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 BradstockCorresponding author: [e-mail] Description1. 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.pdf5. 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 Forests2 = Tropical & Subtropical Dry Broadleaf Forests3 = Tropical & Subtropical Coniferous Forests4 = Temperate Broadleaf & Mixed Forests5 = Temperate Conifer Forests6 = Boreal Forests/Taiga7 = Tropical & Subtropical Grasslands, Savannas & Shrublands8 = Temperate Grasslands, Savannas & Shrublands9 = Flooded Grasslands & Savannas10 = Montane Grasslands & Shrublands11 = Tundra12 = Mediterranean Forests, Woodlands & Scrub13 = Deserts & Xeric Shrublands14 = Mangroves9. 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) Methods11. 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. ReferencesGiglio, 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.GlobalForestBiomes_BA_2000_2019.csv 元数据 作者：Matthias M Boer、Víctor Resco De Dios、Ross A Bradstock 通讯作者：[e-mail] 1. 本CSV文件`GlobalForestBiomes_BA_2000_2019.csv`包含2000年11月至2019年6月期间各大陆森林生物群系的过火面积数据。 2. 本数据用于发表于《Nature Climate Change》的论文中的图1：Boer, M. M., Resco de Dios, V., Bradstock, R. A. 澳大利亚巨型森林火灾的空前过火面积，2020，DOI: 10.1038/s41558-020-0716-1。若您在后续研究中使用本数据，请同时引用该论文与本数据仓储。 3. 数据列及其单位如下：i) continentname：大陆名称；ii) biome：世界自然基金会（World Wide Fund for Nature, WWF）生物群系代码；iii) year：年份；iv) areaforbiome.sum：给定生物群系内的森林表面积（单位：km²）；sumBA.sum：过火面积（单位：km²）；biomefractionBA：给定生物群系与年份内的森林过火面积占比 [km²/km²]。 ## 数据来源 4. 过火面积数据源自MODIS过火产品Collection 6（MODIS Burned Area Collection 6, MCD64A1）。相关用户手册可参阅：https://modis-land.gsfc.nasa.gov/pdf/MODIS_C6_BA_User_Guide_1.0.pdf 5. MODIS过火产品Collection 6的背景论文可参阅Giglio等(2018)。 6. 生物群系分类源自世界自然基金会（World Wide Fund for Nature, WWF）的陆地生态区制图：https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world 7. WWF生物群系分类的背景信息可参阅Olson等(2001)。 8. WWF生物群系地图图例如下： 1 = 热带及亚热带湿润阔叶林 2 = 热带及亚热带干燥阔叶林 3 = 热带及亚热带针叶林 4 = 温带阔叶及混交林 5 = 温带针叶林 6 = 寒温带森林/泰加林 7 = 热带及亚热带草原、稀树草原及灌丛 8 = 温带草原、稀树草原及灌丛 9 = 淹没草原及稀树草原 10 = 山地草原及灌丛 11 = 苔原 12 = 地中海森林、林地及灌丛 13 = 荒漠及旱生灌丛 14 = 红树林 9. 本数据文件仅包含森林生物群系的过火面积数据，即WWF生物群系代码1-6与12。 10. 我们使用全球森林掩膜剔除各生物群系内的非森林区域。相关背景与细节可参阅Schepaschenko等(2015)。 ## 研究方法 11. 所有数据处理与分析均使用R语言（R Core Team, 2019），特别是`raster`地理数据分析与建模工具包（Hijmans等, 2019）。 12. MODIS Collection 6（C6）MCD64A1过火面积（Burned Area, BA）产品是全球分辨率约500米的产品，以24个部分重叠的瓦片形式发布（Giglio等, 2018）。本数据集涵盖2000年11月至2019年6月时段，提供两类栅格数据：i) 过火日期（以年中日表示）；ii) 质量评估数据。 13. 将过火日期栅格重分类为：过火栅格单元赋值为1，未过火栅格单元赋值为0，随后按年份求和并乘以每个栅格单元的面积，得到2000至2019年的年度过火面积栅格（单位：km²）。 14. WWF全球生物群系地图以矢量图层形式提供，将矢量图层的生物群系字段栅格化至过火面积栅格的坐标系，并创建森林生物群系掩膜，对属于以下7种全球森林生物群系的所有栅格单元赋值为1：1 = 热带及亚热带湿润阔叶林；2 = 热带及亚热带干燥阔叶林；3 = 热带及亚热带针叶林；4 = 温带阔叶及混交林；5 = 温带针叶林；6 = 寒温带森林/泰加林；12 = 地中海森林、林地及灌丛。 15. 用于区分树木覆盖与其他植被覆盖类型的全球混合森林掩膜，首先重采样至过火面积数据的栅格坐标系，随后与生物群系1-6和12的WWF生物群系掩膜相乘。此步骤得到的栅格图层中，所有被识别为具有树木覆盖且属于WWF生物群系1-6或12的栅格单元赋值为1。 16. 结合年度过火面积栅格、WWF森林生物群系掩膜与全球混合森林掩膜，计算各大陆森林生物群系类别1-6和12的年度过火面积占比，即给定大陆内森林生物群系1-6或12的年度过火面积除以对应森林生物群系的总面积的比值。 ## 参考文献 Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., Justice, C. O.: 《Collection 6 MODIS过火面积制图算法与产品》，《Remote Sensing of Environment》，217卷，72-85页，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., Wueest, R.: 《raster：地理数据分析与建模工具包》，R包版本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., Kassem, K. R.: 《全球陆地生态区：地球生命的新地图》，《Bioscience》，51卷，933-938页，2001。 R Core Team: 《R：统计计算语言与环境》，奥地利维也纳统计计算R基金会，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., Ontikov, P.: 《通过遥感、众包与FAO统计数据协同开发全球混合森林掩膜》，《Remote Sensing of Environment》，162卷，208-220页，https://doi.org/10.1016/j.rse.2015.02.011，2015。