10-28. Devastation in the Kedarnath (Mandakini) Valley, Garhwal Himalaya, during 16–17 June 2013: a remote sensing and ground-based assessment in NATURAL HAZARDS
  • 2008-03-24. The Alpine-Carpathian-Dinaridic orogenic system: correlation and evolution of tectonic units in SWISS JOURNAL OF GEOSCIENCES
  • 2006-12-01. Interpretation of landslide distribution triggered by the 2005 Northern Pakistan earthquake using SPOT 5 imagery in LANDSLIDES
  • 2013-06-13. September, 2012 landslide events in Okhimath, India—an assessment of landslide consequences using very high resolution satellite data in LANDSLIDES
  • 2014-03-18. Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data in NATURAL HAZARDS
  • 2016-10-14. Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal in LANDSLIDES
  • 2012-06-14. Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake in LANDSLIDES
  • 2009-04-17. Interpretation of earthquake-induced landslides triggered by the 12 May 2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using satellite imagery and Google Earth in LANDSLIDES
  • 2011-02-08. Assessment of ASTER satellite images in landslide inventory mapping: Yenice-Gökçebey (Western Black Sea Region, Turkey) in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2015-11-14. Kedarnath disaster 2013: causes and consequences using remote sensing inputs in NATURAL HAZARDS
  • 2014-06-19. A geotechnical model of the Umka landslide with reference to landslides in weathered Neogene marls in Serbia in LANDSLIDES
  • 2013-11-30. The Varnes classification of landslide types, an update in LANDSLIDES
  • 2014-04-01. Satellite stereoscopic pair images of very high resolution: a step forward for the development of landslide inventories in LANDSLIDES
  • 2012-10-26. The ICL Adriatic-Balkan Network: analysis of current state and planned activities in LANDSLIDES
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