Abstract
Abstract. Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts. The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage. To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. The probability of wind-induced forest damage (PDAM) in southern Finland (61°N, 23°E) was modelled for a 173 km2 study area of mainly managed boreal forests (dominated by Norway spruce and Scots pine) and agricultural fields. Wind damage occurred in the study area in December 2011. LiDAR data were acquired prior to the damage in 2008. High spatial resolution aerial imagery, acquired after the damage event (January, 2012) provided a source of model calibration via expert interpretation. A systematic grid (16 m x 16 m) was established and 430 sample grid cells were identified systematically and classified as damaged or undamaged based on visual interpretation using the aerial images. Potential drivers associated with PDAM were examined using a multivariate logistic regression model. Risk model predictors were extracted from the LiDAR-derived surface models. Geographic information systems (GIS) supported spatial mapping and identification of areas of high PDAM across the study area. The risk model based on LiDAR data provided good agreement with detected risk areas (73 % with kappa-value 0,47). The strongest predictors in the risk model were mean canopy height and mean elevation. Our results indicate that open-access LiDAR data sets can be used to map the probability of wind damage risk without field data, providing valuable information for forest management planning.
Highlights
Forest damage caused by natural disturbances, such as wind and snow, have increased in recent years
Most of the regional power lines that distribute electricity to households are located inside forest, these networks are very vulnerable to fallen trees and cut branches that harm the power lines
CHMmean and CHMmax can be expected to explain stand maturity and they were higher in damaged plots, it can be expected that the damaged plots were more mature
Summary
Forest damage caused by natural disturbances, such as wind and snow, have increased in recent years. As an example in 2012 wind was the most significant abiotic factor causing losses in forest yield in Finland (Heino & Pouttu 2013). This damage has an impact on forest yield value and to sustainable use of forests. Wind damage is the main reason for interruptions in the supply of electricity (Finnish Energy Industries 2013), information about high wind damage risk areas is needed in electricity companies who do not have access to the forest resource information or forest management plans of private forest owners
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