Abstract

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.

Highlights

  • Forest disturbances caused by snow are frequent in high latitude regions [1,2,3,4] and high-altitude areas [5,6,7]

  • The MS-national forest inventory (NFI) data is available for download under CC-BY 4.0 license from http://kartta.luke.fi/index-en.html, the data for snow load on tree crowns is available under CC-BY 4.0 license from http://urn.fi/urn:nbn: fi:csc-kata20180329115425634000, the digital elevation model (DEM) are available under CC-BY 4.0 license from https:// tiedostopalvelu.maanmittauslaitos.fi/tp/kartta? lang=en, the data on biogeographical regions in Finland are available under CC-BY 4.0 license from https://ckan.ymparisto.fi/dataset/%7B664BE696C6A5-4FC4-8D6A-7D2E63D0E9C6%7D

  • The generalized linear models (GLM) model results show that abiotic factors, especially crown snow load, drive the snow damage, as damage probability increases with increasing snow load, relative elevation, and altitude (Table 3, Fig 3)

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Summary

Introduction

Forest disturbances caused by snow are frequent in high latitude regions [1,2,3,4] and high-altitude areas [5,6,7]. In Europe, the estimates of forest damage caused by snow disturbance events. Mapping the probability of forest snow disturbances authors to publicly share this data. The MS-NFI data is available for download under CC-BY 4.0 license from http://kartta.luke.fi/index-en.html, the data for snow load on tree crowns is available under CC-BY 4.0 license from http://urn.fi/urn:nbn: fi:csc-kata20180329115425634000, the DEMs are available under CC-BY 4.0 license from https:// tiedostopalvelu.maanmittauslaitos.fi/tp/kartta? Lang=en, the data on biogeographical regions in Finland are available under CC-BY 4.0 license from https://ckan.ymparisto.fi/dataset/%7B664BE696C6A5-4FC4-8D6A-7D2E63D0E9C6%7D. The data from Finnish Forest Centre can be downloaded under CC-BY 4.0 license from https://www. The data from Finnish Forest Centre can be downloaded under CC-BY 4.0 license from https://www. metsaan.fi/paikkatietoaineistot

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