Kyrgyzstan is landlocked mountainous nation of around five million people, which borders China, Kazakhstan, Tajikistan and Uzbekistan. The total area of high mountainous terrain, alpine meadows and pastures exceeds 70% of the Republic’s territory, whereas the greater part of the Kyrgyz Republic is occupied by the Tien-Shan mountains. Kyrgyzstan is a highly active seismic region and has been shaken by numerous significant earthquakes as a consequence of the ongoing collision between the Indian and Eurasian tectonic plates.
External Contact Person:
Kanayim Teshebaeva
Email:
k.teshebaeva [at] caiag.kg
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Bibliographic reference:
Teshebaeva, K., Sudhaus, H., Wetzel, H. U., Echtler, H., Zubovich, A., & Roessner, S. (2013, October). Radar remote sensing for surveying and monitoring of earthquakes and mass movements in Southern Kyrgyzstan. Inand Young Researchers’ Forum.
Landslide geodatabases, including inventories and thematic data, today are fundamental tools for national and/or local authorities in susceptibility, hazard and risk management. Awell organized landslide geo-database contains different kinds of data such as past information (landslide inventory maps), ancillary data and updated remote sensing (space-borne and ground based) data,which can be integrated in order to produce landslide susceptibility maps, updated landslide inventory maps and hazard and risk assessment maps. Italy is strongly affected by
External Contact Person:
Email:
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Bibliographic reference:
Ciampalini, A., Raspini, F., Bianchini, S., Frodella, W., Bardi, F., Lagomarsino, D., ... & Casagli, N. (2015). Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy) geodatabase.Geomorphology.
Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution.
External Contact Person:
Robert Behling
Email:
behling [at] gfz-potsdam.de
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Bibliographic reference:
Behling, R., Roessner, S., Segl, K., Kleinschmit, B., & Kaufmann, H. (2014). Robust automated image co-registration of optical multi-sensor time series data: Database generation for multi-temporal landslide detection. Remote Sensing, 6(3), 2572-2600.
In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information.
External Contact Person:
Robert Behling
Email:
behling [at] gfz-potsdam.de
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Bibliographic reference:
Behling, R., Roessner, S., Kaufmann, H., & Kleinschmit, B. (2014). Automated spatiotemporal landslide mapping over large areas using rapideye time series data. Remote Sensing, 6(9), 8026-8055.
We present a method for the semi-automatic recognition and mapping of recent rainfall induced shallow landslides. The method exploits VHR panchromatic and HR multispectral satellite images, and was tested in a 9.4 km2 area in Sicily, Italy, where on 1 October 2009 a high intensity rainfall event caused shallow landslides, soil erosion, and inundation.
External Contact Person:
Alessandro Mondini
Email:
Alessandro.Mondini [at] irpi.cnr.it
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Bibliographic reference:
Mondini, A. C., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., & Ardizzone, F. (2011). Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sensing of Environment, 115(7), 1743-1757.
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