Next Generation Fire Severity Mapping (Image Service)

The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. The ecological effects of wildland fire – also termed the fire severity – are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. In turn, these statistical models were used to generate "wall-to-wall" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.本数据集所描述并发布的地理空间产品，展示了美国本土西部多个生态区发生火灾时的高强度火灾发生概率。林地火灾的生态效应——也称为火灾烈度——在时空维度上通常具有高度异质性。这种异质性源于燃料、地形、气候（如年平均气温）等因素的空间变异。此外，每日天气、气候极端事件（如异常暖年）等随时间变化的因子也可能发挥关键作用。 美国林务局落基山研究站（US Forest Service Rocky Mountain Research Station）与蒙大拿大学的科研人员开展了一项研究，利用观测数据构建了统计模型，以描述高强度火灾发生概率作为燃料、地形、气候与火灾天气的函数关系。研究使用了2002至2015年间超过2000场火灾的观测数据，为美国本土19个生态区分别构建了独立模型（详见Parks等人2018年的图1）。 高强度火灾采用相对燃烧比（relativized burn ratio）这一火灾烈度指标进行量化，该指标通过火灾前后的陆地卫星（Landsat）影像来衡量火灾引发的生态变化。燃料数据包含活燃料量的火前度量指标，如归一化植被指数（NDVI）。地形因子涵盖坡度、潜在太阳辐射等。气候数据汇总了研究区内空间变异的因子（如夏季平均气温）的30年平均值。火灾天气则纳入了每日气温、年度气温等随时间变化的因子。 基于上述统计模型，研究人员为19个生态区中的13个生成了「全覆盖」（wall-to-wall）地图，展示发生火灾时的高强度火灾概率。对于模型质量不达标的生态区，则未制作相关地图。所有地图均采用2016年的燃料数据，因此能够较为及时地评估高强度火灾发生潜力。 在火灾天气相对影响较强的6个生态区中，研究人员额外制作了两张地图：一张展示中等天气条件下的高强度火灾发生概率，另一张则展示极端天气条件下的该概率。需要特别说明的是，模型仅以被归类为森林的像元构建，因此生成的地图不包含非森林像元。