Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 539-542, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-539-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 539-542, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-539-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

WATCHING GRASS GROW- A PILOT STUDY ON THE SUITABILITY OF PHOTOGRAMMETRIC TECHNIQUES FOR QUANTIFYING CHANGE IN ABOVEGROUND BIOMASS IN GRASSLAND EXPERIMENTS

M. Kröhnert1, R. Anderson2,3,4, J. Bumberger2, P. Dietrich2,5, W. S. Harpole3,4,6, and H.-G. Maas1 M. Kröhnert et al.
  • 1Institute of Photogrammetry and Remote Sensing, TU Dresden, Germany
  • 2Department of Monitoring and Exploration Technologies, Helmholtz Center for Environmental Research (UFZ), Leipzig, Germany
  • 3Department of Physiological Diversity, Helmholtz Center for Environmental Research (UFZ), Leipzig, Germany
  • 4German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany
  • 5Department of Geosciences, University of Tübingen, Germany
  • 6Institute of Biology, Martin Luther University Halle-Wittenberg, Germany

Keywords: Aboveground Biomass, Volume Derivation, Grassland, Structure-from-Motion, Low-Cost Cameras

Abstract. Grassland ecology experiments in remote locations requiring quantitative analysis of the biomass in defined plots are becoming increasingly widespread, but are still limited by manual sampling methodologies. To provide a cost-effective automated solution for biomass determination, several photogrammetric techniques are examined to generate 3D point cloud representations of plots as a basis, to estimate aboveground biomass on grassland plots, which is a key ecosystem variable used in many experiments. Methods investigated include Structure from Motion (SfM) techniques for camera pose estimation with posterior dense matching as well as the usage of a Time of Flight (TOF) 3D camera, a laser light sheet triangulation system and a coded light projection system. In this context, plants of small scales (herbage) and medium scales are observed. In the first pilot study presented here, the best results are obtained by applying dense matching after SfM, ideal for integration into distributed experiment networks.