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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: Suvanto, Susanne, Lehtonen, Aleksi, Nevalainen, Seppo, Lehtonen, Ilari, Viiri, Heli, Strandström, Mikael, Peltoniemi, Mikko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321231/
https://www.ncbi.nlm.nih.gov/pubmed/34324530
http://dx.doi.org/10.1371/journal.pone.0254876
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author Suvanto, Susanne
Lehtonen, Aleksi
Nevalainen, Seppo
Lehtonen, Ilari
Viiri, Heli
Strandström, Mikael
Peltoniemi, Mikko
author_facet Suvanto, Susanne
Lehtonen, Aleksi
Nevalainen, Seppo
Lehtonen, Ilari
Viiri, Heli
Strandström, Mikael
Peltoniemi, Mikko
author_sort Suvanto, Susanne
collection PubMed
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.
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spelling pubmed-83212312021-07-31 Mapping the probability of forest snow disturbances in Finland Suvanto, Susanne Lehtonen, Aleksi Nevalainen, Seppo Lehtonen, Ilari Viiri, Heli Strandström, Mikael Peltoniemi, Mikko PLoS One Research Article 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. Public Library of Science 2021-07-29 /pmc/articles/PMC8321231/ /pubmed/34324530 http://dx.doi.org/10.1371/journal.pone.0254876 Text en © 2021 Suvanto et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Suvanto, Susanne
Lehtonen, Aleksi
Nevalainen, Seppo
Lehtonen, Ilari
Viiri, Heli
Strandström, Mikael
Peltoniemi, Mikko
Mapping the probability of forest snow disturbances in Finland
title 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_short Mapping the probability of forest snow disturbances in Finland
title_sort mapping the probability of forest snow disturbances in finland
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321231/
https://www.ncbi.nlm.nih.gov/pubmed/34324530
http://dx.doi.org/10.1371/journal.pone.0254876
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