Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape

Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are comprised of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked 1) what is the relative importance of different variables driving fire severity, and 2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 SE) versus BLM forests (398.87 ± 18.23 SE) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

诸多研究已围绕可燃物、地形、气候及火灾气象条件对火灾烈度的影响展开了深入探讨。然而，尽管针对无视权属边界的野火扑救行动耗资巨大且颇具争议，但学界对多权属景观中不同森林经营措施如何影响火灾烈度的认知仍较为匮乏。2013年，道格拉斯复合火灾（Douglas Complex）在美国俄勒冈州西南部焚毁了超过1.9万公顷的俄勒冈加利福尼亚铁路（Oregon & California Railroad, O&C）属地土地。O&C属地由经营目标迥异的私营工业林地与联邦林地（土地管理局，Bureau of Land Management, BLM）以棋盘状交错分布的格局组成，为探究不同经营措施对野火烈度的影响提供了极具独特性的实验景观。本研究依托陆地卫星（Landsat）获取的火灾烈度估算数据（归一化燃烧比差值，Relative differenced Normalized Burn Ratio, RdNBR），以及野火蔓延、天气、地形、火前森林状况与土地权属等地理空间数据，提出了两个研究问题：其一，驱动火灾烈度的各类变量的相对重要性如何？其二，集约化人工林经营是否与更高的火灾烈度存在关联？本研究采用随机森林（Random Forest）集成机器学习方法开展分析，结果显示，逐日火灾气象条件是火灾烈度最重要的预测因子，其次为林分年龄与土地权属，再次为地形特征。火前森林生物量的估算值并非火灾烈度的重要预测因子。在纳入空间自相关效应的广义最小二乘模型中，对所有其他预测变量进行控制后，私营工业林地的平均预测RdNBR值为521.85±18.67标准误，显著高于土地管理局林地的398.87±18.23标准误，而后者的成熟林占比更高。本研究结果表明，以幼龄林与空间均质化可燃物为特征的集约化人工林经营，而非火前生物量，是野火烈度的重要驱动因素。该研究结果对野火风险认知、协同灭火责任的落实，以及在多权属景观中为多重目标构建火灾韧性的实践均具有借鉴意义。