Forest under watch: Remote-sensing data processing to monitor forest structures

Remote-sensing technologies are widely used to study forests but integrated toolkit for data processing is still lacking. FuW aims at proposing comprehensive workflow to estimate forest parameters from the most recent remote-sensing data.

Fiche signalétique

  • Département responsable Haute école des sciences agronomiques, forestières et alimentaires
  • Autres départements Technique et informatique
  • Institut Gestion multifonctionnelle des forêts
  • Unité de recherche Écosystème forestier et gestion
  • Organisation d'encouragement BFH
  • Durée (prévue) 01.09.2023 - 01.09.2024
  • Responsable du projet Dr. Estelle Noyer
  • Direction du projet Dr. Estelle Noyer
  • Équipe du projet Dr. Estelle Noyer
    Dr. Gaspard Dumollard
    Florian Thürkow
  • Partenaire Berner Fachhochschule BFH


Forest ecosystems are important carbon sinks worldwide, playing an essential role in climate mitigation. However, the future of forests is currently uncertain due to climate change, threatening forest ecosystem services (FES, e.g., wood production protection against rockfalls, carbon sequestration) and leading to a rethinking of forest management. Yet, the lack of data on forest conditions for modeling and for carbon balance (CB) estimations requires rapid improvements in forest monitoring. The emergence of remote sensing-technologies and data processing workflows assisted by Artificial Intelligence (AI) displays promising perspectives for sustainable forest resource management policies. Most advanced models have already proved successful but only a few studies provided detailed information, limiting practical application. The aim of the FuW project is therefore to propose an integrated tool based on remote sensing and inventory data to calculate FES and CB estimates, and in extenso, forest health. Through case studies, the following objectives are pursued: 1. to test different models and statistical approaches and assess their efficiency and accuracy; 2. to integrate the most robust models into a data processing process for practical use, and 3. to extend the developed approach to larger study areas.

Ce projet contribue aux objectifs de développement durable suivants

  • 13: Lutte contre le changement climatique
  • 15: Protection de la faune et de la flore terrestres