Greenhouse gas emissions, burned areas, and land cover
As for the previous FAOSTAT version, this study calculates fire-related emissions applying a Tier 1 approach based on the 2006 IPCC Guidelines (IPCC 2006) but replacing GFED4 burned areas activity data with the new MCD64A1 C6 burned areas.
C6 burned area is being produced by the US National Aeronautics and Space Administration (NASA) using an improved version of the MCD64 burned area mapping algorithm (Giglio et al. 2009). The algorithm applies dynamic thresholds to composite MODIS Terra and Aqua imagery, generated from a burn-sensitive spectral band index derived from MODIS 1240 nm and 2130 nm Terra and Aqua surface reflectance, and a measure of temporal variability. Cumulative MODIS 1 km active fire detections are used to guide the selection of burned and unburned training samples and to guide the specification of prior burned and unburned probabilities (Giglio et al. 2018b). Compared to the previous C5.1 algorithm, the C6 algorithm features (Giglio et al. 2018a) are as follows: (i) improved detection of burned areas, especially of small burns (with a lower threshold of 100 ha ca.); (ii) reduced occurrence of unclassified grid cells and, in parallel, unique flagging of missing-data pixels versus water pixels; (iii) reduced temporal uncertainty of burn dates; (iv) expanded product coverage from 219 to 268 tiles; and (v) improved per-pixel quality assurance.
C6 burned area is distributed as a monthly, level-3 gridded 500-m product containing per-pixel burning date and quality information.
Temporal and areal uncertainty of the C6 product are illustrated in Giglio et al. (2018b) and an intercomparison of four global burned area products, including C6, is discussed in Humber et al. (2019), who also give indications on which products, among those analyzed, most reasonably capture the burning regime.
In our study, consistently with the previous version of FAOSTAT, the land cover information data was retrieved from the MODIS Global Land Cover Product (MCD12Q1 Collection 5.1, Friedl et al. 2010) for the available years (2001–2013). For years beyond 2013, it was assumed that no land cover change occurred compared to 2013 due to unavailability of more recent MCD12Q1 data.Footnote 1 The MCD12Q1 product is generated using 5 different land cover classification schemes; in the present study, the University of Maryland (UMD) classification scheme was considered.
Processing of the full time series of original C6 monthly tiles for the extraction of the burned areas by land cover consisted of the following operations: (i) preliminary filtering to extract only valid burn dates: any value (calendar day) between 1 and 366 was considered valid; (ii) transformation of valid burn dates into per-pixel fire count: any day between 1 and 366 was counted as one fire occurred; (iii) transformation of fire counts into burned area surface: this was obtained by multiplying the non-zero fire counts by the pixel surface area, equal to a constant 21.46 ha (pixel X size × Y size) across the globe due to the MCD products’ sinusoidal projection (equal-area); (iv) partitioning of the monthly burned area tiles by land cover.
Following the previously published FAOSTAT methodology (Rossi et al. 2016), C6 monthly burned areas tiles were converted into yearly burned areas tiles (summing up the twelve monthly files), and then mosaicked globally by UMD land cover class.
More precisely, the five UMD land cover classes representing forests (class 1: evergreen needleleaf; class 2: evergreen broadleaf; class 3: deciduous needleleaf; class 4: deciduous broadleaf; class 5: mixed forests) were aggregated to form the three forest-based classes boreal, temperate, and tropical to allow for calculation of the emissions and then further aggregated as “humid tropical forest” and “other forest” for comparison with GFED4s (Rossi et al. 2016).
As for the former FAOSTAT version, burned areas occurring in the cropland land cover class were excluded from the analysis due to the high uncertainty in mapping agricultural burning compared to other land cover classes (Giglio et al. 2018a, b; Zhu et al. 2017; Hall et al. 2016; Zhu et al. 2017).
For peatlands, the FAOSTAT methodology was applied, deriving the map of histosols from the Harmonized World Soil Database (HWSD) (FAO/IIASA/ISRIC/ISSCAS/JRC 2012) as a robust proxy for peatlands (Egglestone et al. 2006) and independently from the MCD12Q1 land cover product. Unlike earlier version, in this study, pixel resampling and reprojection of the map of histosols followed the MCD64A1 specifications. The map of burnt peatlands was thus derived overlaying the map of histosols with the C6 burned areas. The overlay is independent from land cover information, i.e., estimates of fires in peatlands are a combination of fires in all possible types of land cover on organic soils.
The newly generated yearly global mosaics of burned areas by land cover class (plus peatland) were then used as a basis for our analyses. The burned areas were compared with those from C5.1 (extracted from GFED4 burned areas at 0.25° resolution used in the previous FAOSTAT dataset), by land cover class and peatland, globally, and by GFED region.
A further comparison was added with GFED4s burned areas (partitioned using the same land cover datasets and classes as FAOSTAT) as this may offer a good insight into the differences in related emissions with those in previous and current version of FAOSTAT. However, it has to be noted that, opposite to GFED4s emissions, GFED4s burned areas are only distributed as annual global values; therefore, our partitioning did not necessarily produce the same activity data used to generate GFED4s emissions, particularly in the case of peatland.
Fuel biomass consumption values and emission factors
This study calculates all emission estimates from C6 burned areas using the fuel biomass consumption values and emission factors derived from the IPCC 2006, following the same methods presented in Rossi et al. (2016). As in Rossi et al. (2016), the aggregated land cover classes described above in Section 2.2 were combined with the Joint Research Centre (JRC) climate map to locate spatially fuel biomass values and emission factors by vegetation type and climate zones; in the present study, the climate map was resampled at 500 m ground resolution rather than at the 0.25° resolution previously used.
The fuel biomass consumption values and emission factors are reported in Table 1. As a reference, Table 1 reports also the field-derived consumption values summarized from the available literature by van Leeuwen (2014).
New estimates of GHG emissions from burning of biomass were computed for each of the source classes using the FAOSTAT methodology and parameters (“FAOSTAT emissions”) along with the new yearly C6 burned areas (Rossi et al. 2016) and were then re-aggregated to allow for comparison with GFED4s emissions data (Table 2).
More in detail, using the FAO Global Ecological Zones (FAO-GEZ) map, the new emissions from the forest land cover classes (boreal, temperate, tropical) were aggregated into “humid tropical forests” (masking against the FAO-GEZ classes 11, tropical rainforest; and 12, tropical moist forest) and “other forests” (all other FAO-GEZ classes). Following the GFED approach, burning of humid tropical forests was used as a proxy for the GFED4s “deforestation and forest degradation” class (Rossi et al. 2016) while fire emissions from the remaining forests (“Other forest”) were compared against the GFED4s “boreal and temperate class.”
Emissions from all other land cover classes were grouped in the “savanna, grassland, and shrubland” class, consistently with the GFED4s emissions and were then analyzed against the latter. Peatland emissions were compared directly among the two datasets.
Comparison of emissions between GFED4s and FAOSTAT for all aggregated classes was performed in terms of CO2eq emissions, obtained by summing up the CO2, CH4, and N2O emissions for each source of emissions after conversion to CO2eq by use of the Global Warming Potential (GWP) over 100 year time horizon (using the IPCC SAR values of N2O = 310; CH4 = 21).
GFED4s emissions are published in raster and tabular format, the latter aggregated by GFED regions (Giglio et al. 2013) and by source of emissions: savanna, grassland and shrubland, boreal forest, temperate forest, deforestation and forest degradation, peatland. The tabular values and the related regional distribution were employed for comparison with the new FAOSTAT emissions of this exercise. For ease of reference, GFED regions are illustrated in Fig. 1.
It should be noted that while the previous version of GFED emissions (GFED4) was directly calculated based on the C5.1 burned areas, the latest GFED4s dataset also include emissions from small fires (van der Werf et al. 2017).