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Environmental Earth Sciences

, Volume 74, Issue 7, pp 5541–5555 | Cite as

Spatial relationships between landslide occurrences and land cover across the Arno river basin (Italy)

  • Ping Lu
  • Shibiao BaiEmail author
  • Nicola Casagli
Original Article

Abstract

In this study, an investigation was performed of the spatial relationships between the occurrences of four types of landslides (slides, flows, falls and creeps) and three categories of land cover (agricultural areas, artificial surfaces and forested and semi-natural areas) that are found in the Arno river basin of central Italy. The main purpose of the study was to test whether the landslides that are mapped within the basin are spatially clustered (i.e., have a spatial attraction) or randomly spatially distributed (i.e., spatially independent) on different types of land cover. The bivariate K-function was employed to evaluate the spatial dependence or randomness with additional estimates of corresponding clustering or independent distance. The bivariate K-function rejects the null hypothesis that all types of landslides tend to cluster similarly across different categories of land cover. At a 98 % confidence interval, significant spatial attractions can be detected in the active and dormant slides that are located on all three types of land cover, whereas the stabilized slides only exhibit spatial clustering in the forested and semi-natural areas within a clustering distance of 9 km. Additionally, the results suggest a spatial attraction between the dormant flows and the three types of land cover, for which only spatial randomness was detected for the active flows. Moreover, the occurrences of the falls appear to be spatially independent in the agricultural areas and on the artificial surfaces, whereas they are spatially clustered in the forested and semi-natural areas. Finally, no clustering was observed for the creeps on any of the three types of land cover.

Keywords

Landslide Land cover Spatial pattern Bivariate K-function 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 41201424), 973 National Basic Research Program (No. 2013CB733203 and No. 2013CB733204), 863 National High-Tech R&D Program (No. 2012AA121302) and FP6 Project of European Commission “Mountain Risks” (MRTN-CT-2006-035798).

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.College of Surveying and Geo-Informatics, Center for Spatial Information Science and Sustainable Development ApplicationsTongji UniversityShanghaiChina
  2. 2.College of Geographical Sciences, Key Laboratory of Virtual Geographic Environments (National Education Administration)Nanjing Normal UniversityNanjingChina
  3. 3.Department of Earth SciencesUniversity of FirenzeFlorenceItaly

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