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Changes in Global Terrestrial Live Biomass Over the 21St Century advances.sciencemag.org/cgi/content/full/7/27/eabe9829/DC1 Supplementary Materials for Changes in global terrestrial live biomass over the 21st century Liang Xu, Sassan S. Saatchi*, Yan Yang, Yifan Yu, Julia Pongratz, A. Anthony Bloom, Kevin Bowman, John Worden, Junjie Liu, Yi Yin, Grant Domke, Ronald E. McRoberts, Christopher Woodall, Gert-Jan Nabuurs, Sergio de-Miguel, Michael Keller, Nancy Harris, Sean Maxwell, David Schimel *Corresponding author. Email: [email protected] Published 2 July 2021, Sci. Adv. 7, eabe9829 (2021) DOI: 10.1126/sciadv.abe9829 This PDF file includes: Figs. S1 to S10 Tables S1 to S7 Supplementary Materials Supplementary Figures & Tables Fig. S1. Flow chart of procedures estimating global live biomass carbon stocks. It includes the organizations of input data forming training samples for regional and spatio-temporal models, spatially continuous annual remote sensing data sets as predictor layers, the models used, and the final output products at 10km resolution. Fig. S2. Ecoregion maps used in this study. (A) Regional land cover types by separating the biomes based on continents; (B) Combined land cover types aggregated to a total of 5 vegetation classes globally. Maps were derived from the MODIS IGBP (International Geosphere-Biosphere Programme) land cover product. We selected the data in 2001 as the base map for the inclusion of forest clearing and fire events in their original classes. Detailed description of each class can be found in Table S1. Fig. S3. Regional carbon stock series from 2000 to 2019. The land cover classes were derived from MODIS LC product (Fig. S2) and divided continentally to show regional effects. The shadowed area shows one standard error associated with each estimate of regional total carbon. Fig. S4. Comparison of vegetation carbon with FAO reports. (A) Scatter plots of carbon numbers between our estimation and FAO reported numbers for Annex-1 countries in 2000, 2005, 2010 and 2015; (B) Change comparison by comparing the signs of change (positive or negative); (C) Examples of time series plots for selected countries; (D) Comparison of FAO carbon IAV vs. annual IAV of our carbon estimates; (E) Comparison of FAO carbon IAV vs. 5-year-average IAV of our carbon estimates. Our estimates in panels (A) and (B) were carbon numbers averaged over a 5-year period centering at the interested year of observation. In panel (B), “significant” means the change is over 4% of total carbon. The blue curves in panel (C) show values read from the left axes, while the red lines show values read from the right axes. The indicator of IAV in panels (D) and (E) is the coefficient of variation (CV), which is defined as the ratio of the interannual standard deviation to the long-term mean. Fig. S5. Vegetation carbon stock changes from 2000 to 2019 for selected countries and regions. We selected (A) the United States, (B) Russian Federation, (C) China, (D) Brazil, (E) Congo Basin (Gabon, DRC, Congo), (F) Indonesia, (G) Canada, (H) European Union and (I) Australia to plot the country-level carbon stock and changes. The shaded area in each plot shows the confidence interval of regression using bootstrapped samples. Fig. S6. Emissions from forest cover changes caused by combined land use and environmental disturbances (fire, drought, insect, etc.) showing contributions from forest clearing, remaining forest fire, and nonforest fires across (A) boreal ecosystems, (B) temperate ecosystems, (C) comparison of emissions from forest clearing from different continents, and (D) emissions of global forest clearing compared to forest clearing without fire. Fig. S7. Spatial mapping uncertainty of the global live biomass carbon. (A) Pixel-level uncertainty map showing the model residual errors estimated from the spatial mapping process; (B) Scatter plot of the independent validation result of AGB estimates (Unit: Mg/ha) for the spatial mapping model at 10km resolution; (C) AGB estimation errors trained from GLAS data changing with spatial resolution. The relative AGB error in (C) is the average prediction error from pixel-level kriging of GLAS. The error improvement in (C) is calculated as the 1st-order difference of the AGB error (red dots) divided by the 1st-order difference of the blue dots. The numbers in parenthesis on the X axis (Panel C) are the minimum GLAS shots taken as valid training pixels. Fig. S8. Correlations between climate and vegetation carbon stock changes. (A) Land cover-based correlation map between temperature and carbon change (2001-2019); (B) LC-based correlation map between rainfall and carbon change (2001-2019); (C) correlations (in terms of R2) between climate and carbon changes for different spatial scales across the global vegetation, and (D) tropical ecosystems . The “Scale of multi-pixels” represents the total number of 10-km pixels that correlations were calculated. Fig. S9. Systematic error in estimating emissions from forest cover change using mean live biomass at different map resolutions. The percent of detected emission from forest cover loss is calculated using down-sampled AGB map from 1-ha to 10,000 ha while keeping area of forest cover loss derived from 30m forest cover change product the same in all grid cells. The insert shows percent of detected emissions for carbon stocks from 1 to 100 ha. Fig. S10. Estimate of AGB at the landscape scale using GLAS lidar samples and ALOS samples with (A) showing the distribution of GLAS lidar tracks across the global woody vegetation and the ALOS-derived AGB samples for low-vegetation regions, and (B) showing an example of how a minimum of 25 lidar samples (~0.25 ha each) are used to estimate the mean and variance of AGB at the 10 km x 10 km pixel area. For forested regions, we used GLAS-derived AGB that has a good coverage spatially (shown in magenta color). For other vegetated area, ALOS-derived AGB samples were used and the pixels were randomly sampled (shown in red color) with a similar sampling density compared to GLAS-derived samples. The background map is the land cover map in Fig. S2 with 50% transparency. Table S1. Land cover types for continental regions (Fig. S2A) and global regions (Fig. S2B). Regions were derived from the MODIS IGBP (International Geosphere-Biosphere Programme) land cover product using data in 2001 as the base map. Description column shows the IGBP classes combined in each LC region. The total area and forest area (Unit: million km2) are calculated within each region. Forest Area Regions Area Description (Mkm2) (Mkm2) Continental Statistics Moist Tropical Forest 7.23 6.78 Evergreen broadleaf forests in Latin America (Americas) Moist Tropical Forest (Africa) 2.26 2.19 Evergreen broadleaf forests in Central Africa Evergreen broadleaf forests in Southeast Asia and Moist Tropical Forest (Asia) 4.00 3.30 Australia Tropical & Subtropical Dry Woody savannas, savannas and closed shrublands in 5.63 2.70 Forest, Shrubland (Americas) Latin America Tropical & Subtropical Dry Woody savannas, savannas and closed shrublands in 6.23 3.67 Forest, Shrubland (Africa) Africa Woody savannas, savannas and closed shrublands in Tropical & Subtropical Dry 4.52 2.22 South Asia, Southeast Asia, Southern China and Forest, Shrubland (Asia) Australia Open shrublands, grasslands, wetlands, croplands and Tropical & Subtropical Other 5.81 0.61 cropland/natural vegetation mosaics in the Caribbean, Vegetation (Americas) Central America and South America Tropical & Subtropical Other Open shrublands, grasslands, wetlands, croplands and 10.87 0.30 Vegetation (Africa) cropland/natural vegetation mosaics in Africa Open shrublands, grasslands, wetlands, croplands and Tropical & Subtropical Other 11.52 0.44 cropland/natural vegetation mosaics in South Asia, Vegetation (Asia) Southeast Asia, Southern China and Australia Conifer Forest (North America) 1.52 1.22 Evergreen needleleaf forests in North America Deciduous broadleaf forests, mixed forests, woody Temperate Forest, Shrubland 3.45 2.47 savannas, savannas and closed shrublands south of mixed (North America) forests in North America Deciduous needleleaf forests, woody savannas, savannas, Boreal Forest, Shrubland 4.46 2.73 wetlands and closed shrublands north of mixed forests in (North America) North America Open shrublands and grasslands north of boreal forests in Tundra (North America) 3.92 0.16 North America Open shrublands, grasslands, wetlands, croplands and Temperate Other Vegetation 6.76 0.57 cropland/natural vegetation mosaics in the continental (North America) region of North America Evergreen needleleaf forests, deciduous needleleaf Southern Forest (South 0.45 0.35 forests, deciduous broadleaf forests and mixed forests in America) South America Deciduous broadleaf forests, mixed forests, woody Temperate Forest, Shrubland 7.23 4.92 savannas, savannas and closed shrublands south of mixed (Eurasia) forests and north of Subtropical Dry Forest in Eurasia Deciduous needleleaf forests, woody savannas, savannas, Boreal Forest, Shrubland 6.77 4.20 wetlands and closed shrublands north of mixed forests in (Eurasia) Eurasia Open shrublands and grasslands north of boreal forests in Tundra (Eurasia) 4.80 0.41 North America Open shrublands, grasslands, wetlands, croplands and Temperate Other Vegetation 17.04 1.25 cropland/natural vegetation mosaics south of mixed (Eurasia) forests and north of Subtropical Dry Forest in Eurasia Global Statistics Combined class of Moist Tropical Forest (Americas), Moist Tropical Forest 13.50 12.27 Moist Tropical Forest (Africa) and Moist Tropical Forest (Asia)
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