Mapping the probability of forest snow disturbances in Finland.

The changing forest disturbance regimes emphasize the need for improved damage risk information. Here, our aim was to (1) improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on trees, versus forest properties in p...

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Autores principales:	Susanne Suvanto, Aleksi Lehtonen, Seppo Nevalainen, Ilari Lehtonen, Heli Viiri, Mikael Strandström, Mikko Peltoniemi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c79395078c3447496bfbd8a8a6eeab2
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spelling	oai:doaj.org-article:0c79395078c3447496bfbd8a8a6eeab22021-12-02T20:08:58ZMapping the probability of forest snow disturbances in Finland.1932-620310.1371/journal.pone.0254876https://doaj.org/article/0c79395078c3447496bfbd8a8a6eeab22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254876https://doaj.org/toc/1932-6203The changing forest disturbance regimes emphasize the need for improved damage risk information. Here, our aim was to (1) improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on trees, versus forest properties in predicting the probability of snow damage, (2) produce a snow damage probability map for Finland. We also compared the results for winters with typical snow load conditions and a winter with exceptionally heavy snow loads. To do this, we used damage observations from the Finnish national forest inventory (NFI) to create a statistical snow damage occurrence model, spatial data layers from different sources to use the model to predict the damage probability for the whole country in 16 x 16 m resolution. Snow damage reports from forest owners were used for testing the final map. Our results showed that best results were obtained when both abiotic and forest variables were included in the model. However, in the case of the high snow load winter, the model with only abiotic predictors performed nearly as well as the full model and the ability of the models to identify the snow damaged stands was higher than in other years. The results showed patterns of forest adaptation to high snow loads, as spruce stands in the north were less susceptible to damage than in southern areas and long-term snow load reduced the damage probability. The model and the derived wall-to-wall map were able to discriminate damage from no-damage cases on a good level (AUC > 0.7). The damage probability mapping approach identifies the drivers of snow disturbances across forest landscapes and can be used to spatially estimate the current and future disturbance probabilities in forests, informing practical forestry and decision-making and supporting the adaptation to the changing disturbance regimes.Susanne SuvantoAleksi LehtonenSeppo NevalainenIlari LehtonenHeli ViiriMikael StrandströmMikko PeltoniemiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254876 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Susanne Suvanto Aleksi Lehtonen Seppo Nevalainen Ilari Lehtonen Heli Viiri Mikael Strandström Mikko Peltoniemi Mapping the probability of forest snow disturbances in Finland.
description	The changing forest disturbance regimes emphasize the need for improved damage risk information. Here, our aim was to (1) improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on trees, versus forest properties in predicting the probability of snow damage, (2) produce a snow damage probability map for Finland. We also compared the results for winters with typical snow load conditions and a winter with exceptionally heavy snow loads. To do this, we used damage observations from the Finnish national forest inventory (NFI) to create a statistical snow damage occurrence model, spatial data layers from different sources to use the model to predict the damage probability for the whole country in 16 x 16 m resolution. Snow damage reports from forest owners were used for testing the final map. Our results showed that best results were obtained when both abiotic and forest variables were included in the model. However, in the case of the high snow load winter, the model with only abiotic predictors performed nearly as well as the full model and the ability of the models to identify the snow damaged stands was higher than in other years. The results showed patterns of forest adaptation to high snow loads, as spruce stands in the north were less susceptible to damage than in southern areas and long-term snow load reduced the damage probability. The model and the derived wall-to-wall map were able to discriminate damage from no-damage cases on a good level (AUC > 0.7). The damage probability mapping approach identifies the drivers of snow disturbances across forest landscapes and can be used to spatially estimate the current and future disturbance probabilities in forests, informing practical forestry and decision-making and supporting the adaptation to the changing disturbance regimes.
format	article
author	Susanne Suvanto Aleksi Lehtonen Seppo Nevalainen Ilari Lehtonen Heli Viiri Mikael Strandström Mikko Peltoniemi
author_facet	Susanne Suvanto Aleksi Lehtonen Seppo Nevalainen Ilari Lehtonen Heli Viiri Mikael Strandström Mikko Peltoniemi
author_sort	Susanne Suvanto
title	Mapping the probability of forest snow disturbances in Finland.
title_short	Mapping the probability of forest snow disturbances in Finland.
title_full	Mapping the probability of forest snow disturbances in Finland.
title_fullStr	Mapping the probability of forest snow disturbances in Finland.
title_full_unstemmed	Mapping the probability of forest snow disturbances in Finland.
title_sort	mapping the probability of forest snow disturbances in finland.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0c79395078c3447496bfbd8a8a6eeab2
work_keys_str_mv	AT susannesuvanto mappingtheprobabilityofforestsnowdisturbancesinfinland AT aleksilehtonen mappingtheprobabilityofforestsnowdisturbancesinfinland AT sepponevalainen mappingtheprobabilityofforestsnowdisturbancesinfinland AT ilarilehtonen mappingtheprobabilityofforestsnowdisturbancesinfinland AT heliviiri mappingtheprobabilityofforestsnowdisturbancesinfinland AT mikaelstrandstrom mappingtheprobabilityofforestsnowdisturbancesinfinland AT mikkopeltoniemi mappingtheprobabilityofforestsnowdisturbancesinfinland
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Mapping the probability of forest snow disturbances in Finland.

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