Skip to content
FIRECAM: Fire Inventories - Regional Evaluation, Comparison, and Metrics
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs/imgs add global, custom options to FIRECAM, GFEDv4s apps May 18, 2019
fire_inv update readme May 21, 2019


FIRECAM: Fire Inventories - Regional Evaluation, Comparison, and Metrics

FIRECAM is an explorer for regional differences in fire emissions from five global fire emissions inventories:

  1. Global Fire Emissions Database (GFEDv4s; van der Werf et al., 2017)
  2. Fire Inventory from NCAR (FINNv1.5; Wiedinmyer et al., 2011)
  3. Global Fire Assimilation System (GFASv1.2; Kaiser et al., 2012)
  4. Quick Fire Emissions Dataset (QFEDv2.5r1; Darmenov and da Silva, 2013)
  5. Fire Energetics and Emissions Research (FEERv1.0-G1.2; Ichoku and Ellison, 2014)

FIRECAM can be accessed through 1) Earth Engine Apps and 2) the Google Earth Engine (GEE) playground. While EE Apps faciliates access to FIRECAM for any user (does not require a GEE account), accessing the FIRECAM repository in the GEE playground allows custom exports of timeseries and additional data analysis. The latter is also a fallback option if EE Apps is running too slowly.

Ancillary Apps

  • GFEDv4s Explorer: Explore GFEDv4s emissions (1997-2016) for burned area and all available chemical species, partitioned by land use/land cover
    • Note: Burned area from small fires is approximate based on the small fire fraction for emissions


(Earth Engine Apps, no Google Earth Engine account required)

banner image

Step 1: Time Range

Select a time range Use the start year and end year sliders to select a time range for the annual and monthly regional emissions time series charts.

Step 2: Select Bounds Type and Region/Pixel of Interest

Select a bounds type Choose 1) "Global," 2) "Basis Region," 3) "Country/Sub-Region," 4) "Pixel," or 5) "Custom."

  1. Global: all grid cells (Note: monthly time series plot not shown for this option)
  2. Basis Region: 14 broad geographic regions from GFEDv4s (van der Werf et al., 2017).
  3. Country/Sub-Region: countries and sub-regions from simplified Large Scale International Boundary (LSIB) Polygons; those with neglible fire emissions were excluded
  4. Pixel: individual grid cells, 0.5° x 0.5° spatial resolution
  5. Custom: user-defined polygon using an array of longitude, latitude coordinates

    banner image

Step 3: Species

Select a species. The six available species are CO2, CO, CH4, organic carbon (OC), black carbon (BC), and fine particulate matter (PM2.5)

Regional Emissions

After clicking the submit button, please wait a few seconds for the default map layers and three charts to display. Note that for large regions, such as BOAS, and long time ranges, calculations for the monthly and annual time series can take up to a few minutes. The three charts (annual average from 2003-2016 and two time series charts, yearly and monthly emissions by inventory), can be viewed in a new tab and exported as tables or images. Map layers consist of emissions at 0.5° x 0.5° spatial resolution for a given species for each of the five global fire emissions inventories and fire relative fire confidence metrics (described below) at 0.25° x 0.25° spatial resolution. The distribution of peatlands (0.25° x 0.25°), based on GFEDv4s emissions from 2003-2016, and MODIS land use/land cover map (500 m, MCD12Q1 C6), based on FINNv1.0 aggregated vegetation classes, are also available as map layers.

Relative Fire Confidence Metrics

# Metric Range Units Description
1 BA-AFA Discrepancy -1 to 1 unitless discrepancy between burned area (BA; MCD64A1) and active fire area (AFA; MxD14A1), calculated as a normalized index using the area of BA outside AFA and AFA outside BA
2 Cloud-Haze Obscuration 0 to 1 unitless degree to which clouds and/or haze obscure the land surface from satellite observations of fires during fire-prone months
3 Burn Size/ Fragmentation ≥ 0 km2 / fragment average size of burned area per burn scar fragment (large, continuguous versus small, fragmented fire landscapes)
4 Topography Variance ≥ 0 m2 roughness in terrain, expressed as the variance in elevation across neighboring pixels (flat versus mountainous)
5 VIIRS FRP Outside MODIS Burn Extent 0 to 1 unitless additional small fires from VIIRS (375 m), a sensor with higher spatial resolution than MODIS (500 m, 1 km)

(Google Earth Engine account required)

Step 1: Sign up for a free Google Earth Engine account

Google Earth Engine (GEE) is a powerful cloud-computing platform for geospatial analysis and capable of computations with petabyte-scale datasets. To sign up, simply fill out a form and wait for an email. GEE works best with the Google Chrome web browser.

Step 2: The FIRECAM online tool repository

Copy and paste the following link in a tab in Google Chrome to enter the GEE Javascript playground and add the FIRECAM repository to your account under the read-only permissions folder in one step:

The repository should then appear in the top-left panel under 'Reader' as 'users/tl2581/FIRECAM'. The GEE Javascript playground is a code editor with a map and console to display or print results.

Step 3: Diving into the GUI

Click the 'Apps/UI_FIRECAM.js' script in the 'users/tl2581/FIRECAM' repository. The script should appear in the code editor. Click 'Run' in the top-right corner of the code editor to activate the user interface. The repository also contains a script to export monthly and annual timeseries data ('Exports/UI_FIRECAM_Exports.js').


  • Feburary 2019: add data download/processing code to this Github repo under "fire_inv"; added "Country/Sub-Region" and "Pixel" options to FIRECAM app; created ancillary app for GFEDv4s (GFEDv4s Explorer)
  • March 2019: added "Country/Sub-Region" and "Pixel" options to FIRECAM exports
  • May 2019: added "Global" and "Custom" options to FIRECAM, GFEDv4s apps


  1. Liu, T., L.J. Mickley, R.S. DeFries, M.E. Marlier, M.F. Khan, M.T. Latif, and A. Karambelas (in review). Diagnosing spatial uncertainties and relative biases in global fire emissions inventories: Indonesia as regional case study. EarthArXiv:

  2. van der Werf, G.R., J.T. Randerson, L. Giglio, T.T. van Leeuwen, Y. Chen, B.M. Rogers, M. Mu, M.J.E. van Marle, D.C. Morton, G.J. Collatz, R.J. Yokelson, and P.S. Kasibhatla (2017). Global fire emissions estimates during 1997-2016. Earth Syst. Sci. Data 9, 697–720.

  3. Wiedinmyer, C., S.K. Akagi, R.J. Yokelson, L.K. Emmons, J.J. Orlando, and A.J. Soja (2011). Model Development The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 4, 625–641.

  4. Kaiser, J.W., A. Heil, M.O. Andreae, A. Benedetti, N. Chubarova, L. Jones, J.J. Morcrette, M. Razinger, M.G. Schultz, M. Suttie, and G.R. van der Werf (2012). Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554.

  5. Darmenov, A.S. and A. da Silva (2013). The Quick Fire Emissions Dataset (QFED) - Documentation of versions 2.1, 2.2, and 2.4, NASA Technical Report Series on Global Modeling and Data Assimilation, Volume 32.

  6. Ichoku, C. and L. Ellison (2014). Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements. Atmos. Chem. Phys. 14, 6643–6667.

You can’t perform that action at this time.