Department of Forestry and Wildland Resources, Humboldt State University, 1 Harpst Street, Arcata, California, 95521, USA.
Department of Forest Engineering, Resources, and Management, Oregon State University, 280 Peavy Hall, Corvallis, Oregon, 97331, USA.
Ecol Appl. 2018 Jun;28(4):1068-1080. doi: 10.1002/eap.1710. Epub 2018 Apr 26.
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 composed 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 [mean ± SE]) vs. BLM forests (398.87 ± 18.23) 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.
许多研究都探讨了燃料、地形、气候和火险天气如何影响火灾严重程度。尽管代价高昂且颇具争议的野火扑灭行动并未考虑到所有权边界，但对于不同森林管理实践如何影响多所有者景观中的火灾严重程度，人们了解得较少。2013 年，道格拉斯综合体（Douglas Complex）在美国俄勒冈州和加利福尼亚州南部的奥勒冈和加利福尼亚铁路（Oregon & California Railroad，O&C）烧毁了超过 19000 公顷的土地。O&C 土地由私人工业和联邦林地（土地管理局，BLM）组成的棋盘式格局组成，具有不同的管理目标，为了解不同管理实践如何影响野火严重程度提供了一个独特的实验景观。利用基于 Landsat 的火灾严重程度估算值（相对差分归一化燃烧比，RdNBR）和有关火灾蔓延、天气、地形、火灾前森林状况和土地所有权的地理空间数据，我们提出了以下两个问题：（1）不同变量对火灾严重程度的相对重要性是什么？（2）密集种植林业是否与更高的火灾严重程度相关？使用随机森林集成机器学习，我们发现每日火灾天气是火灾严重程度的最重要预测因子，其次是林分年龄和所有权，其次是地形特征。火灾前森林生物量的估计值不是火灾严重程度的重要预测因子。在包含空间自相关的广义最小二乘模型中，调整所有其他预测变量后，私人工业林的平均预测 RdNBR 较高（RdNBR 521.85±18.67[平均值±SE]），而 BLM 林的 RdNBR 较低（398.87±18.23），且前者的老龄林分比例更大。我们的研究结果表明，以年轻森林和空间均匀化燃料为特征的密集种植林业，而不是火灾前的生物量，是野火严重程度的重要驱动因素。这对野火风险的认知、共同的火灾管理责任以及在多所有者景观中实现多种目标的火灾恢复力产生了影响。
