中文摘要：

我国重要的北方针叶林地区大兴安岭是林火高发地区.受气候变暖影响,该地区林火发生频率将会发生显著变化.模拟人为火的发生分布与影响因素之间的关系、加强气候变化下人为火的发生分布预测,对于林火管理和减少森林碳损失具有重要作用.本文采用点格局分析方法,基于大兴安岭1967—2006年的火烧数据,建立人为火空间分布与影响因素之间的关系模型,该模型以林火发生次数为因变量,选取非生物因子（年均温和降水量、坡度、坡向和海拔）、生物因子（植被类型）和人为活动因子（距离道路距离、距离居民点距离、道路密度）共9个因子为自变量.并采用RCP 2.6和RCP 8.5气候情景数据代替当前气候情景预测2050年大兴安岭人为火的空间分布状况.结果表明：点格局模型能够较好地模拟人为火发生分布与空间变量的关系,可以预测未来气候下人为火的发生概率.其中,气候因子对人为火的发生具有明显的控制作用,植被类型、海拔和人为活动等因子对人为火的发生也具有重要影响.林火发生预测结果表明,未来气候变化下,南部地区的林火发生概率将进一步增加,北部和沿主要道路干线附近将成为新的人为火高发区.与当前相比,2050年大兴安岭人为火的发生概率将增加72.2%~166.7%.在未来气候情景下,人为火的发生更多受气候和人为活动因素的控制.

英文摘要：

The Great Xing＇ an Mountains are an important boreal forest region in China with high frequency of fire occurrences. With climate change, this region may have a substantial change in fire frequency. Building the relationship between spatial pattern of human-caused fire occurrence and its influencing factors, and predicting the spatial patterns of human-caused fires under climate change scenarios are important for fire management and carbon balance in boreal forests. We em- ployed a spatial point pattern model to explore the relationship between the spatial pattern of human- caused fire occurrence and its influencing factors based on a database of historical fire records （1967-2006） in the Great Xing＇ an Mountains. The fire occurrence time was used as dependent variable. Nine abiotic （annual temperature and precipitation, elevation, aspect, and slope）, biotic （vegetation type） , and human factors （distance to the nearest road, road density, and distance to the nearest settlement） were selected as explanatory variables. We substituted the climate scenario data （RCP 2.6 and RCP 8.5） for the current climate data to predict the future spatial patterns of human-caused fire occurrence in 2050. Our results showed that the point pattern progress （PPP） model was an effective tool to predict the future relationship between fire occurrence and its spatial covariates. The climatic variables might significantly affect human-caused fire occurrence, while vegetation type, elevation and human variables were important predictors of human-caused fire oc-currence. The human-caused fire occurrence probability was expected to increase in the south of the area, and the north and the area along the main roads would also become areas with high humancaused fire occurrence. The human-caused fire occurrence would increase by 72.2% under the RCP 2.6 scenario and by 166.7% under the RCP 8.5 scenario in 2050. Under climate change scenarios, the spatial patterns of human-caused fires were mainly influenced by the cli