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A streamlined approach for the spatial allocation of fuel removals in wildland–urban interfaces

Author:
Elia, Mario, Lafortezza, Raffaele, Colangelo, Giuseppe, Sanesi, Giovanni
Source:
Landscape ecology 2014 v.29 no.10 pp. 1771-1784
ISSN:
0921-2973
Subject:
case studies, fire spread, fire suppression, forest types, fuel loading, fuels (fire ecology), geostatistics, human settlements, landscape management, landscapes, managers, models, population density, prediction, roads, shrublands, wildfires, Italy
Abstract:
Major concerns are arising on the expansion of wildland–urban interfaces defined as zones where infrastructures and other man-made systems interact with undeveloped areas. Wildland–urban interfaces create an environment in which fire can easily spread from forest fuels to human settlements. In this context, there is a need to prevent fire spread by determining the sound allocation of fuel treatment (fuel removal). To this end, the Spatial Allocation Index was developed as a streamlined approach to determine where and what type of forest areas may be eligible for fuel removal in terms of fire suppression. This approach was developed as a case study example using forest landscapes located in the province of Taranto (Apulia region) in southern Italy. By using geostatistical techniques, we scaled up 210 data points of plot-level fuel load and developed maps for different forest types. These spatial predictions were combined with other landscape-level variables such as population density, urban density, and road density. Through our modelling approach we were able to determine the fuel model types and spatial allocations of wildland areas that are likely to be treated by fuel removal. Our results suggest that the predominant forest typology requiring treatment in the study area is the Mediterranean maquis (shrub-land), which covers 44 % of the wildland–urban interface landscape. The areas on the map where the Spatial Allocation Index reaches its maximum value are those with the highest priority in terms of fuel removal; i.e., the highest number of people, houses, and roads benefitting from wildfire suppression. By adopting this streamlined approach, forest managers and decision makers may avail of a fast and effective tool to improve efforts in landscape management and budgeting of financial resources.
Agid:
1192761