Global Burned Area Data for Forest Biomes 2000-2019
Metadata for GlobalForestBiomes_BA_2000_2019.csv
Authors: Matthias M Boer, Víctor Resco De Dios, Ross A Bradstock
Corresponding author: m.boer@westernsydney.edu.au
Description
1. The csv file ‘GlobalForestBiomes_BA_2000_2019.csv' contains burned area data for forest biomes in each continent over the period November 2000 - June 2019.
2. This data was used to produce Figure 1 in this publication: Boer, M. M., Resco de Dios, V., and Bradstock, R. A.: Unprecedented burn area of Australian mega forest fires, Nature Climate Change, 10.1038/s41558-020-0716-1, 2020. Please cite this paper along with the data repository if you use the data in future work.
3. Data columns and units are as follows: i) continentname: name of continent; ii) biome: WWF biome code; iii) year: year, iv) areaforbiome.sum: surface area (km^2) of forest within the given biome; sumBA.sum: burned area (km^2); biomefractionBA: forested burned area fraction [km^2/km^2] within given biome and year.
Data sources
4. The burned area data is from the MODIS Burned Area Collection 6 product (MCD64A1). For the corresponding user guide, see: https://modis-land.gsfc.nasa.gov/pdf/MODIS_C6_BA_User_Guide_1.0.pdf
5. For the background paper on the MODIS Burned Area Collection 6 data product, see Giglio et al.(2018)
6. The biome classification is from the Worldwide Fund for Nature (WWF) ecoregion mapping: https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world.
7. For background information on the WWF biome classification, see Olson et al. (2001).
8. The legend of the WWF biome map is as follows:
1 = Tropical & Subtropical Moist Broadleaf Forests
2 = Tropical & Subtropical Dry Broadleaf Forests
3 = Tropical & Subtropical Coniferous Forests
4 = Temperate Broadleaf & Mixed Forests
5 = Temperate Conifer Forests
6 = Boreal Forests/Taiga
7 = Tropical & Subtropical Grasslands, Savannas & Shrublands
8 = Temperate Grasslands, Savannas & Shrublands
9 = Flooded Grasslands & Savannas
10 = Montane Grasslands & Shrublands
11 = Tundra
12 = Mediterranean Forests, Woodlands & Scrub
13 = Deserts & Xeric Shrublands
14 = Mangroves
9. The data file contains burned area data for the forest biomes only, i.e. WWF biome codes 1-6 and 12.
10. We used a global forest mask to exclude non-forest areas within each biome. For background and details, see: Schepaschenko et al. (2015)
Methods
11. We used R(R Core Team, 2019) for all data processing and analyses, in particular the ‘raster’ package (Hijmans et al., 2019).
12. The MODIS Collection 6 (C6) MCD64A1 burned area (BA) product is a global ~500m resolution product made available in 24 partially overlapping tiles (Giglio et al., 2018). The data set used here covers the period from November 2000 to June 2019 and provides rasters of: i) the burn date (as a day of year) and ii) a quality assessment.
13. The burn date grids were reclassified to ones for burned grid cells and zeros for unburned grid cells, and then summed by year and multiplied by the area of every grid cell to produce rasters of annual BA (km2) from 2000 to 2019.
14. The WWF global biome map is provided as a vector layer. We rasterized the biome field of the vector layer to the BA grid and created a forest biome mask with ones for all grid cells within any of the seven global forest biomes: 1 = Tropical & Subtropical Moist Broadleaf Forests; 2 = Tropical & Subtropical Dry Broadleaf Forests; 3 = Tropical & Subtropical Coniferous Forests; 4 = Temperate Broadleaf & Mixed Forests; 5 = Temperate Conifer Forests; 6 = Boreal Forests/Taiga; 12 = Mediterranean Forests, Woodlands & Scrub.
15. The global hybrid forest mask, which distinguishes tree cover from other vegetation cover types, was first resampled to the BA data grid and then multiplied with the WWF Biome mask for biomes 1-6 and 12. The result of this step is a raster layer with ones for all grid cells that were identified as having tree cover and classified as WWF biomes 1-6, or 12.
16. Combining the annual BA grids, WWF biome forest biome mask and the global hybrid forest mask, we computed the annual burned area fractions of each continental section of forest biome classes 1-6, and 12, as the ratio of the annual burned area within forest biome 1-6 or 12 divided by the total area of the same forest biome on a given continent.
References
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O.: The Collection 6 MODIS burned area mapping algorithm and product, Remote Sensing of Environment, 217, 72-85, https://doi.org/10.1016/j.rse.2018.08.005, 2018.
Hijmans, R. J., Etten, J. v., Sumner, M., Cheng, J., Bevan, A., Bivand, R., Busetto, L., Canty, M., Forrest, D., Ghosh, A., Golicher, D., Gray, J., Greenberg, J. A., Hiemstra, P., Geosciences, I. f. M. A., Karney, C., Mattiuzzi, M., Mosher, S., Nowosad, J., Pebesma, E., Lamigueiro, O. P., Racine, E. B., Rowlingson, B., Shortridge, A., Venables, B., and Wueest, R.: raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://CRAN.R-project.org/package=raster. 2019.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., and Kassem, K. R.: Terrestrial ecoregions of the world: a new map of life on Earth, Bioscience, 51, 933-938, 2001.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2019.
Schepaschenko, D., See, L., Lesiv, M., McCallum, I., Fritz, S., Salk, C., Moltchanova, E., Perger, C., Shchepashchenko, M., Shvidenko, A., Kovalevskyi, S., Gilitukha, D., Albrecht, F., Kraxner, F., Bun, A., Maksyutov, S., Sokolov, A., Dürauer, M., Obersteiner, M., Karminov, V., and Ontikov, P.: Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics, Remote Sensing of Environment, 162, 208-220, https://doi.org/10.1016/j.rse.2015.02.011, 2015.
