Volume XLI-B1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 443-448, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-443-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 443-448, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-443-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Jun 2016

03 Jun 2016

EVALUATING THE POTENTIAL OF SATELLITE HYPERSPECTRAL RESURS-P DATA FOR FOREST SPECIES CLASSIFICATION

O. Brovkina1, J. Hanuš1, F. Zemek1, V. Mochalov2, O. Grigorieva2, and M. Pikla1 O. Brovkina et al.
  • 1Global Change Research Institute, 60300 Brno, Czech Republic
  • 2St.Petersburg Institute for Informatics and Automation of Russian Academy of Science, 199178 St.Petersburg, Russia

Keywords: satellite, airborne, hyperspectral, forest species

Abstract. Satellite-based hyperspectral sensors provide spectroscopic information in relatively narrow contiguous spectral bands over a large area which can be useful in forestry applications. This study evaluates the potential of satellite hyperspectral Resurs-P data for forest species mapping. Firstly, a comparative study between top of canopy reflectance obtained from the Resurs-P, from the airborne hyperspectral scanner CASI and from field measurement (FieldSpec ASD 4) on selected vegetation cover types is conducted. Secondly, Resurs-P data is tested in classification and verification of different forest species compartments. The results demonstrate that satellite hyperspectral Resurs-P sensor can produce useful informational and show good performance for forest species classification comparable both with forestry map and classification from airborne CASI data, but also indicate that developments in pre-processing steps are still required to improve the mapping level.