DOI: 10.5061/dryad.qnk98sfnn
title: Geospatial data from: Identifying opportunity hot spots for reducing the risk of wildfire-caused carbon loss in western US conifer forests

description: 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.
description: 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 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
 &gt;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.
description: Data can be opened using R or ArcGIS Pro.