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

Suvanto, Susanne; Henttonen, Helena M.; Nöjd, Pekka; Mäkinen, Harri

doi:10.1139/cjfr-2017-0315

Cited by 11 publications

(8 citation statements)

References 37 publications

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“…Such conditions occur on the down-wind edge of recently clear-felled areas. The forest areas most severely exposed to wind damages can be mapped by utilizing topographic and land-use data, and such accounts have been developed, for example by Venäläinen et al (2017) and Suvanto et al (2018). Significant changes in the directional distribution of extreme winds would necessitate recalculation of the risk mapping tools.…”

Section: Discussionmentioning

confidence: 99%

Projected Changes in European and North Atlantic Seasonal Wind Climate Derived from CMIP5 Simulations

2019

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Future changes in geostrophic winds over Europe and the North Atlantic region were studied utilizing output data from 21 CMIP5 global climate models (GCMs). Changes in temporal means, extremes, and the joint distribution of speed and direction were considered. In concordance with previous research, the time mean and extreme scalar wind speeds do not change pronouncedly in response to the projected climate change; some degree of weakening occurs in the majority of the domain. Nevertheless, substantial changes in high wind speeds are identified when studying the geostrophic winds from different directions separately. In particular, in northern Europe in autumn and in parts of northwestern Europe in winter, the frequency of strong westerly winds is projected to increase by up to 50%. Concurrently, easterly winds become less common. In addition, we evaluated the potential of the GCMs to simulate changes in the near-surface true wind speeds. In ocean areas, changes in the true and geostrophic winds are mainly consistent and the emerging differences can be explained (e.g., by the retreat of Arctic sea ice). Conversely, in several GCMs the continental wind speed response proved to be predominantly determined by fairly arbitrary changes in the surface properties rather than by changes in the atmospheric circulation. Accordingly, true wind projections derived directly from the model output should be treated with caution since they do not necessarily reflect the actual atmospheric response to global warming.

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Section: Discussionmentioning

confidence: 99%

Projected Changes in European and North Atlantic Seasonal Wind Climate Derived from CMIP5 Simulations

2019

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“…This may in part result from the use of stand level data, where defining and identifying the open stand borders from the NFI data is more uncertain than in the case of tree-level analysis (see section 2.3.2 for the used methodology). Earlier work with storm damage data from severe autumn storms in Finland showed that the effects of open forest edges on damage probability were more emphasized in tree-level analysis (Suvanto et al, 2018) than in the stand-level analysis of the same data (Suvanto et al, 2016). In the future, potential improvements to the presentation of damage probability at the forest edges in the map could be achieved by combining tree-level results or mechanistic approaches to the current stand-level modeling approach.…”

Section: Drivers Of Forest Susceptibility To Wind Disturbancementioning

confidence: 99%

High-resolution mapping of forest vulnerability to wind for disturbance-aware forestry

Suvanto

Peltoniemi

Tuominen

et al. 2019

Forest Ecology and Management

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Windstorms cause major disturbances in European forests. Forest management can play a key role in making forests more persistent to disturbances. However, better information is needed to support decision making that effectively accounts for wind disturbances. Here we show how empirical probability models of wind damage, combined with existing spatial data sets, can be used to provide fine-scale spatial information about disturbance probability over large areas. First, we created stand-level damage probability models using wind damage observations within 5-year time window in national forest inventory data (NFI). Model predictors described forest characteristics, forest management history, 10-year return-rate of maximum wind speed, and soil, site and climate conditions. We tested three different methods for creating the damage probability models -generalized linear models (GLM 1 ), 1 Abbreviations: AUCarea under curve, BRTboosted regression trees, dddegree days, GAMgeneralized additive model, GAMMgeneralized additive mixed model, GISgeographic information system, GLMgeneralized linear model, GLMMgeneralized linear mixed model, GTK -Geological Survey of Finland, GVIFgeneralized variance inflation factor, MS-NFImulti-source national forest inventory, NFI (NFI11, NFI12) generalized additive models (GAM) and boosted regression trees (BRT). Then, damage probability maps were calculated by combining the models with GIS data sets representing the model predictors. Finally, we demonstrated the predictive performance of the damage probability maps with a large, independent test data of over 33,000 NFI plots, which shows that the maps are able to identify vulnerable forests also in new wind damage events, with area under curve value (AUC) > 0.7. Use of the more complex methods (GAM and BRT) was not found to improve the performance of the map compared to GLM, and therefore we prefer using the simpler GLM method that can be more easily interpreted. The map allows identification of vulnerable forest areas in high spatial resolution (16 m x 16 m ), making it useful in assessing the vulnerability of individual forest stands when making management decisions. The map is also a powerful tool for communicating disturbance risks to forest owners and managers and it has the potential to steer forest management practices to a more disturbance-aware direction. Our study showed that in spite of the inherent stochasticity of the wind and damage phenomena at all spatial scales, it can be modelled with good accuracy across large spatial scales when existing ground and earth observation data sources are combined smartly. With improving data quality and availability, map-based risk assessments can be extended to other regions and other disturbance types.

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“…The overall results of our analysis indicate that ALS-based tree and stand characteristics have potential to be used in assessing spatial patterns of wind damage risk to forests. Canopy height has already been deemed important by many previous studies (Miller 1986;Mitchell et al 2001;Albrecht et al 2012;Locatelli et al 2016;Díaz-Yáñez et al 2017) as has been the distance to the upwind forest stand edge (Suvanto et al 2018;Zubizarreta-Gerendiain et al 2012;. The compelling additional contribution of this study is that we have outlined a new method to estimate these meaningful metrics over large forested areas using ALS and aerial image data.…”

Section: The Utility Of Als and Aerial Image Data In Wind Damage Riskmentioning

confidence: 84%

The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland

Gopalakrishnan

Packalén

Ikonen

et al. 2020

Annals of Forest Science

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Key message The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage. Context Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations. Aims (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects? Methods We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area. Results Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement. Conclusion Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas.

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

Cited by 11 publications

References 37 publications

Projected Changes in European and North Atlantic Seasonal Wind Climate Derived from CMIP5 Simulations

Projected Changes in European and North Atlantic Seasonal Wind Climate Derived from CMIP5 Simulations

High-resolution mapping of forest vulnerability to wind for disturbance-aware forestry

The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland

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