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FIRECAM

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

FIRECAM is an online app for end-users to diagnose and explore 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)

Please see our website for more information.

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

Ancillary Apps

FIRECAM App

(Earth Engine Apps, no Google Earth Engine account required) <br><br> 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," 5) "Custom," or 6) "Draw."

  1. Global: all grid cells within GFEDv4s bounds (Note: monthly time series plot only shown for individual years)
  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 negligible fire emissions were excluded
  4. Pixel: individual grid cells, 0.5° x 0.5° spatial resolution; the centroid of the selected grid cell is displayed on the map
  5. Custom: user-defined polygon using an array of longitude, latitude coordinates; the tool re-defines the polygon to match the 0.5° x 0.5° grid of the basis regions
  6. Draw: user-defined polygon, drawn interactively on the base map; the tool re-defines the polygon to match the 0.5° x 0.5° grid of the basis regions <br><br> banner image

Step 3: Species

Select a species. The six available species are CO<sub>2</sub>, CO, CH<sub>4</sub>, organic carbon (OC), black carbon (BC), and fine particulate matter (PM<sub>2.5</sub>)

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. (Tip: Zoom in or zoom out in the web browser to adjust the displayed text.)

Relative Fire Confidence Metrics

#MetricRangeUnitsDescription
1BA-AFA Discrepancy-1 to 1unitlessdiscrepancy 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
2Cloud-Haze Obscuration0 to 1unitlessdegree to which clouds and/or haze obscure the land surface from satellite observations of fires during fire-prone months
3Burn Size/ Fragmentation≥ 0km<sup>2</sup> / fragmentaverage size of burned area per burn scar fragment (large, contiguous versus small, fragmented fire landscapes)
4Topography Variance≥ 0m<sup>2</sup>roughness in terrain, expressed as the variance in elevation across neighboring pixels (flat versus mountainous)
5VIIRS FRP Outside MODIS Burn Extent0 to 1unitlessadditional 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:

https://code.earthengine.google.com/?accept_repo=users/embrslab/FIRECAM

The repository should then appear in the top-left panel under 'Reader' as 'users/embrslab/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/embrslab/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').

Julian Day (Day of Year)

<b>Non-Leap Years</b>

Day of MonthJanFebMarAprMayJunJulAugSepOctNovDec
11326091121152182213244274305335
22336192122153183214245275306336
33346293123154184215246276307337
44356394124155185216247277308338
55366495125156186217248278309339
66376596126157187218249279310340
77386697127158188219250280311341
88396798128159189220251281312342
99406899129160190221252282313343
10104169100130161191222253283314344
11114270101131162192223254284315345
12124371102132163193224255285316346
13134472103133164194225256286317347
14144573104134165195226257287318348
15154674105135166196227258288319349
16164775106136167197228259289320350
17174876107137168198229260290321351
18184977108138169199230261291322352
19195078109139170200231262292323353
20205179110140171201232263293324354
21215280111141172202233264294325355
22225381112142173203234265295326356
23235482113143174204235266296327357
24245583114144175205236267297328358
25255684115145176206237268298329359
26265785116146177207238269299330360
27275886117147178208239270300331361
28285987118148179209240271301332362
292988119149180210241272302333363
303089120150181211242273303334364
313190151212243304365

<b>Leap Years</b>

Day of MonthJanFebMarAprMayJunJulAugSepOctNovDec
11326192122153183214245275306336
22336293123154184215246276307337
33346394124155185216247277308338
44356495125156186217248278309339
55366596126157187218249279310340
66376697127158188219250280311341
77386798128159189220251281312342
88396899129160190221252282313343
994069100130161191222253283314344
10104170101131162192223254284315345
11114271102132163193224255285316346
12124372103133164194225256286317347
13134473104134165195226257287318348
14144574105135166196227258288319349
15154675106136167197228259289320350
16164776107137168198229260290321351
17174877108138169199230261291322352
18184978109139170200231262292323353
19195079110140171201232263293324354
20205180111141172202233264294325355
21215281112142173203234265295326356
22225382113143174204235266296327357
23235483114144175205236267297328358
24245584115145176206237268298329359
25255685116146177207238269299330360
26265786117147178208239270300331361
27275887118148179209240271301332362
28285988119149180210241272302333363
29296089120150181211242273303334364
303090121151182212243274304335365
313191152213244305366

Updates

Publications

  1. Liu, T., L.J. Mickley, R.S. DeFries, M.E. Marlier, M.F. Khan, M.T. Latif, and A. Karambelas (2020). Diagnosing spatial uncertainties and relative biases in global fire emissions inventories: Indonesia as regional case study. Remote Sens. Environ. 237, 111557. https://doi.org/10.1016/j.rse.2019.111557

  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. https://doi.org/10.5194/essd-9-697-2017

  3. Wiedinmyer, C., S.K. Akagi, R.J. Yokelson, L.K. Emmons, J.J. Orlando, and A.J. Soja (2011). The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 4, 625–641. https://doi.org/10.5194/gmd-4-625-2011

  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. https://doi.org/10.5194/bg-9-527-2012

  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. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.406.7724

  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. https://doi.org/10.5194/acp-14-6643-2014