<p>In recent years, forest fires have become more frequent in central Europe. As the frequency and magnitude of future extreme weather events such as droughts are projected to increase, also the trend of increasing fire frequency in temperate forests is expected to continue. However, knowledge about fire behavior and spread dynamics in these forests is scarce. One of the key drivers of fire behavior is the availability of flammable vegetation, i.e. fuels. In the project ErWiN, we aim to describe the amount and distribution of fuels in different forest types in Southwestern Germany. Detailed field inventories of fuels in all vertical strata of the stands allow a first classification into different fuel types, which can be used in fire behavior simulations to obtain estimates of fire spread and intensity. In a further step, deep learning algorithms will be trained on recognizing these fuel types on GNSS located photos of forest stand situations to provide an efficient solution for mapping fuels in the field. By coupling field data with detailed remotely sensed information on forest structure obtained from airborne laserscanning, continuous fuel maps will be derived. Such fuel maps in turn allow landscape-scale analysis of fire behavior and can be useful in forest management decisions as well as in developing firefighting strategies. We thus hope to make a contribution to a better understanding of fuel-driven fire risk in central European forests and to facilitate the operational use of fire behavior models. In this contribution we present the concept developed in the ErWiN project and present first results obtained from the field survey of fuel types in Southwestern Germany.</p>
Analysis of the wind flow and fire spread dynamics over a sloped–ridgeline hill
