A complex network theory approach for the spatial distribution of fire breaks in heterogeneous forest landscapes for the control of wildland fires

TL;DR

This paper introduces a complex network-based computational method to optimize the placement of fire breaks in heterogeneous forests, aiming to better control wildland fires by identifying critical nodes for intervention.

Contribution

It presents a novel two-tier approach combining cellular automata and network theory to strategically locate fuel breaks, improving fire spread inhibition over traditional methods.

Findings

Method outperforms random fuel break placement in simulations.

Effective in both artificial and real-world landscapes.

Reduces fire spread and size significantly.

Abstract

Based on complex network theory, we propose a computational methodology that addresses the spatial distribution of fuel breaks for the inhibition of the spread and size of wildland fires on heterogeneous landscapes. This is a two-tire approach where the dynamics of fire spread are modeled as a random Markov field process on a directed network whose edge weights, are provided by a state-of-the-art 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 the network nodes among which the fire spreads faster, thus their removal favours the inhibition of the fire propagation. Here this is accomplished exploiting the information 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 a major part of its forest burned in 2008. Simulation results show that the methodology outperforms significantly the benchmark tactic of random distribution of fuel breaks.