Volume XL-3/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 189-196, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W2-189-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 189-196, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W2-189-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Mar 2015

10 Mar 2015

MAPPING THE RISK OF FOREST WIND DAMAGE USING AIRBORNE SCANNING LiDAR

N. Saarinen1,2, M. Vastaranta1,2, E. Honkavaara3, M. A. Wulder4, J. C. White4, P. Litkey3, M. Holopainen1,2, and J. Hyyppä2,3 N. Saarinen et al.
  • 1Dept. of Forest Sciences, University of Helsinki, Finland
  • 2Centre of Excellence in Laser Scanning Research, Finnish Geodetic Institute, Masala, Finland
  • 3Dept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Finland
  • 4Canadian Forest Service, Pacific Forestry Centre, Victoria, Canada

Keywords: wind damage, airborne scanning LiDAR, forest management, forest mensuration, risk modelling, open access

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.