Research project PIONEER

PIONEER- a Prototype servIce for fOrest inventory and health moNitoring using EndurancE dRones and citizen science

Forest ecosystems support biodiversity and resilience, and need better monitoring. PIONEER combines remote sensing and citizen science for sustainable forestry.

The project at a glance

  • Start date:
    01 Mar 2023
  • Duration in months:
    36
  • Funding:
    Institute for Advanced Studies (IAS)
  • Principal Investigator(s):
    Félicia Norma Rebecca TEFERLE
    Ariane KÖNIG

About

Forest ecosystems are significant determinants for biodiversity, bio-geo-chemical cycles, and regional ecosystems against impacts of extreme weather events. Trees are vulnerable to the impacts of anthropogenic climate change, and plant health. The mapping (characterization of trees) thus become crucial for evidence-based future-oriented forests management. Field-based surveys are inadequate to monitor forests. A paradigm shift in forestry monitoring is required to make forestry more resilient and sustainable. The project PIONEER will develop an innovative approach to produce accessible evidence on forest ecosystems and their services by combining remote sensing (RS) and citizen science (CS). PIONEER will investigate the use of unmanned aerial vehicle (UAV) based light detection and ranging (LiDAR) and hyperspectral image (HSI) sensing to collect forest data while developing machine learning (ML) approaches for the extraction of forest metrics. Additionally, a CS approach will be co-designed with experts/professionals, and citizen volunteers to allow the collection of complementary data that presents data on both the state of health of the forest ecosystem and its services. This will fill data gaps in the new environmental accounting approaches that are being proposed and discussed within the EU. Both RS and CS will be leveraged to develop a tree-centric approach to estimate carbon density, biodiversity, and forest health based on individual tree (IT) parameters, such as, tree species, diameters, and volumes. The developed methods will extract IT parameters with spectral signatures for an efficient way providing a digital model for precision forestry to understand tree growth rates and health at IT level. A citizen science toolset for participatory monitoring will help to enrich official data pools and provide educational means that can be used by schools and in life-long learning offered as part of leisure activities to develop an appreciation of complexity and renegotiation of what is valued in the community.

Organisation and Partners

Administration de la nature et des foreêts (ANF)


Project team

Prof Félicia Norma Rebecca TEFERLE

Full professor in Geodesy

Ariane KÖNIG

Assistant professor

Abdul Awal Md NURUNNABI

Postdoctoral researcher

Addisu HUNEGNAW

Postdoctoral researcher