BayCEER Workshop 2023

Are current fire dynamics in temperate forests signaling a novel fire regime? - Using Artificial Intelligence for forest monitoring and prediction of fire events (KIWA)

European forests are becoming increasingly vulnerable to emerging fire regimes. The shifting disturbance regime, and its magnitude, duration, and frequency, are poorly understood. The consequences for biodiversity, habitat degradation, and carbon budgets remain unclear. Novel ways of dealing with forest fire events are needed, in order to prepare for such events and safeguard future biodiversity. Modelling approaches based on artificial intelligence (AI) are a promising tool for early fire detection.

Here, we investigate the shifting fire regime in Germany by using remote sensing to identify the temporal patterns of canopy browning - a typical fire-precondition in terms of fuel quality -, and by carrying out field surveys to record forest architecture and composition. Then, we integrate remotely sensed-, field- and climate data in an AI model for forest monitoring, and fire risk prediction. Thereby, we tackle questions, such as: 1) What are the changing ecological pre-conditions that produce fire and alter ecosystem trajectories? 2) Why are some temperate forest types (differing by species composition, stand structure) more prone to fire than others?

Our research contributes to the understanding of changing fire regimes in the Anthropocene, risk to fire, and new ways of forest fire risk management for ecosystem resilience.

KIWA Akronym und Projektname