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Titel |
Investigating the performance of LiDAR-derived biomass information in hydromechanic slope stability modelling |
VerfasserIn |
Elmar Schmaltz, Stefan Steger, Thom Bogaard, Rens van Beek, Thomas Glade |
Konferenz |
EGU General Assembly 2017
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Medientyp |
Artikel
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Sprache |
en
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Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 19 (2017) |
Datensatznummer |
250153772
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Publikation (Nr.) |
EGU/EGU2017-18791.pdf |
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Zusammenfassung |
Hydromechanic slope stability models are often used to assess the landslide susceptibility of
hillslopes. Some of these models are able to account for vegetation related effects when
assessing slope stability. However, spatial information of required vegetation parameters
(especially of woodland) that are defined by land cover type, tree species and stand density
are mostly underrepresented compared to hydropedological and geomechanical parameters.
The aim of this study is to assess how LiDAR-derived biomass information can
help to distinguish distinct tree stand-immanent properties (e.g. stand density and
diversity) and further improve the performance of hydromechanic slope stability
models.
We used spatial vegetation data produced from sophisticated algorithms that are able to
separate single trees within a stand based on LiDAR point clouds and thus allow an
extraordinary detailed determination of the aboveground biomass. Further, this information is
used to estimate the species- and stand-related distribution of the subsurface biomass using an
innovative approach to approximate root system architecture and development. The
hydrological tree-soil interactions and their impact on the geotechnical stability of the soil
mantle are then reproduced in the dynamic and spatially distributed slope stability model
STARWARS/PROBSTAB.
This study highlights first advances in the approximation of biomechanical reinforcement
potential of tree root systems in tree stands. Based on our findings, we address the advantages
and limitations of highly detailed biomass information in hydromechanic modelling and
physically based slope failure prediction. |
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