Fore(st)cast – Nowcast and predict forest condition

Fore(st)cast is a joint project of WSL and SDSC

It combines the advantages of machine learning methods with the ecological knowledge produced with forest monitoring infrastructure

Forests host about 90% of the world’s terrestrial biomass in the form of carbon and are an important pool for global biodiversity. We need long-term monitoring but a near real-time data processing to fulfill the needs from science and the public. We propose here a further step toward near real-time assessment of the condition of our forests, as has traditionally been done in meteorology or hydrology. Fore(st)cast aims at further developing the TreeNet platform that reports on the current and predicted condition of trees (e.g., growth and drought stress) in relation to environmental variables.

The milestones of the project are

1. implement a recently developed approach to gap-fill data with machine learning,
2. develop a method for anomaly detection,
3. interpolate point data to larger areas, and
4. develop tree growth and drought stress predictions.