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.Data can be opened using R or ArcGIS Pro.Funding provided by: The Nature ConservancyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100014596Award Number: Funding provided by: U.S. Geological SurveyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000203Award Number: G18AC00325Funding provided by: U.S. Geological SurveyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000203Award Number: G18AC00325To 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 pixels with values near 1 as most exposed to wildfire, as these locations contained the most total carbon and experienced the highest annual burn probability. We developed a composite indicator for sensitivity using indicators on potential carbon loss and carbon recovery following wildfire. To quantify carbon loss, we combined the gridded tree and fuel lists and pixel-specific flame length probabilities with the Fire and Fuels Extension Forest Vegetation Simulator (FFE-FVS) to estimate how much carbon would be emitted directly to the atmosphere during a wildfire event (tons/acre, 30 m resolution). Additionally, we applied a 50-year half-life over 30 years to fire-killed biomass to estimate how much carbon would be released indirectly through decomposition over time (tons/acre, 30 m resolution). For carbon recovery, we extracted individual indicators from gridded datasets on site productivity (30 m resolution, site index extracted from tree list and used as proxy) and post-wildfire tree regeneration probability (480 m resolution). Given that site productivity and post-wildfire tree regeneration influence forest recovery and growth after wildfire, we assumed both to be good proxies for likelihood of associated carbon recovery. To ensure individual indicators contributed equally to the composite indicator, we log-transformed total carbon loss and site productivity because both were right-skewed. Afterward, all indicators were min-max normalized, weighted equally, and added together to create a gridded dataset for sensitivity that varied from 0 to 1 (30 m resolution). We interpreted carbon in pixels with values near 1 as most sensitive to wildfire, as these locations would lose the most carbon due to wildfire and had the lowest likelihood of carbon recovery. To aggregate pixel-level findings to broader spatial extents, we estimated whether need and feasibility for proactive forest management could reduce wildfire hazard at project area-levels if communities and agencies leveraged their social adaptive capacity. To estimate need, we excluded infrequent-fire forests because forest thinning in these historically dense forests is not justified based on historical ecological conditions. For remaining frequent-fire forests, we matched plot IDs in the gridded tree and fuels list to corresponding FIA plots to calculate stand density index (SDI) for each pixel. We then divided a pixel's SDI by the maximum SDI corresponding to its forest type and geography – thereby calculating a relative SDI. We assumed that a relative SDI ≤25% indicated a forest structure that was generally climate- and fire-resilient and could be burned safely under moderate weather conditions, whereas a relative SDI >25% suggested high fuel loads that could justify forest thinning. Finally, we applied ecological (e.g. fire behavior fuel models, previous wildfire severity), legal (e.g. land ownership, wilderness areas, distance to perennial stream or wetland), and operational (e.g. distance to road) constraints to identify locations where treatments were not feasible and excluded them from our estimate of social adaptive capacity