As 21st-century climate and fire activity depart from historical baselines, effects on
forests are uncertain. Forest managers need to predict and monitor forest recovery and fuel
accumulation to anticipate future fire behavior and plan appropriate management activities. We
explored how interactions between climate and fire affected post-fire recovery of subalpine
forests, which were historically resilient to infrequent (100-300 year) severe fire, in Greater
Yellowstone (Northern Rocky Mountains, United States). We sampled paired short- (< 30 year)
and long- (> 125 year) interval post-fire plots last burned between 1988 and 2018 to address two
questions: (1) How do short-interval fire, climate, and other factors (topography, distance to live
edge) interact to affect post-fire forest recovery? (2) How do forest biomass and fuels vary
following short- versus long-interval severe fires? Additionally, a low-cost unpersonned aerial
system (i.e., drone) was flown in a subset of post-fire plots to assess: (3) How do different
methods of drone data collection affect derived measurements of forest structure and detection of
standing dead snags? Mean post-fire stem density was an order of magnitude lower following
short- versus long-interval fires (3,240 versus 28,741 stems ha-1 , respectively). Differences
between paired plots increased with greater climate water deficit normal (𝜌 = 0.67) and were
amplified at longer distances to live forest edge. Unlike conifers, density of aspen (Populus
tremuloides), a deciduous resprouter, increased with short- versus long-interval fire (mean 384
versus 62 stems ha-1 , respectively). Live biomass and canopy fuels remained low nearly 30 years
after short-interval fire, in contrast to rapid recovery after long-interval fire, suggesting that
future burn severity may be reduced for several decades following reburns. Short-interval plots
also had half as much dead woody biomass compared to long-interval plots (60 versus 121 Mg
ha-1 ), primarily due to the absence of large snags. Measurements derived from drone imagery
underestimated tree density in young, dense, post-fire plots but characterized snag and tree
height well. The conventional built-in red, green, and blue light sensor outperformed a separate
sensor that detected near-infrared reflectance, and ground control points did not improve output
accuracy. Overall, our results suggest that a trifecta of short-interval fire, large patch size, and
arid post-fire climate could threaten subalpine forest resilience but also reduce future burn
severity. These findings could help forest managers prioritize opportunities for management
activities or identify areas where forest transitions may be most likely. Identifying reburn
locations may be valuable for planning fire suppression activities or identifying potential
firefighter access or escape routes. Low cost and standardized approaches make drones a
promising technology for collecting forest inventory data.