Tracking and classifying Amazon fire events in near-real time

Summary

Time-series (2018-2024) of the Amazon dashboard, including minor updates to the methods.

The Amazon dashboard data product tracks individual fire events across most of South America (10N - 25S, 85W - 30W) in near-real time. The model classifies fires into four key fire types (deforestation, forest, small clearing and agricultural, and savanna and grassland fires) and provides estimates of individual fire carbon emissions. Methods are described in Andela et al. (2022). Near-real time estimates are provided at https://amzfire.servirglobal.net/ and here we archive historic time-series.

Methods

The data archived here (v1.1) include several small updates.

Two updates relate to the use of VIIRS active fire detections. First, VIIRS active fire detections have been updated from collection 1 to collection 2. Second, any full day of missing data from either the VIIRS instrument onboard NOAA-20 or Suomi NPP is now replaced by data of the other instrument. This "gap" filling helps reduce the impact of periods with instrument outage, like those of Suomi NPP VIIRS during the 2024 burning season. 

The other two updates relate to the emissions calculations. First, to convert dry matter burned to carbon emissions, we have introduced fire type specific emissions factors instead of the earlier assumption of 50% carbon content for all fire types. Second, as part of the Sense4Fire project (https://sense4fire.eu/), we provide daily gridded emissions estimates of Dry Matter (DM), C, CO2, CO, and NOx at 0.1 degree resolution. We used emissions factors provided by Andrea (2019) for savanna and grassland fires as well as small clearing and agricultural fires while for forest and deforestation fires we reviewed the literature to select the most relevant emissions factors (Table 1). 

Table 1: Emissions factors (gram species per kg dry matter burned) used to calculate C, CO2, CO, and NOx emissions. 

Fire type / trace gas emissions
C
CO2
CO
NOx

Savanna and grassland
480
1656
69.2
2.5

Small clearing and agricultural
430
1431
76.2
2.4

Forest
480
1561
104.0
2.0

Deforestation
490
1641
95.5
1.7

Dataset description

For full detail, please see Andela et al. (2022). The tables below (Tables 2 - 4) describe the content of the fire event (polygon) and active fire detections (point) shapefiles as well as the gridded emissions product. The active fire detections and associated estimates of dry matter burned can be combined with emissions factors (Table 1) to derive daily trace gas emissions time series for species and areas of interest.

Table 2: Explanation of fire event shapefile attribute table.

Attribute class
Attribute
Explanation / units

Fire type classification
Fire type
(1) savanna and grassland, (2) small clearing andagriculture, (3) forest, and (4) deforestation fires

Confidence
(1) low, (2) moderate, and (3) high

Fire Atlas
Size
Fire size in km2

Start day
Day of new fire start as day of year (1-366)

Duration
Fire duration in days

C Emissions
Fire carbon emissions (ton C)

Fire characterization
Tree cover
Average tree cover fraction within perimeter (%)

Biomass
Average biomass within fire perimeter (ton ha-1)

Deforestation 
Fraction of 550 m grid cells with historicdeforestation (five years prior to fire) within fire perimeter (%)

FRP
Average fire radiative power (FRP) for all firedetections within fire perimeter (MW)

Persistence
Average fire persistence across 550 m grid cellswithin fire perimeter (days)

Progression
Average fire progression fraction across 550 mgrid cells within perimeter (%)

Daytime
Fraction of 1:30 pm detections (%) for all firedetections within fire perimeter

Detections
Total active fire detections within fire perimeter

Table 3: Explanation of active fire detection shapefile attribute table.

Attribute class
Attribute
Explanation / units

VIIRS active fire detections
FRP
Fire radiative power (MW)

DOY
Day of year (1-366)

Fire type classification
Fire type
(1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires

Confidence
(1) low, (2) moderate, and (3) high

Emissions
C Emissions
Fire carbon emissions (ton C) associated with each active fire detection

DM Emissions
Dry matter burned (ton) associated with each active fire detection

Table 4: Content of daily gridded (0.1 degree resolution) emissions netcdf files. The daily emissions product provides emissions estimates of dry matter, C, CO2, CO, and NOx. For DM and CO partitioned emissions are also provided by fire type, for other species these can be derived by multiplying the dry matter burned (DM) estimates with trace gas specific emissions factors (Table 1). Values of each grid cell can be multiplied by the number of seconds per day and grid cell area to calculate total emissions (convert "kg species m-2 s-1" to "kg species day-1 per grid cell").

/ancill
grid_cell_area

/partitioned_DM_emissions
Deforestation emissions

Forest emissions

Savanna and grassland emissions

Small clearing and agricultural emissions

/partitioned_CO_emissions
Deforestation emissions

Forest emissions

Savanna and grassland emissions

Small clearing and agricultural emissions

/total_emissions
DM emissions

C emissions

CO2 emissions

CO emissions

NOx emissions

Results

Despite the various small improvements to the dataset, the data are largely consistent with the original dataset published for 2019-2020 (Table 5). 

Table 5: Comparison of model versions (original from Andela et al., 2022 and v1.1 published here) for April-December 2019 (equator-25S, 85W - 30W). Note that the current version (v1.1) is complete for 2019, but the original dataset had missing data due to incomplete active fire detections from NOAA-20 VIIRS at that time.

Dataset
Fire type
Fire detections (x1,000)
Mean fire radiative power (MW)
Number of events (x1,000)
Emissions (Tg C)

Original
Deforestation
756.65
15.15
24.24
99.18

Original
Forest
637.58
12.73
5.28
85.46

Original
Small clearing and agricultural
348.49
10.91
154.68
10.55

Original
Savanna and grassland
1935.06
12.11
296.42
71.75

v1.1
Deforestation
742.64
14.81
24.02
97.18

v1.1
Forest
626.92
12.06
5.16
77.84

v1.1
Small clearing and agricultural
350.7
10.89
155.56
9.27

v1.1
Savanna and grassland
1877.16
11.89
299.17
70.55

Acknowledgements

The Sense4Fire project is funded by ESA under ESA Contract Number: 4000134840/21/I-NB. 

References

Andela, N., Morton, D.C., Schroeder, W., Chen, Y., Brando, P.M. and Randerson, J.T., 2022. Tracking and classifying Amazon fire events in near real time. Science advances, 8, eabd2713. https://doi.org/10.1126/sciadv.abd2713.

Andreae, M.O., 2019. Emission of trace gases and aerosols from biomass burning–an updated assessment. Atmospheric Chemistry and Physics, 19, 8523-8546. https://doi.org/10.5194/acp-19-8523-2019.