Wildfire-generated snags provide key habitat for wildlife associated with recently disturbed forests, offering nesting and foraging resources for several woodpecker species. Snag harvest through post-fire salvage logging provides economic value but reduces habitat in recently burned forests. Managers of recently burned forests often must identify suitable woodpecker habitat within timeframes precluding field surveys. We developed nesting habitat suitability models for the Lewis's woodpecker (Melanerpes lewis)—a species of conservation concern potentially impacted by salvage logging—to inform post-fire management planning that includes habitat conservation objectives. Using weighted logistic regression, we selected models that maximized predictive performance measured by spatial cross-validation to verify model applicability to novel wildfire locations. From 1994 to 2018, we monitored 348 nest sites, ≤5 years post-fire, at four wildfire locations across the Inland Pacific Northwest, USA. We used both exclusively remotely sensed covariates (to support habitat mapping) and a combination of remotely sensed and field collected covariates (to inform logging prescriptions). Top models proved predictive of nest distribution across novel wildfire locations, describing positive relationships with local and landscape-scale burn severity, landscape-scale open (0–10%) and low (10–40%) canopy cover, large nest-tree diameters, locally high snag densities, and southeast-facing slopes. The top combination model was more discriminating of nest from non-nest sites compared to the top remotely sensed model (RPI = 0.976 vs. 0.733, AUC= 0.794 vs. 0.716), indicating the value of vegetation field measurements for quantifying habitat. Herein is exemplified an effective process for developing and evaluating predictive habitat models, broadly applicable, and useful for prioritization of post-fire forest management objectives.