Investigating the role of bark beetle (Ips typographus) in altering forest fire behaviour: a case study for Italian forests

Insects outbreak and wildland fires are among the most relevant natural disturbances affecting forested ecosystems worldwide. Following the storm Vaia of 2018, many Norway spruce (Picea abies (L.) Karst.) forests of the Eastern Italian Alps have been affected by a severe outbreak of bark beetle (Ips typographus), leading to economic, management and social concerns. In this context, the interaction between bark beetle outbreak and alterations in wildfire behaviour is poorly analysed, especially for Italian forests. This research aimed to detect the effects of bark beetle proliferation in the alteration of potential wildfire behaviour in a forested area (Veneto region, northern Italy). The semi-empirical FlamMap software was used, based on ALS data processing for deriving the spatial distribution of forest attributes and fuels within the study area. The Minimum Travel Time (MTT) algorithm of FlamMap was adopted for wildfire behaviour simulations. The contribution of bark beetle in altering the spatial behaviour of wildfires was explored using ALS point clouds acquired before and after the proliferation of bark beetle within the study area (pre-beetle and post-beetle scenario respectively). From the ALS data 5 meters-resolution Digital Terrain Models (DTMs), Canopy Height Models (CHMs), topographic data and forest metrics were extracted for both scenarios, to model alterations of wildfire behaviour over time. Differences in Rate of Spread (RoS) and Burn Probabilities (BP) were assessed and their correlation with bark beetle effects on standing trees was investigated at the catchment scale. An increase in RoS over 25m/min and in BP greater than 0.5 were estimated in forested areas affected by bark beetle outbreak, confirming the key role of Ips typographus in altering wildfire behaviour. The relation between bark beetle impacts and changes in wildfire attributes was finally estimated by computing regression analysis that led to R² of 0.78 and 0.82 respectively for RoS and BP. This type of analysis could be the starting point to inspect similar issues by the combined use of ALS data and wildfire behaviour models, with the ultimate aim of proposing effective management solutions and strategies for forest stands affected by natural disturbances.