A Complex Network Theory Approach for the Spatial Distribution of Fire Breaks in Heterogeneous Forest Landscapes for the Control of Wildland Fires

• Published in: PloS one Vol. 11; no. 10; p. e0163226
• Main Authors: Russo, Lucia, Russo, Paola, Siettos, Constantinos I
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.10.2016; Public Library of Science (PLoS)
• Subjects: Analysis; Applied mathematics; Biology and Life Sciences; Burning; Cellular automata; Climate change; Computer and Information Sciences; Computer applications; Computer simulation; Conservation of Natural Resources - methods; Density; Ecology and Environmental Sciences; Engineering and Technology; Fires; Flammability; Forest & brush fires; Forestry; Forests; Fuels; Geographic information systems; Greece; Harvest; Harvesting; Landscape ecology; Markov Chains; Markov processes; Meteorological data; Methods; Models, Theoretical; Patches (structures); Physical Sciences; Pinus palustris; Prescribed burning; Prescribed fire; Robots; Social network analysis; Social networks; Spatial distribution; Vegetation; Wildfires; Greece; United States > US; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0163226

Based on complex network theory, we propose a computational methodology which addresses the spatial distribution of fuel breaks for the inhibition of the spread of wildland fires on heterogeneous landscapes. This is a two-level approach where the dynamics of fire spread are modeled as a random Markov field process on a directed network whose edge weights are determined by a Cellular Automata model that integrates detailed GIS, landscape and meteorological data. Within this framework, the spatial distribution of fuel breaks is reduced to the problem of finding network nodes (small land patches) which favour fire propagation. Here, this is accomplished by exploiting network centrality statistics. We illustrate the proposed approach through (a) an artificial forest of randomly distributed density of vegetation, and (b) a real-world case concerning the island of Rhodes in Greece whose major part of its forest was burned in 2008. Simulation results show that the proposed methodology outperforms the benchmark/conventional policy of fuel reduction as this can be realized by selective harvesting and/or prescribed burning based on the density and flammability of vegetation. Interestingly, our approach reveals that patches with sparse density of vegetation may act as hubs for the spread of the fire.