Abstract Background Weather and fuels are among the critical, interacting factors that drive wildland fire behavior, and thus are primary factors in fire operations planning and decision support tools. In mesic forests, variation in stand structure may lead to heterogeneous microclimate and fuel moisture conditions in the understory where fires often ignite and spread. However, such variation in fuel availability is often overlooked by fire behavior models that assume spatially uniform weather and fuel conditions. In this study, we analyze a combination of field-based understory meteorology and fuel moisture data with terrestrial laser scanning (TLS) and traditional forest inventory data to develop new knowledge about relationships between forest structure, microclimate, and dead fuel moisture in USA northern conifer forests that can inform fire operations planning and decision support. Results We found that open canopy plots were significantly warmer (+ 7.82 °C average daily maximum air temperature) and drier (-24.1% average daily relative humidity) and with drier fuels (+ 8.03% fuel moisture) at midday in summer compared to closed canopy plots. Directly using microclimate variables (i.e., air temperature and relative humidity) resulted in better predictions of dead fuel moisture content (mean R 2 = 0.88) than using forest structure variables such as canopy openness (R 2 = 0.60). Furthermore, forest structure variables derived from TLS were better predictors of dead fuel moisture content (R 2 = 0.74) than traditional forest inventory metrics. Conclusions Our study used multi-modal measurements to demonstrate that dense forest cover linearly reduces fuel availability by buffering microclimate and maintaining fuel moisture. This research can be used to develop thinning prescriptions to achieve certain thresholds of understory temperature, relative humidity, and dead fuel moisture. Moreover, our results highlight the microclimate buffering effect of shaded fuel breaks used in fire suppression and containment tactics. Finally, our work suggests that tools like TLS can be used to fine tune fuel-weather relationships in fire behavior models that use spatially explicit fuels data to inform planning and predict fuels treatment effectiveness. This research enhances fire managers’ ability to plan and implement fuel treatments by highlighting how changes in forest stand structure drive fine scale heterogeneity in fuel availability.
Breigenzer et al. (Fri,) studied this question.