Forest wildfires consume and redistribute carbon within forest carbon pools. Because the incidence of wildfires is unpredictable, quantifying wildfire effects is challenging due to the lack of prefire data or controls from experiments over a large landscape. We explored a quasi‐experimental method, propensity score matching, to estimate wildfire effects on aboveground forest woody carbon mass in Washington and Oregon, United States. Observational data, including national forest inventory plot measurements and satellite imagery metrics, were utilized to obtain a control set of unburned plots that are comparable to burned plots in terms of environmental conditions as well as spatial locations. Three matching methods were implemented: propensity score matching (PSM), spatial matching (SM), and distance‐adjusted propensity score matching (DAPSM). We investigated if propensity score matching with and without spatial adjustment led to different outcomes in terms of (1) balance in covariate distributions between burned and control plots, (2) mean carbon mass obtained from the selected control plots compared to burned and all unburned plots, and (3) estimates of wildfire effects by burn severity. We found that PSM and SM, which use only the environmental covariate set or the spatial distance for estimating propensity scores, respectively, did not appear to produce a comparable set of control plots in terms of the estimated propensity scores and the outcomes of mean carbon mass. DAPSM was the preferred method both in balancing the observed covariates and in dealing with unobservable confounding variables through spatial adjustment. The average wildfire effects estimated by DAPSM showed clear evidence of redistribution of carbon among aboveground woody pools, from live to dead trees, but the consumption of total woody carbon by wildfire was not substantial. Only moderate burn severity led to significant reduction of total woody carbon mass across Washington and Oregon forests (64% of control plots remained on average). This study provides an applied example of a quasi‐experimental approach to quantify the effects of a natural disturbance for which experimental settings are unavailable. The study results suggest that incorporating spatial information in addition to environmental covariates would yield a comparable set of control plots for wildfire effects quantification.

中文翻译：

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匹配方法以量化野火对美国西北太平洋地区森林碳质量的影响

森林野火消耗并重新分配了森林碳库中的碳。由于野火的发生是不可预测的，因此由于缺乏预火数据或大景观实验的控制，难以量化野火影响。我们探索了一种准实验方法，即倾向得分匹配，以评估野火对美国华盛顿和俄勒冈州地上森林木本碳素质量的影响。利用观测数据，包括国家森林清单样地测量和卫星图像度量，获得了一组未燃烧样地的对照，这些样地在环境条件和空间位置方面都可与燃烧样地相媲美。实施了三种匹配方法：倾向得分匹配（PSM），空间匹配（SM）和距离调整的倾向得分匹配（DAPSM）。我们调查了在进行和不进行空间调整的情况下倾向得分匹配是否导致以下不同结果：（1）燃烧图和控制图之间协变量分布的平衡；（2）与燃烧图和所有未燃烧图相比，从选定控制图获得的平均碳质量，以及（3）根据烧伤严重程度估算野火影响。我们发现，分别使用环境协变量集或空间距离来估计倾向得分的PSM和SM似乎没有根据估计的倾向得分和平均碳质量的结果生成一组可比较的控制图。 。在平衡观察到的协变量和通过空间调整处理不可观察的混杂变量方面，DAPSM是首选方法。DAPSM估计的平均野火影响显示了从活树到枯死树木的地上木本池中碳重新分布的明显证据，但是野火对木本碳的总消耗量并不大。只有中等程度的烧伤严重程度才能导致华盛顿和俄勒冈州森林的总木碳量显着减少（平均仍留有64％的对照地块）。这项研究提供了一个准实验方法的应用实例，该方法可以量化没有实验设置的自然干扰的影响。研究结果表明，除环境协变量外，还纳入空间信息将产生一组可比较的对照图，用于量化野火效果。但是野火消耗的木质碳总量并不多。只有中等程度的烧伤严重程度才能导致华盛顿和俄勒冈州森林的总木碳量显着减少（平均仍留有64％的对照地块）。这项研究提供了一个准实验方法的应用实例，该方法可以量化没有实验设置的自然干扰的影响。研究结果表明，除环境协变量外，还纳入空间信息将产生一组可比较的对照图，用于量化野火效果。但是野火消耗的木质碳总量并不多。只有中等程度的烧伤严重程度才能导致华盛顿和俄勒冈州森林的总木碳量显着减少（平均仍留有64％的对照地块）。这项研究提供了一个准实验方法的应用实例，该方法可以量化没有实验设置的自然干扰的影响。研究结果表明，除环境协变量外，还纳入空间信息将产生一组可比较的对照图，用于量化野火效果。这项研究提供了一个准实验方法的应用示例，该方法可以量化没有实验设置的自然干扰的影响。研究结果表明，除环境协变量外，还纳入空间信息将产生一组可比较的对照图，用于量化野火效果。这项研究提供了一个准实验方法的应用实例，该方法可以量化没有实验设置的自然干扰的影响。研究结果表明，除环境协变量外，还纳入空间信息将产生一组可比较的对照图，用于量化野火效果。