A public Cloud-based China’s Landslide Inventory Database (CsLID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011
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Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.
KeywordsLandslide Inventory Zone Distribution Cloud computing
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- Cruden DM (1991) A simple definition of a landslide. Bulletin of Engineering Geology and the Environment 43: 27–29. DOI: 10.1007/BF02590167Google Scholar
- Institute of Hydrogeology and Engineering Geology, CAS (1992) China's Environmental Geological Zone Maps. Sinomaps Press, Beijing, China.Google Scholar
- Jelínek R, Hervás J, Wood M (2007) Risk mapping of landslides in New Member States. European Commission, Joint Research Centre, Geneva, Switzerland.Google Scholar
- Liu C, Li WY, Lu P, et al. (2012) Susceptibility evaluation and mapping of China's landslide disaster based on multitemporal ground and remote sensing satellite data. XXXIXB 8: 45–50. DOI: 10.5194/isprsarchives-XXXIX-B8-45-2012Google Scholar