Geospatial data from: Identifying opportunity hot spots for reducing the risk of wildfire-caused carbon loss in western US conifer forests

The geospatial dataset includes raster and vector data for visualizing the spatial distribution of risk of wildfire-caused carbon loss in Peeler et al. 2023. Raster data evaluate carbon exposure, sensitivity, and vulnerability at the pixel-level across western US carbon forests. Vector data aggregate pixel-level findings into project area and fireshed spatial units to identify target geographies (or “opportunity hot spots”) where proactive forest management could reduce the greatest risk from wildfire to carbon. Vector data also identifies firesheds in which proactive forest management could simultaneously reduce the risk from wildfire to carbon and human communities., To form a composite indicator for exposure at the full extent of western US conifer forests, we aggregated individual indicators for annual burn probability (30 m resolution) and total carbon (tons/acre, 30 m resolution). Annual burn probability was extracted from a gridded dataset on wildfire hazard. Total carbon was estimated by matching plot IDs in gridded tree and fuel lists to corresponding plots in the US Forest Inventory and Analysis (FIA) program. Living and dead biomass in the corresponding FIA plot were converted to units of carbon using a conversion factor of 0.5, while litter and duff used a conversion factor of 0.37. All carbon stocks were summated to quantify total carbon. We used min-max normalization to scale minimum and maximum values of annual burn probability and total carbon to 0 and 1. Afterward, we weighted the normalized individual indicators equally and added them together to create a gridded dataset for exposure that varied from 0 to 1. We interpreted carbon in ..., Data can be opened using R or ArcGIS Pro.

本地理空间数据集包含栅格与矢量数据，用于可视化Peeler等人2023年研究中野火引发的碳损失风险空间分布。栅格数据以像素级为单位，评估美国西部碳汇森林的碳暴露度、敏感性与脆弱性。矢量数据将像素级分析结果聚合至项目区域与火灾流域空间单元，以识别可通过主动森林管理最大程度降低野火对碳库风险的目标区域（或称"机遇热点"）；同时，矢量数据还可识别出主动森林管理能够同时降低野火对碳库及人类社区风险的火灾流域。
为构建覆盖美国西部针叶林全域的暴露度复合指标，我们将年度燃烧概率（分辨率30米）与总碳储量（单位：吨/英亩，分辨率30米）这两项独立指标进行聚合。年度燃烧概率取自野火风险网格化数据集。总碳储量则通过将网格化树木与燃料清单中的样地ID，与美国森林清查与分析（Forest Inventory and Analysis, FIA）计划的对应样地进行匹配来估算。利用0.5的转换系数将对应FIA样地中的活生物量与死生物量转换为碳单位，而凋落物与腐殖质则采用0.37的转换系数。将所有碳储量求和以得到总碳储量。我们采用最小-最大归一化方法，将年度燃烧概率与总碳储量的最小值和最大值分别缩放至0与1。随后，我们对归一化后的两项独立指标赋予相等权重并求和，从而生成取值范围为0至1的暴露度网格化数据集。我们对碳相关结果进行解读……
本数据集可通过R语言或ArcGIS Pro软件打开。