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Multi-temporal coastal zone landscape change detection using remote sensing imagery and in situ data

  • Sergey Victorov
  • Eugene Kildjushevsky
  • Leontina Sukhacheva
  • Tatiana Popova
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

At present remote sensing (RS) methods are widely used for solving large amount of applied and scientific environmental problems. Among them are: assessment of the state of the marine and coastal environments, monitoring of water quality, study of water dynamics, change detection in coastal zone (CZ), study of drainage basins; detection, study and prediction of environmental disasters (floods, emergency pollution, soil erosion, landslides, etc.) through the prism of environmental security. Case studies of landscape change detection in various geographical sites located in the coastal zones of the Black Sea, the Azov Sea, the Ladoga Lake (Baltic Sea basin), and the Volga River reservoirs are presented. The focus is on erosion and accumulation processes. Both multitemporal aerial photos and satellite imagery are used to study these phenomena during a 30 year observational period. Customized knowledge bases are widely used. They contain historical data, maps and charts. Of special interest are the examples dealing with the disputed geographical objects (Russia-Ukraine border conflict in the Azov Sea) and the dangerous situation with landslides in the Volga River reservoirs. Landscape ecology aspects are reflected when historical data and new remotely sensed data are used to analyze dynamics of coastal landscape in the vicinity of the Syas pulp factory located in the coastal zone of the largest European Lake of Ladoga – the source of drinking water for the city of St. Petersburg with its 5 million inhabitants.

Keywords: Environmental security; landscape ecology; remote sensing; coastal zone

Keywords

Coastal Zone Satellite Imagery Environmental Security Coastal Landscape Taman Peninsula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2008

Authors and Affiliations

  • Sergey Victorov
    • 1
  • Eugene Kildjushevsky
    • 1
  • Leontina Sukhacheva
    • 1
  • Tatiana Popova
    • 1
  1. 1.Research Institute of Remote Sensing Methods for GeologyRussia

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