DOI: 10.5061/dryad.mcvdnck6c
title: Data from: Learning from wildfires: a scalable framework to evaluate treatment effects on burn severity

description: Interruption of frequent burning in dry forests across western North
 America and the continued impacts of anthropogenic climate change have
 resulted in increases in fire size and severity compared to historical
 fire regimes. Recent legislation, funding, and planning have emphasized
 increased implementation of mechanical thinning and prescribed burning
 treatments to decrease the risk of undesirable ecological and social
 outcomes due to fire. As wildfires and treatments continue to interact,
 managers require consistent approaches to evaluate treatment effectiveness
 at moderating burn severity. In this study, we present a repeatable,
 remote sensing-based, analytical framework for conducting fire-scale
 assessments of treatment effectiveness that informs local management while
 also supporting cross-fire comparisons. We demonstrate this framework on
 the 2021 Bootleg Fire in Oregon and Schneider Springs Fire in Washington.
 Our framework used 1) machine learning to identify key bioclimatic,
 topographic, and fire weather drivers of burn severity in each fire, 2)
 standardized workflows to statistically sample untreated control units,
 and 3) spatial regression modeling to evaluate the effects of treatment
 type and age on burn severity. Application of our framework showed that,
 in both fires, sites recently treated with prescribed burning were the
 most effective at reducing burn severity relative to untreated controls.
 Thinning-only treatments, even when followed by piling and burning of
 surface fuels, only produced low/moderate-severity effects under the more
 moderate fire weather conditions in the Schneider Springs Fire. Our
 framework offers a robust approach for evaluating treatment effects on
 burn severity at the scale of individual fires, which can be scaled up to
 assess treatment effectiveness across multiple fires. As climate change
 brings increased uncertainty to dry forest ecosystems of western North
 America, our framework can support more strategic management actions to
 reduce wildfire risk and foster resilience.
description: # Data and code in support of: Learning from wildfires: a scalable
 framework to evaluate treatment effects on burn severity
 [https://doi.org/10.5061/dryad.mcvdnck6c](https://doi.org/10.5061/dryad.mcvdnck6c) Author: Caden P. Chamberlain Contact: [cc274@uw.edu](mailto:cc274@uw.edu) Creation Date: October 21, 2024 This dataset supports the analyses conducted in "Learning from wildfires: a scalable framework to evaluate treatment effects on burn severity." Please contact the primary author at [cc274@uw.edu](mailto:cc274@uw.edu) if you plan to run these analyses on another fire; we are constantly evolving our code and can provide the most up-to-date and streamlined version. ## Description of the data and file structure The parent directory contains a folder for each of the two fires analyzed in the study - the 2021 Oregon Bootleg Fire ('bootleg') and the 2021 Washington Schneider Springs Fire ('schneider*springs'). Each folder contains five subfolders - 'scripts' which contains the script to run analyses for each fire; 'datasets' which contains the raw data used to run the analyses; 'csvs' which contains csv files used for file naming and figure production; 'figures' which contains the output figures from the fully run analyses; and 'controls' which contains the matched untreated control units produced from the analyses. Below we describe the contents of the 'bootleg' and 'schneider*_springs' folders. We refer the reader to the main manuscript and supplemental material for additional details. All original data sources are cited in-text below and a full citation is provided at the end of the document in the References Cited section.  ### Bootleg Fire files: #### bootleg\scripts\\ 'bootleg_full_analyses.Rmd': R script to run the full analyses and produce all figures for the Bootleg Fire. The number of cores can be set on line 51 depending on user's computer specifications. On line 54, the user will need to reset the "pdir" file directory to the location where the "bootleg" folder is located on the user's computer. Identifying appropriate lag distances for the SAR modeling is one step that takes particularly long. If users wish to speed up processing time, this step can be skipped by 1) commenting out lines 733-749 and 2) setting "best_d" on line 752 to '35' to match results from our analyses.  #### bootleg\datasets\predictors\climate\\ 'annual_aet_1981_2010.tif': Mean actual evapotranspiration across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'annual_deficit_1981_2010.tif': Mean climatic water deficit across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'annual_pet_1981_2010.tif': Mean potential evapotranspiration across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'annual_ppt_anl_total_1981_2010.tif': Mean annual total precipitation across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'annual_tmmean_anl_mean_1981_2010.tif': Mean average annual temperature across years 1981-2010 (degrees C) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'annual_tmmin_anl_mean_1981_2010.tif': Mean annual minimum annual temperature across years 1981-2010 (degrees C) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) #### bootleg\datasets\predictors\distto\\ 'distance_to_roads.img': distance (m) to department of natural resources (DNR) and Forest Service (FS) roads at 10-m spatial resolution (USDA FS Roads 2022; USCB Roads 2022) 'distance_to_streams_wetlands.img': distance (m) to stream and wetlands layers at 10-m spatial resolution (OR DFW 2022; OR DSL 2022; US FWS 2022) 'distance_to_trt_edge.img': distance (m) to treatment edge (measured outward from treatment boundary) at 10-m spatial resolution (treatment data provided by The Nature Conservancy) #### bootleg\datasets\predictors\frs\_stevens\\ 'frs.tif': Community Fire Resistance Score at 250-m spatial resolution (Stevens et al. 2020) #### bootleg\datasets\predictors\gedi\ 'gedi_rh100_mean.tif': Global Ecosystems Dynamics Investigation (GEDI) mean 100th percentile relative height (m) at 1000-m spatial resolution (Dubayah et al. 2021) 'gedi_rh100_sd.tif': Global Ecosystems Dynamics Investigation (GEDI) standard deviation relative height (m) at 1000-m spatial resolution (Dubayah et al. 2021) #### bootleg\datasets\predictors\gridmet\_byprog\\ 'erc.tif': GRIDMET daily energy release Component extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'fm100.tif': GRIDMET daily 100-hour fuel moisture (%) extracted to NIROPS-derived burn progression maps maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'fm1000.tif': GRIDMET daily 1000-hour fuel moisture (%) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'minrh.tif': GRIDMET daily relative humidity (%) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'tmmx.tif': GRIDMET daily maximum temperature (degrees C) extracted to NIROPS-derived burn progression map at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'vpd.tif': GRIDMET daily mean vapor pressure deficit extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) #### bootleg\datasets\predictors\landfire\_fuels\\ 'LF2020_CBD.tif': pre-fire LANDFIRE canopy bulk density (kg per meters cubed) at 30-m spatial resolution (LANDFIRE 2016) 'LF2020_CC.tif': pre-fire LANDFIRE canopy cover (%) at 30-m spatial resolution (LANDFIRE 2016) 'LF2020_CH.tif': pre-fire LANDFIRE canopy height (m) at 30-m spatial resolution (LANDFIRE 2016) #### bootleg\datasets\predictors\masks\ 'forest_mask.tif': binary layer indicating forest vs. non-forest based on Region 6 Potential Vegetation Type layer (Region 6 PVT 2023); 1 = non-private, 0 = private 'ownership_mask.tif': binary layer indicating private vs. non-private ownership (ownership layer provided by The Nature Conservancy Oregon); 1 = forest, 0 = non-forest #### bootleg\datasets\predictors\snow\_cloud\_metrics\\ 'scf.tif': snow cover frequency measured as percent of days with snow in a year, measured at 2000-m spatial resolution (Crumley et al. 2020) 'sdd.tif': snow disappearance date (Julian date) measured at 2000-m spatial resolution (Crumley et al. 2020) #### bootleg\datasets\predictors\usgs_topo\ 'elevation_10res.tif': elevation above sea level (m) at 10-m spatial resolution (USGS 2022) 'hli_10res.tif': heat load index derived from USGS 10-m digital elevation model (USGS 2022; Evans and Murphy 2021) 'slope_10res.tif': slope derived from USGS 10-m digital elevation model (USGS 2022; Hijmans et al. 2023) 'sri_10res.tif': solar radiation index derived from USGS 10-m digital elevation model (USGS 2022; Evans and Murphy 2021) 'tpi_10res_410win.tif': topographic position index derived from USGS 10-m digital elevation model using a 410-m window (USGS 2022; Evans and Murphy 2021) 'tpi_10res_2010win.tif': topographic position index derived from USGS 10-m digital elevation model using a 2010-m window (USGS 2022; Evans and Murphy 2021) 'tpi_10res_8010win.tif': topographic position index derived from USGS 10-m digital elevation model using a 8010-m window (USGS 2022; Evans and Murphy 2021) 'tri_10res_410win.tif': topographic ruggedness index derived from USGS 10-m digital elevation model using a 410-m window (USGS 2022; Evans and Murphy 2021) #### bootleg\datasets\predictors\windninja\_byprog\\ 'mx_speed_20230501.tif': maximum daily wind speed (mph) derived from WindNinja and extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Wagenbrenner et al. 2016; NIROPS 2022) 'eastwestness_mx_speed_direction_20230501.tif': sine of WindNinja maximum wind direction (sine transformed radians) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Wagenbrenner et al. 2016; NIROPS 2022) 'northsouthness_mx_speed_direction_20230501.tif': cosine of WindNinja maximum wind direction (cosine transformed radians) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Wagenbrenner et al. 2016; NIROPS 2022) #### bootleg\datasets\severity\ '2021_Bootleg_rdnbr_w_offset_DATESADJUSTED.tif': relativized differenced normalized burn ratio at 30-m spatial resolution (Parks et al. 2018; Parks et al. 2021) #### bootleg\datasets\study\_area\\ '2021_Bootleg_for_analysis.shp': shapefile of Bootleg Fire perimeter from Monitoring Trends in Burn Severity datasets (MTBS 2022) #### bootleg\datasets\treatments\ 'manuscript_treatments_20240222.shp': shapefile of Bootleg Fire treatments; only treatments analyzed in final analyses after treatments were excluded that did not meet certain criteria (see details in manuscript) (treatment data provided by The Nature Conservancy) 'treatments_combined_w_wx_20240321.shp': shapefile of all Bootleg Fire treatments (treatment data provided by The Nature Conservancy) #### bootleg\csvs\ 'predictor_variables': table used to link variable names to figure names when creating figures; Data_Category = data category, File_Name = raw file name, Figure_Name = figure name; name_w_units = figure name with metric units 'treatment_names': table used to link treatment codes to figure names when creating figures; trt_comb = character treatment code; trt_code = numeric treatmet code; color = color for figure; new_code = new numeric code after consolidating treatments; new_nm = figure name #### bootleg\figures\\ this folder contains all primary figures produced from the full analyses #### bootleg\controls\\ 'bl_controls.shp': shapefile of control polygons produced from the Bootleg Fire analyses ### Schneider Springs Fire files: #### schneider\_springs\scripts\\ 'ss_full_analyses.Rmd': R script to run the full analyses and produce all figures for the Schneider Springs Fire. The number of cores can be set on line 51 depending on user's computer specifications. On line 54, the user will need to reset the "pdir" file directory to the location where the "bootleg" folder is located on the user's computer. Identifying appropriate lag distances for the SAR modeling is one step that takes particularly long. If users wish to speed up processing time, this step can be skipped by 1) commenting out lines 719-735 and 2) setting "best_d" on line 738 to '35' to match results from our analyses.  #### schneider\_springs\datasets\predictors\climate\\ 'Annual_AET_V2_1981_2010.tif': Mean actual evapotranspiration across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'Annual_Deficit_V2_1981_2010.tif': Mean climatic water deficit across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'Annual_PET_1981_2010.tif': Mean potential evapotranspiration across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'Annual_PPT_anl_total_1981_2010.tif': Mean annual total precipitation across years 1981-2010 (mm) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'Annual_Tave_anl_mean_1981_2010.tif': Mean average annual temperature across years 1981-2010 (degrees C) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) 'Annual_Tmin_anl_mean_1981_2010.tif': Mean annual minimum annual temperature across years 1981-2010 (degrees C) derived at 90-m spatial resolution (Cansler et al. 2022, Appendix A) #### schneider\_springs\datasets\predictors\distto\\ 'distance_to_roads_20221021.img': distance (m) to department of natural resources (DNR) and Forest Service (FS) roads at 10-m spatial resolution (USDA FS Roads 2022; USCB Roads 2022) 'distance_to_strms_and_wetlands.img': distance (m) to stream and wetlands layers at 10-m spatial resolution (streams layer provided by WA DNR; US FWS 2022) 'distance_to_trt_edge.img': distance (m) to treatment edge (measured outward from treatment boundary) at 10-m spatial resolution (treatment data provided by the Washington Department of Natural Resources) #### schneider\_springs\datasets\predictors\frs\_stevens\\ 'frs_ss_clipped.tif': Community Fire Resistance Score at 250-m spatial resolution (Stevens et al. 2020) #### schneider_springs\datasets\predictors\gedi\ 'gedi_rh100_mean.tif': Global Ecosystems Dynamics Investigation (GEDI) mean 100th percentile relative height (m) at 1000-m spatial resolution (Dubayah et al. 2021) 'gedi_rh100_sd.tif': Global Ecosystems Dynamics Investigation (GEDI) standard deviation relative height (m) at 1000-m spatial resolution (Dubayah et al. 2021) #### schneider\_springs\datasets\predictors\gridmet\_byprog\\ 'SS_erc.tif': GRIDMET daily energy release Component extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'SS_fm100.tif': GRIDMET daily 100-hour fuel moisture (%) extracted to NIROPS-derived burn progression maps maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'SS_fm1000.tif': GRIDMET daily 1000-hour fuel moisture (%) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'SS_minrh.tif': GRIDMET daily relative humidity (%) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'SS_tmmx.tif': GRIDMET daily maximum temperature (degrees C) extracted to NIROPS-derived burn progression map at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) 'SS_vpd.tif': GRIDMET daily mean vapor pressure deficit extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Abatzoglou et al. 2013; NIROPS 2022) #### schneider\_springs\datasets\predictors\landfire\_fuels\\ 'LF2019_CBD.tif': pre-fire LANDFIRE canopy bulk density (kg per meters cubed) at 30-m spatial resolution (LANDFIRE 2016) 'LF2019_CC.tif': pre-fire LANDFIRE canopy cover (%) at 30-m spatial resolution (LANDFIRE 2016) 'LF2019_CH.tif': pre-fire LANDFIRE canopy height (m) at 30-m spatial resolution (LANDFIRE 2016) #### schneider_springs\datasets\predictors\masks\ 'forest_mask.tif': binary layer indicating forest vs. non-forest based on Region 6 Potential Vegetation Type layer (Region 6 PVT 2023); 1 = non-private, 0 = private #### schneider\_springs\datasets\predictors\snow\_cloud\_metrics\\ 'scf_20221011.tif': snow cover frequency measured as percent of days with snow in a year, measured at 2000-m spatial resolution (Crumley et al. 2020) 'sdd_20221011.tif': snow disappearance date (Julian date) measured at 2000-m spatial resolution (Crumley et al. 2020) schneider_springs\datasets\predictors\usgs_topo\ 'elevation_10res.tif': elevation above sea level (m) at 10-m spatial resolution (USGS 2022) 'hli_10res.tif': heat load index derived from USGS 10-m digital elevation model (USGS 2022; Evans and Murphy 2021) 'slope_10res.tif': slope derived from USGS 10-m digital elevation model (USGS 2022; Hijmans et al. 2023) 'sri_10res.tif': solar radiation index derived from USGS 10-m digital elevation model (USGS 2022; Evans and Murphy 2021) 'tpi_10res_410win.tif': topographic position index derived from USGS 10-m digital elevation model using a 410-m window (USGS 2022; Evans and Murphy 2021) 'tpi_10res_2010win.tif': topographic position index derived from USGS 10-m digital elevation model using a 2010-m window (USGS 2022; Evans and Murphy 2021) 'tpi_10res_8010win.tif': topographic position index derived from USGS 10-m digital elevation model using a 8010-m window (USGS 2022; Evans and Murphy 2021) 'tri_10res_410win.tif': topographic ruggedness index derived from USGS 10-m digital elevation model using a 410-m window (USGS 2022; Evans and Murphy 2021) #### schneider\_springs\datasets\predictors\windninja\_byprog\\ 'mx_speed_20230310.tif': maximum daily wind speed (mph) derived from WindNinja and extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Wagenbrenner et al. 2016; NIROPS 2022) 'eastwestness_mx_speed_direction_20230314.tif': sine of WindNinja maximum wind direction (sine transformed radians) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Wagenbrenner et al. 2016; NIROPS 2022) 'northsouthness_mx_speed_direction_20230314.tif': cosine of WindNinja maximum wind direction (cosine transformed radians) extracted to NIROPS-derived burn progression maps at 10-m spatial resolution (Wagenbrenner et al. 2016; NIROPS 2022) #### schneider\_springs\datasets\severity\\ '2021_SchneiderSprings_rdnbr_w_offset_DATESADJUSTED.tif': relativized differenced normalized burn ratio at 30-m spatial resolution (Parks et al. 2018; Parks et al. 2021) #### schneider_springs\datasets\study_area\ 'ss_perimeter_w_hole.shp': shapefile of Bootleg Fire perimeter from Monitoring Trends in Burn Severity datasets (MTBS 2022) #### schneider\_springs\datasets\treatments\\ 'mansucript_treatments_20240321.shp': shapefile of Bootleg Fire treatments; only treatments analyzed in final analyses after treatments were excluded that did not meet certain criteria (see details in manuscript) (treatment data provided by Washington Department of Natural Resources) 'global_treatments_w_wx_20221011.shp': shapefile of all Bootleg Fire treatments (treatment data provided by The Nature Conservancy) #### schneider\_springs\csvs\\ 'predictor_variables_20221108': table used to link variable names to figure names when creating figures; Data_Category = data category, File_Name = raw file name, Figure_Name = figure name; name_w_units = figure name with metric units 'treatment_names': table used to link treatment codes to figure names when creating figures; trt_comb = character treatment code; trt_code = numeric treatmet code; figure_nm = figure name prior to consolidating treatmetns; color = color for figure; new_code = new numeric code after consolidating treatments; new_nm = figure name ### schneider\_springs\figures\\ this folder contains all primary figures produced from the full analyses #### schneider\_springs\controls\\ 'ss_controls.shp': shapefile of control polygons produced from the Bootleg Fire analyses ### Reference Cited Abatzoglou, John T. 2013. “Development of gridded surface meteorological data for ecological applications and modelling.” International Journal of Climatology 33 (1): 121–31. 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