A spatially explicit UAV-based decision support tool to assess the protective effect of lying deadwood on snow avalanche release

Mountain forests provide ecosystem services, including protection against snow avalanches. Severe disturbances such as windthrow and bark beetle outbreaks generate large amounts of lying deadwood and may affect avalanche protection. Climate change and past land-use legacies are increasing disturbance frequency and severity, while management resources remain limited, highlighting the need for decision support tools (DSTs). Current assessments rely largely on expert judgment, limiting their objectivity and reproducibility. We present a spatially explicit DST that integrates terrain, forest structure, and deadwood characteristics to derive objective indicators of the protective effect of lying deadwood against avalanche release. Using high-resolution UAV-derived data, the DST characterizes deadwood structure, models winter terrain with increasing snow depth, and identifies snow depths at which surface roughness is substantially reduced. Comparisons with snow-on orthophotos support the plausibility of the modeled winter terrain. Application across UAV sensor systems, including photogrammetry and ULS, demonstrates the framework’s applicability to different data sources, with low-cost photogrammetric systems producing plausible results. Analyses across multiple Alpine sites and post-disturbance management strategies indicate that deadwood structure and retention influence protective effects. At the investigated sites, uncleared areas provided the highest protection, maintaining substantial roughness up to snow depths of ∼ 1.6 m, with only minor reductions over 5–6 years (up to 15%). Complete deadwood removal considerably reduced protection, whereas partial removal retained some protection under low-snow conditions. Our open-source DST provides a transparent, reproducible, and practitioner-oriented framework for assessing spatial indicators of the protective effect of lying deadwood against avalanche release under changing disturbance regimes.

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Publication Details

Journal
Ecological Indicators
Published
2026-09-30
DOI
https://doi.org/10.1016/j.ecolind.2026.115505
Primary Topic
Forest Ecology and Biodiversity Studies
Type
article
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article

A spatially explicit UAV-based decision support tool to assess the protective effect of lying deadwood on snow avalanche release

Michaela Teich, Frederik Schulte, Emanuele Lingua, Tommaso Baggio et al.
Ecological Indicators
Forest Ecology and Biodiversity Studies
article

A spatially explicit UAV-based decision support tool to assess the protective effect of lying deadwood on snow avalanche release

Michaela Teich, Frederik Schulte, Emanuele Lingua, Tommaso Baggio, Lukas Winiwarter, Thomas Marke, Marc S. Adams, Kathrin Holstein, Leon J. Bührle, Peter Bebi, Paul Richter
article en

Abstract

Mountain forests provide ecosystem services, including protection against snow avalanches. Severe disturbances such as windthrow and bark beetle outbreaks generate large amounts of lying deadwood and may affect avalanche protection. Climate change and past land-use legacies are increasing disturbance frequency and severity, while management resources remain limited, highlighting the need for decision support tools (DSTs). Current assessments rely largely on expert judgment, limiting their objectivity and reproducibility. We present a spatially explicit DST that integrates terrain, forest structure, and deadwood characteristics to derive objective indicators of the protective effect of lying deadwood against avalanche release. Using high-resolution UAV-derived data, the DST characterizes deadwood structure, models winter terrain with increasing snow depth, and identifies snow depths at which surface roughness is substantially reduced. Comparisons with snow-on orthophotos support the plausibility of the modeled winter terrain. Application across UAV sensor systems, including photogrammetry and ULS, demonstrates the framework’s applicability to different data sources, with low-cost photogrammetric systems producing plausible results. Analyses across multiple Alpine sites and post-disturbance management strategies indicate that deadwood structure and retention influence protective effects. At the investigated sites, uncleared areas provided the highest protection, maintaining substantial roughness up to snow depths of ∼ 1.6 m, with only minor reductions over 5–6 years (up to 15%). Complete deadwood removal considerably reduced protection, whereas partial removal retained some protection under low-snow conditions. Our open-source DST provides a transparent, reproducible, and practitioner-oriented framework for assessing spatial indicators of the protective effect of lying deadwood against avalanche release under changing disturbance regimes.

Ecological IndicatorsVol. 191
University of Padua (IT), Universität Innsbruck (AT), Austrian Research Centre for Forests (AT), WSL Institute for Snow and Avalanche Research SLF (CH)
Climate action
Openalex Percentile: Top 12%
Forest Ecology and Biodiversity Studies
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