Volume XL-7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 165-171, 2014
https://doi.org/10.5194/isprsarchives-XL-7-165-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 165-171, 2014
https://doi.org/10.5194/isprsarchives-XL-7-165-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Sep 2014

19 Sep 2014

Mapping Biomass Availability to Decrease the Dependency on Fossil Fuels

T. Steensen1, S. Müller2, M. Jandewerth3, and O. Büscher2 T. Steensen et al.
  • 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hanover, Germany
  • 2EFTAS Fernerkundung Technologietransfer GmbH, Münster, Germany
  • 3Fraunhofer-Institut für Umwelt-, Sicherheits- und Energietechnik UMSICHT, Oberhausen, Germany

Keywords: Vegetation, Satellite, LIDAR, GIS, Hyperspectral, Mapping

Abstract. To decrease the dependency on fossil fuels, more renewable energy sources need to be explored. Over the last years, the consumption of biomass has risen steadily and it has become a major source for re-growing energy. Besides the most common sources of biomass (forests, agriculture etc.) there are smaller supplies available in mostly unused areas like hedges, vegetation along streets, railways, rivers and field margins. However, these sources are not mapped and in order to obtain their potential for usage as a renewable energy, a method to quickly assess their spatial distribution and their volume is needed. We use a range of data sets including satellite imagery, GIS and elevation data to evaluate these parameters. With the upcoming Sentinel missions, our satellite data is chosen to match the spatial resolution of Sentinel-2 (10–20 m) as well as its spectral characteristics. To obtain sub-pixel information from the satellite data, we use a spectral unmixing approach. Additional GIS data is provided by the German Digital Landscape Model (ATKIS Base-DLM). To estimate the height (and derive the volume) of the vegetation, we use LIDAR data to produce a digital surface model. These data sets allow us to map the extent of previously unused biomass sources. This map can then be used as a starting point for further analyses about the feasibility of the biomass extraction and their usage as a renewable energy source.