The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 437–440, 2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 437–440, 2013

  25 Sep 2013

25 Sep 2013


H. Torabzadeh, F. Morsdorf, R. Leiterer, and M. E. Schaepman H. Torabzadeh et al.
  • Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland

Keywords: Fusion, Airborne Laser Scanning, Imaging Spectrometry, Forest Species

Abstract. Accurate mapping of forest species composition is an important aspect of monitoring and management planning related to ecosystem functions and services associated with water refinement, carbon sequestration, biodiversity, and wildlife habitats. Although different vegetation species often have unique spectral signatures, mapping based on spectral reflectance properties alone is often an ill-posed problem, since the spectral signature is as well influenced by age, canopy gaps, shadows and background characteristics. Thus, reducing the unknown variation by knowing the structural parameters of different species should improve determination procedures. In this study we combine imaging spectrometry (IS) and airborne laser scanning (ALS) data of a mixed needle and broadleaf forest to differentiate tree species more accurately as single-instrument data could do. Since forest inventory data in dense forests involve uncertainties, we tried to refine them by using individual tree crowns (ITC) position and shape, which derived from ALS data. Comparison of the extracted spectra from original field data and the modified one shows how ALS-derived shape and position of ITCs can improve separablity of the different species. The spatially explicit information layers containing both the spectral and structural components from the IS and ALS datasets were then combined by using a non-parametric support vector machine (SVM) classifier.