This readme file was generated on [2022-04-25] by Qingqing Xu


GENERAL INFORMATION

1. Title of Dataset: Wildfire burn severity and emissions inventory

2. Author Information

        Corresponding Investigator 
		Name: Qingqing Xu
		Institution: University of California: Merced, California, US
		Email: qxu6@ucmerced.edu

        Co-investigator 1
		Name: Dr LeRoy Westerling
		Institution: University of California: Merced, California, US
                Email: leroy.westerling@icloud.com

	Co-investigator 2
		Name: Dr Jonathan Baldwin
		Institution: University of California: Merced, California, US
                Email: wbaldwin@ucmerced.edu

3. Date of data collection: 1984-2020 

4. Geographic location of data collection: California, The United States

5. Information about funding sources that supported the collection of the data: 

This work was funded by the California Energy Commission EPC-18-026, National Oceanic and Atmospheric Administration Climate Program—NOAA NA170AR4310284, California Department of Insurance 18028CA-AM 1, Strategic Growth Council of California CCR20021, UC Lab Fees LFR-20-651032, and University of California Research Initiatives UCOP MRPI 20170261-01. M H acknowledges support from the California Department of Forestry and Fire Protection as part of the California Climate Investments Program, Grant #8GG14803. This research was supported in part by the USDA Forest Service, Rocky Mountain Research Station, Aldo Leopold Wilderness Research Institute. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.

6. SHARING/ACCESS INFORMATION

Links to publications that cite or use the data: Xu, Q., Westerling, A. L., Notohamiprodjo, A., Wiedinmyer, C., Picotte, J. J., Parks, S. A., ... & Ade, C. (2022). Wildfire burn severity and emissions inventory: an example implementation over California. Environmental Research Letters, 17(8), 085008.

Links to other publicly accessible locations of the data:  https://data.pyregence.org/wg4/WBSE/

Recommended citation for this dataset: Xu, Qingqing et al. (2022), Wildfire burn severity and emissions inventory, Dryad, Dataset, https://doi.org/10.6071/M3QX18


DATA & FILE OVERVIEW
1. Description of dataset

These data were generated to map spatial burn severity and emissions of each historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California in the US. Event-based assessments were conducted at 30-m resolution for all fires and daily emissions were calculated at 500-m resolution for fires burned since 2002. A total of 1697 wildfires were assessed using the Wildfire Burn Severity and Emissions Inventory(WBSE) framework developed by Xu et al 2022. The comprehensive, long-term event and daily emissions records described here could be used to study health effects of wildfire smoke, either by combining them with transport modeling to model air quality and estimate exposures, or by incorporating them into statistical models predicting health impacts as a direct function of estimated emissions. These data will also facilitate analyses of changing emissions impacts on the carbon cycle over the last three decades. High resolution severity and emissions raster maps are generated for each fire event to support further spatial analysis. While the emissions calculated for California with WBSE are not a substitute for real-time daily emissions estimates, it is designed to extend the estimated emissions record back to 1984 with a finer spatial resolution and provide more up-to-date estimates on emissions factors reflecting information from California's recent extreme fires.

2. File List: 
        Folder Name: burn_severity_CA
        Folder Description: A set of raster files named <FireID>_burn_severity_CA.tif which contains burn severity rasters for the fires of interest by fire ID (either MTBS fire ID (e.g."ca3282611639220020619"), or constructed from CAL FIRE fire start/end dates with name and id(e.g."CA20200816_20201111_UCM300_AUGUST_COMPLEX_FIRES_CF00008864"). Each raster is 30-m by 30-m resolution with a pixel value of: 
                            Raster value 0 -- non-processing area
                            Raster value 1 -- unburned
                            Raster value 2 -- low severity
                            Raster value 3 -- moderate severity
                            Raster value 4 -- high severity 
                            Raster value 5 -- grass burn

        Recommended software to read the data: (1) ArcGIS Pro
                                               (2) R programming with raster()
                                                     e.g. severity <- raster("CA3282611639220020619_burn_severity_CA.tif") # explanation: use the raster() function from the "raster" package to read the data and assign it to the "severity" variable


        Folder Name: emission_CA(folder)
	Folder Description: A set of R native format raster stacks with layers of vegetation general classification(evt_class), burn severity(cbi_severity), and 12 types of emissions(CO2, CO, CH4, NMOC, SO2, NH3, NO, NO2, NOx, PM2.5, OC, BC) named <FireID>_stack.grd/gri for the fires of interest by fire ID (either MTBS fire ID (e.g."CA3282611639220020619_"), or constructed from CAL FIRE fire start/end dates with fire name and id(e.g."CA20200816_20201111_UCM300_AUGUST_COMPLEX_FIRES_CF00008864"). Each raster stack is 30-m by 30-m resolution. Emissions are in unit of kg.
                            evt_class raster values 1 -- grass
                                             values 2 -- shrub
                                             values 3 -- forest under 5500 ft
                                             values 4 -- forest between 5500-7500
                                             values 5 -- forest above 7500 ft
                                             values 0/6 -- ignored

        Recommended software to read the data: (1) ArcGIS Pro
                                               (2) R programming with stack()
                                                     e.g. emissions <- raster::stack("CA3282611639220020619_stack.grd") # explanation: use the stack() function from the "raster" package to read the data and assign it to the "emissions" variable
  
        Folder Name: daily_emission_CA(folder)
	Folder Description: A set of daily emissions shapefiles named <FireID>_daily_emission.shp/dbf/prj/shx which contain emissions by day for the fires of interest by fire ID (either MTBS fire ID (e.g."ca3282611639220020619"), or constructed from CAL FIRE fire start/end dates with name and id(e.g."CA20200816_20201111_UCM300_AUGUST_COMPLEX_FIRES_CF00008864"). Each polygon has attributes of Day-of-Year(dob), and 12 types of emissions.
        Recommended software to read the data: (1) ArcGIS Pro
                                               (2) R programming with st_read()
                                                      e.g. daily <-  sf::st_read("CA3282611639220020619_daily__emission.shp") # explanation: use the st_read() function from the "sf" package to read the data and assign it to the "daily" variable
  
        File Name: Area burned in each burn severity class and emissions per fire 1984_2020.csv
	File Description: a summary table of area burned in different severity categories (unchanged, low severity, moderate severity, high severity, grass burned), total area burned, and emissions by fire ID (Event_ID), and fire year

        File Name: daily emissions per fire 2002_2020.csv
	File Description: a summary table of emissions aggregated by Day-of-Year, fire ID (Event_ID), and fire year


METHODOLOGICAL INFORMATION

Burn severity calculation:
Fire records for California from 1984 to 2019 were retrieved from MTBS (https://mtbs.gov/viewer/index.html) via interactive viewer on 8 May 2021, resulting in a dataset with a total of 1623 wildfires. We also acquired fire perimeters for 74 large wildfires in 2020 from CAL FIRE (https://frap.fire.ca.gov/frap-projects/fire-perimeters/) and calculated dNBR for each 2020 fire using the dNBR calculation tool with Google Earth Engine (GEE). This process first selects either initial assessment or extended assessment for each fire. The initial assessment utilizes Landsat images acquired immediately after a fire to capture first-order fire effects. The extended assessment uses images obtained during the growing season following the fire to identify delayed first-order effects and dominant second-order effects (Eidenshink et al 2007). We utilized LANDFIRE Biophysical Settings (BPS) to determine which assessment type to apply for each fire burned in 2020. After Picotte et al (2021), we used extended assessment if the majority of general vegetation groups within the fire perimeter are forests, while initial assessment is used when the majority of general vegetation groups are grassland/shrubland. By contrast, MTBS uses extended assessment for forest and shrubland types. We did not delineate grasslands into burn severity categories. Instead, we classified them as burned ('grass burn') because of difficulties in assessing vegetation change. Post-fire images for extended assessment were selected during the next peak of the green season (June–September) using the mean compositing approach suggested by Parks et al (2018). Composite post-fire images acquired immediately within two months after the fire containment dates were used for the initial assessment. Composite pre-fire images for extended and initial assessments were acquired with the matching periods from the preceding year. The dNBR images were produced by quantifying the spectral difference between composite pre-fire and post-fire Landsat scenes. We calculated the unitless, continuous CBI variable from dNBR/NBR values using the linear and Sigmoid B regression models developed for the CONUS by Picotte et al (2021). CBI values were then classified following thresholds modified based on Crotteau et al(2014) into six severity classes: unburned, low severity, moderate severity, high severity, grass burn, and non-processing area.

Emissions calculation:
Emissions of all species are calculated as a function of area burned, fuel loading, the fraction of vegetation burned based on burn severity, and an emissions factor specific to each vegetation type using the following equation modified from the FINN model (Wiedinmyer et al 2011). Fuel categories were assigned from LANDFIRE EVT products. For emissions calculations, EVT data were then categorized into five general vegetation categories: grass, shrub, forest under 5500 feet (1676 m), forest between 5500–7500 feet (1676–2286 m), and forest above 7500 feet (2286 m), updated for California ecosystems. Fuel consumption was determined following Hurteau et el 2014 assigning fuel loading and consumption values for each severity class for the five general vegetation categories based on the First Order Fire Effects Model v5 (Reinhardt et al 1997). Emission factors for greenhouse gases, particulate matter, and reactive trace gases were updated with recent data for each general vegetation class using results from recent field campaigns and studies specific for California ecosystems and Western U.S. ecosystems.

Day of burning and daily emissions:
To assign the day of burning for individual pixels, NASA fire information for resource management system (FIRMS) active fire products from MODIS (Collection 6) within 750 m of the fire perimeter shapefiles supplied by MTBS or CAL FIRE were selected for interpolation to account for detections that might be outside the boundary due to detection radius. VIIRS 375 m data, when available since 2012, was added to complement MODIS data with improved performance to assign burn dates using the fire progression raster tool (figure 4). We filtered the MODIS/VIIRS detection points to the date range of interest and created a 500 m buffer around each point. Points were then converted to circle polygons to represent each point's detection extent properly. The average date was selected as the proper date in regions of overlapping buffers. We then calculated daily emissions and assigned them to the centroids of the aggregated daily progression polygons. 

For more detailed mythological information, please check out our paper: Xu, Q., Westerling, A. L., Notohamiprodjo, A., Wiedinmyer, C., Picotte, J. J., Parks, S. A., ... & Ade, C. (2022). Wildfire burn severity and emissions inventory: an example implementation over California. Environmental Research Letters, 17(8), 085008.

The code that generated this dataset is available at the following GitHub repository: https://github.com/qxu6/WBSE.git


DATA-SPECIFIC INFORMATION FOR: Area burned in each burn severity class and emissions per fire 1984_2020.csv

Number of variables: 24

Number of cases/rows: 1697

Variable List: 
     [1] Event_ID: unique ID to identify a wildfire; Event_IDs for fires burned during 1984-2019 are the same as MTBS fire IDs
     [2] Fire_Name: fire name 
     [3] Lat: latitude information from fire records                 
     [4] Long: longitude information from fire records               
     [5] Fire_Year: the year that fire burned
     [6] Fire_Month: the month that fire started           
     [7] Total_area_burned(ha): total area burned in hectares
     [8] Unchanged(ha): area burned in unchanged burn severity category      
     [9] Low_Severity(ha)：area burned in low burn severity category  
     [10] Moderate_Severity(ha): area burned in moderate burn severity category
     [11] High_Severity(ha): area burned in high burn severity category
     [12] Grass_Burned(ha):  area burned in grassland   
     [13] CO2(kg): total CO2 emissions from the fire
     [14] CO(kg): total CO emissions from the fire             
     [15] CH4(kg): total CH4 emissions from the fire
     [16] NMOC(kg): total NMOC emissions from the fire           
     [17] SO2(kg): total SO2 emissions from the fire
     [18] NH3(kg): total NH3 emissions from the fire              
     [19] NO(kg): total NO emissions from the fire
     [20] NO2(kg): total NO2 emissions from the fire              
     [21] NOx(kg): total NOx emissions from the fire
     [22] PM2.5(kg): total PM2.5 emissions from the fire
     [23] OC(kg): total OC emissions from the fire
     [24] BC(kg): total BC emissions from the fire

DATA-SPECIFIC INFORMATION FOR: Daily emissions per fire 2002_2020.csv

Number of variables: 18

Number of cases/rows: 8158

Variable List: 
     [1] Event_ID: unique ID to identify a wildfire; Event_IDs for fires burned during 1984-2019 are the same as MTBS fire IDs
     [2] Fire_Name: fire name
     [3] Lat: latitude information of the burn day centroid
     [4] Long: longitude information of the burn day centroid 
     [5] Fire_Year: the year that fire burned
     [6] Day-of-Year: burn day as the day of the year                              
     [7] CO2(kg): total CO2 emissions from the fire on the Day-Of-Year
     [8] CO(kg): total CO emissions from the fire on the Day-Of-Year             
     [9] CH4(kg): total CH4 emissions from the fire on the Day-Of-Year
     [10] NMOC(kg): total NMOC emissions from the fire on the Day-Of-Year          
     [11] SO2(kg): total SO2 emissions from the fire on the Day-Of-Year
     [12] NH3(kg): total NH3 emissions from the fire on the Day-Of-Year             
     [13] NO(kg): total NO emissions from the fire on the Day-Of-Year
     [14] NO2(kg): total NO2 emissions from the fire on the Day-Of-Year            
     [15] NOx(kg): total NOx emissions from the fire on the Day-Of-Year
     [16] PM2.5(kg): total PM2.5 emissions from the fire on the Day-Of-Year
     [17] OC(kg): total OC emissions from the fire on the Day-Of-Year
     [18] BC(kg): total BC emissions from the fire on the Day-Of-Year

Reference:

Crotteau J S, Ritchie M W and Varner J M 2014 A mixed-effects heterogeneous negative binomial model for postfire conifer regeneration in Northeastern California, USA For. Sci. 60 275–87
Eidenshink J, Schwind B, Brewer K, Zhu Z-L, Quayle B and Howard S 2007 A project for monitoring trends in burn severity Fire Ecol. 3 3–21
Hurteau M D, Westerling A L, Wiedinmyer C and Bryant B P 2014 Projected effects of climate and development on California wildfire emissions through 2100 Environ. Sci. Technol. 48 2298–304
Parks S, Holsinger L, Voss M, Loehman R and Robinson N 2018 Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potential Remote Sens. 10 879
Picotte J J, Cansler C A, Kolden C A, Lutz J A, Key C, Benson N C and Robertson K M 2021 Determination of burn severity models ranging from regional to national scales for the conterminous United States Remote Sens. Environ. 263 112569
Wiedinmyer C, Akagi S K, Emmons L K, Al-Saadi J A, Orlando J J, Soja A J and Soja A J 2011 The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning Geosci. Model Dev. 4 625–41