Forest harvest scheduling has been modeled using deterministic and stochastic programming models. Past models seldom address explicit spatial forest management concerns under the influence of natural disturbances. In this research study, we employ multistage full recourse stochastic programming models to explore the challenges and advantages of building spatial optimization models that account for the influences of random stand-replacing fires. Our exploratory test models simultaneously consider timber harvest and mature forest core area objectives. Each model run reports first-period harvesting decisions for each stand based on a sample set of random fire. We integrate multiple model runs to evaluate the persistence of period-one solutions under the influence of stochastic fires. Follow-up simulations were used to support multiple comparisons of different candidate forest management alternatives for the first time period. Test case results indicate that integrating the occurrence of stand-replacing fire into forest harvest scheduling models could improve the quality of long-term spatially explicit forest plans.