Biogas plants are an important producer of electricity and heat based on locally available, renewable resources. However, they can endanger the environment by intensifying the farming of monocultures. One consequence is, for example, increased bee and insect mortality - which is pretty bad ...

What it does

The Landscape Optimizer is a tool designed to protect the environment and help operators of biogas plants. It finds the ideal cultivation of parcels and a suitable location for new biogas plants in order to achieve high biodiversity and high efficiency at the same time. Just four simple steps are required:

  1. Choosing the type of biogas plant.
  2. Marking your own area on a map.
  3. Placing the desired plant location on the map.
  4. Running an assessment and maybe adjust your scenario.

As a result, a dashboard with various key indicators is displayed: Degree of Edge Density, Shannon diversity Index and Patch density. The so-called “biodiversity points” indicate with a single number how biodiversity-rich is the setting with the chosen plantation. By trying out different alternatives, you can interactively change these results.

How we built it

  • Functional prototype with R, Shiny, Leaflet.js, and OpenStreetMap.
  • Using the R programming language and the QGIS app for processing spatial data.
  • Sketches/Wireframes with InVision.

What data did we use?

For the demo we chose to focus on a region in eastern Thuringia (counties Altenburger Land, Greiz and Gera). Agricultural parcels (Flurstücke) were derived from the open data portal of Thuringia ( Data on location of existing biogas plants and the road network were derived from OpenStreetmap ( via the QuickOSM plugin of QGIS. Crops were assigned to the parcels using remote sensing based landcover classes from a 20-m resolution product provided by the Helmholtz-Centre for Environmental Reseach ( All data processing took place in R and QGIS.

Challenges we ran into

Biodiverse cultivation of crops for the supply of biogas plants is more complex than originally assumed.

Challenges during the implementation of the functional prototype: Defining flexible options for the user to add their spatial data about crops; Having a fast reliable server host for our interactive dashboard; Smooth and fast integration between frontend and backend; Data complexity is given also its spatial component and data privacy concerns.

Setting up an effective team in a fast pace environment without knowing each other is quite a challenge.

Accomplishments that we're proud of

  • Finding together as a team within one day.
  • Gathering knowledge on BioDiversity.
  • Learning from each other.
  • Understanding business environments better.
  • A good technical infrastructure for the product to kick off with.

What we learned

Structure and clear guideline in a team are needed to work most efficient and target oriented. Good solutions need time though “the market” moves fast. What is possible on the tech side.

What's next for Landscape Optimizer

Refining the core logic. Wrapping the core logic into a web app and developing a user-friendly interface. Tests with data from real-life examples and challenging the solution with the target group.

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