High-resolution topographical information improves tree-level storm damage models

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http://hdl.handle.net/10138/307294

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Suvanto , S , Henttonen , H M , Nöjd , P & Mäkinen , H 2018 , ' High-resolution topographical information improves tree-level storm damage models ' , Canadian Journal of Forest Research , vol. 48 , no. 6 , pp. 721-728 . https://doi.org/10.1139/cjfr-2017-0315

Title: High-resolution topographical information improves tree-level storm damage models
Author: Suvanto, Susanne; Henttonen, Helena M.; Nöjd, Pekka; Mäkinen, Harri
Contributor: University of Helsinki, Department of Geosciences and Geography
Date: 2018-06
Language: eng
Number of pages: 8
Belongs to series: Canadian Journal of Forest Research
ISSN: 0045-5067
URI: http://hdl.handle.net/10138/307294
Abstract: Storms cause major forest disturbances in Europe. The aim of this study was to model tree-level storm damage probability based on the properties of a tree and its environment and to examine whether fine-scale topographic information is connected to the damage probability. We used data documenting effects of two autumn storms on over 17 000 trees on permanent Finnish National Forest Inventory plots. The first storm was associated with wet snowfall that damaged trees, while exceptionally strong winds and gusts characterized the second storm. During the storms, soils were unfrozen and deciduous trees were without leaves. Generalized linear mixed models were used to study how topographical variables calculated from digital elevation models (DEM) with resolutions of 2 and 10 m (TOPO2 and TOPO10, respectively) were related to damage probability, in addition to variable groups for tree (TREE) and stand (STAND) characteristics. We compared models containing different variable groups with Akaike information criteria. The best model contained the variable groups TREE, STAND, and TOPO2. Increase in slope steepness calculated from the high-resolution DEM decreased tree-level damage probability significantly in the model. This suggests that the local topography affects the tree-level damage probability and that high-resolution topographical data improves the tree-level damage probability models.
Subject: windthrow
wind storm
wind damage
snow damage
digital elevation model
NATIONAL FOREST INVENTORY
NORWAY SPRUCE
EUROPEAN FORESTS
WINTER STORM
SNOW DAMAGE
SCOTS PINE
FINLAND
WIND
WINDTHROW
STANDS
114 Physical sciences
1172 Environmental sciences
4112 Forestry
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