Abstract. Interactions between wind and trees control energy exchanges between the atmosphere and forest canopies. This energy exchange can lead to the widespread damage of trees, and wind is a key disturbance agent in many of the world's forests. However, most research on this topic has focused on conifer plantations, where risk management is economically important, rather than broadleaf forests, which dominate the forest carbon cycle. This study brings together tree motion time-series data to systematically evaluate the factors influencing tree responses to wind loading, including data from both broadleaf and coniferous trees in forests and open environments. We found that the two most descriptive features of tree motion were (a) the fundamental frequency, which is a measure of the speed at which a tree sways and is strongly related to tree height, and (b) the slope of the power spectrum, which is related to the efficiency of energy transfer from wind to trees. Intriguingly, the slope of the power spectrum was found to remain constant from medium to high wind speeds for all trees in this study. This suggests that, contrary to some predictions, damping or amplification mechanisms do not change dramatically at high wind speeds, and therefore wind damage risk is related, relatively simply, to wind speed. Conifers from forests were distinct from broadleaves in terms of their response to wind loading. Specifically, the fundamental frequency of forest conifers was related to their size according to the cantilever beam model (i.e. vertically distributed mass), whereas broadleaves were better approximated by the simple pendulum model (i.e. dominated by the crown). Forest conifers also had a steeper slope of the power spectrum. We interpret these finding as being strongly related to tree architecture; i.e. conifers generally have a simple shape due to their apical dominance, whereas broadleaves exhibit a much wider range of architectures with more dominant crowns.

Abstract. 1. Interactions between wind and trees control energy exchanges between the atmosphere and forest canopies. This energy exchange can lead to the widespread damage of trees and wind is a key disturbance agent in many of the world’s forests. However, most research on this topic has focused on conifer plantations, where risk management is economically important, rather than broadleaf forests, which dominate the forest carbon cycle. This study brings together all available tree motion time-series data to systematically evaluate the factors influencing tree responses to wind loading, including data from both broadleaf and coniferous trees in forests and open environments. 2. We found that the two most descriptive features of tree motion were: (a) the fundamental frequency, which is a measure of the speed at which a tree sways and is strongly related to tree height, and (b) the slope of the power spectrum, which is related to the efficiency of energy transfer from wind to trees. Intriguingly, the slope of the power spectrum was found to remain constant from medium to high wind speeds for all trees in this study. This suggests that, contrary to some predictions, damping or amplification mechanisms do not change dramatically at high wind speeds and therefore wind damage risk is related, relatively simply, to wind speed. 3. Conifers from forests were distinct from broadleaves in terms of their response to wind loading. Specifically, the fundamental frequency of forest conifers was related to their size according to the cantilever beam model (i.e. vertically distributed mass), whereas broadleaves were better approximated by the simple pendulum model (i.e. dominated by the crown). Forest conifers also had a steeper slope of the power spectrum. We interpret these finding as being strongly related to tree architecture, i.e. conifers generally have a simple shape due to their apical dominance, whereas broadleaves exhibit a much wider range of architectures with more dominant crowns.

Severe storms caused the largest amount of damaged timber in European forests in the past 70 years. Storm damage occurs when wind loads exceed the failure limits of trees. A decisive factor in assessing storm damage is comprehensive knowledge of interactions between the aerial parts of trees and the high-impact airflow. This paper describes the inexpensive multiple sensor system TreeMMoSys that can measure aerial tree parts' wind-induced reactions, including branches and the stem. The output of TreeMMoSys includes acceleration and angular rate data converted to tilt angles in the postprocessing. The system consists of a scalable number of light-weight tree response sensors and ground receivers that communicate through a WLAN network. The weatherproofed system is highly portable, reusable, and allows for an efficient monitoring and a maximization of the number of study trees. Due to the stable measurement performance and accuracy of TreeMMoSys, it can be deployed in the field for long-term monitoring of single tree reactions or neighboring trees' reactions to wind excitation.

The parameterization of hybrid-mechanistic storm damage models is largely based on the results of tree pulling tests. The tree pulling tests are used for imitating the quasi-static wind load associated with the mean wind speed. The combined effect of dynamic and quasi-static wind loads associated with wind load maxima is considered by either linearly increasing the quasi-static wind load by a gust factor or by using a turning moment coefficient determined from the relationship between maxima of wind-induced tree response and wind speed. To improve the joint use of information on dynamic and quasi-static wind loading, we present a new method that uses the coupled components of momentum flux time series and time series of stem orientation of a plantation-grown Scots pine tree. First, non-oscillatory tree motion components, which respond to wind excitation, are isolated from oscillatory components that are not coupled to the wind. The non-oscillatory components are detected by applying a sequence of time series decomposition methods including bi-orthogonal decomposition and singular spectrum analysis. Then, the wind-excited tree response components are subjected to dynamic time warping, which maximizes the coincidence between the processed data. The strong coincidence of the time-warped data allows for the estimation of the wind-induced tree response as a function of the effective wind load using simple linear regression. The slope of the regression line represents the rate of change in the tree response as the effective wind load changes. Because of the strength of the relationship, we argue that the method described is an improvement for the analysis of storm damage in forests and to individual trees.

Deadwood mapping is of high relevance for studies on forest biodiversity, forest disturbance, and dynamics. As deadwood predominantly occurs in forests characterized by a high structural complexity and rugged terrain, the use of remote sensing offers numerous advantages over terrestrial inventory. However, deadwood misclassifications can occur in the presence of bare ground, displaying a similar spectral signature. In this study, we tested the potential to detect standing deadwood (h > 5 m) using orthophotos (0.5 m resolution) and digital surface models (DSM) (1 m resolution), both derived from stereo aerial image matching (0.2 m resolution and 60%/30% overlap (end/side lap)). Models were calibrated in a 600 ha mountain forest area that was rich in deadwood in various stages of decay. We employed random forest (RF) classification, followed by two approaches for addressing the deadwood-bare ground misclassification issue: (1) post-processing, with a mean neighborhood filter for “deadwood”-pixels and filtering out isolated pixels and (2) a “deadwood-uncertainty” filter, quantifying the probability of a “deadwood”-pixel to be correctly classified as a function of the environmental and spectral conditions in its neighborhood. RF model validation based on data partitioning delivered high user’s (UA) and producer’s (PA) accuracies (both > 0.9). Independent validation, however, revealed a high commission error for deadwood, mainly in areas with bare ground (UA = 0.60, PA = 0.87). Post-processing (1) and the application of the uncertainty filter (2) improved the distinction between deadwood and bare ground and led to a more balanced relation between UA and PA (UA of 0.69 and 0.74, PA of 0.79 and 0.80, under (1) and (2), respectively). Deadwood-pixels showed 90% location agreement with manually delineated reference to deadwood objects. With both alternative solutions, deadwood mapping achieved reliable results and the highest accuracies were obtained with deadwood-uncertainty filter. Since the information on surface heights was crucial for correct classification, enhancing DSM quality could substantially improve the results.