Abstract Introduction Altered wildfire regimes, exacerbated by unsustainable management, threaten natural ecosystem recovery post‐fire. Effective restoration requires timely fire impact assessments and tailored, evidence‐based management. While fire databases and Environmental Impact Assessment (EIA) frameworks partially support decision‐making, a holistic platform linking assessment, planning, and operational actions is still lacking. Objectives Our goal was to develop and test a web‐based Post‐Fire Spatial Decision Support System (PF‐SDSS) that facilitates decision‐making across three post‐fire management levels: problem definition, strategic planning, and operational management. Methods PF‐SDSS integrates satellite imagery with high‐resolution cartography in a participatory multi‐criteria analysis (MCA), using server‐ and cloud‐based computing for real‐time analyses. The generated soil erosion risk (SER) and vegetation recovery potential (VRP) maps underpin rule‐based restoration prioritization and recommendations that provide site‐specific practices derived from a comprehensive literature review. Field validation (Spearman's correlation), sensitivity analysis (MCA weight variations), and usability evaluation (System Usability Scale SUS method) assessed the system's performance. Results PF‐SDSS is freely available online, with a demonstration for Ávila Province, Spain. Validation showed significant correlations for SER ( ρ = 0.56) and VRP ( ρ = 0.42). Sensitivity analysis confirmed MCA robustness under 20% weight variations, and the 75% SUS score indicated satisfactory usability and acceptance among end‐users. Conclusions This study automated the post‐wildfire management planning cycle within a modular framework. The EIA module supports problem definition by mapping fire impacts. The strategic planning module identifies priority areas and sets site‐specific management objectives. The operational planning module offers spatially oriented, evidence‐based management alternatives.
Cristal et al. (Thu,) studied this question.