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Dendrogeomorphology of landslides: principles, results and perspectives

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Abstract

Landslides are dangerous and destructive geomorphic processes that cause annual damage to human infrastructure or even loss of life. As recovery is very costly, knowledge of past landslide activities, a detailed analysis of triggers and prediction of future landslide development are important. Dendrogeomorphic (tree-ring-based) dating is the best solution of chronological data obtaining in forested areas, where trees annually produce increment rings. A moving landslide mass affects trees that grow on its surface. Trees respond to this influence in different ways that are recordable and subsequently visible in tree ring series. Thus, tree rings represent an ideal natural archive of past landslide behaviour. Depending on the tree species, the length of a landslide chronology can be several centuries with sub-annual resolution. Although dendrogeomorphic approaches have some limitations, provided data are unique because they represent insight into the past without the need for long-term monitoring. Nevertheless, trees as landslide archives are suitable for medium-magnitude events because excessively small movements can be disregarded and catastrophic movements destroy trees. This review introduces details regarding tree-landslide interactions, provides a historical overview of applied methods, presents and assesses methodical approaches and summarises basic advantages and contributions to the knowledge of landslide chronology, spatial behaviour and triggers. Finally, limitations, the potential for subsequent research directions and calls for future fundamental studies in new world regions are presented.

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Acknowledgements

The language was reviewed by American Journal Experts.

Funding

The Czech Science Foundation project 19-01866S and the Specific Research Project of University of Hradec Králové Nr. 2102/2020 supported this study.

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Correspondence to Karel Šilhán.

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Šilhán, K. Dendrogeomorphology of landslides: principles, results and perspectives. Landslides 17, 2421–2441 (2020). https://doi.org/10.1007/s10346-020-01397-4

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