Use of machine learning techniques to model wind damage to forests

This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory data damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested. All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment at the stand or single tree level over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage

Developing a method of wind damage risk assessment: case study in Aquitaine

Developing a method of wind damage risk assessment: case study in Aquitaine. Mathematical Modelling of Wind Damage Risk to Forest

Developing a method of wind damage risk assessment: case study in Aquitaine

Developing a method of wind damage risk assessment: case study in Aquitaine. Mathematical Modelling of Wind Damage Risk to Forest

Developing a method of wind damage risk assessment: case study in Aquitaine

Developing a method of wind damage risk assessment: case study in Aquitaine. Mathematical Modelling of Wind Damage Risk to Forest

Observations and predictions of wind damage to Larix kaempferi trees following thinning at an early growth stage

It is important to find effective forest management strategies to reduce wind damage risk, which is expected to increase due to climate change and increasing areas of planted forests. Thinning is one of the most important forest management activities, but it initially increases tree vulnerability due to an increase in wind penetration into the forest. In this paper, we analysed the damage to trees remaining after the thinning of Larix kaempferi (Lamb.) Carr. at an early growth stage in order: (1) to find the critical factors related to wind damage using statistical models and (2) to calculate the critical wind speeds for damage using a modified version of the mechanistic model GALES. Tree damage caused by a storm in 2006 was examined in plots of different stem densities (300, 500 and 1000 stem ha(-1)), which were thinned at a young age in a replicated silvicultural trial. Subsequently, tree-pulling experiments were conducted to obtain the parameters required in the GALES model. The logistic regression models based on the observations indicated that a longer crown length and a faster annual increase in crown width were significantly related to a reduction in damage occurrence. Estimation of wind damage to trees using GALES did not agree with the observed damage, probably due to the model not accounting for the influence of neighbouring trees and the degree of tree acclimation to the local wind climate after thinning. However, our research is based on only one example of tree damage in stands with different stem densities. Furthermore, our assessment might be biased because we used the trees remaining after the storm to derive parameters for the GALES model, and these trees might on average be more resistant than the storm damaged trees

Wind damage in forests associated with fragmented landscape in Aquitaine, France

On 24 January 2009 approximately 37 million m3 of maritime pine trees were damaged by storm Klaus in the south-western region (Aquitaine), France. This region consists of more than 90 % private forest ownership and the mean area of parcel is 14 ha, but 75 % of the forest owners have a less than 4 ha parcel. This shows that a large number of small forests with differing management plans are found in the region, but the direct relationship between such a fragmented landscape and wind damage has not been clearly demonstrated. The aim of our research was to find out how fragmented landscapes during storm conditions might affect the degree of wind damage in the Aquitaine region. First, we created a map showing the location and area of wind damage caused by storm Klaus in addition to the land-use type using aerial photos taken after the damage. The landscape was classified as four types of forest based on height and stem density, residential areas, open fields, water, and we also determined three degree of damage. The map was subsequently used to determine the neighbouring land-use type and difference of height between the particular land-uses. Wind speeds during the storm were estimated using data from the Application of Research to Operations at Mesoscale (AROME), developed by Météo France. A logistic regression analysis described that the difference of the windward height (west), land-use change, and maximum wind speed during the storm were significantly related to more than 50% of wind damage in forests. Further analysis will be conducted to understand the influence on the levels of wind damage of wind speed variation and intensity during the storm, and the spatial variability of land-use

Emergence and propagation of wind damage in forests: applying agent-based modelling to wind damage analysis

International audienc

Wind damage in forests associated with fragmented landscape in Aquitaine, France

International audienceOn 24 January 2009 approximately 37 million m3 of maritime pine trees were damaged by storm Klaus in the south-western region (Aquitaine), France. This region consists of more than 90 % private forest ownership and the mean area of parcel is 14 ha, but 75 % of the forest owners have a less than 4 ha parcel. This shows that a large number of small forests with differing management plans are found in the region, but the direct relationship between such a fragmented landscape and wind damage has not been clearly demonstrated. The aim of our research was to find out how fragmented landscapes during storm conditions might affect the degree of wind damage in the Aquitaine region. First, we created a map showing the location and area of wind damage caused by storm Klaus in addition to the land-use type using aerial photos taken after the damage. The landscape was classified as four types of forest based on height and stem density, residential areas, open fields, water, and we also determined three degree of damage. The map was subsequently used to determine the neighbouring land-use type and difference of height between the particular land-uses. Wind speeds during the storm were estimated using data from the Application of Research to Operations at Mesoscale (AROME), developed by Météo France. A logistic regression analysis described that the difference of the windward height (west), land-use change, and maximum wind speed during the storm were significantly related to more than 50% of wind damage in forests. Further analysis will be conducted to understand the influence on the levels of wind damage of wind speed variation and intensity during the storm, and the spatial variability of land-use

Emergence and propagation of wind damage in forests: applying agent-based modelling to wind damage analysis

International audienc