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Exclusive application of tree-ring anatomy for reconstruction of past landslide behavior
Abstract Tree-ring-based analyses currently represent a modern, continuously evolving, and effective tool for the spatio-temporal reconstruction of past landslide movements. Although the use of standard macroscopic landslide signals is still more common, this classical approach has significant limitations. Recent research, however, shows that changes in the anatomical structure of tree rings can serve as a more efficient source of landslide signals. Thus, this unique pioneering study was conducted on two active landslides with the aim of reconstructing their spatio-temporal behavior using only anatomical responses, which are not accompanied by macroscopic manifestations. A total of 54 disturbed trees (Fagus sylvatica L. and Acer pseudoplatanus L.) were sampled. The only anatomical parameter examined was the change in vessel lumen area (VLA), which was analyzed to detect landslide signals. The results identified 17 and 12 landslide signals at the two sites, respectively, corresponding to four and three landslide events over the past several decades. The study found that anatomical changes in vessel lumen area effectively captured landslide events, offering advantages over traditional macroscopic approaches by minimizing noise and avoiding the masking of younger events by older ones. However, this methodology is most suitable for certain types of landslides, particularly flow-like and translational types with tension cracks that affect the root systems of trees. The results suggest that further refinement, including geophysical surveys to optimize sampling locations, could enhance the accuracy of this dendrogeomorphic approach. Thus, this study demonstrates the potential of using exclusively anatomical responses in tree rings for landslide reconstruction. While this method requires more resources than traditional approaches, it provides a clearer, less noisy record of landslide activity.
Exclusive application of tree-ring anatomy for reconstruction of past landslide behavior
Abstract Tree-ring-based analyses currently represent a modern, continuously evolving, and effective tool for the spatio-temporal reconstruction of past landslide movements. Although the use of standard macroscopic landslide signals is still more common, this classical approach has significant limitations. Recent research, however, shows that changes in the anatomical structure of tree rings can serve as a more efficient source of landslide signals. Thus, this unique pioneering study was conducted on two active landslides with the aim of reconstructing their spatio-temporal behavior using only anatomical responses, which are not accompanied by macroscopic manifestations. A total of 54 disturbed trees (Fagus sylvatica L. and Acer pseudoplatanus L.) were sampled. The only anatomical parameter examined was the change in vessel lumen area (VLA), which was analyzed to detect landslide signals. The results identified 17 and 12 landslide signals at the two sites, respectively, corresponding to four and three landslide events over the past several decades. The study found that anatomical changes in vessel lumen area effectively captured landslide events, offering advantages over traditional macroscopic approaches by minimizing noise and avoiding the masking of younger events by older ones. However, this methodology is most suitable for certain types of landslides, particularly flow-like and translational types with tension cracks that affect the root systems of trees. The results suggest that further refinement, including geophysical surveys to optimize sampling locations, could enhance the accuracy of this dendrogeomorphic approach. Thus, this study demonstrates the potential of using exclusively anatomical responses in tree rings for landslide reconstruction. While this method requires more resources than traditional approaches, it provides a clearer, less noisy record of landslide activity.
Exclusive application of tree-ring anatomy for reconstruction of past landslide behavior
Landslides
Šilhán, Karel (author)
2025-02-18
Article (Journal)
Electronic Resource
English
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