Rocky Mountain Research (United States)

Right on time: evaluating accuracy and timing of burn-severity assessments to support post-fire management

Abstract Effective wildfire impact assessment and post-fire management rely on burn-severity maps that accurately characterize fire effects on vegetation and soils. These maps inform time-sensitive activities such as post-fire flood and debris-flow modeling, risk mitigation for h...

Automated workflow demonstrates mixed treatment impacts on burn severity across ecoregion scales

Background Intensifying wildfire activity in western North America has motivated focus on fuel treatments as a tool for moderating fire severity. Treatment effectiveness is influenced by complex interactions among biophysical settings, treatments and weather. Aims This study intr...

Adapting for solitude: Stress-coping, substitution behaviors, and intentions to return in wilderness recreation

Increasing visitation to Wilderness Areas is intensifying pressure on ecological conditions and visitor experience quality, highlighting gaps in current monitoring systems that rely heavily on biophysical indicators while overlooking visitor-based psychological and experiential d...

Biomass inventory optimization for willow-based pellet production integrating seasonal supply and stochastic demand

Biomass supply chains face both internal challenges (low energy density, bulkiness, seasonality) and external challenges (market uncertainty). We developed a two-stage stochastic mixed-integer linear programming (MILP) model that configures an (s, S) inventory control policy for...

Investigation of influence of fuel moisture content on surface fire transition to crown fire using physics-based modelling

Background The transition from surface fire to crown fire depends on various factors, including atmospheric conditions and forest characteristics. Aims In this study, we investigate the role of fuel moisture content in the transition from surface fire to crown fire in a pine fore...

An interpretable remote sensing–machine learning framework for wildfire soil burn severity prediction in the northwestern United States

Background Although soil burn severity (SBS) is traditionally mapped post-fire, pre-fire predictive approaches remain limited despite their potential to identify areas at risk of soil degradation, runoff and erosion. Aim To develop a large-scale model for predicting post-fire SBS...