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.

诸多研究已探讨可燃物、地形、气候与火灾天气如何影响火灾烈度。尽管针对不顾权属边界的野火开展的扑救工作代价高昂且颇具争议，但人们对不同森林经营措施如何影响多权属林地景观内的火灾烈度仍知之甚少。2013年，美国俄勒冈州西南部的道格拉斯复合火场（Douglas Complex）过火面积逾1.9万公顷，波及俄勒冈与加利福尼亚铁路（Oregon & California Railroad, O&C）所辖林地。O&C林地由棋盘格状分布的私有工业林地与联邦林地（土地管理局，Bureau of Land Management, BLM）构成，二者经营目标迥异，这为探究不同经营措施如何影响野火烈度提供了独一无二的实验性景观样本。本研究借助基于陆地卫星（Landsat）的火灾烈度估算数据——相对差值归一化燃烧比（Relative differenced Normalized Burn Ratio, RdNBR）——以及火灾蔓延、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相关？本研究采用随机森林（Random Forest）集成机器学习方法，结果显示：每日火灾天气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。在纳入空间自相关的广义最小二乘模型中，对所有其他预测变量进行校正后，私有工业林地的预测平均RdNBR值为521.85 ± 18.67 SE，显著高于BLM林地的398.87 ± 18.23 SE，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，而非火前森林生物量，是影响野火烈度的关键驱动因素。该研究结果对于野火风险认知、协同火灾管理责任制定，以及在多权属林地景观中构建多目标火灾韧性均具有参考意义。