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https://doi.org/10.1038/s41558-020-00976-6
1World Resources Institute, Washington DC, USA. 2Woodwell Climate Research Center, Falmouth, MA, USA. 3Laboratory of Geo-Information Science
and Remote Sensing, Wageningen University and Research, Wageningen, the Netherlands. 4Biospheric Sciences Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA. 5Department of Geographical Sciences, University of Maryland, College Park, MD, USA. 6Center for International
Forestry Research (CIFOR), Bogor, Indonesia. 7Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. 8Institute of Environment,
University of California, Los Angeles, CA, USA. 9The Sustainability Consortium, University of Arkansas, Fayetteville, AR, USA. 10Present address:
Department of Earth and Environment, Boston University, Boston, MA, USA. 11Present address: Department of Land Resources and Environmental
Sciences, Montana State University, Bozeman, MT, USA. ✉e-mail: nharris@wri.org
Climate change must be addressed by various actors includ-
ing scientists, policymakers, companies, investors and civil
society, all of whom operate under different mandates and
capabilities. Both IPCC reports1,2 and the Paris Agreement3 rec-
ognize that climate change mitigation goals cannot be achieved
without a substantial contribution from forests but monitoring the
extent to which forests impact atmospheric greenhouse gas (GHG)
concentrations is challenging. Opposing fluxes (emissions from
sources (+) and removals by sinks (-)) occur simultaneously within
regions on the basis of where and when disturbance and manage-
ment take place, interannual variability can be high and land-use
patterns are more dynamic and operate on finer spatiotemporal
scales than reflected in most global models4. Furthermore, ability to
distinguish anthropogenic from non-anthropogenic effects is lim-
ited on the basis of direct observation2 and most estimation meth-
ods offer few details about where, when and why forest fluxes occur.
Yet understanding the magnitude, drivers and spatial distribution of
carbon fluxes across the world’s forests, and how they can be man-
aged both to reduce emissions and enhance removals, is increas-
ingly important for climate policy and the various actors developing
nature-based solutions5.
Current estimates of terrestrial GHG fluxes vary with respect
to scope, definitions, assumptions and level of transparency and
completeness. At the global scale, the net annual carbon diox-
ide (CO2) flux from anthropogenic land-use and land-cover
change—driven mainly by tropical deforestation—is estimated in
IPCC reports1,2 and the Global Carbon Project6 by a bookkeeping
model7,8 or by dynamic global vegetation models6. The remaining
non-anthropogenic sink of atmospheric carbon on land—predomi-
nantly forests9—is then inferred as the residual of the other terms
of the global carbon budget1. Another approach compiles national
GHG inventories (GHGIs), which reflect methodologies developed
by the IPCC and agreed to under the United Nations Framework
Convention on Climate Change10,11. The quality, methodological
complexity and sources of data used by each country vary, as do the
completeness and frequency of reporting. These approaches produce
dissimilar global net forest fluxes; GHGI estimates compiled from
country reports are 4.3 GtCO2 yr−1 lower than global estimates from
models summarized in IPCC reports—a discrepancy larger than the
total annual emissions of India, the world’s third highest emitter12.
A substantial part of this discrepancy (about 3.2 GtCO2 yr−1) can
be explained by conceptual differences in what is counted in the
anthropogenic forest sink. Beyond this large disparity in global esti-
mates, data and methodological mismatches also exist across proj-
ect, subnational and national forest GHG measurement systems,
leading to complications around integrating smaller-scale activities
into larger national or subnational monitoring programmes13 and
around the potential international transfer of forest-related emis-
sion reductions versus those achieved as part of a country’s own
nationally determined contribution14. In sum, the complexity and
lack of spatial detail in GHG measurement systems contributes to
confusion about the role forests play in climate mitigation targets
and discourages the transformational action and ambition needed
in the forest sector to achieve global climate goals.
Here, we introduce a transparent, independent and spatially
explicit global system for monitoring the collective impact of
Global maps of twenty-first century forest carbon
fluxes
Nancy L. Harris 1 ✉ , David A. Gibbs 1, Alessandro Baccini2,10, Richard A. Birdsey2, Sytze de Bruin 3,
Mary Farina2,11, Lola Fatoyinbo4, Matthew C. Hansen 5, Martin Herold 3, Richard A. Houghton2,
Peter V. Potapov 5, Daniela Requena Suarez 3, Rosa M. Roman-Cuesta6, Sassan S. Saatchi7,8 ,
Christy M. Slay 9, Svetlana A. Turubanova5 and Alexandra Tyukavina5
Managing forests for climate change mitigation requires action by diverse stakeholders undertaking different activities with
overlapping objectives and spatial impacts. To date, several forest carbon monitoring systems have been developed for differ-
ent regions using various data, methods and assumptions, making it difficult to evaluate mitigation performance consistently
across scales. Here, we integrate ground and Earth observation data to map annual forest-related greenhouse gas emissions
and removals globally at a spatial resolution of 30 m over the years 2001–2019. We estimate that global forests were a net
carbon sink of −7.6 ± 49 GtCO2e yr−1, reflecting a balance between gross carbon removals (−15.6 ± 49 GtCO2e yr−1) and gross
emissions from deforestation and other disturbances (8.1 ± 2.5 GtCO2e yr−1). The geospatial monitoring framework introduced
here supports climate policy development by promoting alignment and transparency in setting priorities and tracking collective
progress towards forest-specific climate mitigation goals with both local detail and global consistency.
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forest-related climate policies implemented by diverse actors across
multiple scales. We complement existing global forest carbon flux
estimation approaches of large area vegetation models and aggrega-
tion of national inventories with a third approach that capitalizes
on recent advances in Earth observation. Using recently revised
IPCC guidelines as a methodological framework10,11, we separately
map GHG emissions (sources) and carbon dioxide removals (sinks)
from global forest lands at 30-m resolution between 2001 and 2019
(Methods). Areas of forest extent, loss and gain from the Global
Forest Change product of Hansen et al.15 form the basis of the activ-
ity data. By co-locating activity data with spatially explicit emission
and removal factors developed from integrating ground and Earth
observation monitoring data on land use and management type,
forest type, forest age class, fire history and biomass and soil carbon
stocks, we separately map gross annual carbon removals occurring
within natural, seminatural and planted forests and gross annual
emissions arising from five dominant drivers of forest disturbance.
We then map the difference between gross emissions (+) and gross
removals (−) as the net annual forest-related GHG flux, which
may be positive or negative in an area depending on the balance
of gross fluxes. Tracking gross emissions and removals separately,
rather than solely the net balance between the two, underscores the
dual role of forests as sources and sinks in the global carbon cycle
and facilitates more complete and transparent accounting of the
individual pathways involved in forest-based mitigation (reducing
emissions and increasing removals).
Global distribution of forest emissions and removals
Between 2001 and 2019, deforestation and other satellite-observed
forest disturbances resulted in global gross GHG emissions of
8.1 ± 2.5 GtCO2e yr−1 (mean ± s.d.). Carbon dioxide (CO2) was the
dominant GHG; methane (CH4) and nitrous oxide (N2O) emis-
sions from stand-replacing forest fires and drainage of organic
soils in deforested areas accounted for 1.1% of gross emissions
(0.088 GtCO2e yr−1). Over the same period, gross carbon removals by
forest ecosystems were −15.6 ± 49 GtCO2e yr−1. Taken together, the
balance of these opposing fluxes (gross emissions and gross remov-
als) yields a global net GHG forest sink of −7.6 ± 49 GtCO2e yr−1
(Table 1 and Fig. 1). The large uncertainties in global gross remov-
als and net flux are almost entirely due to extremely high uncer-
tainty in removal factors from the IPCC Guidelines11 applied to old
secondary temperate forests outside the United States and Europe
(Supplementary Table 1).
Tropical and subtropical forests contributed the most to
global gross forest fluxes, accounting for 78% of gross emis-
sions (6.3 ± 2.4 GtCO2e yr−1) and 55% of gross removals
(−8.6 ± 7.6 GtCO2e yr−1) (Table 1). While these forests removed
more atmospheric carbon than temperate and boreal forests on a
gross basis (−8.6 versus −4.4 and −2.5 GtCO2e yr−1, respectively),
tropical and subtropical forests contributed just 30% to the global
net carbon sink; about two-thirds of the global net sink was in tem-
perate (47%) and boreal (21%) forests, resulting from substantially
lower gross emissions there than in the subtropics and tropics (0.87
and 0.88 versus 6.3 GtCO2e yr−1, respectively).
Just six large forested countries (Brazil, Canada, China,
Democratic Republic of the Congo, Russia and the United States)
accounted for 51% of global gross emissions, 56% of global gross
removals and 60% of net flux. Forests in nearly all countries were
net carbon negative, that is, gross carbon removals from established
and regrowing forests exceeded gross emissions from land-use
change and other forest disturbances. The main exceptions were
in Indonesia, Malaysia, Cambodia and Laos, where annual gross
emissions across these countries (1.36 GtCO2e yr−1), including peat
drainage and burning (0.14 GtCO2e yr−1), exceeded gross removals
(−0.83 GtCO2e yr−1) (Fig. 2). Globally, 72% of gross removals were
concentrated in older (>20 yr) secondary natural and seminatural
forests, 12% in tropical primary forests, 10% in plantations, 3.5%
in young (<20 yr) forest regrowth, 1.3% in mangroves and 0.34% in
boreal and temperate intact forest landscapes (Table 1).
Fluxes for specific localities and drivers of forest change
Our analysis enables consistent evaluation of forest GHG dynam-
ics across scales and in custom geographies beyond national or cli-
mate domain boundaries (Fig. 1). For example, ~27% of the global
net forest GHG sink occurred within protected areas16. Forests in
the Brazilian Amazon were a net carbon source of 0.22 GtCO2e yr−1
between 2001 and 2019, whereas forests across the larger Amazon
River basin—encompassing 514 Mha of forests across nine coun-
tries—were a net carbon sink of −0.10 GtCO2e yr−1. Although
smaller in extent than the Amazon, the net sink in forests of Africa’s
Congo River basin (298 Mha) was approximately six times stron-
ger (−0.61 GtCO2e yr−1), reflecting nearly identical gross removals
(−1.1 versus −1.2 GtCO2e yr−1) but gross emissions that were half
those of the Amazon basin (0.53 versus 1.1 GtCO2e yr−1).
From overlaying forest GHG flux maps in Fig. 1 with a global
map of dominant drivers of forest disturbance17, we estimate that
commodity-driven deforestation was the largest source of gross
forest-related emissions between 2001 and 2019 (2.8 GtCO2e yr−1)
and occurred primarily in the rainforests of South America and
Southeast Asia. Forests in shifting agriculture landscapes, a domi-
nant land use in the tropics characterized by cycles of small-scale
forest clearing of both primary and secondary forests followed by
secondary regrowth, contributed another 2.1 GtCO2e yr−1 to gross
emissions and −3.3 GtCO2 yr−1 to gross removals, leading to a
net sink in these areas of −1.2 GtCO2e yr−1. Gross emissions from
stand-replacing forest fires, occurring primarily in temperate and
boreal forests, averaged 0.69 GtCO2e yr−1. Forestry-dominated land-
scapes, comprised of both plantations and natural and seminatural
forests, were a net sink of −3.3 GtCO2e yr−1 between 2001 and 2019.
This reflects 2.4 GtCO2 yr−1 of gross emissions from harvest offset
by −5.5 GtCO2 yr−1 of gross removals from forest management and
regeneration and −0.16 GtCO2e yr−1 of increased carbon storage in
harvested wood products.
A flexible data integration framework
The IPCC Guidelines used as the overarching methodological
framework in this analysis10,11 provide three tiers of methods, param-
eters and data sources for GHG flux estimation, where progression
from Tier 1 to Tier 3 generally results in more accurate and precise
estimates at the expense of more analytical complexity and larger
data requirements. For forests, Tier 3 estimates are characterized by
the incorporation of repeated, country-specific measurements over
time but the land-use definitions and the spatial scale of data sources
chosen can impact the resulting estimates. Therefore, in addition
to estimating uncertainty in GHG estimates within geographies for
which information was available to do so (climate domains), we also
conducted sensitivity analyses to demonstrate how estimates change
as data inputs and model assumptions are varied within our spa-
tial data integration framework (Supplementary Information). At
the global scale, GHG flux estimates were relatively insensitive to
changes in model assumptions; estimates for most pixels changed
less than 15% in either direction and sources stayed sources while
sinks stayed sinks.
However, estimates were more affected by changes in data
sources, particularly at local scales. For example, replacing the
global 30-m biomass map developed in this study as the basis of
emission factors (Extended Data Fig. 1) with a coarser (1-km)
resolution biomass map produced by Saatchi et al.18 for the trop-
ics produced 12% lower gross GHG emissions there than our
original estimate. Replacing the 30-m annual tree cover loss data
from Hansen et al.15 in the Brazilian Amazon with annual forest
loss data from Brazil’s national forest monitoring system19, which
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excludes deforestation events smaller than 6.25 ha, reduced aver-
age gross emissions there from 1.1 to 0.74 GtCO2e yr−1. This differ-
ence arises from increased detection of emissions from small forest
clearings. Both examples highlight the value of our spatially detailed
approach in capturing more changes and larger fluxes occurring
at small scales where many human-induced forest changes are
occurring. In the United States, replacing Tier 3 removal factors esti-
mated specifically for US forest types and age classes from repeated
inventory measurements with generalized Tier 1 defaults from the
updated IPCC Guidelines11 led to a 38% stronger net carbon sink
there than the original estimate. (See Supplementary Table 2 and
Extended Data Figs. 2–8 for additional examples.) These analyses
quantitatively and spatially demonstrate tradeoffs between globally
consistent analyses and locally derived values that are difficult to
aggregate globally and may not be available or comparable across
regions. The flexible spatial data integration framework introduced
here enhances science-policy coordination by providing a more
systematic, structured, transparent and verifiable system for explor-
ing differences in data, assumptions and resulting estimates than
what has been available previously.
Forest fluxes in the global carbon budget
Our results are not directly comparable to other global estimates
because other estimates typically reflect all terrestrial fluxes (versus
forests only), report only net fluxes (versus gross and net fluxes),
include only CO2 (versus all relevant GHGs) and make assump-
tions to partition between anthropogenic and non-anthropogenic
net fluxes2,12. While the spatial, observation-based framework intro-
duced here permits estimation of fluxes for any forest definition and
the inclusion (or exclusion) of any geographic area of interest, it can-
not distinguish between anthropogenic versus non-anthropogenic
effects or between managed versus unmanaged land until the req-
uisite spatial data become available to differentiate them20. When
considering only CO2 fluxes to improve comparability with the
Table 1 | Forest-related GHG fluxes by climate domain and forest type
Climate
domain Forest type Forest
extent
2000
(Mha)
GtCO2eyr−1, 2001–2019
Gross
emissions Percentage
of global
total
Gross removals Percentage
of global
total
Net GHG flux Percentage
of global
totald
Boreal Primarya38 0.26 3.2 −0.044 0.28 0.22
Old secondary (>20 yr) 1,030 0.60 7.4 −2.4 15 −1.8
Young secondary (≤20 yr) 22 0.015 0.19 −0.037 0.24 −0.022
Plantations/tree cropsb0.21 0.000056 0.00070 −0.0027 0.017 −0.0027
Total boreal 1,090 0.88 ± 0.42 11 −2.5 ± 0.96 16 −1.6 ± 1.1 21
Temperate Primarya2.3 0.036 0.45 −0.0092 0.059 0.027
Old secondary (>20 yr) 560 0.71 8.8 −4.2 27 −3.5
Young secondary (≤20 yr) 16 0.049 0.60 −0.039 0.25 0.0092
Plantations/tree cropsb12 0.071 0.88 −0.14 0.92 −0.073
Total temperate 590 0.87 ± 0.60 11 −4.4 ± 48 28 −3.6 ± 48 47
Subtropical Primarya3.6 0.0062 0.076 −0.0058 0.037 0.00035
Old secondary (>20 yr) 270 0.46 5.7 −0.84 5.4 −0.38
Young secondary (≤20 yr) 13 0.11 1.3 −0.067 0.43 0.040
Plantations/tree cropsc54 0.40 5.0 −0.71 4.6 −0.31
Mangroves 0.070 0.000066 0.00082 −0.0040 0.026 −0.0040
Total subtropical 340 1.0 ± 0.59 12 −1.6 ± 0.56 10 −0.65 ± 0.81 8.6
Tropical Primarya1,010 1.8 22 −1.9 12 −0.12
Old secondary (>20 yr) 880 1.9 23 −3.8 24 −1.9
Young secondary (≤20 yr) 47 0.76 9.5 −0.40 2.5 0.37
Plantations/tree cropsc47 0.89 11 −0.73 4.7 0.16
Mangroves 7.2 0.010 0.12 −0.16 1.0 −0.15
Total tropical 1,990 5.3 ± 2.4 66 −7. 0 ± 7.6 45 −1.7 ± 8.0 22
Global Primary 1,060 2.1 26 −2.0 13 0.13
Old secondary (>20 yr) 2,750 3.7 45 −11 72 −7.7
Young secondary (≤20 yr) 99 0.9 12 −0.54 3.5 0.39
Plantations/tree crops 113 1.4 17 −1.6 10 −0.23
Mangroves 8.7 0.012 0.14 −0.20 1.3 −0.19
Total global 4,029 8.1 ± 2.5 100 −16 ± 49 100 −7.6 ± 49 100
Average annual gross GHG emissions, gross GHG removals and net GHG fluxes across global forest lands between 2001 and 2019. Estimates reflect forest ecosystem fluxes only; harvested wood
products are excluded. Uncertainties are expressed as s.d. Large uncertainties in net flux estimates should be interpreted with caution; s.d. are very large relative to the estimates in part because net flux
estimates reflect the sum of negative (removals) and positive (emissions) terms, complicating the combination of their error terms. aThe extent of primary forests was delineated differently for tropical and
extratropical regions (Methods). bFluxes occurring within seminatural managed forests are reported in the relevant secondary forest category (old or young). cFluxes reported in the plantation/tree crop
category include those associated with conversion of natural forests to plantations or tree crops (for example, oil palm) over the 2001–2019 analysis period. dCalculating percentages of net flux by forest
type is complicated by the mixture of sources and sinks among forest types, and is thus omitted.
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Global Carbon Budget, we estimate a larger net CO2 sink by forest
ecosystems (−7.8 GtCO2 yr−1) than its estimate of −5.2 GtCO2 yr−1
for all terrestrial fluxes over the same time period6. One potential
reason for this difference is that our model underestimates gross
forest-related emissions due to the exclusion of forest disturbances
that go undetected and unquantified in the medium resolution sat-
ellite observations that underpin our analysis. Gross emissions from
tropical forest degradation have been estimated as 2.1 GtCO2e yr−1,
with selective logging, fuelwood harvest and non-stand-replacing
fires accounting for 53, 30 and 17% of the total, respectively21.
Adding this (non-spatial) estimate of gross degradation emissions
to our satellite-based gross carbon emission and removal estimates
occurring within forest ecosystems, as well as −0.16 GtCO2 yr−1
of net removals in harvested wood products, yields a revised net
forest-atmosphere CO2 flux of −5.8 GtCO2 yr−1 (Table 2). Taken
together, these estimates of gross removals (−15.6 GtCO2 yr−1)
and gross emissions related to forests (including degradation:
10 GtCO2 yr−1) appear to nearly balance the global carbon budget
Net forest
GHG flux
MtCO2e yr–1 (2001–2019)
0.17
0
–0.087
Gross forest
GHG emissions
MtCO2e yr–1 (2001–2019)
0.21
0
Gross forest
GHG removals
MtCO2e yr–1 (2001–2019)
0
–0.089
a
b
c
Fig. 1 | Forest-related GHG fluxes (annual average, 2001–2019). a, Gross annual GHG emissions. b, Gross annual GHG removals. c, Net annual GHG flux.
For display purposes, maps have been resampled from the 30-m observation scale to a 0.04° geographic grid. Values in the legend reflect the average
annual GHG flux from all forest dynamics occurring within a grid cell, including emissions from all observed disturbances and removals from both forest
regrowth after disturbance as well as removals occurring in undisturbed forests.
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(Table 2) but other important fluxes are omitted from our analysis
such as those occurring within grasslands, semi-arid savannas and
shrublands22 (due to the 30% per 5 m of tree cover definition used
in our analysis), non-stand-replacing fires23, degradation outside
the tropics and other terrestrial fluxes not previously included in
any global budget to date24. We include Table 2 to highlight how
our gross estimates of forest-related fluxes fit within the context of
the global carbon budget but our research is geared towards high-
lighting forest emission and removal hotspots for policy-relevant
applications and stakeholders (Fig. 1), not towards producing
a comprehensive and precise accounting of the full terrestrial
carbon budget.
Limitations and future improvements
All forest monitoring systems reflect a balance between data avail-
ability, s cale of applicability, measurement costs, reducing uncertain-
ties and other constraints. Given the urgency of addressing climate
change, the time and costs required to develop monitoring systems
that reduce uncertainties as far as practicable25 must be balanced
against the potential benefits of publicly accessible, operational and
fit-for-purpose systems that provide enough spatial detail to incen-
tivize real, near-term and sustained investment in nature-based cli-
mate solutions on the ground. In this study, we combined publicly
available data into a global monitoring framework that generates
consistent information on forest carbon fluxes cost-effectively over
large spatial scales. However, this approach encounters limitations
that should be addressed as research progresses.
First, the global forest change data used as the basis of activ-
ity data in our analysis are spatially detailed but contain temporal
inconsistencies. While the forest loss product is updated annually
through 2019, gain has not been updated past 2012 and represents a
cumulative total (2000–2012). Therefore, although gross emissions
can be estimated annually (Extended Data Fig. 9), estimating annual
trends in gross removals and net flux is limited by a lack of a con-
sistent time series on forest regrowth. Globally, GHG flux estimates
were relatively insensitive to this limitation; we estimate that expan-
sion of forest extent observed after 2000 accounted for less than 5%
of global gross carbon removals, with the vast majority occurring
Democratic Republic
of the Congo
Angola
Ivory Coast
Mozambique
Madagascar
Other countries
France
Sweden
Poland
Spain
Finland
Other countries
Australia
Papua New Guinea
New Zealand
Solomon Islands
Brunei
Other countries
Russia
China
Myanmar
Malaysia
Indonesia
Other countries
Canada
United States
Mexico
Guatemala
Nicaragua
Other countries
Brazil
Colombia
Peru
Bolivia
Paraguay
Other countries
Oceania South America
Europe North America/Central America/Caribbean
Africa Asia
−2,000 −1,000 0 1,000 2,000 −2,000 −1,000 0 1,000 2,000
Average annual flux (MtCO2e yr–1)
Annual gross removals
Annual gross emissions
Annual net flux
Fig. 2 | Gross and net GHG fluxes from forests by region (annual average, 2001–2019). Net forest-related fluxes (grey bars) are shown with their two
component gross fluxes: gross emissions from land-use change and other forest disturbances (purple) and gross removals occurring in undisturbed
forests as well as removals from forest regrowth after disturbance (green). The top five countries per region are ranked high to low on the basis of gross
emissions, with all other countries in the region grouped into ‘other countries’.
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instead in forests established before 2000. However, accurate moni-
toring of the timing of recent regrowth becomes more important in
local contexts where rapid forest loss/gain dynamics are occurring,
such as in plantations with short rotation cycles and other dynamic
areas dominated by intensive forestry or short-fallow shifting cul-
tivation systems (Extended Data Fig. 5). Temporal inconsistencies
are also present within the global loss product; one algorithm cov-
ers years 2001–2010 and another covers 2011–2019, with later years
of loss likely to be more sensitive to changes related to small-scale
agriculture, fires and other forms of forest degradation. For these
reasons, we report only long-term averages and not annual trends in
forest GHG fluxes. A forthcoming ‘version 2’ global tree cover loss
product and an improved global gain product, already piloted for
the lower Mekong region of Southeast Asia26, will improve temporal
consistency. Incorporating these improvements into the forest GHG
flux model will more accurately capture interannual variability in
emissions and removals over time and will thus provide a consistent
basis for more temporally detailed monitoring of the long-term net
impact of forests on atmospheric GHGs27.
Second, information is currently lacking to develop globally con-
sistent and spatially detailed maps of forest carbon removals. In our
analysis, uncertainty in gross removals is substantially higher than
uncertainty in gross emissions, driven primarily by high uncer-
tainty in removal factors for established forests in temperate regions
(Table 1 and Supplementary Table 1). Through the integration of
ground and Earth observation data, several biomass and soil carbon
maps have been developed that inform spatially explicit emission
factors. However, accurate and precise estimation of forest carbon
removal factors requires information derived from long-term forest
inventories applied consistently and repeatedly through time across
different forest types and age classes. For many of the world’s for-
ests, this information does not exist28. Many developing countries
have not completed their first forest inventory, let alone repeated
inventories. Efforts to combine georeferenced plot networks with
other spatially explicit data inputs to create maps over large scales
of forest carbon accumulation rates over time, similar to what has
been done to develop biomass density maps at a single point in
time, have begun but are still in their infancy29. We therefore applied
removal factors using a stratification approach, where each forest
pixel is assigned a removal factor on the basis of its geographic
region, forest type and age class (Methods). Removal factors reflect
both ecological forest dynamics (tree growth, mortality and recruit-
ment through natural regeneration) and indirect effects (long-term
increases in atmospheric CO2 concentrations and temperature,
nutrient fertilization). Going forward, new satellite missions such
as GEDI, ICESAT-2 and BIOMASS will provide repeated measure-
ments of forest height and biomass over time that should improve
understanding of spatial variation in rates of carbon removal across
heterogeneous forest landscapes.
The global forest carbon monitoring framework introduced here,
and the main improvements identified above, allow for efficient pri-
oritization and evaluation of how data updates and improvements
influence GHG flux estimates and their uncertainties. As satellite-
and ground-based forest monitoring improve, so too will the associ-
ated forest GHG flux estimates.
Conclusions
Our analysis reinforces the need to reduce gross emissions from
tropical deforestation as a climate change mitigation strategy,
while also highlighting the substantial but often underappreciated
contribution of intact primary and older secondary forests to car-
bon dioxide removals. Quantifying gross emissions and removals
separately and consistently across all forest lands—and producing
maps in addition to tabular statistics—improves transparency in
the accounting of factors and geographies contributing to the global
net forest GHG flux. It also provides a framework to integrate new
and improved data sources over time. Governments interested in
spatially prioritizing implementation and tracking of national and
subnational forest mitigation targets can increasingly make use of
such data. Non-government actors, such as companies aiming to
reduce emissions from deforestation associated with commodity
supply chains and emerging market mechanisms considering the
inclusion of forests for carbon offset programs, could benefit from
a globally consistent and spatially explicit forest monitoring sys-
tem developed using the same internationally accepted methods as
national governments use but based on independent observations
and with GHG estimates that can be linked to individual actions
and generated at scales relevant to diverse climate-related policies,
programmes and stakeholders.
The goals of the Paris Agreement—primarily, net zero anthro-
pogenic emissions in the second half of this century—create an
imperative to track forest-related emissions and removals trans-
parently and at scales that link more closely to mitigation activities
on the ground. As the capacity of national governments to collect,
process and analyse data continues to improve, the global forest
carbon monitoring framework introduced here can help to enhance
transparency, inform forest-related climate policy and implemen-
tation initiatives, underpin independent technical assessments,
reconcile differences between national reports and scientific
studies, and provide a more consistent and comparable basis for
tracking progress at local scales and for assessing atmospheric
Table 2 | Comparison of results from this study to the Global
Carbon Project, 2001–2018
Global carbon budget, 2001–2018 (GtCO2yr−1)
Global Carbon Project This study
Sources
Fossil fuel and cement 32.0 Fossil fuel and cement 32.0
Land-use change (net,
anthropogenic)a
5.3 Forests (gross, all observed
disturbances)b
7.9
Forests (gross, unobserved
emission sources)c
2.1
Total sources 37. 3 42.0
Sinks
Atmosphere 16.9 Atmosphere 16.9
Ocean 8.7 Ocean 8.7
Terrestrial (net,
non-anthropogenic)d
10.5 Forests (gross, all forests)e15.6
Harvested wood products 0.16
Total sinks 36.1 41.4
Land (net, all land) −5.2 Forests (net, all forests)f−5.8
Budget imbalanceg1.2 0.6
Estimates from the Global Carbon Project (GCP)6 and this study are not directly comparable
due to differences in scope (all land versus forests, respectively), data, methodologies and
reporting structure. In GCP reporting, land-use change emissions (sources) reflect the net
balance between anthropogenic emissions (+) and removals (–), thus the net emission estimate
is lower than gross emissions reported in this study. Similarly, gross removals reported in this
study reflect removals across all forest lands, including removals implicit (but unreported) in the
net land-use change estimate of GCP. aEstimates only net direct anthropogenic effects, including
deforestation, afforestation/reforestation and wood harvest. Gross fluxes higher but not reported.
bGross emissions from all forest disturbances (anthropogenic and non-anthropogenic) observed
from Landsat data. Estimate includes CO2 only for comparability with GCP; non-CO2 emissions
are 0.086 GtCO2e yr−1. cGross emissions from forest degradation in 74 developing countries
covering 2.2 billion hectares of forest, from Pearson et al.21. dIn IPCC’s Fifth Assessment Report1,
calculated as the residual of all other terms in the carbon budget. eGross removals from all forest
processes (direct, indirect and natural). fCalculated as the net balance between gross forest
ecosystem emissions and removals (7.9 + 2.1–15.6 GtCO2 yr−1) plus an additional net removal of
−0.16 GtCO2 yr−1 in harvested wood products. gBudget imbalance is the difference between total
sources and total sinks.
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impacts of global forest change under the Paris Agreement’s forth-
coming Global Stocktake30.
Online content
Any methods, additional references, Nature Research report-
ing summaries, source data, extended data, supplementary infor-
mation, acknowledgements, peer review information; details of
author contributions and competing interests; and statements of
data and code availability are available at https://doi.org/10.1038/
s41558-020-00976-6.
Received: 15 May 2020; Accepted: 3 December 2020;
Published: xx xx xxxx
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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2021
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Articles Nature Climate ChaNge
Methods
Study design and scope. We mapped gross and net GHG emissions by sources and
removals by sinks from global forest lands by synthesizing information collected
from more than 637,000 ground plots, 707,561 waveform lidar observations
and other satellite data into a spatial forest carbon monitoring framework. e
analysis covers 2001 to 2019 but can be extended to include later years as data are
updated. To the extent possible, we adhered to IPCC Guidelines developed for
the agriculture, forestry and other land use (AFOLU) sector10,11. In the context of
IPCC land-use categories, our analysis covers only forest-related transitions (forest
to non-forest, non-forest to forest and forest remaining forest). We applied the
IPCC gain-loss method (versus the stock-dierence method10), in which forest
carbon (C) stocks in ve ecosystem pools were estimated for a base year (2000)
aer which changes in C stocks were estimated by considering both annual C
losses from land-use change and disturbance (conventionally represented by a
+ sign) as well as annual C gains from forest regrowth (represented by a – sign).
We included harvested wood products as a sixth (human-created) carbon pool.
We also included methane (CH4) and nitrous oxide (N2O) emissions from
stand-replacement forest res and drainage of organic soils associated with a loss
of tree cover. We summarized GHG uxes across all relevant gases and reported in
units of CO2 equivalents (CO2e) using 100-yr Global Warming Potentials (without
climate feedbacks) from the IPCC Fih Assessment Report1.
We set all data inputs to a common resolution of 0.00025° × 0.00025° to
match the resolution of Landsat-based tree cover change data of Hansen et al.15.
Gross emissions and removals were modelled at this common resolution across
approximately 90 billion individual pixels of global forest cover (defined below).
We resampled all input layers to this resolution so that outputs can be flexibly
aggregated to larger scales. Extended Data Fig. 10 summarizes the overall
conceptual approach and Supplementary Table 3 provides a list of data inputs.
Forest definition and extent. Initially, we defined forest extent in the year 2000
similarly to Hansen et al.15, that is, any 30-m Landsat pixel that met a tree canopy
threshold of at least 30% with trees taller than 5 m in height. This initial definition
included natural and seminatural forests, plantations and agricultural tree crops
such as oil palm and agroforestry systems where minimum height and cover
thresholds were met. On the basis of available data, we made four modifications to
the original tree cover map to refine our global map of forest extent:
1. We included pixels of tree cover gain since 2000 in addition to tree cover
already present in the year 2000.
2. We included only tree cover pixels that also had a corresponding value in
the aboveground biomass density map (0.031% of tree cover pixels lacked a
biomass value).
3. We excluded all areas of tree cover falling within oil palm plantation bounda-
ries mapped for the year 2000 in Indonesia and Malaysia31–34.
4. We replaced tree cover extent from Hansen et al.15 with mangrove forest
extent using data from Giri et al.35; in areas of geographic overlap, mangroves
had priority.
Forest aboveground live biomass density in 2000. We created a year 2000 map
of aboveground live biomass density (AGB, in Mg ha−1) at 30-m resolution by
combining two maps: one developed specifically for mangroves36 and the other
developed to cover all woody vegetation globally (Supplementary Data 1). In areas
of geographic overlap, the mangrove biomass map had priority. The basic approach
is the same as that used to map tropical biomass at 500-m (ref. 37) and 30-m (ref. 38)
resolution; published height–biomass equations were applied to estimate biomass
over specific regions and forest types around the world (Extended Data Fig. 1a).
These equations, developed by linking observations from airborne or spaceborne
lidar to 20,347 ground-measured biomass plots, were applied to estimate
aboveground biomass density from spaceborne lidar observations across 707,561
locations globally. To create a continuous biomass map (Extended Data Fig. 1b),
separate random forest models were trained for each of six biogeographic realms
using predictor variables of Landsat imagery (bands 3, 4, 5 and 7), normalized
difference vegetation index (NDVI), normalized difference infrared index (NDII),
mean percentage tree cover, mean elevation, mean slope and monthly mean
precipitation, temperature and bioclimatic data. Additional details are provided in
Supplementary Data 1.
Forest ecosystem carbon pools in 2000. From the 30-m global AGB map, we
mapped belowground live biomass density (BGB) using a forest root-to-shoot
ratio39 with mangrove-specific ratios based on defaults provided in Table 4.5 of
the 2013 IPCC Wetlands Supplement40. AGB and BGB values were converted to
C density values using a biomass-to-carbon ratio of 0.45 for mangroves40 and 0.47
for all other forest types10,11. From the final 30-m AGB map we estimated dead
wood and litter biomass densities per pixel as constant fractions of AGB using a
lookup table based on global ecological zone, elevation and precipitation regime41
(Supplementary Table 4). Dead wood and litter biomass densities were converted
to C densities using IPCC conversion factors10.
Soil organic carbon density in the top 30 cm of mineral soils was mapped using
SoilGrids250 (v.2.0)42 after resampling from its original spatial resolution of 250 m
to match the common 30-m resolution of our analysis. For mangrove forests, we
used a 30-m soil carbon map developed specifically for mangroves43. We delineated
locations of organic (peat) soils using maps summarized in Supplementary Table 3.
We used these five forest carbon pool maps as the basis for estimating emission
factors associated with various forest disturbances (see below).
Activity data. Activity data were defined using the global forest change product of
Hansen et al.15 with loss updated annually on Global Forest Watch. In the model,
all pixels defined as forest were classified into one of four categories: (1) loss only;
(2) gain only; (3) both loss and gain; or (4) no change over the period 2001–2019.
Loss is defined by Hansen et al. as a stand-replacement disturbance and includes
all disturbances (natural and anthropogenic) observable in Landsat imagery. Gain
is defined as a non-forest to forest change, which includes tree cover gain observed
after harvest and other disturbance. The loss product is annual, while the gain
product represents a cumulative total (2000–2012). Loss and gain can co-occur
on pixels undergoing forest management or other forms of disturbance and
regrowth. Lack of annually updated gain data is addressed through the sensitivity
analysis (Extended Data Fig. 5). Due to a lack of information about tree cover gain
after 2012, we assumed no additional areas of gain from 2012 to 2019. Areas of
no change reflect forest areas established before 2000 that showed no observable
disturbance in Landsat imagery between 2000 and 2019.
Emission factors. We assigned emission factors to tree cover loss pixels following
an IPCC land-use classification framework, on the basis of whether each pixel
maintained its land use or was converted to a new use over the analysis period.
Since forest may remain in the same use despite a temporary loss of tree cover, we
used the global 10-km map of Curtis et al.17 (updated through 2019) to attribute
tree cover loss to one of five dominant drivers; these influence the C pools affected
(Supplementary Table 5) and thus the emission factors assigned to each individual
loss pixel. Supplementary Table 6 summarizes emission factors by forest type
within each climate domain.
Commodity-driven deforestation and shifting agriculture. The initial change in C
stocks was estimated as a full loss of C in aboveground, belowground, dead wood
and litter pools. In addition to CO2 emissions resulting from a loss of C stocks, we
used IPCC equation 2.27 (ref. 10) and a 1-km global burned area map44 to calculate
CH4 and N2O emissions in loss pixels that overlapped with areas that burned the
same year or the year before (to account for lag effects between fire occurrence
and observed tree cover loss). For deforestation on mineral soils, soil C loss was
estimated using IPCC equation 2.25 (ref. 10); default soil stock change factors vary
by ecological zone and were assigned spatially using ecozone boundaries45. Per
IPCC guidelines, 1/20th of the total soil C stock change was apportioned annually
from the year of loss through the last year of the analysis period (2019) but
assigned to the year of observed tree cover loss. Due to lack of information in the
driver attribution map17 about the specific land use established after forest clearing,
we assumed for the purposes of soil emission accounting that all deforested land
on mineral soils for commodity-driven deforestation was converted to annual
cropland with full tillage and medium inputs. A different factor was used to
estimate loss of soil C on mineral soils (Table 5.10 in the IPCC Guidelines10) in
areas of shifting agriculture, which were assumed to represent transient land-use
conversions to cropland under shortened fallow, where vegetation recovery is
not attained before re-clearing. Soil emissions were not estimated for areas of
loss on mineral soils that overlapped with forest and wood fibre plantations,
even if they fell within the broader commodity-driven deforestation or shifting
agriculture classes, consistent with the assumption that loss of tree cover within
tree plantations follows the forestry assumptions listed in Supplementary Table 5
(see emissions from Forestry below). For loss on organic soils that overlapped with
tropical plantations and tree crops planted since 2000, GHG emissions associated
with drainage were estimated using CO2 and CH4 emission factors provided in the
IPCC Wetlands Supplement40. Like emissions from mineral soils, emissions from
peat drainage were assumed to continue in each year after loss up through the last
year of the analysis period (2019) but were assigned to the year of observed tree
cover loss. Emissions (CO2, CH4 and N2O) from peat burning were also calculated
on the basis of methods provided in the IPCC Wetlands Supplement40 where a loss
pixel overlapped with areas burned the same year, or the year before, the loss event
(on the basis of global burned area data).
Urbanization. The same assumptions and calculations were used for calculating
gross emissions from urbanization as for commodity-driven deforestation and
shifting agriculture, except a different factor was used to estimate the loss of soil C
on mineral soils. We assumed that forest land converted to settlement was paved
over and applied the IPCC default assumption11 that 20% of the soil C relative to
the previous land use was lost as a result of disturbance, removal or relocation.
Forestry. Emission factors for loss attributed to forestry were estimated as the loss
of C in live biomass only, following assumptions outlined in Supplementary Table 5
that there is no net change to the dead organic matter or soil C pools in the case of
mineral soils. Emissions from peat drainage and burning associated with forestry
activities, as well as non-CO2 emissions in the case of forest fires, were included in
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the same way as for deforestation and shifting agriculture above. Emission factors
for loss pixels within the ‘zero or minor loss’ category of the driver attribution map
also followed assumptions for forestry (Supplementary Table 5).
Wildfire. Within 10-km grid cells of the drivers map labelled wildfire, wildfire
emission factors were applied only for 30-m pixels where loss occurred in the year
of, or year after, a fire event in the 1-km burned area map. In these cases, we used
IPCC equation 2.27 (ref. 10) to estimate both CO2 and non-CO2 emissions from
forest fire. The AGB map determined the mass of fuel available for combustion and
a lookup table (Table 2.6 of the IPCC Guidelines10) provided default combustion
and emission factors that were applied on the basis of forest type (primary versus
secondary). For boreal and temperate forests, combustion factors were applied on
the basis of the assumption of a land-clearing fire, given that forest loss is defined
in Hansen et al.15 as a stand-replacement disturbance. In cases where organic soils
overlapped with burned areas, emissions from peat burning (CO2, CH4 and N2O)
were estimated following guidance in the IPCC Wetlands Supplement40. Forestry
emission factors, rather than wildfire factors, were applied where loss did not
overlap with a fire event in the 1-km burned area map.
Removal factors. We developed removal factors spatially by linking information
about each pixel’s geographic region, ecological zone, forest type and age
class to corresponding growth rates on the basis of best available information.
Supplementary Table 6 summarizes removal factors by forest type in each climate
domain. In areas of geographic overlap, the priority of assigning removal factors to
a given pixel reflects the order of data sources listed below. Removal factors include
accumulation in live biomass only and reflect the net increase, accounting for both
productivity and mortality. We assumed no change to the dead organic matter and
soil organic carbon pools, consistent with the IPCC Tier 1 assumption of no net
change to non-biomass pools in forest land remaining forest land. The number
of years of carbon accumulation was assigned as 19 yr for undisturbed forest, 6 yr
for areas of new tree cover gain and one less than the year in which tree cover loss
occurred for loss-only forest.
Mangroves. We applied mangrove-specific growth rates and root-to-shoot ratios
from IPCC Tables 4.4 and 4.5 of the Wetlands Supplement40, respectively.
Europe. We assigned removal factors spatially according to a map of dominant
tree species developed from 260,000 national inventory plot locations46. For each
species, we estimated mean annual increment (MAI) values from Table 4.11 of
the updated IPCC Guidelines11, FAO Planted Forest Assessment47 and national
inventories48 (Supplementary Table 3). These were converted to aboveground
biomass growth rates using species-specific biomass conversion and expansion
factors and belowground biomass increment was added on the basis of a
root-to-shoot ratio39.
Plantations and tree crops. Outside Europe, we assigned removal factors for
plantations and tree crops using a variety of published data sources49. For
common plantation species, we used MAI and biomass conversion and expansion
factors summarized in the updated IPCC Guidelines11 to estimate aboveground
biomass increment and added belowground biomass increment on the basis of a
root-to-shoot ratio39. Rates in plantations were assigned on the basis of mapped
species when known or, when unknown, the most common mix of plantation
species grown in the region. Removal factors for tree crops such as oil palm
and rubber as well as various types of agroforestry systems were estimated for
areas mapped as such on the basis of regionally specific values derived from the
published literature and from Tables 5.1 and 5.3 of the updated IPCC Guidelines11.
All removal factors used for plantations and tree crops, along with data sources and
assumptions applied, are provided in the companion spatial attribute file associated
with the global compilation of planted tree maps used in this analysis49.
United States. We developed removal factors for three age classes (0–20, 20–100
and >100 yr) for forest types across 11 geographic regions using methods
broadly similar to those of Smith et al.50, except that we included more forest
types in each region, as well as more recent and comprehensive data from the US
Forest Inventory and Analysis database. Removal factors were developed from
approximately 130,000 inventory plot locations. Pixels were assigned removal
factors on the basis of dominant forest type51, age class52 and geographic
inventory region.
Young secondary forests. Outside the United States and Europe, areas of tree cover
gain that fell outside boundaries of mangroves35 and planted trees49 were assumed
to be secondary natural forest regrowth <20 years old. We assigned natural forest
regrowth removal factors to these areas using the 1-km map of Cook-Patton et al.29.
Primary forests. We used removal factors by ecological zone and continent from
IPCC Table 4.9 of the 2019 IPCC Refinement11 and assigned them spatially
between 30° N and 30° S within a tropical primary humid forest map53. Outside
30° N and 30° S, we used a map of intact forest landscapes54 as a proxy for primary
forests, which is likely to be highly conservative due to the relatively large extent
criterion applied but represents the best available information by which to spatially
delineate primary from old secondary forests in boreal and temperate regions.
Old secondary forests. We assigned removal factors from IPCC Table 4.9 (>20 yr)
to all forest areas that fell outside the types identified above. Given no observed
disturbance occurred in these areas since the year 2000, we assumed they were
secondary natural forests at least 20 years old.
Harvested wood products. We used statistics reported in FAOSTAT and methods
outlined in the 2019 Refinement11 to estimate emissions and/or removals arising
from harvested wood products. Losses of harvested wood products in use
were assumed to result in CO2 emissions to the atmosphere, with no explicit
representation of the subsequent retention of disposed wood in solid waste
disposal sites (SWDS) and eventual CO2 emissions from SWDS. Calculations rely
on statistics reported by countries on production, import and export volumes
for three aggregate semifinished wood product commodity classes: sawnwood,
wood-based panels and paper and paperboard.
Uncertainty analysis. We estimated uncertainty in GHG flux estimates globally
and at the scale of climate domains by combining uncertainties in the activity
data and emission/removal factors following a Taylor series statistical approach
as in Roman-Cuesta et al.55 and Carter et al.56. This approach underlies the
IPCC Approach 1 (simple error propagation)10 and produces similar results but
reflect exact calculations of variances and s.d., whereas IPCC Approach 1 is an
approximated approach that yields 95% confidence intervals.
Uncertainties of all major components of the flux model were included
(activity data, affected C pools of the emission/removal factors, combustion and
emission factor uncertainties for fire-related emissions). Errors were assumed to
be statistically independent (uncorrelated), normally distributed and without bias.
Supplementary Table 1 shows the contribution of each uncertainty component
for domain and global gross emissions, removals and net flux, reported as the
percentage reduction in output variances as each of the uncertainty components
were assumed to have no variance. Variance of the net GHG flux was reduced
the most when removing variance of the removal factor for temperate forests
older than 20 yr. Variances are likely to be lower when estimated across smaller
geographic regions. Estimation of uncertainty is currently limited to the global
and biome scales based on available data for estimating uncertainty in the
activity data.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
Geospatial data generated from the current study are publicly available on Global
Forest Watch’s Open Data Portal (http://data.globalforestwatch.org/) and from the
corresponding author upon request. Summary geospatial statistics are available
from the corresponding author upon request. All data inputs used in the current
study are publicly available or were obtained by the corresponding author.
Code availability
To ensure full reproducibility and transparency of our research, we provide all of
the scripts used in our analysis. Codes used for this study are permanently and
publicly available on GitHub (https://github.com/wri/carbon-budget).
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Acknowledgements
We thank S. Gibbes for her work on preliminary model development and T. Maschler
for his contributions to workflows enabling efficient data processing and generation
of summary statistics. Support for this research was funded in part by the Norwegian
Ministry of Foreign Affairs (18/2721 Global Forest Watch Achieving Sustainability and
Scaling Impact), the UK Department for International Development (DFID FGMC
grant no. FGMC2018-21-WRI) and the US Agency for International Development
(cooperative agreement no. 7200AA19CA00027 Global Forest Watch 3.0) in support
of the Global Forest Watch Partnership convened by the World Resources Institute, by
National Aeronautics and Space Administration Earth Science Division NNH12ZDA001
NICESAT2: studies with ICESAT and CryoSat-2 grant no. 12-ICESAT212-0022 to the
Woods Hole Research Center and by the NASA Carbon Monitoring System Program
Project ‘Estimating Total Ecosystem Carbon in Blue Carbon and Tropical Peatland
Ecosystems’ (16- 30 CMS16-0073) to NASA Goddard. The contribution of M.H., S.deB.
and D.R.S. was supported by CIFOR’s global comparative study on REDD+ (funded
by NORAD), the European Space Agency CCI-Biomass project and the European
Commission Horizon 2020 projects VERIFY (grant no. 776810) and REDD-Copernicus
(grant no. 821880). Data used in part of this publication were made possible, in part, by
an agreement from the United States Department of Agriculture’s Forest Service. This
publication may not necessarily express the views or opinions of the Forest Service.
Author contributions
N.L.H. was involved in conceptualization, data curation, formal analysis, funding
acquisition, investigation, methodology, project administration, visualization and
writing. D.A.G. contributed to data curation, formal analysis, investigation, methodology,
software, validation, visualization and writing. A.B., R.A.B., R.R.C., M.F., L.F., M.C.H.,
R.A.H., P.V.P., C.M.S., D.R.S., S.S.S. and S.A.T. contributed to data curation, formal
analysis, methodology and writing. M.H. contributed to data curation, visualization and
writing. S.deB. and A.T. contributed to formal analysis and methodology.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/s41558-020-00976-6.
Supplementary information is available for this paper at https://doi.org/10.1038/
s41558-020-00976-6.
Correspondence and requests for materials should be addressed to N.L.H.
Peer review information Nature Climate Change thanks Gert-Jan Nabuurs, Seth Spawn
and the other, anonymous, reviewer(s) for their contribution to the peer review of this
work.
Reprints and permissions information is available at www.nature.com/reprints.
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Extended Data Fig. 1 | Aboveground live woody biomass density in the year 2000. a, Subsets of ecoregions over which different height–biomass
equations were applied. Patterned shading indicates equations that were only applied to conifer GLAS shots within the specified ecoregion. b, Global 30-m
map of aboveground live woody biomass density in the year 2000.
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Articles Nature Climate ChaNge
Extended Data Fig. 2 | Results of sensitivity analysis when the source of tree cover loss data used in the forest GHG flux model is changed from the
30-m tree cover loss product of Hansen et al.15 in the standard model to PRODES, Brazil’s 250-m forest loss monitoring product for the Brazilian
Amazon19, in the alternative model. Top panel: Percent change in net GHG flux between standard model and sensitivity analysis model; Bottom panel:
Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net
sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04-degree
geographic grid.
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Extended Data Fig. 3 | Results of sensitivity analysis when the source of biomass data used in the forest GHG flux model is changed from a 30-m global
AGB map in the standard model to a 1-km tropical AGB map in the alternative model. Top panel: Percent change in net GHG flux between standard model
and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that
switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the
30-m observation scale to a 0.04-degree geographic grid.
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Articles Nature Climate ChaNge
Extended Data Fig. 4 | Results of sensitivity analysis when rates of AGB accumulation derived from inventory data for different forest types of the
United States in the standard model are replaced by IPCC Tier 1 default rates in the alternative model. Top panel: change in net GHG flux between
standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model
vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been
resampled from the 30-m observation scale to a 0.04- degree geographic grid.
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Extended Data Fig. 5 | Results of sensitivity analysis when the number of years of growth in the GHG flux model is assumed to be 19 in the alternative
model vs. 6 in the standard model for pixels of tree cover gain since the year 2000. Top panel: Percent change in net GHG flux between standard model
and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that
switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the
30-m observation scale to a 0.04-degree geographic grid.
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Articles Nature Climate ChaNge
Extended Data Fig. 6 | Results of sensitivity analysis when tree cover loss in the GHG flux model is attributed to commodity-driven deforestation in
the alternative model vs. shifting agriculture in the standard model. Top panel: Percent change in net GHG flux between standard model and sensitivity
analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being
a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation
scale to a 0.04-degree geographic grid.
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Extended Data Fig. 7 | Results of sensitivity analysis when the post- deforestation land-use assumption in the GHG flux model is changed from
cropland in the standard model to grassland in the alternative model. Top panel: Percent change in net GHG flux between standard model and sensitivity
analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being
a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation
scale to a 0.04-degree geographic grid.
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Extended Data Fig. 8 | Results of sensitivity analysis when assumptions about carbon uptake in primary forests and intact forest landscapesare
changed to zero carbon uptake in the alternative model vs. positive carbon uptake in the standard model. Top panel: Percent change in net GHG flux
between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis
model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been
resampled from the 30-m observation scale to a 0.04-degree geographic grid.
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Articles Nature Climate ChaNge
Extended Data Fig. 10 | Conceptual framework for modelling forest- related GHG fluxes. For each 30-m pixel included in the model, gross forest-related
emissions and removals are estimated as the product of activity data and emission/removal factors. Net forest GHG flux is the sum of gross fluxes. Text
and arrows in orange are portions of the removals methodology that are passed into the emissions methodology.
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Corresponding author(s): Nancy Harris, nharris@wri.org
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