Modeling Biomass

The Integrated Energy and Climate Program (IEKP) and the national Biomass Action Plan of the Federal Republic of Germany have set ambitious goals for the expansion of biomass energy conversion by 2020. Energy supplied from biomass to meet the nation’s total electricity requirements is supposed to increase from today’s 5.5% to 8%, and the share for heating requirements from today’s 7.7% to 9.7%. By 2020, the share of biofuels in fuel consumption is to be increased by 7.5% to 12% (energy content). Already today, bioenergy makes the second largest contribution to Germany‘s renewable energy supply, after wind energy (6%). If the current trend continues (ca. 19% increase compared with 2009), this resource has the potential to become the most important renewable energy source. Meeting this goal requires detailed planning on the one hand, and due regard to sustainable cultivation practices on the other.

Because it provides high temporal and spatial resolution, remote sensing technology is already making available quality-controlled geodata and information. This now makes it possible to support the routine and standardized monitoring of biomass resources over large areas. Remote sensing can also improve mid-term the data basis for estimating agricultural and forestry biomass resources, especially for countries where the supply of statistical data is fragmentary.

In order to determine the energy potential of vegetation resources (such as forests, agriculture, grasslands) it is first necessary to have information about the existing biomass. As a complement to direct, small-scale ground measurements, remote sensing offers diverse possibilities to derive this information. Airborne lidar measurements can supply data about vegetation height and spatial distribution, from which the amount of biomass can be directly calculated. This application for satellite measurement is still being tested. Vegetation indices (such as NDVI) can be derived from data supplied by spaceborne sensors, and these indices in turn used to calculate the amount of biomass, an approach which is being followed in forestry. Another option is to use remote sensing data as a driver for vegetation models which calculate the annual increase in carbon known as the net primary production (NPP) of vegetation.

The German Remote Sensing Data Center (DFD) uses the vegetation model BETHY/DLR to balance energy potentials; it is based on meteorological and earth observation time series. Current DFD research in this area concentrates on balancing bioenergy potentials on both regional (Germany, Pakistan, South Africa) and continental (Europe) levels. In addition, issues relating to the effects on the environment of energy consumption are being investigated in international projects, as is the development of monitoring systems to certify bioenergy products (EU GMES ENDORSE). Another focus in this context is on deriving degradation indices, which should provide insights about changes in soil usability in semi-arid regions.