The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 59–63, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-59-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 59–63, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-59-2016

  22 Jun 2016

22 Jun 2016

A NEW METHOD TO DETECT REGIONS ENDANGERED BY HIGH WIND SPEEDS

P. Fischer, S. Ehrensperger, and T. Krauß P. Fischer et al.
  • Remote Sensing Technology Istitute, German Aerospace Center (DLR), Münchener Str. 20, 82234 Wessling, Germany

Keywords: Non-Parametric Regression, Boosted Regression Trees, Wind Speeds, Spatial Predictions

Abstract. In this study we evaluate whether the methodology of Boosted Regression Trees (BRT) suits for accurately predicting maximum wind speeds. As predictors a broad set of parameters derived from a Digital Elevation Model (DEM) acquired within the Shuttle Radar Topography Mission (SRTM) is used. The derived parameters describe the surface by means of quantities (e.g. slope, aspect) and quality (landform classification). Furthermore land cover data from the CORINE dataset is added. The response variable is maximum wind speed, measurements are provided by a network of weather stations. The area of interest is Switzerland, a country which suits perfectly for this study because of its highly dynamic orography and various landforms.