AI Driven Forest Health Risk Indicator

Sentinel-2 based classification of bark beetle infestation mostly maps the red attack stage, where needle color changes indicate the dying infested trees. At this infestation stage, the beetles have often left the host tree and spread to other forest stands in the neighborhood. AI frameworks are trained on rule-based risk maps and Sentinel-2 data. Our risk prediction solutions take into account the vicinity of infested areas and critical meteorological conditions, such as drought stress. This allows us to identify and monitor forest areas with high risk of infestation.

Hard Facts

The project goal of AIDForHeRI was to test AI frameworks for the prediction of bark beetle infestation risk in a continuous monitoring scenario. The workflows integrate meteorological data from the SPARTACUS data set, Sentinel-2 data, bark beetle infestation maps and various forest stand related and topography related parameters to train several AI models. Partners: Joanneum Research; Beetle ForTech, Geosphere Austria

Duration: 2022-08-01 to 2023-12-31
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Service readiness level: Research/desk study

I would like to be contacted by:
  • Business/Marketing/Media Partners
  • Commercial Clients
  • Governmental Clients
  • Research Partners
  • Start-ups
  • Start-up Hubs
Areas of Application (EARSC Categories):
  • Agriculture
  • Alternative Energy
  • Biodiversity
  • Ecosystems
  • Environmental, Pollution and Climate
  • Forestry
Addressed SDGs: