DOI: 10.5061/dryad.t4b8gtj62
title: Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk

description: Historically, fire has been essential in Southwestern US forests. However,
 a century of fire-exclusion and changing climate created forests which are
 more susceptible to uncharacteristically severe wildfires. Forest managers
 use a combination of thinning and prescribed burning to reduce forest
 density to help mitigate the risk of high-severity fires. These treatments
 are laborious and expensive, therefore optimizing their impact is crucial.
 Landscape simulation models can be useful in identifying high risk areas
 and assessing treatment effects, but uncertainties in these models can
 limit their utility in decision making. In this study we examined
 underlying uncertainties in the initial vegetation layer by leveraging a
 previous study from the Santa Fe fireshed and using new inventory plots
 from 111 stands to interpolate the initial forest conditions. We found
 that more inventory plots resulted in a different geographic distribution
 and wider range of the modelled biomass. This changed the location of
 areas with high probability of high-severity fires, shifting the optimal
 location for management. The increased range of biomass variability from
 using a larger number of plots to interpolate the initial vegetation layer
 also influenced ecosystem carbon dynamics, resulting in simulated forest
 conditions that had higher rates of carbon uptake. We conclude that the
 initial forest layer significantly affects fire and carbon dynamics and is
 dependent on both number of plots, and sufficient representation of the
 range of forest types and biomass density.
description: <strong>Initial Communities Data</strong>
 The initial communities layer is the base vegetation layer that
 sets the starting conditions for the exchange of carbon, water, energy,
 species interactions, disturbance effects, and other landscape processes.
 The initial treatment optimization study in this landscape (Krofcheck et
 al., 2019) used 68 Forest Inventory and Analysis (FIA) plots from within
 the Santa Fe National Forest that had been inventoried in 2010 or later
 and had not burned since 2005. Forest types represented by the FIA plots
 were piñon-juniper, ponderosa pine, Douglas-fir (<em>Pseudotsuga
 menziesii</em>), Engelman spruce (<em>Picea
 engelmannii</em>) and limber pine (<em>Pinus
 flexilis</em>). The latter three were grouped into a general
 mixed-conifer forest type. The authors then used elevation, transformed
 aspect using Topographic Radiation Aspect Index, TRASP (Roberts &
 Cooper, 1989), and a tasseled cap transformation of spectral data from
 Landsat 8 (available at https://www.usgs.gov/landsat-missions/landsat-8) as predictors for Random Forest models and used the rfUtilities library (Evans & Murphy, 2018) to select the most parsimonious model for each forest category separately. Existing vegetation classification from the Southwest Regional Gap Analysis (SWReGap, https://swregap.org/) using the ‘yaImpute’ library (Crookston & Finley, 2008) was used to stratify the measured plots for the imputation. We determined plot sampling intensity by calculating the relative area of land each plot represents within its forest type. To evaluate the influence of additional plot data on the initial communities layer and its effects on model behavior, we used data collected as part of the planning process by the US Forest Service. These data were located entirely within the study area and included 1072 plots from 111 stands inventoried in 2011, where each stand included between 3 and 31 plots. Plot data were collected using a common stand exam protocol using variable radius plots. The specific Basal Area Factor (BAF) prism chosen for each stand was a function of stand density and they ranged from 10 to 30 BAF. We had coordinates for the centroid of each stand, but not for each individual plot, which effects the imputation process. We used the tree data from each plot to determine a specific forest type and generalized category (e.g. piñon-juniper, ponderosa pine, mixed-conifer), corresponding to the FIA classification, and added an aspen forest type, resulting in a total of four generalized forest types. We also defined non-forested areas and included two generalized species parameterizations to represent shrubs that resprout and shrubs that do not resprout following fire. We used all FIA and common stand exam (CSE) plot data (n=1140) to generate a new initial communities layer following the same method as Krofcheck et al. (2019) in R v4.1.2 (R Core Team, 2021). <strong>Simulation Analysis</strong> To estimate the uncertainty in the initial communities layer that is due to not having coordinates for all CSE plots, we re-ran interpolations randomly selecting one plot for each set of stand coordinates. This led to 31 initial communities layers, which we used to initialize the model with the five climate projections, for a total of 155 simulations. We compared the aboveground carbon following model initialization of these initial communities layers with the initial communities layer that we created using all plot data and that we used for our management simulations. We calculated the difference in aboveground carbon between each layer and the one we used in our simulations to determine how much the initial communities layer is influenced by this source of uncertainty. To determine the influence of the number of plots used in the development of the initial communities layer, we produced five additional initial communities layers with differing numbers of underlying plot data. For four of the five layers, we halved the number of CSE plots used in the interpolation each time (e.g. 536, 268, 134, 67) and combined those with the FIA data. For the fifth initial communities layer, we only used the 68 FIA plots. For each of the layers, we randomly selected plots from each forest type proportional to the prevalence of each forest type on the landscape. We then initialized the model using each of these initial communities layers using the five climate projections and compared the aboveground carbon following model initialization to that of the initial communities layer that we used for our simulations. We quantified differences between our primary initial communities layer and that of Krofcheck et al., (2019) by comparing the difference in quantity and distribution of aboveground carbon at the beginning and end of the simulations. We ran an independent t-test to assess the difference in carbon between the two studies at each site every 10 years for each of the climate models, and computed the percent of area with a significant difference (p &lt; 0.01) in aboveground carbon. We compared treatment location as determined by the probability of high-severity fire between our initial communities layer and that of Krofcheck et al. (2019). We calculated Net Ecosystem Carbon Balance (NECB) by subtracting carbon lost from the system (treatment and wildfires) from carbon gained (photosynthesis) and then relativized the treatment scenario NECB values to the no-management scenario for both our simulations and those of Krofcheck et al. (2019). Data processing and analysis was conducted using R v4.1.2 (R Core Team, 2021). <strong>Treatment scenarios</strong> To develop the optimized treatment placement scenario, we first ran simulations that included no management to identify locations where landscape conditions were such that there was a high probability of high-severity wildfire. We ran the no-management simulations using the same five projected climate data sets and fire weather data described above. We ran 25 replicate simulations using each of five projected climate data sets, for a total of 6250 simulation years. We used fire severity raster data from these model outputs to quantify the probability of high-severity by dividing the number of years with high-severity fires by the total number of fire years per site. We then identified sites with a probability of high-severity fire greater than 0.3 and targeted those locations in the treatment scenario simulations, assigning treatment to those areas first.  To determine the type of treatment we used the probability of high-severity fire in combination with slope and forest type. We limited our management simulations to the ponderosa pine and dry mixed-conifer forest where the combined ponderosa pine and Douglas-fir aboveground carbon was at least 65% of the total. We used the same thinning and prescribed burning treatments as Krofcheck et al. (2019), which were designed to approximate common treatments for the region. Thinning treatments simulated thinning from below by removing approximately 30% of the biomass, preferentially removing the youngest cohorts (Hurteau et al., 2011, 2016) and was only applied to ponderosa pine forest and confined  to slopes &lt;30%, to account for a common limitation on mechanical thinning. We simulated prescribed burning based on historic mean fire return intervals, with all ponderosa pine burned using a 10-year return interval and forests co-dominated by ponderosa pine and Douglas-fir burned using a 15-year return interval. The forest type and probability of high-severity fire are highly dependent on the initial communities layer which defines the initial forest conditions. As a result, our treatment placement map differed substantially from the one in Krofcheck et al. (2019). To examine the effects of the treatment on the landscape we produced a new probability of high-severity fires raster and calculated the difference in aboveground carbon between the management and no management scenarios of this study at the end the simulations. We ran simulations over a 50-year period, using climate model projections for years 2000-2050. We ran 25 replicates for each of the five climate projections, totaling 125 simulations each for the no-management and management scenarios. Fire weather distributions tracked projected climate and were updated each decade to account for changes in temperature and precipitation.
description: R
description: These data are outputs of LADIS-II simulations using the photosynthesis
 and evapotranspiration (PnET) succession, Dynamic Fuels and Fire, and
 Biomass Harvest extensions to simulation forest growth and disturbance
 using a 100m resolution. We ran 25 simulations for each of 5 different
 climate models (CCSM, CNRM, FGOALS, GFDL and MIROC5) for a total of 125
 replicates,both for the management and no management scenarios. Each
 simulation lasted 50 years. The objective of the study was to examine the
 effects of additional plot data on the initial communites layer and the
 subsequent model outputs. We compared results from this study and
 Krofcheck et al., 2019 in the no management scenario, and results from
 this study between the management and no management scenarios. Files with
 the word 'new' refer to files from this study derived from an
 initial communities layer made using Forest Inventory and Analysis (FIA) +
 Common Stand Exam (CSE) plots. Files with the word 'old' refer
 to files from Krofcheck et al., 2019, derived from an initial communities
 layer made using FIA plots only. In all cases biomass is represented by
 aboveground carbon. ### The files included by directory are: 1\)
 BioDifMgmt Files regarding a comparisson between biomass at the end of the
 simulations for management and no magement scenarios in this study.
 Biomass maps the mean biomass of all 125 simulations for the respective
 situations. 'NewYear50MeanBiomassMgmt.tif' A raster of biomass
 at year 50 for the management scenario in this study (in Mg C per
 hectare). 'NewYear50MeanBiomassNoMan.tif' A raster of biomass at
 year 50 for the no managmenet scenario in this study (in Mg C per
 hectare). 2\) BioDifOldNew Files regarding a comparison between biomass
 derived from initial communities made using FIA plots versus FIS + CSE
 plots. Biomass maps the mean biomass of all 125 simulations for the
 respective situations. 'NewYear1MeanBiomassNoMan.tif' A raster
 of biomass at year 1 for the no management scenario in this study (in Mg C
 per hectare). 'NewYear50MeanBiomassNoMan.tif' A raster of
 biomass at year 50 for the no management scenario in this study (in Mg C
 per hectare). 'OldYear1MeanBiomassNoMan.tif' A raster of biomass
 at year 1 for the no management scenario from Krofcheck et al., 2019 (in
 Mg C per hectare). 'OldYear50MeanBiomassNoMan.tif' A raster of
 biomass at year 50 for the no managmenet scenario from Krofcheck et al.,
 2019 (in Mg C per hectare). 'allttestbyclimate.csv' A csv file
 of percent of sites with significanlty similar ("percent"
 comlun) and different ("different" column) biomass between this
 study and Krofcheck et al., 2019 by climate model. 3\) NECB Files
 regarding the Net Ecosystem Carbon Balance in this study.
 'NewMgmtNetPsn.csv' Net photosynthesis summed for the whole
 fireshed in the management scenario in megagrams C per hectare. This is
 pulled from a monthly raster output and stored in a csv file for all
 replicates. First column is time in months. Further columns are Net
 photosynthesis as described for each replicate where the first number in
 column title indicates the climate scenario (1-CCSM, 2-CNRM, 3-fgoals,
 4-GFDL, 5-MIROC5) and the next number is replicate name (ex. 401, 402)
 'NewMgmtTreatmentLoss.csv' Mg Biomass removed from the whole
 fireshed by treatment, both thinning and prescribed burns, in the
 management scenario. This is extracted from a csv summary log output,
 summed by year and stored in a csv file for all replicates. First column
 is time in year. Further columns are Mg Biomass removed as described for
 each replicate where the first part in column title includes the climate
 name and the next part is replicate name (ex. 401, 402)
 'NewMgmtWildfireEmissions.csv' Grams biomass lost due to
 wildfire in the management scenario. Calculated as the difference in
 biomass over burn scars between the year of the burn and the year prior to
 the burn, stores in a csv for all replicates. First column is time in
 year. Further columns are grams biomass as described for each replicate
 where the first part in column title includes the climate name and the
 next part is replicate name (ex. 401, 402)
 'NewNoManNetPsn.csv'Net photosynthesis summed for the whole
 fireshed in the no management scenario in megagrams C per hectare. This is
 pulled from a monthly raster output and stored in a csv file for all
 replicates. First column is time in months. Further columns are Net
 photosynthesis as described for each replicate where the first number in
 column title indicates the climate scenario (1-CCSM, 2-CNRM, 3-fgoals,
 4-GFDL, 5-MIROC5)and the next number is replicate name (ex. 1, 2)
 'NewNoManWildfireEmissions.csv' Biomass lost due to wildfire in
 grams in the no management scenario. Calculated as the difference in
 biomass over burn scars between the year of the burn and the year prior to
 the burn, stores in a csv for all replicates.First column is time in year.
 Further columns are grams biomass as described for each replicate where
 the first part in column title includes the climate name and the next part
 is replicate name (ex. 1, 2) 4\) pHS Files regarding probability of high
 severity fire analysis. 'NewfireTotalsMgmt.tif' A raster of
 total number of fires in the management scenario of this study summed
 across all 125 replicates and all simulation years.
 'NewfireTotalsNoMan.tif' A raster of total number of fires in
 the no management scenario of this study summed across all 125 replicates
 and all simulation years. 'NewHSTotalMgmt.tif'A raster of total
 number of high severity fires in the management scenario of this study
 summed across all 125 replicates and all simulation years.
 'NewHSTotalNoMan.tif' A raster of total number of high severity
 fires in the no management scenario of this study summed across all 125
 replicates and all simulation years. 'OldfireTotalsNoMan.tif' A
 raster of total number of fires in the no management scenario from
 Krofcheck et al., 2019,summed across all 125 replicates and all simulation
 years. 'OldHSTotalNoMan.tif' A raster of total number of high
 severity fires in the no management scenario from Krofcheck et al., 2019,
 summed across all 125 replicates and all simulation years. 5\) random plot
 selection Files regarding analysis of the effects of the lack of plot
 location of the CSE plots. 31 Initial communities layer were produced
 using data from randomly selected plots of the same stand. simulations
 were run using each of these layers and each of the 5 climate models for a
 total of 155 replicates. 'NewYear1MeanBiomassNoMan.tif' A raster
 of biomass at year 1 for the no managmenet scenario in this study (Mg C
 per hectare). 'randICbioMean.tif' A raster of mean biomass of
 year 1 for all 155 replicates (Mg C per hectare).
 'randomPlotSelectionBiomass.csv' Total aboveground biomass
 summed across the whole fireshed for each of the replicates. column names:
 climate- climate name; rep- replicate name; totBiomass_MgC- total biomass
 in Mg C; MgC_ha- biomass in Mg C per hectare; TgC- biomass in teragrams C.
 6\) sampling intensity 'PlotSampleIntensity' A csv of sampling
 intensity for each plot. This is how much of the landscape per forest type
 is represented by each plot. columns are: PLOT_ID; Area - area represented
 by this plot in hectares; plot_num - number of CSE plots used to create
 the initial communities layer in addition to all FIA plots; sf_fortyp -
 forest type code corresponds to fortyp column; totForType - total area for
 thisforest type in hectares; sampleIntensity- Area divided by totForType
 (unitless ratio); fortype- forest type. 7\) treatment Files regarding
 treatment maps. 'NewMgmtMap.tif' A raster of the treatment map
 in this study. 'NewTretmentReclass.csv' A file for converting
 treatment codes in this study to a simplified version. Unitless
 'OldMgmtMap.tif' A raster of the treatment map from Krofcheck et
 al., 2019. 'OldTretmentReclass.csv' A file for converting
 treatment codes from Krofcheck et al., 2019 to a simplified version.NAs in
 this file correspond to sites outside the study area. Unitless
 'TreatmentDifReclass.csv' A file for reclassifying the
 difference between treatments to a simplified version. Unitless 8\)
 varying number of plots Files regarding analysis of the effect of
 additional plot data. We created new initial communities layers using a
 varying number of plots available for the interpolation. We then ran one
 simulation for each climate model for a total of 5 replicates for each new
 initial communities layer. The biomass presented is a mean of all 5
 replicates. All initial communities layers were produced using all FIA
 plots and the number in the file name represents the number of added CSE
 plots. For example: 'biomassMean135.tif' A raster of biomass at
 year 1 derived from an initial communities layer produced using all FIA
 plots with an additional 135 available CSE plots.
 'biomassPerCellPerNumPlots.csv' A csv file sumarizing data from
 all these rasters. column names: x y- coordinates of site (pixel); layer-
 biomass in Mg C per hectare; plot_num: number of CSE plots used to produce
 the initial comuunities later in addition to FIA plots;fortyp- forest
 type.