Downstream protection value: Detecting critical zones for effective fuel-treatment under wildfire risk

摘要: Abstract The destructive potential of wildfires has been exacerbated by climate change, causing their frequencies and intensities to continuously increase globally. Generating fire-resilient landscapes via efficient calculated fuel-treatment plans is critical protecting native forests, agricultural resources, biodiversity, human communities. To tackle this challenge, we propose a framework that integrates fire spread, optimization, simulation models. We introduce the concept Downstream Protection Value (DPV), flexible metric assays ranks impact treating unit landscape, modeling forest as network propagation tree graph. Using our open-source decision support system, custom performance metrics can be optimized minimize wildfire losses, obtaining effective treatment plans. Experiments with real forests show model able consistently outperform alternative methods accurately detect high-risk ignition areas, focusing on most zones. Results indicate methodology decrease expected area burned rate more than half in comparison under weather uncertainty.