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) Combined class of Boreal Forest, Shrubland (North Boreal Forest, Shrubland 11.54 7.18 America) and Boreal Forest, Shrubland (Eurasia) Combined class of Temperate Forest, Shrubland (North America), Conifer Forest (North America), Southern Temperate Forest, Shrubland 12.34 8.71 Forest (South America) and Temperate Forest, Shrubland (Eurasia) Combined class of Tropical & Subtropical Dry Forest, Tropical & Subtropical Dry Shrubland (Americas), Tropical & Subtropical Dry 16.38 8.59 Forest, Shrubland Forest, Shrubland (Africa) and Tropical & Subtropical Dry Forest, Shrubland (Asia) Combined class of Tropical & Subtropical Other Vegetation (Americas), Tropical & Subtropical Other Vegetation (Africa), Tropical & Subtropical Other Other Vegetation 60.73 3.74 Vegetation (Asia), Tundra (North America), Temperate Other Vegetation (North America), Tundra (Eurasia) and Temperate Other Vegetation (Eurasia) Global Total 115.08 40.54 All vegetation types globally

Table S2. Total carbon stocks and changes across global ecological regions with losses and gains from 2001 to 2019. The total C stocks (Unit: PgC) are reported as the long-term mean. The net C changes, emissions and sinks (Unit: PgC yr-1) are reported as the long-term average fluxes. Uncertainty is estimated as one standard error including prediction and modeling error assuming the allometric models are correct. Regions Total C Stock Net C Change C Emission Regional C Sink Continental Statistics Moist Tropical Forest (Americas) 81.37±0.36 +0.051±0.021 0.483±0.015 -0.431±0.026 Moist Tropical Forest (Africa) 36.10±0.26 -0.023±0.012 0.244±0.007 -0.267±0.014 Moist Tropical Forest (Asia) 36.78±0.80 +0.021±0.015 0.364±0.011 -0.343±0.019 Tropical & Subtropical Dry 15.16±0.20 -0.004±0.007 0.257±0.007 -0.261±0.010 Forest, Shrubland (Americas) Tropical & Subtropical Dry 21.41±0.22 -0.014±0.008 1.577±0.060 -1.591±0.061 Forest, Shrubland (Africa) Tropical & Subtropical Dry 17.97±0.54 -0.075±0.037 0.254±0.007 -0.329±0.037 Forest, Shrubland (Asia) Tropical & Subtropical Other 5.77±0.10 -0.000±0.004 0.051±0.002 -0.051±0.004 Vegetation (Americas) Tropical & Subtropical Other 12.35±0.13 -0.001±0.004 0.468±0.019 -0.469±0.019 Vegetation (Africa) Tropical & Subtropical Other 7.36±0.13 -0.012±0.006 0.089±0.003 -0.101±0.006 Vegetation (Asia) Conifer Forest (North America) 11.64±0.51 -0.025±0.011 0.090±0.004 -0.115±0.012 Temperate Forest, Shrubland 19.30±0.23 -0.018±0.009 0.147±0.006 -0.165±0.010 (North America) Boreal Forest, Shrubland (North 11.93±0.12 -0.016±0.005 0.067±0.003 -0.083±0.006 America) Tundra (North America) 3.57±0.06 -0.002±0.003 0.001±0.000 -0.003±0.003 Temperate Other Vegetation 7.66±0.12 -0.012±0.004 0.027±0.001 -0.040±0.004 (North America) Southern Forest (South America) 2.47±0.08 -0.006±0.003 0.023±0.001 -0.029±0.003 Temperate Forest, Shrubland 33.12±0.45 -0.048±0.028 0.174±0.007 -0.221±0.029 (Eurasia) Boreal Forest, Shrubland 27.19±0.20 -0.029±0.014 0.230±0.010 -0.259±0.017 (Eurasia) Tundra (Eurasia) 8.00±0.09 -0.015±0.004 0.017±0.001 -0.032±0.004 Temperate Other Vegetation 19.28±0.29 -0.003±0.010 0.062±0.002 -0.066±0.010 (Eurasia) Global Statistics Tropical Moist Ecosystems 154.25±1.04 +0.049±0.032 1.091±0.033 -1.042±0.046 Boreal Ecosystems 41.26±0.60 -0.042±0.022 0.308±0.013 -0.350±0.022 Temperate Ecosystems 64.43±0.54 -0.100±0.030 0.423±0.017 -0.523±0.041 Tropical & Subtropical Dry 54.54±0.65 -0.093±0.042 2.088±0.071 -2.181±0.083 Forest and Savanna Other Vegetation 64.00±0.50 -0.045±0.015 0.716±0.025 -0.761±0.030 Global Total 381.30±2.08 -0.232±0.087 4.626±0.130 -4.858±0.156

Table S3. Country-level live biomass carbon changes. The statistics in each country include the total forest area and nonforest woody area (Unit: Mha), annual total carbon stocks in 2000 and 2019 (Unit: PgC), and the average gross carbon emissions and removals for the period 2000-2019 (Unit: TgC yr-1). Countries/Regions with area of more than 1Mha were selected to report carbon numbers.

Forest Nonforest Total C Total C Country Emission Removal Area Area (2000) (2019) Algeria 1.19 1.04 0.14 0.15 0.74 -0.99 Angola 58.55 50.33 2.58 2.63 197.14 -198.92 Benin 0.19 10.49 0.31 0.31 16.40 -16.22 Botswana 0.01 9.59 0.23 0.20 4.93 -5.07 Burkina Faso 0.00 7.16 0.24 0.25 10.71 -10.99 Burundi 0.56 1.85 0.08 0.08 0.61 -0.73 Cameroon 32.64 10.52 4.84 4.99 72.95 -78.50 Central African Republic 49.21 12.19 3.67 3.88 277.60 -289.52 Chad 0.51 22.63 0.52 0.52 45.01 -45.62 Egypt 0.16 1.37 0.08 0.08 0.15 -0.16 Nigeria 10.82 44.07 2.15 2.18 75.70 -75.72 Congo 27.42 6.59 4.33 4.35 30.78 -33.00 Congo, DRC 205.29 23.98 22.34 22.69 382.37 -398.41 Cote d'Ivoire 16.14 15.64 1.65 1.62 55.52 -51.05 Djibouti 0.00 0.00 0.01 0.01 0.00 -0.01 Equatorial Guinea 2.61 0.01 0.50 0.49 2.51 -2.29 Eritrea 0.00 0.04 0.12 0.12 0.28 -0.38 Ethiopia 13.10 37.19 2.53 2.59 59.72 -60.92 Gabon 24.76 1.24 5.09 5.16 20.21 -22.68 Ghana 7.25 14.22 0.91 0.91 60.56 -60.41 Guinea 9.11 15.00 1.02 1.02 61.69 -61.33 Guinea-Bissau 1.12 1.98 0.11 0.11 6.33 -6.37 Kenya 3.42 14.62 1.01 1.05 2.69 -3.27 Lesotho 0.00 0.31 0.01 0.01 0.06 0.03 Liberia 9.35 0.10 1.18 1.21 15.08 -15.30 Libya 0.00 0.03 0.04 0.04 0.00 0.00 Madagascar 17.22 27.97 1.10 1.12 23.98 -22.85 Malawi 1.71 7.31 0.19 0.19 5.23 -4.87 Mali 0.03 13.86 0.44 0.44 32.44 -32.38 Mauritania 0.00 0.01 0.00 0.00 0.02 -0.04 Morocco 0.64 0.94 0.19 0.19 0.10 -0.16 Mozambique 31.74 43.76 2.37 2.30 183.36 -181.58 Namibia 0.00 6.17 0.43 0.43 9.03 -9.47 Niger 0.00 0.15 0.04 0.05 0.62 -0.67 Rwanda 0.54 1.84 0.06 0.06 0.35 -0.45 Senegal 0.04 6.34 0.23 0.22 28.38 -28.52 Sierra Leone 5.87 1.28 0.30 0.30 13.23 -12.73 Somalia 0.10 6.61 0.90 0.91 0.26 0.42 Sudan 0.02 10.44 0.43 0.44 25.13 -25.70 Tanzania 28.94 48.08 2.01 2.05 110.31 -110.49 South Africa 6.15 33.29 0.64 0.60 10.48 -9.40 South Sudan 12.73 47.06 1.26 1.37 223.15 -227.60 Swaziland 0.51 1.16 0.04 0.04 0.79 -0.77 The Gambia 0.00 0.71 0.01 0.01 0.78 -0.81 Togo 0.62 4.49 0.17 0.17 11.00 -10.96 Tunisia 0.22 0.17 0.02 0.02 0.06 -0.04 Uganda 8.60 11.37 0.45 0.47 18.25 -18.12 Western Sahara 0.00 0.00 0.00 0.00 0.00 0.00 Zambia 26.34 43.77 1.83 1.85 177.62 -177.81 Zimbabwe 1.41 27.83 0.63 0.60 17.23 -16.72 Portugal 2.33 1.97 0.22 0.22 4.39 -4.46 Afghanistan 0.21 0.37 0.34 0.33 0.02 0.35 Bangladesh 1.81 1.72 0.21 0.23 2.25 -3.07 Bhutan 2.69 0.39 0.23 0.23 0.73 -0.70 Myanmar 43.40 5.63 3.92 4.03 69.71 -74.04 Cambodia 8.90 2.94 0.71 0.65 36.00 -31.73 China 162.92 74.16 18.34 20.20 94.14 -193.55 India 38.37 22.25 5.21 5.42 37.01 -46.33 Japan 26.20 2.99 2.11 2.31 3.59 -15.15 Kazakhstan 3.95 4.97 0.49 0.55 3.26 -1.82 Kyrgyzstan 0.60 1.01 0.34 0.34 0.08 0.08 Laos 19.46 1.67 1.48 1.50 28.12 -29.43 Mongolia 3.55 4.83 0.84 0.87 3.82 -5.07 Nepal 5.29 2.14 0.46 0.47 3.93 -4.40 North Korea 5.17 1.91 0.62 0.64 1.81 -2.61 Pakistan 0.97 1.51 0.58 0.58 0.31 -0.50 Russia 754.02 257.97 54.33 54.56 362.30 -407.23 South Korea 5.22 1.40 0.62 0.68 1.76 -4.49 Sri Lanka 4.06 1.00 0.22 0.23 1.32 -2.03 Taiwan 2.33 0.24 0.25 0.26 0.97 -1.30 Tajikistan 0.04 0.24 0.15 0.15 0.01 -0.06 Thailand 20.06 5.92 1.64 1.70 23.69 -28.81 Turkmenistan 0.01 0.04 0.02 0.02 0.01 -0.01 Uzbekistan 0.08 0.34 0.07 0.08 0.04 -0.01 Vietnam 16.51 5.52 1.47 1.54 25.86 -29.31 Albania 0.68 0.55 0.09 0.09 0.26 -0.26 Armenia 0.34 0.17 0.04 0.04 0.08 -0.14 Austria 4.56 1.01 0.34 0.34 1.25 -1.06 Azerbaijan 1.25 0.72 0.10 0.10 0.31 -0.33 Belarus 9.38 2.27 0.62 0.64 3.11 -4.45 Belgium 0.88 0.55 0.09 0.09 0.35 -0.32 Bosnia & Herzegovina 2.84 0.82 0.19 0.20 0.28 -0.55 Bulgaria 4.30 1.19 0.28 0.29 0.66 -0.91 Croatia 2.45 0.75 0.19 0.20 0.31 -0.41 Czech Republic 3.18 0.96 0.25 0.26 1.51 -1.74 Denmark 0.58 0.55 0.06 0.06 0.25 -0.40

Estonia 2.65 0.47 0.15 0.15 1.55 -1.68 Finland 22.00 4.98 1.40 1.41 13.70 -15.31 France 17.48 7.94 1.44 1.44 4.11 -4.13 Georgia 3.22 0.80 0.25 0.25 0.11 -0.47 Germany 12.81 4.77 1.07 1.07 3.07 -3.23 Greece 3.68 2.40 0.36 0.38 0.77 -1.36 Hungary 2.02 0.95 0.17 0.17 0.56 -0.39 Iceland 0.00 0.00 0.08 0.07 0.00 0.20 Iran 1.75 0.65 0.62 0.63 0.47 -0.38 Iraq 0.01 0.06 0.07 0.08 0.28 -0.34 Ireland 0.69 1.47 0.11 0.12 0.47 -0.64 Israel 0.03 0.05 0.01 0.01 0.01 -0.02 Italy 9.68 4.05 0.86 0.89 1.67 -2.55 Jordan 0.00 0.01 0.01 0.01 0.00 -0.01 Kosovo 0.39 0.16 0.04 0.04 0.06 -0.09 Kuwait 0.00 0.00 0.00 0.00 0.00 0.00 Latvia 3.68 0.78 0.21 0.21 2.35 -2.59 Lebanon 0.06 0.08 0.03 0.03 0.02 -0.03 Lithuania 2.43 0.65 0.17 0.18 1.21 -1.69 Macedonia 0.86 0.38 0.08 0.08 0.20 -0.31 Moldova 0.35 0.27 0.03 0.03 0.08 0.09 Montenegro 0.66 0.26 0.05 0.05 0.12 -0.16 Netherlands 0.54 0.69 0.06 0.06 0.07 -0.10 Norway 11.09 4.64 0.97 1.00 3.17 -3.80 Oman 0.00 0.01 0.04 0.04 0.00 0.01 Poland 10.54 3.61 0.82 0.85 4.30 -5.67 Qatar 0.00 0.00 0.00 0.00 0.00 0.00 Romania 8.19 2.21 0.54 0.57 1.73 -1.83 Saudi Arabia 0.00 0.00 0.24 0.24 0.00 0.00 Serbia 2.95 1.13 0.22 0.23 0.36 -0.69 Slovakia 2.44 0.44 0.16 0.16 0.76 -0.79 Slovenia 1.36 0.22 0.09 0.09 0.16 -0.14 Spain 11.25 7.53 1.01 1.01 4.33 -4.28 Jan Mayen 0.00 0.00 0.00 0.00 0.00 -0.02 Sweden 28.37 6.11 1.99 1.97 18.59 -17.01 Switzerland 1.62 0.58 0.16 0.16 0.15 -0.21 Syria 0.11 0.13 0.05 0.05 0.12 -0.11 Turkey 10.30 6.47 1.28 1.32 2.53 -5.39 Ukraine 11.01 6.93 0.84 0.76 7.28 0.07 United Arab Emirates 0.00 0.00 0.01 0.01 0.00 0.00 United Kingdom 3.27 4.35 0.49 0.50 1.97 -2.31 Yemen 0.00 0.07 0.21 0.22 0.00 -0.06 Belize 1.83 0.14 0.15 0.15 1.81 -1.72 Canada 420.52 112.64 24.63 24.48 126.18 -155.88 Colombia 83.30 9.51 8.69 8.85 48.63 -50.22 Costa Rica 4.02 0.59 0.30 0.32 2.31 -2.78 Cuba 3.89 1.48 0.29 0.32 3.00 -4.33 Dominican Republic 2.64 0.87 0.19 0.20 1.83 -2.27 El Salvador 0.98 0.54 0.08 0.08 0.53 -0.68 Greenland 0.00 0.00 0.17 0.18 0.00 0.09 Guatemala 7.83 1.59 0.60 0.59 9.93 -8.59 Haiti 0.82 0.68 0.08 0.08 0.32 -0.35 Honduras 7.89 1.83 0.58 0.58 8.80 -8.54 Jamaica 0.77 0.12 0.06 0.07 0.44 -0.68 Mexico 53.77 24.91 4.90 4.97 31.99 -37.29 Nicaragua 8.03 2.02 0.67 0.65 13.55 -11.48 Panama 5.59 0.80 0.56 0.57 4.25 -4.59 The Bahamas 0.17 0.12 0.01 0.01 0.07 -0.09 United States 289.95 93.11 26.84 27.67 208.71 -246.81 Australia 40.62 66.93 5.84 5.90 163.31 -171.60 Fiji 1.35 0.17 0.16 0.17 0.73 -0.80 Indonesia 158.56 12.61 16.16 15.46 154.38 -125.20 Malaysia 29.56 1.35 2.79 2.56 44.38 -32.96 New Caledonia 1.32 0.28 0.14 0.14 0.60 -0.43 New Zealand 10.89 2.89 0.58 0.64 3.89 -6.21 Papua New Guinea 42.03 1.66 5.10 5.08 25.74 -25.65 Philippines 17.83 4.44 1.56 1.59 10.71 -11.04 Solomon Is. 2.19 0.03 0.31 0.31 2.18 -2.11 Timor Leste 0.73 0.37 0.05 0.05 0.31 -0.47 Vanuatu 0.85 0.01 0.11 0.11 0.34 -0.49 Argentina 38.15 14.65 1.82 1.88 20.04 -22.09 Bolivia 64.69 6.02 5.19 5.13 58.94 -64.39 Brazil 519.84 98.76 54.98 54.88 483.52 -426.09 Chile 18.39 5.68 1.50 1.56 11.25 -13.22 Ecuador 19.28 2.52 1.90 1.92 9.38 -9.40 French Guiana 8.07 0.04 1.58 1.59 3.38 -3.46 Falkland Is. 0.00 0.00 0.00 0.00 0.00 0.09 Guyana 19.12 0.36 2.88 2.89 6.12 -5.68 Suriname 14.00 0.19 2.31 2.31 4.77 -4.37 Paraguay 24.48 6.26 0.70 0.72 22.87 -25.44 Peru 78.36 3.54 9.96 9.99 38.53 -35.39 Uruguay 1.60 1.54 0.10 0.18 0.96 -2.24 Venezuela 56.90 8.64 5.59 5.68 28.68 -31.07

Table S4. Alternative estimations of live biomass C changes across global ecological regions from 2001 to 2019. The calculations separate C changes from primary forests and other low- biomass regions. The intact primary forests are defined as areas with AGB greater than 200 Mg/ha (Scenario 1) or AGB greater than 100 Mg/ha (Scenario 2). C changes in primary forests are calculated using the growth rates (Unit: MgC ha-1) estimated from field-based studies, while low-AGB C changes are still estimated using global C maps generated from this study. Numbers are reported as long-term averages of regional total C changes (Unit: PgC yr-1). Positive signs indicate vegetation C losses, while negative signs are vegetation C gains. Scenario 1 (primary forests Scenario 2 (primary forests Alternative Estimations defined as AGB>200 Mg/ha) defined as AGB>100 Mg/ha) Growth rate Intact Low- Total Net Intact Low- Total Regions for primary Forest C AGB C C Forest C AGB C Net C forests Change Change Change Change Change Change Continental Statistics Moist Tropical Forest -0.23 -0.067 +0.040 -0.027 -0.127 +0.007 -0.117 (Americas) Moist Tropical Forest (Africa) -0.63 -0.092 -0.002 -0.095 -0.114 -0.001 -0.116 Moist Tropical Forest (Asia) -0.38 -0.031 +0.017 -0.014 -0.086 +0.004 -0.081 Tropical & Subtropical Dry -0.48 -0.000 -0.004 -0.004 -0.005 -0.006 -0.011 Forest, Shrubland (Americas) Tropical & Subtropical Dry -0.48 -0.002 -0.014 -0.016 -0.009 -0.014 -0.023 Forest, Shrubland (Africa) Tropical & Subtropical Dry -0.48 -0.003 -0.072 -0.075 -0.016 -0.051 -0.067 Forest, Shrubland (Asia) Tropical & Subtropical Other -0.35 -0.000 -0.000 -0.000 -0.001 -0.000 -0.001 Vegetation (Americas) Tropical & Subtropical Other -0.35 -0.000 -0.001 -0.001 -0.001 -0.001 -0.001 Vegetation (Africa) Tropical & Subtropical Other -0.12 -0.000 -0.012 -0.012 -0.000 -0.012 -0.013 Vegetation (Asia) Conifer Forest (North -0.3 -0.005 -0.014 -0.019 -0.018 -0.011 -0.029 America) Temperate Forest, Shrubland -1.25 -0.001 -0.018 -0.020 -0.113 -0.018 -0.131 (North America) Boreal Forest, Shrubland -0.59 -0.000 -0.016 -0.016 -0.005 -0.017 -0.022 (North America) Tundra (North America) -0.32 -0.000 -0.002 -0.002 -0.000 -0.001 -0.002 Temperate Other Vegetation -0.48 -0.000 -0.012 -0.012 -0.003 -0.011 -0.014 (North America) Southern Forest (South -0.46 -0.000 -0.006 -0.006 -0.006 -0.005 -0.011 America) Temperate Forest, Shrubland -1.25 -0.005 -0.046 -0.051 -0.116 -0.032 -0.148 (Eurasia) Boreal Forest, Shrubland -0.59 -0.000 -0.028 -0.029 -0.032 -0.029 -0.061 (Eurasia) Tundra (Eurasia) -0.32 -0.000 -0.015 -0.015 -0.000 -0.015 -0.015 Temperate Other Vegetation -0.32 -0.000 -0.003 -0.003 -0.001 -0.002 -0.003 (Eurasia) Global Statistics Tropical Moist Ecosystems -0.34 -0.176 +0.055 -0.122 -0.327 +0.010 -0.310 Boreal Ecosystems -0.59 -0.000 -0.042 -0.042 -0.046 -0.047 -0.093 Temperate Ecosystems -1.02 -0.023 -0.087 -0.110 -0.246 -0.065 -0.313 Tropical & Subtropical Dry -0.48 -0.004 -0.091 -0.095 -0.031 -0.070 -0.101 Forest and Savanna Other Vegetation -0.34 -0.000 -0.045 -0.045 -0.006 -0.043 -0.050 Global Total -0.48 -0.204 -0.211 -0.416 -0.656 -0.216 -0.880

Table S5. Height-biomass allometric equations and root:shoot ratios for calculation of BGB. Equations are collected from various sources. North America FIA regional models and root:shoot ratios were based on FIA plot level data (67). General tropical broadleaf evergreen models are from (25).

Height to AGB RMSE Number Vegetation Type 푹ퟐ Root:Shoot Ratio Conversion (Mg/ha) of plots Africa Broadleaf Evergreen 퐴퐺퐵 = 0.2788퐻2.12 0.80 75 *0.205|0.235 Woodlands † 퐴퐺퐵 = 0.12363퐻2.3533 0.86 95.5 49 †0.42AGB Australia Broadleaf Evergreen 퐴퐺퐵 = 0.39194퐻2.1506 0.96 34.5 31 *0.205|0.235 Eurasia Broadleaf Deciduous (east) 퐴퐺퐵 = 0.26089퐻2.1192 0.45 84.5 60 *0.456|0.226|0.241 Boreal (east) 퐴퐺퐵 = 0.2퐻2.2194 0.39 19.1 202 *0.392|0.239 Boreal (west) 퐴퐺퐵 = 3.9314퐻1.2103 0.84 18.5 468 0.23803AGB Mediterranean 퐴퐺퐵 = 1.4243퐻1.5953 0.71 257 *0.322 North America FIA Southern Conifer 퐴퐺퐵 = 0.396퐻2.2 0.63 35.6 11113 0.22371 Mixed 퐴퐺퐵 = 2.2721퐻1.5 0.65 35.6 3604 0.20844 Deciduous 퐴퐺퐵 = 0.2퐻2.3 0.61 44.6 20279 0.19621 FIA North Eastern Conifer 퐴퐺퐵 = 2.35퐻1.55 0.55 34.8 2804 0.22149 Mixed 퐴퐺퐵 = 0.3퐻2.37 0.58 36.8 500 0.20753 Deciduous 퐴퐺퐵 = 4.13퐻1.3 0.43 49.7 14252 0.19677 FIA North Central Conifer 퐴퐺퐵 = 0.588퐻1.8661 0.43 32.6 5590 0.22544 Mixed 퐴퐺퐵 = 0.1퐻2.65 0.47 32.5 944 0.20916 Deciduous 퐴퐺퐵 = 1.6145퐻1.5 0.48 36.9 22031 0.19684 FIA Interior West Conifer 퐴퐺퐵 = 3.1429퐻1.2 0.54 46.2 4044 0.22797 Deciduous 퐴퐺퐵 = 3.486퐻1.2 0.58 37.5 497 0.20634 FIA Pacific Conifer 퐴퐺퐵 = 2.335퐻1.3 0.67 99.3 4031 0.22394 Deciduous 퐴퐺퐵 = 5.4311퐻1.1 0.51 90.1 960 0.20541 Canada Montane Cordillera 퐴퐺퐵 = 1.5248퐻1.5512 0.65 38.3 25 *0.392|0.239 Pacific Maritime 퐴퐺퐵 = 3.4819퐻1.2558 0.80 30.2 25 *0.403|0.292|0.201 Boreal Cordillera 퐴퐺퐵 = 0.61335퐻1.8054 0.47 23.0 25 *0.392|0.239 Boreal Plains 퐴퐺퐵 = 2.1745퐻1.5258 0.81 13.5 25 *0.392|0.239 Boreal Shields 퐴퐺퐵 = 2.0657퐻1.5548 0.67 20.0 25 *0.392|0.239 Atlantic Maritime 퐴퐺퐵 = 0.47767퐻2.0538 0.51 21.1 25 *0.456|0.226|0.241 Temperate Savanna, *0.642 퐴퐺퐵 = 1.3403퐻1.4694 0.60 32.6 3089 Shrubland Mediterranean Forests, *0.322 퐴퐺퐵 = 2.3053퐻1.3171 0.44 49.2 322 Woodlands Tropical Dry Broadleaf 퐴퐺퐵 = 0.24888퐻2.4469 0.35 28.9 4833 *0.563|0.275 Tropical Conifer 퐴퐺퐵 = 4.0685퐻1.1974 0.40 34.5 2770 *0.563|0.275 South America General Broadleaf Evergreen 퐴퐺퐵 = 0.6011퐻1.894 0.86 298 *0.205|0.235 Southern Amazon 퐴퐺퐵 = 3.1721퐻1.3257 0.82 *0.205|0.235 Central Amazon 퐴퐺퐵 = 2.4673퐻1.4706 0.74 *0.205|0.235 Western Amazon 퐴퐺퐵 = 0.23065퐻2.217 0.79 *0.205|0.235 Northeastern Amazon 퐴퐺퐵 = 0.2163퐻2.1835 0.45 59.7 33 *0.205|0.235 Colombia 퐴퐺퐵 = 0.00945퐻3.18566 0.63 16.4 25 *0.205|0.235 Eastern Coastal 퐴퐺퐵 = 0.95843퐻1.8102 0.79 *0.205|0.235 Southeast Asia Broadleaf Evergreen 퐴퐺퐵 = 0.21608퐻2.1604 0.58 303 0.489AGB^0.89 Mangrove 퐴퐺퐵 = 0.7067퐻1.7862 0.61 102.8 11 ‡0.4 ∗ Estimation based on (75). † Estimation based on (76). ‡ Middle value (2.5:1) between a typical range of AGB:BGB ratio of 2:1 to 3:1 in mangroves (77).

Table S6. Information on sources of field inventory plots used for developing allometric models. The data sources are from journal publications, listed websites, or key personnel with contact information. The collected inventory data were used to build region-specific allometric models between plot-level AGB and Lidar-derived Lorey’s height metrics (see Materials and Methods).

Regions / Countries Vegetation Type Number of Plots / Size Source North America Canada Boreal Samples from biomass map (66) Mexico Dry and Moist 114 (0.1 ha) Winrock/CONAFOR; https://www.winrock.org Mexico Dry 29 (0.1 ha) Winrock/USAID; https://www.winrock.org Temperate Evergreen, Deciduous, United States 131,619 (0.4 ha) USDA Forest Service (FIA); https://www.fia.fs.fed.us/tools-data Mixed Eurasia Norway Boreal 201 (0.02 - 0.04 ha) (78) Russia Taiga 2797 (79) Jurisdictional (NUTS) Carbon Estimates from Norway, Sweden, Temperate Evergreen, Deciduous, Europe Netherlands, Germany, Ireland, (80); https://www.efi.int/knowledge/models/efiscen/inventory Mixed, Boreal Conifers Poland, France, Spain, Switzerland, Italy, Portugal South & Central

America Pan Amazon Moist Tropical Forests 413 (0.25-2.25 ha) (81) Argentina Primary, Secondary Forests 56 (0.1 ha) (82) Pine Grassland and Woodland, Shrub Grassland, Pine-Oak Belize 187 Winrock/The Nature Conservancy; https://www.winrock.org Woodland, Upland Forest, Bajo Forest, Burned Forest Colombia Tropical Moist Forests 25 (1-ha) (83) Tropical Moist Forests, Montane Colombia 134 (0.1 ha), 16 (1.0 ha), (63, 84) Forests Colombia Tropical Wet Forests Choco 225 (0.25 ha) (63) Inundated, Liana, Secondary, Bolivia Semi-Deciduous, Deciduous, 176 (0.1-0.25 ha) (25) Evergreen Tropical Moist, Woodland, Brazil 737 (0.1-5 ha) (25) Savanna Atlantic Forests, Tropical Moist Brazil 844 (0.1 ha) (85) Forests, Cerrado Mixed Conifer Forests, Araucaria Santa Catarina Forest and Floristic Inventory (IFFSC); Brazil 449 (0.1 ha) Moist Forests https://www.iff.sc.gov.br Chile Lenga and Coigue Forest 35 (0.1 ha) Winrock/Wildlife Conservation Society; https://www.winrock.org Terra Firme, Inundated, Colombia 164 (0.1 - 1 ha) (25) Agroforestry Costa Rica Tropical Rain Forest 238 (0.5 ha) (86) Guyana Moist Tropical Forest 28 (1 ha) (87) Northwest South Tropical Inundated Forests 34 (1.0 ha) (88) America Panama Tropical Forest 50 plots (1 ha) FORESTGeo; https://forestgeo.si.edu Peru Terra Firme, Floodplain Forests 184 (0.1 - 1 ha) (25) Africa Botswana Woodland Savanna 3 (1 ha) (89) Cameroon Forest-Savanna Transition 11 (90) Central and West Old Growth Forests 79 (91) Africa Gabon Tropical Rain Forest 18 (0.1 - 1 ha) Ministry of Forest; http://www.eaux-forets.gouv.ga Guinea Various 574 (0.1 ha) Winrock; https://www.winrock.org Primary and Secondary, Mountain Kenya 240 (0.04 - 1 ha) (25) and Sub-Mountain, Woodland Madagascar Primary and Secondary 202 (0.1 - 1 ha) (25) Malawi Woodland Savanna 183 (0.1 ha) (92) Woodland Savanna, Mountain Mozambique Forests, Coastal Forests, Miombo 170 (0.1 - 1 ha) (93) Woodlands Republic of Congo Tropical Rain Forest 10 (0.1 ha) Winrock/USAID; https://www.winrock.org Tanzania Miombo Woodlands 88 (0.07 ha) (65) Moist Tropical Forests, (30); https://wwf.panda.org/?300412/A-National-Forest-Carbon- DRC 135 (1-ha) Woodlands Map-for-the-DRC Tropical Rian Forest, Forest- Uganda 141 (0.5-2 ha) (90) Savanna Transition Tropical Moist, Woodland, http://www.fao.org/forestry/16185- Republic of Congo 864 (0.5 ha) Savanna 0d614ff2aad80fd21ea10c619c0a28276.pdf Asia/Australia Australia Tropical Moist 25 (1 ha) (94) Primary, Peat Swamp, Indonesia 58 (0.1 ha) (25) Agroforestry Malaysia, Indonesia Tropical Old Growth, Peat Swamp 480 (0.1 - 1 ha) (95) Tropical Rain Forest, Undisturbed Philippines Dipterocarp Forest, Secondary 39 (0.1 - 1 ha, 250 m transect) (25) Tropical Rain Forest Vietnam Tropical Moist Forest 49 (0.1 ha) Winrock/USAID; https://www.winrock.org Indonesia Tropical Moist Forest 104(0.1-0.25) (64) Nepal Various Broadleaf and Needleleaf 216 (0.05 - 0.075 ha) ICIMOD, Nepal; https://www.icimod.org

Table S7. Sources of uncertainty in the global carbon estimation. The columns list different modeling procedures used in this study. The sources of error (assuming independent from each other) are described from pixel level to regional scale estimations. The treatments (in parenthesis) show how the uncertainty terms are used in the study.

Model ID A B C D Model procedure Plot-level Spatial mapping Univariate trend Flux calculations to allometries building using random forest model to estimate estimate the relationships machine learning the long-term committed between field model to predict change of carbon emissions of carbon biomass and wall-to-wall stocks. from forest clearing, lidar/radar samples biomass maps degradation, and trained from fire events. lidar/radar samples Pixel-level Predictor data Lidar/radar Wall-to-wall Time (accurate) Carbon estimates uncertainty measurements satellite data layers (inherited) (treatment) (ignored) (ignored) Model residual Available (ignored Estimable Estimable (not Not applicable uncertainty for time series (included) used) (treatment) analysis) Parameter modeling Not Available Estimable Estimable Estimable (inherited uncertainty (ignored) (included) (inherited C uncertainty; (treatment) uncertainty from modeled uncertainty Model B) for emission efficiency factors) Regional-scale Regional uncertainty Not Available Estimable (error Estimable Estimable (inherited (treatment) (ignored) covariance (inherited C uncertainty; approximated) uncertainty from modeled uncertainty Model B) for emission efficiency factors)