Tree roots can prevent landslides through root reinforcement. Modelling root reinforcement means combining density and biomechanical properties of the roots. Slope stability analysis requires the estimation of root reinforcement on large areas. In the present study, we analyze the relationship between a spatially explicit model of root reinforcement and LiDAR metrics from a sample of Norway spruce stands. Data were collected in twenty 20-m radius circular plots covered by a low-resolution airborne LiDAR-derived canopy height model. Trees diameter and position were used as input variables to calculate root reinforcement. Then, we fitted the relationship between root reinforcement and area-based stand metrics from canopy height model. Best regression was achieved plotting root reinforcement against canopy height modelderived tree height standard deviation (R2 = 0.73; relative RMSE = 0.096). Therefore, root reinforcement values might be spatially extrapolated through available canopy height models. Further research will integrate the extrapolated values into landslide susceptibility models.

Extrapolating a spatially explicit tree root reinforcement model with field and LiDAR-derived stand data

Edoardo Alterio
;
Tommaso SItzia;Andrea Rizzi;Niccolò Marchi;Emanuele LIngua;
2020

Abstract

Tree roots can prevent landslides through root reinforcement. Modelling root reinforcement means combining density and biomechanical properties of the roots. Slope stability analysis requires the estimation of root reinforcement on large areas. In the present study, we analyze the relationship between a spatially explicit model of root reinforcement and LiDAR metrics from a sample of Norway spruce stands. Data were collected in twenty 20-m radius circular plots covered by a low-resolution airborne LiDAR-derived canopy height model. Trees diameter and position were used as input variables to calculate root reinforcement. Then, we fitted the relationship between root reinforcement and area-based stand metrics from canopy height model. Best regression was achieved plotting root reinforcement against canopy height modelderived tree height standard deviation (R2 = 0.73; relative RMSE = 0.096). Therefore, root reinforcement values might be spatially extrapolated through available canopy height models. Further research will integrate the extrapolated values into landslide susceptibility models.
EGU2020: Sharing Geoscience Online conference
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3337682
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