IA 2.4. Optimizing landscape configuration and fire management policies to minimize expected losses from EWE



What kind of result is this?

Updated tool  

What’s the area addressed?

Ecosystem Conservation and Landscape Design
Landscape Management and Fuel Treatment Planning

What’s the covered phase?

Prevention & Preparedness

What’s the addressed challenge?

This Innovation Action has 2 components dealing with the same problem using different approaches.

  • Component 1: Prioritizing investments in forest and fuel management projects and measuring outcomes is a significant challenge. The scrutiny over fuel management programs requires a broader understanding of how the myriad of often conflicting management objectives concerning conservation, restoration of fire-adapted forests and agency wood production targets influence the process of prioritizing limited budgets to conduct forest management activities. From a cost perspective, fuel treatment cannot be performed for very large portions of a study area, as the costs of the fuel treatment operations could exceed the benefits of the reduction in losses from wildfires. As a result, the need to prioritize and strategically place fuel treatments requires a methodological approach that accounts for the implementation costs and prioritization based on different management objectives for different landscapes.
  • Component 2: The utilization of wildfire simulators has enhanced our comprehension of forest fire dynamics, leading to the formulation of spatially explicit problems in forest management that consider the fire risk. Within these formulations, the likelihood of a forest stand catching fire is influenced by stand-level characteristics and biophysical factors. This probability also considers the relationships with neighboring stands and the potential for wildfire spread among adjacent sets. The design of resilient landscapes building from the knowledge of these parameters, requires the use of effective, but simple, problem formulations in which the practitioners can fully understand the driving mechanisms of the models and algorithms involved. The implementation of exact methods can be constrained by problem size in the context of modern forest planning, and heuristics approaches are often preferred in the optimization arena to solve complex combinatorial problems. However, the tuning process can be optimized and nearly optimal outcomes for very complex formulations can be achieved at acceptable computational cost. Based on a proposed methodological framework on a forest matrix scenario that reflects alternative spatial distributions of forest biophysical attributes, the specific objectives of this component of the IA are: i) to obtain an optimized spatial landscape configuration to increase resilience and address concerns with EWE ii) to minimize potential losses caused by EWE by the optimal allocation of management options over the landscape.

What value is proposed?

This IA will help forest management agencies to modernize their approach to prioritizing land management investments. The IA will build a system that can model spatially explicit management scenarios across scales ranging from projects, forests, regions and nationwide. The framework can be coupled with other models and management assessments and applied to a wide range of scenarios.

Who can use it?

Policymakers and Forest Managers 

What type of tool is it?

Model, Maps, Reports 

How does it look like?

Optimization tools, model-ready datasets, guidelines 

This tool is…

⊠  a new tool

⊠ an improved tool

What are the vision & mission statement?

Combining fire simulators and fuel management evaluation systems, this IA will test and optimize short term fuel management policies. It will use stochastic fire simulations to estimate landscape locations with expected community and other values-at-risk exposure and the Landscape Treatment Designer (LTD) / ForSys to quickly test different management policies. Afterwards, it will use new optimization methods to select the most efficient way to allocate fuel management to mitigate EWE impact.

When will it be complete?

Spring 2025



This IA is implemented in the Living Lab(s)…

Catalonia, Chile, Greece, Portugal, Nouvelle Aquitaine


Prof. Kostas Kalabokidis

Palaiologou Palaiologos

José Borges