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.
- Lead school School of Agricultural, Forest and Food Sciences
- Additional schools School of Engineering and Computer Science
- Institute Multifunctional Forest Management
- Research unit Forest Ecosystem and Management
- Funding organisation BFH
- Duration (planned) 01.09.2023 - 01.09.2024
- Project management Dr. Estelle Noyer
- Head of project Dr. Estelle Noyer
Dr. Estelle Noyer
Dr. Gaspard Dumollard
- Partner 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.