ArticlePDF Available

Attribution of net carbon change by disturbance type across forest lands of the conterminous United States


Abstract and Figures

Background Locating terrestrial sources and sinks of carbon (C) will be critical to developing strategies that contribute to the climate change mitigation goals of the Paris Agreement. Here we present spatially resolved estimates of net C change across United States (US) forest lands between 2006 and 2010 and attribute them to natural and anthropogenic processes. ResultsForests in the conterminous US sequestered −460 ± 48 Tg C year−1, while C losses from disturbance averaged 191 ± 10 Tg C year−1. Combining estimates of net C losses and gains results in net carbon change of −269 ± 49 Tg C year−1. New forests gained −8 ± 1 Tg C year−1, while deforestation resulted in losses of 6 ± 1 Tg C year−1. Forest land remaining forest land lost 185 ± 10 Tg C year−1 to various disturbances; these losses were compensated by net carbon gains of −452 ± 48 Tg C year−1. C loss in the southern US was highest (105 ± 6 Tg C year−1) with the highest fractional contributions from harvest (92%) and wind (5%). C loss in the western US (44 ± 3 Tg C year−1) was due predominantly to harvest (66%), fire (15%), and insect damage (13%). The northern US had the lowest C loss (41 ± 2 Tg C year−1) with the most significant proportional contributions from harvest (86%), insect damage (9%), and conversion (3%). Taken together, these disturbances reduced the estimated potential C sink of US forests by 42%. Conclusion The framework presented here allows for the integration of ground and space observations to more fully inform US forest C policy and monitoring efforts.
This content is subject to copyright. Terms and conditions apply.
Harris et al. Carbon Balance Manage (2016) 11:24
DOI 10.1186/s13021-016-0066-5
Attribution ofnet carbon change
bydisturbance type acrossforest lands
ofthe conterminous United States
N. L. Harris1,5*†, S. C. Hagen2†, S. S. Saatchi3, T. R. H. Pearson1, C. W. Woodall4, G. M. Domke4, B. H. Braswell2,
B. F. Walters4, S. Brown1, W. Salas2, A. Fore3 and Y. Yu3
Background: Locating terrestrial sources and sinks of carbon (C) will be critical to developing strategies that contrib-
ute to the climate change mitigation goals of the Paris Agreement. Here we present spatially resolved estimates of net
C change across United States (US) forest lands between 2006 and 2010 and attribute them to natural and anthropo-
genic processes.
Results: Forests in the conterminous US sequestered 460 ± 48 Tg C year1, while C losses from disturbance
averaged 191 ± 10 Tg C year1. Combining estimates of net C losses and gains results in net carbon change
of 269 ± 49 Tg C year1. New forests gained 8 ± 1 Tg C year1, while deforestation resulted in losses of
6 ± 1 Tg C year1. Forest land remaining forest land lost 185 ± 10 Tg C year1 to various disturbances; these
losses were compensated by net carbon gains of 452 ± 48 Tg C year1. C loss in the southern US was highest
(105 ± 6 Tg C year1) with the highest fractional contributions from harvest (92%) and wind (5%). C loss in the west-
ern US (44 ± 3 Tg C year1) was due predominantly to harvest (66%), fire (15%), and insect damage (13%). The north-
ern US had the lowest C loss (41 ± 2 Tg C year1) with the most significant proportional contributions from harvest
(86%), insect damage (9%), and conversion (3%). Taken together, these disturbances reduced the estimated potential
C sink of US forests by 42%.
Conclusion: The framework presented here allows for the integration of ground and space observations to more
fully inform US forest C policy and monitoring efforts.
Keywords: Forests, Disturbance, Harvest, Insects, Fire, Drought, Greenhouse gas, Land use, Climate change, FIA,
© The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(, which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made.
e 2015 Paris Climate Change Agreement, with con-
sensus from 192 signatories, calls for achieving a balance
between anthropogenic emissions by sources and remov-
als by sinks in the second half of this century [1]. Forests
are currently responsible for the capture and storage of
an estimated 25% of global anthropogenic emissions [2].
If Paris goals are to be achieved, further enhancement of
forest-based carbon (C) removals to mitigate emissions
in other sectors will be a critical component of any col-
lective global strategy [3], especially as no alternative sink
technologies have yet been proven at scale. us, spa-
tially identifying terrestrial sources and sinks of carbon,
and understanding them well enough to predict how they
will respond to management decisions or future climate
change, will pose major science and policy challenges in
the years to come.
Remote sensing products can provide regular and con-
sistent observations of Earth’s surface to help identify the
condition of forest ecosystems and changes within them
at a range of spatial and temporal scales [4]. Over the past
Open Access
N. L. Harris and S. C. Hagen contributed equally to this work
5 Present Address: Forests Program, World Resources Institute,
10 G Street NE Suite 800, Washington, DC 20002, USA
Full list of author information is available at the end of the article
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 2 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
several years, the remote sensing research community
has used these products to monitor tropical deforesta-
tion, forest C stocks and associated C emissions, largely
in support of REDD+ initiatives in developing countries
[512]. In many developed countries, periodic national
forest inventories form the basis of annual greenhouse
gas (GHG) reporting to the United Nations Framework
Convention on Climate Change (UNFCCC). e sample-
based design of these inventories may offer little in the
way of detailed and spatially-explicit information on the
distribution of forest biomass [13], timing and location of
timber harvesting in managed forests, or the cause and
timing of other types of forest disturbances. If the ulti-
mate aim of the Paris Agreement is to introduce practices
that lead to reduced emissions and enhanced removals of
C from the world’s managed forests, including in temper-
ate and boreal biomes, then a lack of disaggregated, spa-
tially-explicit information could pose challenges over the
coming years related to knowledge of where changes are
occurring and where interventions are likely to be most
Several C budget models have been developed to sim-
ulate ecosystem response to climate drivers and other
disturbances, and these models represent an established
approach to estimating C fluxes at national to regional
scales. For example, Canada’s National Forest Carbon
Monitoring Accounting and Reporting System (NFC-
MARS) uses the Carbon Budget Model of the Canadian
Forest Sector (CBM-CFS3), and is used also as a deci-
sion support tool for forest managers to quantify forest C
dynamics at a landscape scale. Different models empha-
size different aspects of ecosystem dynamics, with some
accounting for competition between plant functional
types, nutrient limitation, and natural disturbances. Time
series of anthropogenic land-cover changes are usually
prescribed based on spatially explicit data. e mod-
els can reflect spatial and temporal variability in C den-
sity and response to environmental conditions, but their
modeled C stocks may differ markedly from observations
Such models are not used explicitly in the GHG inven-
tory for the US to report forest C fluxes. Instead, the cur-
rent US inventory system uses the C stock-difference
accounting approach [15] enabled by the annual national
forest inventory conducted by the United States Depart-
ment of Agriculture (USDA) Forest Service Forest Inven-
tory and Analysis (FIA) program. e difference in C
stocks in five C pools is estimated via sequential re-meas-
urements of permanent ground inventory plots. When
forest stocks decline, it is assumed that C emissions
have occurred from the land to the atmosphere if not
reconciled with a transfer to another land use category.
Conversely, when forest C stocks increase it is assumed
that C has been sequestered from the atmosphere by ter-
restrial vegetation. In this way, estimated net C change
in the US forest sector is the integrated result of both
anthropogenic and natural processes—harvest, land use
change, fire, drought, insect infestation, wind damage—
all of which influence the magnitude of forest C stocks
in each pool. Results are most statistically robust when
compiled at large spatial scales (e.g., state or regional),
such that quantification of finer-scale spatial patterns
is less precise. ough changes are well constrained via
sequential re-measurements on inventory plots, the US
[16, 17] has only recently begun using methods to disag-
gregate the effects of various disturbance types on for-
est stocks and fluxes (although this separation is not a
requirement of IPCC Good Practice Guidance, [18]).
e objective of this study was to synthesize informa-
tion from remote sensing observations of forest car-
bon stocks and disturbance with information collected
by various US agencies into a framework that (1) more
explicitly attributes C losses to major disturbance types
(land use change, harvesting, forest fires, insect damage,
wind damage and drought); and (2) disaggregates net C
change into relevant IPCC reporting categories of non-
forest land converted to forest land, forest land converted
to non-forest land, and forest land remaining forest land.
is framework allows for the integration of ground and
space observations to more fully inform US forest C pol-
icy and monitoring efforts.
We built a spatially-explicit empirical model that com-
bines information from many data sources to infer
disturbance and resulting C dynamics within each hec-
tare of forest land in the 48 conterminous states of the
US, totaling an area of more than 2.1 million km2. For
the purposes of regional comparison and analyses, we
divided the US into three broad regions (North, South,
West) based on similar histories of forestland use ([19],
Fig.1) and into nine smaller subregions based on those
used in the US FIA program. Forest types were defined
as hardwood or softwood, following the National Land
Cover Data (NLCD) classification (deciduous forest class:
hardwoods; evergreen forest class: softwoods). e time
period of analysis is 1 January 2006 to 31 December 2010.
Data inputs
Forest area map (2005)
Forest extent in the base year 2005 was determined from
the NLCD and the global tree cover and tree cover
change products of Hansen etal. [8]. Specifically, an area
was determined to be forested if categorized as
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 3 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Fig. 1 a Map of aboveground live woody biomass carbon density (Mg C ha1) and b uncertainty across forest lands of the conterminous US at
1-ha resolution for circa the year 2005. c The regional analysis was performed by dividing the US into three sub-regions as recommended by Heath
and Birdsey [19]. The above and belowground carbon density maps and the uncertainty maps can be downloaded from NASA’s distributed Data
Active Archive Center (
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 4 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
hardwood or softwood in the NLCD 2006 dataset1 and,
according to the Hansen etal. [8] dataset, it (a) met the
tree cover threshold of 25% in the year 2000 and was not
lost between 2001 and 2005 or (b) did not meet the tree
cover threshold of 25% in 2000 but was identified as hav-
ing gained tree cover (i.e., afforestation/reforestation)
between 2000 and 2012. e NLCD has been shown to
significantly underestimate tree cover [20] and thus the
forest area estimates used in this analysis—defined by
both NLCD and Hansen etal. [8]—are likely to be con-
servative. However, these two data products currently
represent the best available spatially explicit data for for-
est extent in the conterminous US (CONUS).
Forest biomass density maps (circa 2005)
We developed maps of C stocks (50% of biomass) in
aboveground live biomass in US forest land as part of
NASA’s C Monitoring System (CMS) program based
on a combination of remote sensing observations and
FIA data (Fig.1). e overall methodology used in map-
ping the aboveground live forest biomass C density is
described in Saatchi et al. [5]. After filtering for cloud
effects, slopes, and signal-to-noise ratio, more than
700,000 samples of lidar (light detecting and ranging)
data acquired between 2003 and 2008 from the Geo-
science Laser Altimeter System (GLAS), onboard the
Ice, Cloud and land Elevation Satellite (ICESat) were
used as samples of the vertical structure of US for-
est land. We used the Lorey’s height [21] measured in
65,000 single-condition FIA plots (i.e., plots with a sin-
gle domain mapped on each plot) to calibrate the lidar-
derived height metric and used the relationship between
Lorey’s height and aboveground C density for 28 forest
types to convert the lidar data into estimates of above-
ground live C density. All FIA plots with a probability of
disturbance causing reduced canopy cover (<50%) were
removed from the height-biomass model development
to reduce any potential discrepancy between ground
and lidar height metrics. Lidar-derived biomass sam-
ples were then extrapolated over the landscape using a
combination of optical and radar satellite imagery that
captures the variations of forest structure and cover to
create wall-to-wall maps of forest aboveground live bio-
mass C density. We used nine remote sensing imagery
layers as spatial predictor variables. Optical and thermal
data from Landsat imagery (bands 3, 4, 5 and 7) were
aggregated to 100m spatial resolution from 30m native
1 Within each 1ha pixel, the wet woodland class was included as forest
but was not used to determine whether the pixel was hard- or softwood.
Hard- or softwood was determined based on the plurality of NLCD hard-
or softwood 30m pixels within the hectare, ignoring the sub-fraction of wet
woodlands and selecting softwood when hard- and softwood fractions were
resolution along with the leaf area index derived from
Landsat imagery [22]. In addition, we used the advanced
land observing satellite (ALOS) phased area L-band syn-
thetic aperture radar (PALSAR) imagery at two polariza-
tions (HH and HV backscatter) along with topographical
data of surface elevation and slope from Shuttle Radar
Topography Mission (SRTM) resampled to 100m reso-
lution from 20 and 30m native resolutions, respectively.
ALOS PALSAR plays an important role in quantifying
variation in forest biomass. In particular, the HV polari-
zation provides the largest contribution among the data
layers to predicted biomass because it has a strong direct
sensitivity to biomass up to 100–150Mgha1 (depend-
ing on forest type), is less impacted by soil moisture
and other environmental variables, and may contrib-
ute significantly in extrapolating larger biomass forests
through texture and spatial correlation. Similarly, SRTM
data include information on topography and also forest
height. We used the national elevation data (NED) to
represent the ground surface elevation and used the dif-
ference between SRTM and NED as an indicator of for-
est height. is variable also contributed significantly to
explaining the spatial variation of biomass over forests
with biomass values >150Mgha1.
e aboveground C density samples derived from
GLAS data were combined with satellite imagery using
the maximum entropy estimation (MaxEnt) algorithm
to estimate aboveground biomass density for each 1-ha
pixel. MaxEnt is a probability-based algorithm that esti-
mates the posterior likelihood distribution of a variable
by maximizing the entropy of said probability distribu-
tion while maintaining the constraints provided by the
training samples [23]. We selected a random subset
consisting of 70% of the samples (~500,000 samples)
for model input and used the remaining 30% for model
evaluation and validation. e product from the Max-
Ent estimator includes both the mean aboveground
carbon (AGC) density for each 1-ha pixel and the esti-
mation of the error derived from a Bayesian probability
estimator for each pixel. Spatial uncertainty analysis and
uncertainty propagation were used to evaluate the over-
all uncertainty of AGC at the pixel level. is process
included the quantification of error at each step of the
process and the use of the Gaussian error propagation
where each of the terms are the relative errors at that
pixel and represent the measurement errors of lidar for
capturing the forest height, the error associated with
the lidar aboveground C allometry model for each forest
type, the error associated with sampling the 1-ha pixel
measurement +ε2
allometry +ε2
sampling +ε2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 5 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
with GLAS footprint size (~0.25 ha), and the MaxEnt
prediction error. In evaluating the errors at the state and
county level, we also included the spatial correlation of
the prediction error from the MaxEnt approach [24].
In the FIA, belowground forest biomass is quantified
using a root-shoot ratio [25]. Knowledge of root bio-
mass dynamics is fundamental to improving our under-
standing of carbon allocation and storage in terrestrial
ecosystems [26]. We used the relationship between
belowground carbon (BGC) and AGC from the FIA data
to develop a BGC spatial distribution at the same scale
as AGC [5, 27]. In estimating the uncertainty in BGC, we
followed the same approach as AGC with the addition of
including the errors associated with the model used in
relating AGC to BGC.
FIA stock change data (2006–2010)
To estimate average net changes in the stock of live AGC
and BGC between 2006 and 2010 in forests disaggre-
gated by disturbance type, we queried the FIA database
to extract more than 141,000 records associated with re-
measured permanent plots, where each extracted record
represents a “condition” (i.e., domain(s) mapped on each
plot according to attributes such as land use, forest type,
stand size, ownership, tree density, stand origin, and/or
disturbance history) of a measured plot at two points in
time, typically 5years apart. Disturbed plots were strati-
fied into a lookup table by geographic region (North,
South, or West), forest type (hardwood or softwood), dis-
turbance type (fire, insect, wind, conversion, or harvest),
and disturbance intensity (Table 1). A similar lookup
table was developed for undisturbed plots stratified by
geographic region, forest type, and base C stock in the
year 2005 (Table2).
Disturbance maps (2006–2010)
Sources of disturbance data used in this analysis are sum-
marized in Table3 and include spatially-explicit data on
locations of fire, insect damage, wind damage, land use
change, drought, and timberlands. e timberlands map
was used to attribute net carbon gains occurring within
vs. outside timberland areas. Because harvested wood
may come from intermediate treatments (treatments
not intended to cause regeneration), partial harvest or
clearcutting forests, deforestation, and non-forest land
trees, the area of clearcuts as observed within timberland
areas through remote sensing imagery cannot represent
all these wood sources [28]. erefore for estimating
C losses from timber harvest, we used data collected in
the US based on mill surveys rather than remote sensing
Timber product output data (TPO 2007)
e volume of roundwood products, mill residues and
logging residues reported in the TPO database (Table3),
separated by product class and detailed species group,
were used to estimate C losses from wood harvest. e
spatial resolution of the data was the “combined county”,
which represented the minimum reportable scale from
the timber product output (TPO; FIA Fiscal Year 2013
Business Report, [29]) data while retaining necessary
Model assumptions
IPCC Tier 2 estimation
e terrestrial C cycle includes changes in C stocks due
to both continuous processes (i.e., growth, decomposi-
tion) and discrete events (i.e., disturbances such as har-
vest, fire, insect outbreaks, land-use change). Continuous
processes can affect C stocks in all areas every year, while
discrete events (i.e., disturbances) cause emissions and
redistribute C in specific areas in the year of the event.
In accounting for net C change in this analysis, we use
country-specific data (Tier 2) and apply the simplifying
methodological assumption [15] that all post-disturbance
emissions (after accounting for C storage in harvested
wood products) occur as part of the disturbance event,
i.e., in the year of disturbance, rather than modeling these
emissions through time as in IPCC’s Tier 3 approach.
e application of lower tier methods also assumes
that the average transfer rate into dead organic matter
(dead wood and litter) is equal to the average transfer
out of dead organic matter, so that the net stock change
in these pools is zero [15]. is assumption means that
dead organic matter (dead wood and litter) C stocks need
not be quantified for land areas that remain forested. e
rationale for this approach is that dead organic matter
stocks, particularly dead wood, are highly variable and
site-specific, depending on forest type and age, distur-
bance history and management. Because the FIA data
used in this analysis do not include measurements of soil
C or dead C pools and no robust relationships currently
exist that relate these pools to a more easily measured
pool (such as the derivation of belowground biomass
from aboveground biomass using root:shoot ratios), we
excluded the soil C and dead C pools from our analysis.
As a result, our estimate of net C change using the stock-
difference approach is equal to the net change in C stocks
in the aboveground and belowground live biomass pools
only, with a fraction of the aboveground live biomass
assumed to be transferred to the wood products pool,
where a portion is permanently sequestered in long-lived
products and the remainder emitted to the atmosphere
(see below).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 6 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Table 1 Look-up table of annual fractional change (average = µ; standard error = σ) in aboveground carbon (AGC)
andbelowground carbon (BGC) indisturbed forests based onFIA plot data
Region Forest type Disturbance Initial C N AGC µ AGC σ BGC µ BGC σ
North Softwood Fire Low 20.003 0.012 0.001 0.013
North Softwood Fire Medium 3 0.052 0.031 0.053 0.031
North Softwood Fire High 5 0.150 0.030 0.157 0.030
North Softwood Weather Low 63 0.013 0.016 0.014 0.016
North Softwood Weather High 10 0.163 0.013 0.169 0.013
North Softwood Insect Low 85 0.003 0.007 0.003 0.008
North Softwood Insect Medium 82 0.044 0.023 0.046 0.023
North Softwood Insect High 45 0.126 0.035 0.133 0.032
North Softwood Harvested Low 521 0.046 0.035 0.048 0.036
North Softwood Harvested High 246 0.152 0.026 0.158 0.025
North Hardwood Fire Low 40 0.003 0.009 0.003 0.009
North Hardwood Fire Medium 29 0.045 0.024 0.048 0.023
North Hardwood Fire High 11 0.131 0.034 0.136 0.034
North Hardwood Weather Low 412 0.011 0.016 0.011 0.016
North Hardwood Weather High 34 0.160 0.017 0.164 0.016
North Hardwood Insect Low 656 0.002 0.008 0.002 0.008
North Hardwood Insect Medium 432 0.045 0.020 0.046 0.020
North Hardwood Insect High 118 0.132 0.029 0.136 0.028
North Hardwood Harvested Low 2177 0.047 0.035 0.047 0.035
North Hardwood Harvested High 806 0.154 0.023 0.157 0.023
South Softwood Fire Low 127 0.002 0.007 0.003 0.008
South Softwood Fire Medium 174 0.048 0.021 0.052 0.022
South Softwood Fire High 52 0.124 0.027 0.131 0.028
South Softwood Weather Low 78 0.016 0.016 0.017 0.016
South Softwood Weather High 16 0.161 0.026 0.168 0.023
South Softwood Insect Low 46 0.002 0.008 0.004 0.008
South Softwood Insect Medium 66 0.054 0.022 0.059 0.023
South Softwood Insect High 60 0.135 0.030 0.142 0.029
South Softwood Harvested Low 1787 0.044 0.034 0.048 0.036
South Softwood Harvested High 586 0.149 0.025 0.157 0.024
South Hardwood Fire low 112 0.002 0.008 0.003 0.008
South Hardwood Fire Medium 86 0.042 0.021 0.045 0.022
South Hardwood Fire High 37 0.131 0.033 0.139 0.030
South Hardwood Weather Low 484 0.014 0.016 0.015 0.016
South Hardwood Weather High 32 0.162 0.019 0.167 0.017
South Hardwood Insect Low 145 0.000 0.013 0.002 0.011
South Hardwood Insect Medium 121 0.047 0.022 0.051 0.022
South Hardwood Insect High 38 0.133 0.031 0.138 0.031
South Hardwood Harvested Low 1235 0.048 0.036 0.051 0.036
South Hardwood Harvested High 609 0.146 0.029 0.152 0.027
West Softwood Fire Low 13 0.007 0.008 0.007 0.008
West Softwood Fire Medium 8 0.049 0.023 0.050 0.026
West Softwood Fire High 0 0.126 NA 0.133 NA
West Softwood Weather Low 50.003 0.008 0.003 0.008
West Softwood Weather High 0 0.162 NA 0.168 NA
West Softwood Insect Low 12 0.001 0.007 0.001 0.007
West Softwood Insect Medium 3 0.041 0.016 0.044 0.018
West Softwood Insect High 0 0.131 NA 0.138 NA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 7 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Disturbance attribution
Forest land was assumed to be disturbed if included in at
least one of the disturbance maps (Table3) during the
2006–2010 time period: (1) maximum burn severity
score of at least two (low) over the 5years of fire data; (2)
insect damage of at least three trees per acre over the
5year study period; (3) within a path of a tornado or a
buffered region around the hurricane path where wind
speeds typically exceeded 95 miles per hour (category 2
hurricane)2 between 2006 and 2010; (4) converted to
agriculture, barren land or settlement in the NLCD layer
between 2006 and 2011 (considered as deforestation
events); or (5) had an average drought intensity score of
more than two in the NDMC Drought Monitor map
between the years of measurement. For fire and insect
disturbance, three levels of disturbance intensity were
assigned based on burn severity score (from the MTBS
dataset) or insect damage per acre (from the Aerial
Detection Survey), respectively. Two levels of wind dis-
turbance intensity were assigned and areas determined to
have been converted to agriculture or settlement were
assumed to experience one uniform intensity of distur-
bance. All other forest land was assumed to be undis-
turbed between 2006 and 2010. In areas where multiple
types of disturbance were identified within a 1ha forest
land pixel, we assumed only one disturbance type was
driving the C loss. Disturbance type priority was set
based on the intensity of the disturbance and level of
confidence in the data sets. In general, more intense
2 is wind speed threshold was selected based on the Saffir Simpson Hur-
ricane Wind Scale, which indicates that trees start to be uprooted and fall at
category 2 sustained wind speeds between 96 and 110mph. e hurricane
tracks were buffered to a symmetrical width of 100km.
disturbances and higher quality products took priority
over less intense disturbances and those products
assessed as having more uncertainty. e disturbance
location and intensity products were assumed to be in
the following quality order, from least to most inherent
uncertainty: conversion, fire, wind, insect damage. For
instance, a pixel identified as experiencing an intense fire
disturbance and a low intensity insect disturbance was
assigned the high intensity fire disturbance as the single
disturbance driving loss. is assumption simplified the
processing but added additional uncertainty to the esti-
mates. e assigned disturbance type priority varied
across multiple iterations of our uncertainty analysis. It
was not possible to attribute harvest disturbance to spe-
cific pixels, therefore C losses from harvest were esti-
mated at the county scale using TPO data.
Estimation ofnet carbon change
Net carbon change fromre, wind, insect damage, land use
change, anddrought
If a hectare of forest land in the US was categorized as
disturbed between 2006 and 2010 based on the distur-
bance maps, then the intensity and type of disturbance
was identified. e pixel was then linked to an annual-
ized percent net change in C stock estimate, based on
its identified category in the FIA-based lookup tables.
ese annualized percent change values were multiplied
by the initial base C stock in 2005 in each pool (above-
ground biomass, belowground biomass) and multiplied
by 5years to estimate total net change in C within the
pixel between 2006 and 2010.
Net carbon change fromharvest
Annual C losses associated with harvest activities were
estimated using mill surveys compiled into the USDA
Table 1 continued
Region Forest type Disturbance Initial C N AGC µ AGC σ BGC µ BGC σ
West Softwood Harvested Low 28 0.027 0.030 0.028 0.031
West Softwood Harvested High 0 0.150 NA 0.157 NA
West Hardwood Fire Low 40.002 0.008 0.002 0.008
West Hardwood Fire Medium 3 0.057 0.021 0.059 0.021
West Hardwood Fire High 0 0.131 NA 0.138 NA
West Hardwood Weather Low 00.013 NA 0.013 NA
West Hardwood Weather High 0 0.161 NA 0.165 NA
West Hardwood Insect Low 13 0.003 0.008 0.003 0.009
West Hardwood Insect Medium 3 0.041 0.025 0.044 0.028
West Hardwood Insect High 0 0.132 NA 0.136 NA
West Hardwood Harvested Low 40.039 0.031 0.039 0.033
West Hardwood Harvested High 0 0.151 NA 0.155 NA
Italics imputed from other regions
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 8 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Table 2 Look-up table of annual fractional change (average = µ; standard error = σ) in aboveground carbon (AGC)
andbelowground carbon (BGC) inundisturbed forests, based onFIA plot data
Region Forest type Drought Initial C n AGC µ AGC σ BGC µ BGC σ
North Softwood No <25 5167 0.064 0.135 0.080 0.199
North Softwood No 25–50 3459 0.023 0.034 0.023 0.034
North Softwood No 50–100 2085 0.016 0.024 0.016 0.024
North Softwood No 100 345 0.013 0.034 0.013 0.034
North Softwood Yes <25 50 0.028 0.030 0.031 0.035
North Softwood Yes 25–50 50 0.008 0.034 0.008 0.035
North Softwood Yes 50–100 12 0.016 0.040 0.016 0.040
North Softwood Yes 100 2 0.013 0.017 0.013 0.016
North Hardwood No <25 12,559 0.074 0.102 0.087 0.131
North Hardwood No 25–50 13,656 0.025 0.036 0.025 0.036
North Hardwood No 50–100 14,173 0.014 0.026 0.014 0.026
North Hardwood No 100 3265 0.010 0.030 0.010 0.030
North Hardwood Yes <25 19 0.016 0.058 0.016 0.062
North Hardwood Yes 25–50 12 0.006 0.040 0.006 0.041
North Hardwood Yes 50–100 7 0.001 0.026 0.000 0.027
North Hardwood Yes 100 1 0.006 NA 0.005 NA
South Softwood No <25 3648 0.314 0.355 0.452 0.621
South Softwood No 25–50 2940 0.082 0.069 0.085 0.072
South Softwood No 50–100 2345 0.039 0.049 0.039 0.050
South Softwood No 100 673 0.021 0.050 0.020 0.051
South Softwood Yes <25 464 0.340 0.407 0.487 0.694
South Softwood Yes 25–50 348 0.081 0.071 0.084 0.074
South Softwood Yes 50–100 299 0.038 0.039 0.038 0.041
South Softwood Yes 100 110 0.020 0.038 0.020 0.039
South Hardwood No <25 6585 0.133 0.191 0.176 0.291
South Hardwood No 25–50 6180 0.040 0.044 0.041 0.045
South Hardwood No 50–100 8244 0.021 0.032 0.021 0.032
South Hardwood No 100 2697 0.014 0.032 0.014 0.032
South Hardwood Yes <25 630 0.140 0.184 0.185 0.272
South Hardwood Yes 25–50 498 0.042 0.062 0.044 0.064
South Hardwood Yes 50–100 756 0.021 0.029 0.021 0.030
South Hardwood Yes 100 275 0.011 0.029 0.011 0.029
West Softwood No <25 56 0.061 0.102 0.079 0.123
West Softwood No 25–50 45 0.027 0.048 0.028 0.049
West Softwood No 50–100 61 0.022 0.026 0.022 0.027
West Softwood No 100 80 0.014 0.019 0.014 0.019
West Softwood Yes <25 0 0.310 NA 0.443 NA
West Softwood Yes 25–50 0 0.072 NA 0.075 NA
West Softwood Yes 50–100 0 0.037 NA 0.037 NA
West Softwood Yes 100 0 0.020 NA 0.020 NA
West Hardwood No <25 33 0.037 0.055 0.043 0.061
West Hardwood No 25–50 26 0.023 0.026 0.025 0.028
West Hardwood No 50–100 45 0.026 0.041 0.027 0.043
West Hardwood No 100 38 0.019 0.025 0.020 0.027
West Hardwood Yes <25 0 0.137 NA 0.180 NA
West Hardwood Yes 25–50 0 0.041 NA 0.043 NA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 9 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
TPO database for the year 2007. Due to the periodic
nature of the TPO report for 2007 data, harvest emission
estimates were assumed to be representative for all
5years included in our analysis (2006–2010). Volumes of
roundwood products, mill residue and logging residues
were converted to biomass using oven-dry wood densi-
ties [30]. e fraction of C in primary wood products
remaining in end uses or in landfills after 100years per
product class3 was assumed to be permanently seques-
tered, and was estimated from values published in Smith
etal. [31]. Fuelwood, posts/poles/pilings and miscellane-
ous product classes were assumed to be fully emitted.
Emissions from mill residues were considered equal to
3 e TPO and Smith et al. [31] product classes were mapped to one
another as follows: Sawlog = softwood/hardwood lumber (depending on
species); veneer=softwood plywood; pulp=paper; composite=oriented
the summed mill residues from fuel by-products, miscel-
laneous by-products and unused mill residues, plus emis-
sions from fiber by-products. All fiber by-products were
assumed to form pulp and to follow the emissions
assumptions of pulp products. All logging residues were
assumed to be emitted. Timberlands were delineated
based on the boundaries of the US timberlands map
(Table 3), and annual net C gains within timberlands
were estimated following the look-up tables for growth in
undisturbed forests as described below.
Net carbon change fromforest growth/regrowth
Forest land in the US that did not experience deforesta-
tion through land use conversion or significant dam-
age by wind, insect, fire, or drought over the analysis
period, as well as new forest land (i.e., afforestation/
reforestation), were linked to values of annual net change
Table 2 continued
Region Forest type Drought Initial C n AGC µ AGC σ BGC µ BGC σ
West Hardwood Yes 50–100 0 0.021 NA 0.021 NA
West Hardwood Yes 100 0 0.011 NA 0.011 NA
Italics imputed from other regions
Table 3 Fourteen independent datasets were integrated andused to produce net carbon change estimates by distur-
bance type
Product Source Spatial coverage Temporal coverage Url
Tree cover
Tree cover change [8] Complete CONUS Tree cover: single snapshot in
Loss: annual 2001–2010
Gain: 2000–2012
Fire Monitoring trends in burn
severity Complete CONUS Annual 2006–2010
Wind NOAA’s storm prediction
center—tornado tracks Complete CONUS Annual 2006–2010
Wind NOAA’s storm prediction
center—hurricane paths Complete CONUS Annual 2006–2010
Insect USFS aerial detection survey Sub-set of CONUS Annual 2006–2010
Forest type National land cover database—
hardwood or softwood Complete CONUS Single snapshot in 2000
Conversion National land cover database Complete CONUS Snapshots in 2006 and 2011
Drought NDMC drought monitor Complete CONUS Weekly between 2006 and
Timberlands Mark Nelson USFS for 2007
resources planning act Complete CONUS Snapshot in 2007 N/A
Biomass density
Carbon stocks Sassan Saatchi Complete CONUS Snapshot in 2005
Harvest USFS timber products output Combined county CONUS Survey in 2007
FIA USFS forest inventory and
analysis program Sites in CONUS Between 1997 and 2013
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 10 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
in C stock, based on the area’s identified category in the
lookup tables derived from FIA measurement data. ese
annualized percent change values were multiplied by the
initial C stock in 2005 in each pool (aboveground bio-
mass, belowground biomass) and multiplied by 5 years
to estimate total net change in C within each 1-ha pixel
between 2006 and 2010.
Total annual net carbon change
e FIA-based estimated net change in C represents
the sum of net C losses (caused by disturbances) and
net C gains (caused by forest growth) that occurred
between FIA measurement dates at the site. Similarly,
our estimate of net C change (ΔCnet) during the 5-year
period at the combined county scale was calculated as:
net =
undist +
+Ctimberlands +Cinsect +C
wind +
where ΔCundist is the net C change in forest land out-
side of timberlands that did not experience land use con-
version or significant damage by wind, insects, fire or
drought. ΔCA/R is the net C change in new forest land.
ΔCconversion, ΔCwind ΔCinsect, and ΔCfire represent the net
C change in forestland that was converted or significantly
disturbed by conversion, wind, insects, and fire, respec-
tively. ΔCdrought is the net C reduction in sequestration in
forest land experiencing drought from what was expected
during non-drought periods. ΔCtimberlands is the net C
change on timberlands (as delineated by the timberlands
map), calculated as the sum of net C gains (as estimated
from FIA lookup tables) and C losses (as estimated from
the TPO data, accounting for the fraction of harvested
C stored permanently in the long-lived product pool).
By convention, C losses are represented as positive val-
ues and C gains as negative values. Consequently, various
forms of disturbance result in a weaker (i.e., less negative)
overall sink than would occur otherwise in the absence of
Uncertainty analysis
We estimated statistical bounds for the estimates of net C
change by conducting a Monte Carlo uncertainty analy-
sis [32]. e four sources of uncertainty included in the
simulation were associated with the forest biomass den-
sity maps, the stock-change lookup tables derived from
FIA data, each of the disturbance maps, and the TPO
data. e simulation was conducted at the combined
county scale. Uncertainty in the biomass density maps
was derived from a secondary simulation in which the
input datasets were resampled to generate 100 replicate
training datasets, or realizations, that had the same quali-
ties of the original training dataset, but different random
error. A new MaxEnt model was fit to each of these 100
replicated datasets and used to create 100 full resolution
biomass maps. Uncertainty in the FIA-based ΔC values
were calculated using the variance in the look-up tables:
Uncertainty in the area affected by disturbance was
estimated to be 30%, with an estimated 5% bias in under
reported area. We conducted the simulation using three
separate rule sets for selecting a disturbance type for
pixels identified as experiencing multiple disturbances
during the 5-year study period. Uncertainty in the TPO
data at the combined county scale was also assumed to
be 30%.
We ran 10,000 Monte Carlo simulations with stochastic
elements in place for the four uncertainty components.
We assumed that 80% of the randomly generated error
was random and 20% of the error was systematic within
the simulation. To implement this assumption, we esti-
mated the error associated with each component twice—
once at the simulation iteration level and again for each
individual combined county. e iteration level uncer-
tainty was multiplied by 0.2 before it was added to the
original combined county estimate, while the combined
county level stochastic element was multiplied by 0.8
before it was added. In this way, we accounted for both
random error as well as systematic error in our estimates.
is uncertainty analysis was intended to provide
context to the estimates and assist in the process of
identifying methods and data in need of refinement or
replacement. e uncertainty analysis is not exhaustive,
in the sense that additional sources of uncertainty exist
that are not accounted for in the analysis presented here.
ese additional sources include but are not limited to
(a) potential temporal mismatch between the biomass
data providing initial carbon stocks in 2005 and the activ-
ity data beginning in 2006 and (b) uncertainty in the
equations and factors used in the FIA to convert tree
measurements to estimates of wood volume and carbon
stocks. Given these additional sources of uncertainty, the
uncertainty bounds presented here are almost certainly
an underestimate of the actual uncertainty.
Forest land in the conterminous US, as defined
here totaling 221 million ha in 2005, sequestered
460 ± 48 Tg C year1 between 2006 and 2010,
while average C losses from forest disturbances were
191 ± 10 Tg C year1. Combining estimates of net
C gains and net C losses results in net C change of
269±49TgCyear1 (Fig.2). ese results are broadly
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 11 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
consistent with estimates reported in the US. GHG
inventory for forests in 2010 (293TgCyear1, [33]) but
we estimate a larger net sink than reported in Zheng etal.
[28] (181TgCyear1), although the spatial and tem-
poral domains varied across these analyses, as did the C
pools included.
New forests, averaging 0.4 million ha per year, seques-
tered 8 ± 1 Tg C year1, while deforestation, aver-
aging 0.1 million ha per year, resulted in C losses of
6±1TgCyear1. Forest land remaining forest land lost
184±10TgCyear1 to disturbance (13% from natural
disturbance, 87% from harvest); these were compen-
sated by net carbon gains of 452±48TgCyear1, 75%
of which occurred within timberland areas (Table4). C
losses from natural and human induced disturbances
reduced the potential net C sink in US forests by 42%
compared to the potential sink estimated without distur-
bance effects included, an estimate that is similar to other
studies [28, 34].
Regional variation in net C change across the
nation was substantial. e South sequestered
more C in growing forests (271 ± 28 Tg C year1)
than the North (97 ± 10 Tg C year1) or the
West (92 ± 11 Tg C year1), while at the same
time losing more C to the atmosphere from distur-
bances (105 ± 6 Tg C year1) than the other regions
(41±2TgCyear1 for the North and 44±3TgCyear1
for the West). Forest C change in the South was substan-
tial, in terms of both C losses and gains, because this
region is home to a majority of the wood harvest occur-
ring in the US (60% of all C loss from harvest occurred in
the South), and is therefore also home to the largest area
of regenerating forests that are sequestering C at high
rates. At the state level, the highest C losses occurred in
the forests of Georgia, Alabama, Washington, Missis-
sippi, Louisiana, and Oregon, with each of these states
losing more than 11TgCyear1 (Table5). Georgia, Flor-
ida, Alabama, Mississippi, and North Carolina gained the
most forest C in the time period, with each sequestering
at least 24TgCyear1. C gains exceeded C losses in all
states. Forests in approximately 6% of combined counties
were a net source of C to the atmosphere (Fig.2).
We estimated net C losses from six separate distur-
bance processes: fire, insect infestation, wind, tim-
ber harvest, land use conversion, and drought (Fig.3).
C losses from harvest (162 ± 9.9 Tg C year1) were
more than five times higher than losses from all other
processes combined (30 ± 2.6 Tg C year1). Fire
(7±1.0TgCyear1), wind (5±0.7TgCyear1), insect
infestation (10 ± 1.3 Tg C year1), and deforestation
(6±0.7 TgCyear1) each contributed a similar mag-
nitude of C losses across the CONUS, while drought
Fig. 2 Average annual net carbon change (Tg C year1) at the combined county scale across the CONUS. Most combined counties (91%) are net C
sinks while areas with extensive forest disturbance can be net C sources to the atmosphere
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 12 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
accounted for about 1±0.2TgCyear1. Individual dis-
turbances had spatially distinct distributions (Fig.4a). On
average, drought affected areas had C sequestration rates
20% lower than drought-free areas.
C losses in the South were highest (105±6TgCyear1)
with the highest fractional contributions from harvest
(92%) and wind (5%), with a particularly high concen-
tration of loss coming from the South Central region
(including the states of Texas, Oklahoma, Mississippi,
Louisiana, Kentucky, Tennessee, Alabama, and Arkan-
sas; Fig. 4b). e West had the second highest C loss
(44±3TgCyear1) with significant contributions from
harvest (66%), fire (15%), and insects (13%). e North
had the lowest C loss (41±2TgCyear1) with most sig-
nificant proportional contributions coming from harvest
(86%), insect damage (9%), and conversion (3%).
Our results can also be used to estimate net C impacts
of localized disturbances at finer spatial scales. A tornado
struck Lakewood, Wisconsin on 7 June 2007 and caused
severe forest damage, resulting in net C loss of more than
0.3TgC across a 13,000ha swath (Fig.5a). e wild fire
in southern California’s Santa Barbara County, termed
the “Zaca” fire, started on 4 July 2007 and caused exten-
sive damage to more than 97,000ha of forest in the Los
Padres National Forest, resulting in net C loss of more
than 4Tg C (Fig.4b).
e highest fractional contribution of C loss in all states
was from harvest (Table4), and 64% of these losses were
from logging residues [both above- (19%) and below-
ground (23%)] and mill residues (22%). Across all wood
product classes, the production of pulpwood resulted in
the highest forest C losses (26TgCyear1), followed by
saw logs (18TgCyear1), although a high proportion of
C in saw logs is in use or in landfills, both which are con-
sidered to be long-term C storage (Fig.6).
Comparison withother studies
We estimate that Hurricanes Gustav and Ike in 2008,
the only two hurricanes above category 2 to make
landfall during the study period, damaged forests in
Texas and Louisiana and led to net C change of more
than 22±2 Tg C (or 4± 0.5TgC year1 on average
over the 5 year period). Other studies report average
annual C loss in US forests due to hurricane damage
in the 20th century of 14TgCyear1 [35]. Zhou etal.
[36] estimate total C emissions from wood harvest
in 35 eastern US states as 168 Tg C year1 between
2002 and 2010, while our estimate for the same geo-
graphic extent is 132 ± 8 Tg C year1 between 2006
and 2010. Other national scale estimates of emissions
from wood harvest are lower, such as that of Williams
etal. [37] (107Tgyear1 in 2005) and Powell etal. [34]
(74TgCyear1 between 1986 and 2004). Hicke and Zep-
pel [38] estimated that bark beetles and fire together
resulted in gross emissions of 32TgCyear1 in the west-
ern US between 1997 and 2010. We estimate that insects
and fire resulted in net C change of 17±2TgCyear1
between 2006 and 2010. We conclude that, given the dif-
ferent spatial extents, time periods and C pools included,
results from our analysis that cover all disturbance types
are broadly consistent with these and other more special-
ized studies (see Williams etal. [39] for a comprehensive
Priorities forimproved forest carbon change estimates
Results generated from this analysis are dependent on
the algorithm that assigns each hectare of forest land to
a category that is then associated with a C stock change
value. By including spatial data sets of carbon stocks
and disturbance from remote sensing observations, the
Table 4 Average annual net C change (TgCyear1) acrossUS forests between2006 and2010, disaggregated intocatego-
ries ofnon-forest land toforest land, forest land tonon-forest land, andforest land remaining forest land
Results are further disaggregated by disturbance type within the forest land remaining forest land category
Category Area (Mhayear1) Net C gain (TgCyear1) Net C loss (TgCyear1)
Non-forest land to forest land 0.4 8 ± 1
Forest land to non-forest land 0.1 6 ± 1
Forest land remaining forest land 221.1 452 ± 47 185 ± 10
Insect damage 0.9 9 ± 1
Forest fire 0.6 7 ± 1
Wind damage 0.6 5 ± 1
Drought 0.8 1 ± 0
Timberlands 152.0 342 ± 42 162 ± 10
Undisturbed forest 54.9 109 ± 19
Total 221.6 460 ± 48 191 ± 10
Net C change 269 ± 49
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 13 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Table 5 State level estimates offorest area in2005 (millions ofha), net C gains, net C losses, andnet C change (TgCyear1) together withthe percent ofC loss
attributable toharvest, drought, re, wind, insect infestation, andland use conversion withinthe state
State Forest area C gain C loss Net C change Fire (%) Insect (%) Wind (%) Conversion (%) Drought (%) Harvest (%)
Alabama 8.5 27.3 12.5 14.9 0 1 0 1 0 97
Arizona 2.0 2.4 0.4 1.9 22 0 1 0 0 77
Arkansas 7.4 22.6 8.6 14.0 1 2 0 2 0 95
California 9.3 16.8 9.4 7.4 32 0 7 1 0 60
Colorado 5.1 6.7 0.3 6.3 8 0 0 1 0 92
Connecticut 0.9 1.2 0.2 1.0 0 0 1 31 0 68
Delaware 0.2 0.2 0.1 0.1 0 0 0 4 0 95
District of Columbia <0.1 0.0 0.0 0.0 0 0 0 100 0 0
Florida 6.4 28.5 6.3 22.2 3 0 0 3 0 94
Georgia 9.4 33.2 14.4 18.8 1 1 0 2 0 96
Idaho 7.1 10.2 4.9 5.3 29 0 23 0 0 48
Illinois 2.3 2.8 1.1 1.7 0 0 0 3 0 97
Indiana 2.3 2.8 1.7 1.1 0 0 3 1 0 95
Iowa 1.2 1.5 0.4 1.1 0 1 0 3 0 96
Kansas 0.9 1.1 0.2 0.9 0 1 0 3 0 95
Kentucky 5.7 11.5 3.3 8.2 1 0 0 6 0 93
Louisiana 5.4 18.0 11.1 6.9 0 19 0 1 0 79
Maine 6.8 7.7 6.7 0.9 0 0 15 1 0 84
Maryland 1.2 1.5 0.8 0.8 0 0 6 7 0 86
Massachusetts 1.5 1.9 0.6 1.3 0 0 4 18 0 78
Michigan 8.5 10.3 4.3 6.0 0 0 1 1 11 87
Minnesota 7.7 9.5 3.2 6.3 1 0 3 1 0 96
Mississippi 7.0 24.3 11.6 12.7 0 2 0 2 0 96
Missouri 7.1 8.7 2.7 6.0 1 2 0 4 0 93
Montana 7.3 8.6 5.0 3.5 14 0 49 0 0 37
Nebraska 0.3 0.4 0.1 0.2 2 1 0 0 0 97
Nevada 0.7 0.8 0.1 0.7 15 0 0 0 0 84
New Hampshire 2.1 2.6 0.8 1.8 0 2 4 6 0 88
New Jersey 1.0 1.3 0.5 0.8 2 0 40 14 0 43
New Mexico 2.6 3.2 0.3 2.8 33 0 16 0 0 51
New York 8.3 10.7 3.1 7.6 0 0 5 4 0 91
North Carolina 7.6 23.7 9.6 14.1 0 0 0 1 2 95
North Dakota 0.2 0.3 0.0 0.3 0 1 0 2 0 96
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 14 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Table 5 continued
State Forest area C gain C loss Net C change Fire (%) Insect (%) Wind (%) Conversion (%) Drought (%) Harvest (%)
Ohio 3.6 4.4 1.2 3.2 0 0 7 7 0 86
Oklahoma 3.6 9.0 1.6 7.3 2 2 0 3 0 94
Oregon 9.2 20.6 11.1 9.6 4 0 2 6 0 88
Pennsylvania 7.6 9.8 4.0 5.8 0 0 13 3 0 84
Rhode Island 0.2 0.2 0.1 0.2 0 0 3 11 0 85
South Carolina 4.8 18.4 6.5 11.9 1 1 0 2 0 97
South Dakota 0.5 0.6 0.2 0.3 2 0 0 0 0 98
Tennessee 6.2 14.2 4.0 10.1 0 1 0 3 0 95
Texas 7.9 23.3 9.8 13.6 1 23 0 2 0 74
Utah 2.2 2.2 0.3 1.8 24 0 38 0 0 38
Vermont 2.0 2.5 0.6 1.9 0 0 2 1 0 96
Virginia 6.7 16.5 6.1 10.4 1 0 0 2 0 97
Washington 7.9 17.3 11.7 5.6 3 0 8 19 0 70
West Virginia 5.3 6.9 2.5 4.4 0 0 1 6 0 93
Wisconsin 7.2 8.4 6.3 2.0 0 1 23 0 5 70
Wyoming 2.7 3.3 0.8 2.5 21 0 25 0 0 54
Total 221.5 459.5 191.1 268.4 4 3 5 3 1 85
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 15 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
methodology avoids making gross assumptions on the
regional distribution of carbon stocks and disturbance,
thus improving estimates of C loss. e strength of
this approach is estimated in the uncertainty analysis.
Our framework is therefore completely dependent on
the underlying data sources and, as the data improve,
so will the estimates. Although the US is among the
world’s leaders in technology and open data, where
high quality geospatial datasets are publicly available
and inventory programs are maintained by various fed-
eral and state agencies, opportunities for improvement
Fig. 3 Average annual net carbon loss (Tg C year1) attributed to the most likely disturbance type and estimated at the combined county scale
for harvest, fire, land use conversion, wind, insect, and drought. Combining these six sources results in estimates of total annual net C loss from
disturbance occurring between 2006 and 2010
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 16 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Fig. 4 Average annual net carbon change by disturbance type in a the North (79 million ha of forest), South (87 million ha), and West (56 million
ha) regions and b by FIA region: northeast (NE; 41 million ha), southeast (SE; 35 million ha), southcentral (SC; 52 million ha), northern lake states
(NLS; 23 million ha), northern plains states (NPS; 15 million ha), pacific west (PW; 17 million ha), rocky mountain northern (RMN; 14 million ha), rocky
mountain southern (RMS; 15 million ha), and the pacific southwest (PSW; 9 million ha)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 17 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
Priorities forFIA data collection
All forest inventory data used to estimate changes in
the above- and belowground C stocks in this analysis
come from FIA plots measured more than once. How-
ever, many more FIA plots have been re-measured in the
North and South regions of the US than in the West. e
limited number of re-measured FIA plots in the West
resulted in higher uncertainties in net C stock change
estimates and, in some disturbance categories, required
the imputation of estimates obtained from other regions
(Tables 1, 2). As the FIA program continues national
implementation of an annual inventory (including re-
measurement), the FIA data used in this analysis can be
revised accordingly so that the sample size of plots per
disturbance type increases and uncertainties decrease.
Until the early 2000s, the FIA program measured only
live tree attributes (e.g., tree diameter) allowing for the
estimation of aboveground C and modelling of the other
pools based on regions, live tree, and site characteristics
(although the dead wood pool was measured in some
states). erefore, we estimated changes in the above-
ground C pool using measured data while we relied on
models to estimate belowground C. e FIA program is
in the process of replacing model predictions of C in the
dead wood, litter, and soil organic C pools with estimates
obtained from measurements of these pools on a subset
of FIA plots [40]. ese pools, excluded from the current
Fig. 5 The forest carbon accounting framework implemented here can be useful in assessing carbon impacts of localized disturbances. a 2007
tornado in Lakewood, Wisconsin. The tornado track from NOAA (right) resulted in extensive impacts to the forest, which is evident in an aerial
photo (left) and in the resulting estimate of net carbon change (center, in units of Mg C ha1). b 2007 wild fire in southern California’s Santa Barbara
County, termed the “Zaca” fire. A photo of the blaze (left) highlights the fire intensity, which is mirrored in the burn severity map (right, MTBS) and
the resulting net carbon change estimate (center, in units of Mg C ha1)
C Transfer to Wood Products (Tg C yr-1
Harvest Component
Emied In Use Landfill
Fig. 6 Fate of C harvested from US forestlands in the year 2007, with
some stored in use and landfills and the rest emitted within 100 years.
PPP posts, poles and pilings
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 18 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
analysis, can be included in our framework as new data
are collected.
Priorities fornon‑forest lands
Our analysis focused on forest areas defined in part by the
NLCD data that is based on the interpretation of Landsat
imagery. Comparison of our 1-ha map of carbon density
of forestlands based on NLCD with high resolution lidar
data over the state of Maryland has shown a significant
underestimation of carbon stocks in highly fragmented
and mixed urban and forest landscapes [41]. ese small
scale forests cover substantial areas of densely populated
and fragmented landscapes of the eastern United States
and appear to be highly dynamic. ere is information
on the disturbance and recovery of these forests over the
time frame of our study, but our analysis has ignored car-
bon sources and sinks from these lands. By improving
the carbon inventory and satellite observations to cap-
ture small scale changes, the uncertainty of carbon fluxes,
particularly over the Eastern states, may be reduced. In
the future (post-2020), planned satellite observations of
the aboveground structure of forests by GEDI and NISAR
from the National Aeronautics and Space Administration
(NASA) and BIOMASS from the European Space Agency
should improve the annual inventory of forest C change,
as should the planned collection of FIA plot data in urban
and woodland areas.
Priorities forUNFCCC reporting
Although the US has data on the magnitude of area
change across land use categories, it does not have
reliable and comprehensive estimates of C stocks
across the entire reporting time series (e.g., 1990–2014
for the most recent UNFCCC submission) and full
matrix of land use and land-use change categories to
report these changes separately. For this reason, in
its GHG inventory submission the US has historically
deviated from IPCC guidance by reporting together
C stock changes from afforestation and forest man-
agement as “forest land remaining forest land”, while
emissions associated with a land use conversion from
forest land to a non-forest land use are reported in
the non-forest land use category (per IPCC guidance).
For the first time in its 2016 submission [16, 17], the
US delineated net C stock changes from afforestation
separately from forest land remaining forest land. An
additional data need is refined C stock monitoring on
non-forest lands and better coordination among land
use categories to ensure complete accounting and
avoidance of double counting. Our spatially resolved
analysis approach allowed us to disaggregate net C
change into subcategories of non-forest land to forest
land (8± 1TgCyear1), forest land to non-forest
land (6±1 TgCyear1), and forest land remaining
forest land (267TgCyear1). While the sole focus
on net processes within the forest land use category
in this study does not fully solve complete C account-
ing issues across all land uses, the methods used in
this research are an incremental improvement toward
resolving components of net C change within the for-
est land category, and these results can help inform
and refine US reporting in the future.
Priorities forimproving disturbance attribution
Insect and disease aerial detection surveys (ADS) are
conducted annually using a variety of light aircraft by
the USDA Forest Service in collaboration with other
state and federal cooperators. Overview surveys map
the current year’s forest impact, and some regions have
been conducting ADS for more than 60years while oth-
ers have become more active only within the last decade.
erefore, annual maps of insect damage with full cover-
age of all US forestlands are not available, but areas most
likely to be affected by insect damage are surveyed more
frequently. We accounted for the lack of continuous data
coverage in our uncertainty analysis by assuming a 5%
bias in underreported area. e Monitoring Trends in
Burn Severity (MTBS) dataset, sponsored by the Wild-
land Fire Leadership Council, consistently maps the
burn severity and perimeters across all lands of the US
since 1984. Although 30m resolution imagery is used for
analysis, the minimum mapping unit for delineating fire
perimeters is greater than 1000 acres (404ha) in the West
and 500 acres (202ha) in the East. erefore, burned for-
est areas smaller than these patch sizes were excluded
from our analysis.
Priorities forwood harvest data collection
Information on the primary anthropogenic source of C
loss in US forests—wood harvest—is available only at the
level of combined counties. TPO data allow for the esti-
mation of C losses from the extraction of wood products
that are not readily detected by remote sensing observa-
tions, including the most recent Landsat based tree cover
loss data from Hansen etal. [8]. We examined the rela-
tionship between TPO estimated C losses and a remote
sensing-based estimate of C losses from forest distur-
bance that could not be readily linked to another dis-
turbance type (i.e. wind, insect, fire, or conversion). For
this comparative analysis, we assumed all tree cover loss
pixels in Hansen etal. [8] data that could not be linked
to another disturbance type were harvested, and sub-
sequent C loss was estimated via our FIA look-up table
approach. When aggregated to the state level, these two
independent estimates of C loss associated with har-
vest were highly correlated (Fig. 7), and the remote
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 19 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
sensing-based estimates of (net) C loss from harvest were
approximately half of the (gross) TPO-based estimates.
is provides indications that: (1) Landsat-based remote
sensing observations likely miss a significant proportion
of harvest activity due to partial loss, rather than full
loss, of tree canopy cover; and (2) the additional C loss
not identified by the remote sensing approach is spatially
proximate to larger scale C losses from harvest, at least
at the state scale. Increased transparency on the spatial
location, timing and type of harvesting occurring across
the US would allow more explicit attribution of forest C
fluxes to specific forest management activities.
Managing US forests forclimate change mitigation
Globally, the US ranks fourth in terms of forest area [42,
8]. Although large C losses occur from US forests as a
result of an active wood products industry, particularly
in the US South, 76% of the total US net carbon sink
(342TgCyear1) occurred within timberland areas, more
than half of which are privately owned [43]. e income
received by landowners from Intensive forest manage-
ment may reduce the likelihood of forest conversion to
development, but in the absence of all disturbance effects,
we estimate a potential C sink between 2006 and 2010 of
460 and 436TgCyear1 if only non-harvest distur-
bance effects (fire, drought, wind, insect damage, land-use
conversion) are considered. e US has also committed to
restoring 15Mha of forest land [44], which could further
increase the C sink capacity of US forests. is implies
that the US C sink could be increased substantially if
existing forest land were managed to achieve this goal.
In addition to sequestering and storing atmospheric car-
bon, US forests also generate wood products that support
the energy, industry, transport and building sectors both
domestically and internationally. Given that wood har-
vest represents the majority of C losses from US forests,
increasing the US net forest C sink would require shifts
in current forest management practices as well as more
refined and disaggregated information to reduce the uncer-
tainty of these estimates and resolve these with correct esti-
mation of net C change. For example, national debate has
grown over the production of wood pellets as a renewable
energy source, particularly from the southeast US, with
demand driven by European policies to reduce emissions of
greenhouse gases and increase the use of renewable energy.
Georgia, Florida, Alabama and Virginia currently account
for nearly all US wood pellet exports [45]. Although wood
pellets are claimed by the industry to be made from resi-
dues at lumber mills or logging sites, the industry’s growth
could lead to a substantial increase in demand on South-
ern forests, potentially creating incentives to expand plan-
tations. e potential of bioenergy to reduce greenhouse
gas emissions inherently depends on the source of the bio-
mass and its net land use effects; bioenergy reduces green-
house gas emissions only if the growth and harvesting of
the biomass used for energy sequesters carbon above and
beyond what would be sequestered anyway [46]. is addi-
tional carbon must result from land management changes
that increase tree C uptake or from the use of biomass that
would otherwise decompose rapidly.
New global emphasis on climate change mitigation as
one of the many benefits that forests provide gives US
Fig. 7 Relation between C losses from harvest as estimated from timber product output (TPO) data and from an independent remote sensing-
based estimate. TPO = 1.98 × RS + 767,777; R2 = 0.91). Data points represent results aggregated to the state-level
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 20 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
decision makers the opportunity to re-evaluate national
and state policy agendas to consider not only the pro-
duction of merchantable wood volume and biomass for
bioenergy, but also enhanced C sequestration and stor-
age for climate change mitigation. As recognized in the
2014 Farm Bill [47], there is a growing need to both
reduce the uncertainty associated with estimating forest
biomass and the associated monitoring of C dynamics
across US forests. As it currently stands, the statistical
power of detecting changes in forest C stocks exists only
at large regional scales [48], disallowing the detection
of C change at policy-relevant scales such as encoun-
tered in the pellet industry. Continued research to both
downscale forest C inventories and correctly attribute
C change to natural and anthropogenic disturbance
events is needed to empower forest management policy
Achieving a global, economy-wide “balance between
anthropogenic emissions by sources and removals by sinks”
[1] will require both more emission reductions and more
C sequestration from the forest sector. Results from this
analysis indicate the location and estimated magnitude of
C losses from different disturbances in absolute and relative
terms, and can be used to track more explicitly which losses
result from natural or anthropogenic disturbances. Our
national net C change estimate of 269±49TgCyear1
is within the range of previously reported estimates, and
provides spatially explicit estimates and attribution of
changes to different types of disturbances. Data are synthe-
sized from various US agencies into a common framework,
which could improve inter-agency dialogue to ensure com-
plete accounting and to avoid double counting within and
between land use categories. is work may also improve
collaboration that drives a more efficient and participa-
tory process for allocating resources towards activities
that meet common goals, including an increased focus
on climate change mitigation. e methodological frame-
work and accompanying results allow US policymakers
and negotiators to better understand the causes of for-
est C change more completely so that they can participate
more effectively in domestic policy discussions about for-
est management and monitoring as well as in international
negotiations. Integration of results from this and other
studies should further enable the development of future US
GHG inventories that include disturbance attribution and
full land use change accounting in expectation of post-2020
commitment requirements.
Authors’ contributions
NH, SH, SS, CW, SB and WS designed the study. SH, NH and TP conducted the
analysis. CW, BW and GD compiled the FIA and TPO datasets. SS, YY and AF
produced the biomass maps. SH implemented model runs and designed and
conducted the uncertainty analysis. BB provided guidance on C modeling and
on technical implementation of the methods. SH produced figures and tables.
NH and SH wrote the paper. All authors reviewed the final manuscript. All
authors read and approved the final manuscript.
Author details
1 Ecosystem Services Unit, Winrock International, 2121 Crystal Drive Suite
500, Arlington, VA 22202, USA. 2 Applied Geosolutions, 55 Main Street Suite
125, Newmarket, NH 03857, USA. 3 NASA Jet Propulsion Laboratory, California
Institute of Technology, Pasadena, CA 91109, USA. 4 USDA Forest Service,
Northern Research Station, Saint Paul, MN 55108, USA. 5 Present Address: For-
ests Program, World Resources Institute, 10 G Street NE Suite 800, Washington,
DC 20002, USA.
This study was made possible by an inter-institutional grant from NASA’s Car-
bon Monitoring System Program (Grants # NNX12AN72G and NNX12AP07G)
that supported Applied GeoSolutions, Winrock International, the USDA Forest
Service, NASA JPL and NASA Ames. We thank C. Ipsan for assistance with the
creation of figures.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The datasets supporting the conclusions of this article are available on the
ORNL DAAC website (
Received: 1 August 2016 Accepted: 3 November 2016
1. UNFCCC. Report of the conference of the parties on its twenty-first ses-
sion, held in Paris from 30 November to 13 December 2015. 2016. http:// Accessed Mar 2016.
2. Ciais P, Sabine C, Bala G, Bopp L, Brovkin V, Canadell J, Chhabra A, DeFries
R, Galloway J, Heimann M, Jones C, Quéré C, Myneni RB, Piao S, Thornton
P. Carbon and other biogeochemical cycles. In: Stocker TF, Qin D, Plattner
G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM,
editors. Climate change 2013: the physical science basis. Contribution of
working group I to the fifth assessment report of the intergovernmental
panel on climate change. Cambridge University Press, Cambridge; 2013.
3. Houghton, et al. A role for tropical forests in stabilizing atmospheric CO2.
Nat Clim Change. 2015;5:1022–3.
4. Moscarro VS, Coops NC, Kurz WA, Olguin M. Choice of satellite imagery
and attribution of changes to disturbance type strongly affects forest
carbon balance estimates. Carbon Balance Manag. 2015;10:30.
5. Saatchi SS, et al. Benchmark map of forest carbon stocks in
tropical regions across three continents. Proc Natl Acad Sci USA.
2011;108(24):9899–904. doi:10.1073/pnas.1019576108.
6. Harris NL, Brown S, Hagen SC, Saatchi SS, Petrova S, Salas W, Lotsch A.
Baseline map of carbon emissions from deforestation in tropical regions.
Science. 2012;336(6088):1573–6.
7. Baccini A, Goetz S, Walker WS, et al. Estimated carbon dioxide emissions
from tropical deforestation improved by carbon-density maps. Nat Clim
Change. 2012;2:182–5.
8. Hansen MC, et al. High-resolution global maps of 21st-century forest
cover change. Science. 2013;342(6160):850–3.
9. Achard F, Beuchle R, Mayaux P, Stibig HJ, Bodart C, Brink A, Carboni S,
Desclee B, Donnay F, Eva HD, Lupi A, Rasi R, Seliger R, Simonetti D. Deter-
mination of tropical deforestation rates and related carbon losses from
1990 to 2010. Glob Change Biol. 2014;20:2540–54.
10. Tyukavina A, Baccini A, Hansen MC, Potapov PV, Stehman SV, Houghton RA,
Krylov AM, Turubanova S, Goetz S. Aboveground carbon loss in natural and
managed tropical forests from 2000 to 2012. Environ Res Lett. 2015;10:101002.
11. Kim DH, Sexton JO, Townshend JR. Accelerated deforestation in
the humid tropics from the 1990s to the 2000s. Geophys Res Lett.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 21 of 21
Harris et al. Carbon Balance Manage (2016) 11:24
12. Zarin DJ, Harris NL, Baccini A, et al. Can carbon emissions from
tropical deforestation drop by 50% in 5 years? Glob Change Biol.
13. Marvin DC, Asner GP. Spatially explicit analysis of field inventories for
national forest carbon monitoring. Carbon Balance Manag. 2016;11:9.
14. Houghton RA, House JI, Pongratz J, van der Werf GR, DeFries RS, Hansen
MC, Le Quere C, Ramankutty N. Carbon emissions from land use and land
cover change. Biogeosciences. 2012;9:5125–42.
15. IPCC. Guidelines for national greenhouse gas inventories—volume 4:
agriculture, forestry, and other land use (AFOLU). 2006. http://www. Accessed Mar 2016.
16. Woodall CW, Coulston JW, Domke GM, Walters BF, Wear DN, Smith JE,
Anderson H-E, Clough BJ, Cohen WB, Griffith DM, Hagan SC, Hanou IS,
Nichols MC, Perry CH, Russell MB, Westfall JA, Wilson BT. The US forest
carbon accounting framework: stocks and stock change 1990–2016. Gen
Tech Rep NRS-154. Newtown Square: US Department of Agriculture, For-
est Service, Northern Research Station; 2015.
17. US EPA. Forest sections of the Land use change and forestry chapter, and
Annex. US Environmental Protection Agency, inventory of US greenhouse
gas emissions and sinks: 1990–2014. EPA 430-R-16-002. 2016. https://
Inventory-2016-Main-Text.pdf. Accessed Oct 2016.
18. Penman J, Gytarsky M, Hiraishi T, Krug T, Kruger D, Pipatti R, Buendia
L, Miwa K, Ngara T, Tanabe K, Wagner F. Intergovernmental panel on
climate change Good practice guidance for land use, land-use change
and forestry. Hayama: Institute for Global Environmental Strategies (IGES)
for the Intergovernmental Panel on Climate Change; 2003. Available
GPG_LULUCF_FULL.pdf. Accessed 18 Nov 2016.
19. Heath LS, Birdsey RA. Carbon trends of productive temperate forests of the
coterminous United States. In: Terrestrial biospheric carbon fluxes quantifi-
cation of sinks and sources of CO2. Netherlands: Springer; 1993. p. 279–93.
20. Nowak DJ, Greenfield EJ. Evaluating the national land cover database
tree canopy across the conterminous United States: a comparison with
photo-interpreted estimates. Environ Manag. 2010;46:378.
21. Curtis RO, Marshall DD. Why quadratic mean diameter? West J Appl For.
22. Zhang G, Ganguly S, Nemani RR, White MA, Milesi C, Hashimoto H,
Myneni RB. Estimation of forest aboveground biomass in California using
canopy height and leaf area index estimated from satellite data. Remote
Sens Environ. 2014;151:44–56.
23. Phillips SJ, Anderson RP, Shapire RE. Maximum entropy modeling of spe-
cies geographic distributions. Ecol Model. 2006;190:231–59.
24. Weisbin CR, Lincoln W, Saatchi S. A systems engineering approach to
estimating uncertainty in above-ground biomass (AGB) derived from
remote-sensing data. Syst Eng. 2014;17(3):361–73.
25. Jenkins J, Chojnacky D, Heath L, Birdsey R. National-scale biomass estima-
tors for United States tree species. For Sci. 2003;49(1):12–35.
26. Cairns MS, Brown S, Helmer E, Baumgardner GA. Root biomass allocation
in the world’s upland forests. Oecologia. 1997;111:1–11.
27. Mokany K, Raison R, Prokushkin AS. Critical analysis of root:shoot ratios in
terrestrial biomes. Glob Change Biol. 2006;12:84–96.
28. Zheng D, Heath LS, Ducey MJ, Smith JE. Carbon changes in conterminous
US forests associated with growth and major disturbances: 1992–2001.
Environ Res Lett. 2011;6:014012. doi:10.1088/1748-9326/6/1/014012.
29. FIA fiscal year 2013 business report.
org-documents/docs/FIA_Annual_Report_2013.pdf. Accessed Apr 2016.
30. Miles PD, Smith WB. Specific gravity and other properties of wood and
bark for 156 tree species found in North America. Newtown Square:
USDA For Ser North Res Stn Res Note NRS-38; 2009.
31. Smith JE, Heath LS, Skog KE, Birdsey RA. Methods for calculating forest
ecosystem and harvested carbon with standard estimates for forest types
of the United States. Newtown Square: USDA For Ser North Res Stn Gen
Tech Rep NE-243; 2006.
32. Hagen SC, Braswell BH, Linder E, Frolking S, Richardson AD, Hollinger DY.
Statistical uncertainty of eddy flux-based estimates of gross ecosystem
carbon exchange at Howland forest, Maine. J Geophys Res Biosph.
33. US EPA. Forest sections of the land use change and forestry chapter, and
Annex. US Environmental Protection Agency, inventory of US greenhouse
gas emissions and sinks: 1990–2012. EPA 430-R-14-003. 2013. http://www. Accessed
June 2016.
34. Powell SL, et al. Observation of trends in biomass loss as a result of distur-
bance in the conterminous US: 1986–2004. Ecosystems. 2014;17:142–57.
35. Fisk JP, et al. The impacts of tropical cyclones on the net carbon balance
of eastern US forests (1851–2000). Environ Res Lett. 2013;8(4):045017.
36. Zhou D, Liu S, Oeding J, Zhao S. Forest cutting and impacts on carbon in
the eastern United States. Sci Rep. 2013;3:3547. doi:10.1038/srep03547.
37. Williams CA, Collatz GJ, Masek J, Goward SN. Carbon consequences of
forest disturbance and recovery across the coterminous United States.
Global Biochem Cycles. 2012;26:GB1005. doi:10.1029/2010GB003947.
38. Hicke JA, Zeppel MJB. Climate-driven tree mortality: insights from the
piñon pine die-off in the United States. New Phytol. 2013;200:301–3.
39. Williams CA, Gu H, MacLean R, Masek JG, Collatz GJ. Disturbance and the
carbon balance of US forests: a quantitative review of impacts from har-
vests, fires, insects and droughts. Global Planet Change. 2016;143:66–80
40. Domke GM, Walters BF, Perry CH, Woodall CW, Russell MB, Smith JE. Esti-
mating litter carbon stocks on forest land in the United States. Sci Total
Environ. 2016;557–558:469–78.
41. Huang W, Swatantran A, Johnson K, Duncanson L, Tang H, O’Neil Dunne
J, Hurtt G, Dubayah R. Local discrepancies in continental scale biomass
maps: a case study over forested and non-forested landscapes in Mary-
land, USA. Carbon Balance Manag. 2015;10:19.
42. FAO. Forest resources assessment report. 2015.
resources-assessment/current-assessment/en/n. Accessed Nov 2016.
43. Hewes Jaketon H, Butler Brett J, Liknes Greg C, Nelson Mark D, Snyder
Stephanie A. Map of distribution of six forest ownership types in the
conterminous United States. Newtown Square: US Department of Agri-
culture, Forest Service, Northern Research Station; 2014.
44. IUCN. The Bonn challenge. 2011.
tent/challenge. Accessed June 2016.
45. Goetzl A. Developments in the global trade of wood pellets. Office of
industries working paper no. ID-039, US International Trade Commission.
final.pdf. Accessed June 2016.
46. Searchinger, et al. Fixing a critical climate accounting error. Science.
47. Agricultural Act of 2014. H. R. 2642; Pub. L. 113–79.
48. Westfall JA, Woodall CW, Hatfield MA. A statistical power analy-
sis of woody carbon flux from forest inventory data. Clim Change.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
... Based on the FIA data, there are an estimated 1.38 trillion live trees across all size classes over more than 250 Mha of forestland in the CONUS (Domke et al 2020a). These trees collectively store about 19.5 PgC in live above and below ground biomass and sequester approximately 150 TgC yr −1 (Harris et al 2016, Domke et al 2020a. ...
... There are numerous examples of using high-resolution aerial photography and airborne lidar measurements to enhance NFI inference at the county or the state levels, where sample size of FIA plots may not be enough (Hudak et al 2012, Skowronski and Lister 2012, Cohen 2014, Tinkham et al 2018. National and regional forest carbon maps have also been produced by integrating forest inventory and satellite imagery to provide maps of forest carbon stocks for a variety of applications such as the assessment of the forest fragmentation and urbanization, disturbance from forest fires, and wildlife habitat management (Blackard et al 2008, Saatchi 2012, Wilson et al 2013, Harris et al 2016. ...
... CONUS forestlands), can be approximated with a probability distribution that has maximum entropy. The Modified MaxEnt approach has been successfully used for the estimation of AGC (Saatchi et al 2011, Harris et al 2016, Xu et al 2017. To apply the method, the plot estimates of AGC were separated into bins of biomass range and trained a MaxEnt model for each bin. ...
Full-text available
Signatories to the United Nations Framework Convention on Climate Change (UNFCCC) are required to annually report economy-wide greenhouse gas emissions and removals, including the forest sector. National forest inventory (NFI) is considered the main source of data for reporting forest carbon stocks and changes to UNFCCC. However, NFI samples are often collected asynchronously across regions in intervals of 5–10 years or sub-sampled annually, both introducing uncertainties in estimating annual carbon stock changes by missing a wide range of forest disturbance and recovery processes. Here, we integrate satellite observations with forest inventory data across the conterminous United States to improve the spatial and temporal resolution of NFI for estimating annual carbon stocks and changes. We used more than 120 000 permanent plots from the US forest inventory and analysis (FIA) data, surveyed periodically at sampling rate of 15%–20% per year across the US to develop non-parametric remote sensing-based models of aboveground biomass carbon density (AGC) at 1 ha spatial resolution for the years 2005, 2010, 2015, 2016, and 2017. The model provided a relatively unbiased estimation of AGC compared to ground inventory estimates at plot, county, and state scales. The uncertainty of the biomass maps and their contributions to estimates of forest carbon stock changes at county and state levels were quantified. Our results suggest that adding spatial and temporal dimensions to the forest inventory data, will significantly improve the accuracy and precision of carbon stocks and changes at jurisdictional scales.
... In general, in order to improve the odds of fire encountering a treated area, ten times more area than the specific site would be needed, which means even more treatment related emissions and co-lateral damages can be expected. Likewise, in a synthesis of emissions estimated from natural disturbances vs. logging, Harris et al. (2016) concluded that logging during 2006-2010 nationwide released up to 10 x more emissions than wildfire and insects combined. Thus, putting more carbon dioxide into the atmosphere in attempts to limit fire effects may create a dangerous feedback loop (or "landscape trap," Lindenmayer et al., 2011) such that logging produces emissions (Harris et al., 2016) that then contribute to climate-related increases in extremefire weather and the Sisphean response. ...
... Likewise, in a synthesis of emissions estimated from natural disturbances vs. logging, Harris et al. (2016) concluded that logging during 2006-2010 nationwide released up to 10 x more emissions than wildfire and insects combined. Thus, putting more carbon dioxide into the atmosphere in attempts to limit fire effects may create a dangerous feedback loop (or "landscape trap," Lindenmayer et al., 2011) such that logging produces emissions (Harris et al., 2016) that then contribute to climate-related increases in extremefire weather and the Sisphean response. ...
Full-text available
Debate remains about the effectiveness of commercial thinning as a wildfire management strategy, with some studies reporting somewhat lower severity in thinned forests, and some reporting higher severity, during wildfires. However, while vegetation severity is a measure of basal area tree mortality, research on this question generally omits tree mortality from thinning itself. We investigated whether cumulative tree mortality, or cumulative severity, from commercial thinning and wildfire was different between thinned and unthinned forests in the Caldor Fire of 2021 in the northern Sierra Nevada mountains of California, USA. We found significantly higher cumulative severity in commercial thinning areas compared to unthinned forests. More research is needed to determine whether cumulative severity is higher in commercially thinned forests in other large western US wildfires.
... Another important aspect is the quality and functionality of these sinks (e.g., in terms of their gross primary productivity). EO data already provide useful information for both global and national monitoring of forest/ land use carbon sources and sinks (Harris et al., 2016(Harris et al., , 2021Nesha et al., 2021). ...
Full-text available
Space-based Earth observation (EO), in the form of long-term climate data records, has been crucial in the monitoring and quantification of slow changes in the climate system—from accumulating greenhouse gases (GHGs) in the atmosphere, increasing surface temperatures, and melting sea-ice, glaciers and ice sheets, to rising sea-level. In addition to documenting a changing climate, EO is needed for effective policy making, implementation and monitoring, and ultimately to measure progress and achievements towards the overarching goals of the United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement to combat climate change. The best approach for translating EO into actionable information for policymakers and other stakeholders is, however, far from clear. For example, climate change is now self-evident through increasingly intense and frequent extreme events—heatwaves, droughts, wildfires, and flooding—costing human lives and significant economic damage, even though single events do not constitute “climate”. EO can capture and visualize the impacts of such events in single images, and thus help quantify and ultimately manage them within the framework of the UNFCCC Paris Agreement, both at the national level (via the Enhanced Transparency Framework) and global level (via the Global Stocktake). We present a transdisciplinary perspective, across policy and science, and also theory and practice, that sheds light on the potential of EO to inform mitigation, including sinks and reservoirs of greenhouse gases, and adaptation, including loss and damage. Yet to be successful with this new mandate, EO science must undergo a radical overhaul: it must become more user-oriented, collaborative, and transdisciplinary; span the range from fiducial to contextual data; and embrace new technologies for data analysis (e.g., artificial intelligence). Only this will allow the creation of the knowledge base and actionable climate information needed to guide the UNFCCC Paris Agreement to a just and equitable success.
... The long-term high resolution (10-30 m) records of Landsat and the recent Sentinel-2 data are now used to derive tree cover loss [42] and land-cover changes [1,75,103], and to improve the mapping of small fires which can result in a doubling of burned area [12]. These data can be combined with satellite-based biomass maps to estimate biomass carbon changes [45] as discussed above. Recently launched or planned sensors with high spatial resolution and temporal revisit frequencies such as ESA'Sentinel-1 and 2 or the EnMAP launched in 2022 [39], are expected to further improve our capacity to map land-cover (Zanaga et al. [108]), land-cover changes occurring at small spatial scales such as selective logging [20,77] or tree decline and mortality [79,109]. ...
Full-text available
The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement.
... In west coast states, overall harvest-related emissions were about 5 times fire emissions, and California's fire emissions were a few percent of its fossil fuel emissions [59]. In the conterminous 48 states, harvest-related emissions are 7.5 times those from all natural causes [60]. It is understandable that the public wants action to reduce wildfire threats, but false solutions that make the problem worse and increase global warming are counterproductive. ...
Full-text available
This paper provides a review and comparison of strategies to increase forest carbon, and reduce species losses for climate change mitigation and adaptation in the United States. It compares forest management strategies and actions that are taking place or being proposed to reduce wildfire risk and to increase carbon storage with recent research findings. International agreements state that safeguarding biodiversity and ecosystems is fundamental to climate resilience with respect to climate change impacts on them, and their roles in adaptation and mitigation. The recent Intergovernmental Panel on Climate Change report on impacts, mitigation, and adaptation found, and member countries agreed, that maintaining the resilience of biodiversity and ecosystem services at a global scale is “fundamental” for climate mitigation and adaptation, and requires “effective and equitable conservation of approximately 30 to 50% of Earth’s land, freshwater and ocean areas, including current near-natural ecosystems.” Our key message is that many of the current and proposed forest management actions in the United States are not consistent with climate goals, and that preserving 30 to 50% of lands for their carbon, biodiversity and water is feasible, effective, and necessary for achieving them.
... It is well established that conventional forestry management and harvest of naturally regenerating forests for commodity production (i.e. logging trees for timber, pulp and energy) (Matricardi et al 2020) causes significant CO 2 emissions (Puettmann et al 2015, Harris et al 2016, Mildrexler et al 2020 and that about 70% of the world's forests are managed in this way (FAO 2020b). Logging therefore results in CO 2 emissions and the depletion of forest ecosystem carbon stocks. ...
Full-text available
Meeting the Paris Agreement global warming target requires deep and rapid cuts in CO2 emissions as well as removals from the atmosphere into land sinks, especially forests. While international climate policy in the land sector does now recognize forest protection as a mitigation strategy, it is not receiving sufficient attention in developed countries even though they experience emissions from deforestation as well as from logging of managed forests. Current national greenhouse gas inventories obscure the mitigation potential of forest protection through net carbon accounting between the fossil fuel and the land sectors as well as within the different categories of the land. This prevents decision-makers in national governments, the private sector and civil society having access to all the science-based evidence needed to evaluate the merits of all mitigation strategies. The consequences of net carbon accounting for global policy were investigated by examining annual inventory reports of four high forest cover developed countries (Australia, Canada, USA, and Russia). Net accounting between sectors makes a major contribution to meeting nationally determined contributions with removals in Forest Land offsetting between 14-38% of the fossil fuel emissions for these countries. Analysis of reports for Australia at a sub-national level revealed that the State of Tasmania delivered negative emissions due to a change in forest management – a large and rapid drop in native forest logging – resulting in a gross mitigation benefit of ~40 Mt CO2_e over the reported period (2006/7-2018/19). This is precisely the outcome required globally to meet the Paris Agreement temperature goal. All CO2 emissions from, and atmospheric removal into, forest ecosystem carbon stocks now matter and should be counted and credited to achieve the deep and rapid cuts in emissions needed over the coming decades. Accounting and reporting systems therefore need to show gains and losses of carbon stocks in each reservoir. Changing forest management in naturally regenerating forests to avoid emissions from harvesting and enabling forest regrowth is an effective mitigation strategy that can rapidly reduce anthropogenic emissions from the forest sector and simultaneously increase removals of CO2 from the atmosphere.
Biofuel is an alternative energy that is considered equivalent to natural fossil fuel. It has been existed and commercialized since decades. However, the implementation of biofuel policy in a country or organization required massive input from various stakeholders. The most important aspect that needs to be considered includes the impact on the environment, ecosystem, economy and biodiversity. Thus, this work presents a review on the major factor of biofuel regulations worldwide, current legislation and target for biofuel production and consumption and finally impact assessment on the biofuel policy on biodiversity specifically on the land requirement. The information and data were collected based on official academic published materials, government official website, relevant publication by the trusted news portals and conference or proceeding materials.
•Uniquely important aspects of the boreal forest carbon budget relative to other regions include: smaller human populations with less direct anthropogenic influences and management, extensive areas of slow-growing coniferous forests, large areas of peatland and wetland complexes, substantial amounts of carbon stored below-ground in the region’s soils, and the presence of permafrost in many of these soils. •Boreal forest carbon budget accounting and reporting relies heavily on the national forest inventory programs of the major countries in the region, with other modeling approaches used to fill in the gaps in undersampled geographies and component pools. •Both top-down, atmospheric inversion modeling and bottom-up, terrestrial biosphere modeling have been challenged by a paucity of available data over the large extent of the mostly remote forest lands of the boreal region. •Uncertainties are being addressed—and confidence in budget assessments is improving—as new methods and expanded data collections are coming online, particularly with remote sensing. •How the land base is defined and reported as “managed forest” in boreal nations (that have large areas of noninventoried forest) will have important, global-scale implications for policy actions to mitigate GHG emissions.
Full-text available
Context Information on the maturity of forests is important for conservation planning. However, available information for the USA is inadequate to support national conservation assessment and planning. Objectives The main objective was to spatially model at a high resolution the relative level of maturity and stand development for forests across conterminous USA. A secondary objective was to explore which younger forests could be attributed to the impacts of severe natural disturbances. Methods We modelled the relative level of maturity for forests at a 30 m pixel resolution using spatial data for forest cover, height and biomass, stratified by forest types and ecoregions. National plot data were used to validate modelled results. The impact on Young forest from severe wildfire, insects and disease, and tornados was examined for the years 2000-2019. Results Of a total forest area of 248.9M ha, Young forest covered 52.9 M ha (22%); Intermediate 100.3 M ha (42%); and Mature 86.0 M ha (36%). Results suggest that the modelled data are tracking observed forest structure and stand development. 1.4 M ha (2.67%) of modelled Young forest was impacted by severe natural disturbances, with 51.5 (97.33%) M ha of Young forest unimpacted. The distribution of the disturbance factors varied geographically. The unimpacted Young plus Mature forest are where primary forests are most likely found. Conclusions The forest maturity data can assist forest decision makers in meeting environmental commitments regarding mitigating forest sector emissions, biodiversity conservation and water quality, including through prioritizing land for meeting protected area and ecosystem restoration targets.
Multi-angle surface reflectance data from the NASA Jet Propulsion Laboratory Multi-angle Imaging Spectro-Radiometer (MISR) were used to map aboveground biomass density (AGB, Mg ha⁻¹) in the forests of the southwestern United States inter-annually from 2000 to 2015. The approach uses a multi-angle index that has a loge relationship with AGB estimates in the National Biomass and Carbon Dataset 2000 (NBCD 2000). MISR Level 1B2 Terrain radiance data from May 15–June 15 of each year were converted to mapped surface bidirectional reflectance factors (BRFs) and leveraged to adjust the kernel weights of the RossThin-LiSparse-Reciprocal Bidirectional Reflectance Distribution Function (BRDF) model. The kernel weights with the lowest model-fitting RMSE were selected as the least likely to be cloud-contaminated and were used to generate synthetic MISR datasets. An optimal index calculated using BRFs modeled in the solar principal plane was found with respect to NBCD 2000 estimates for 19 sites near Mt. Lindsey, Colorado. These relationships were found in areas with AGB ranging from 20 to 190 Mg ha⁻¹, with the model yielding R² = 0.91 (RMSE: 15.4 Mg ha⁻¹). With spectral-nadir metrics, the R² values obtained were 0.07, 0.32, and 0.37 for NIR band BRFs, NDVI, and red band BRFs, respectively. For regional application, a simplified single coefficient model was fitted to the NBCD 2000 data, to account for variations in forest type, soils, and topography. The resulting AGB maps were consistent with estimates from up-scaled 2005 ICESat GLAS data and 2013 NASA Carbon Monitoring System airborne lidar-derived estimates for the Rim Fire area in California; and with the 2005 GLAS-based map across the southwestern United States. Trajectories were stable through time and losses from fire and beetle disturbance matched historical data in published sources. MISR estimates were found to reliably capture ABG compared to radar- and lidar-derived estimates across the southwestern United States (N = 11,019,944), with an RMSE of 37.0 Mg ha⁻¹ and R² = 0.9 vs GLAS estimates.
Full-text available
The net flux of carbon from land use and land-cover change (LULCC) is significant in the global carbon budget but uncertain, not only because of uncertainties in rates of deforestation and forestation, but also because of uncertainties in the carbon density of the lands actually undergoing change. Furthermore, there are differences in approaches used to determine the flux that introduce variability into estimates in ways that are difficult to evaluate, and there are forms of management not considered in many of the analyses. Thirteen recent estimates of net carbon emissions from LULCC are summarized here. All analyses consider changes in the area of agricultural lands (croplands and pastures). Some consider, also, forest management (wood harvest, shifting cultivation). None of them includes the emissions from the degradation of tropical peatlands. The net flux of carbon from LULCC is not the same as "emissions from deforestation", although the terms are used interchangeably in the literature. Means and standard deviations for annual emissions are 1.14 ± 0.23 and 1.13 ± 0.23 Pg C yr<sup>−1</sup> (1 Pg = 10<sup>15</sup> g carbon) for the 1980s and 1990s, respectively. Four studies also consider the period 2000–2009, and the mean and standard deviations for these four are 1.14 ± 0.39, 1.17 ± 0.32, and 1.10 ± 0.11 Pg C yr<sup>−1</sup> for the three decades. For the period 1990–2009 the mean global emissions from LULCC are 1.14 ± 0.18 Pg C yr<sup>−1</sup>. The errors are smaller than previously estimated, as they do not represent the range of error around each result, but rather the standard deviation across the mean of the 13 estimates. Errors that result from data uncertainty and an incomplete understanding of all the processes affecting the net flux of carbon from LULCC have not been systematically evaluated but are likely to be on the order of ±0.5 Pg C yr<sup>−1</sup>.
Full-text available
Background: Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO2 emissions. Policies that incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimates of national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitations of field inventory sampling, we tested whether two common field-plot sampling approaches could accurately estimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map of carbon stocks was used as a model of the country's carbon geography. Results: Both field inventory sampling approaches worked well in estimating total national carbon stocks, almost always falling within 10 % of the model national total. However, the sampling approaches were unable to produce accurate spatially-explicit estimates of the carbon geography of Perú, with estimates falling within 10 % of the model carbon geography across no more than 44 % of the country. We did not find any associations between carbon stock errors from the field plot estimates and six different environmental variables. Conclusions: Field inventory plot sampling does not provide accurate carbon geography for a tropical country with wide ranging environmental gradients such as Perú. The lack of association between estimated carbon errors and environmental variables suggests field inventory sampling results from other nations would not differ from those reported here. Tropical forest nations should understand the risks associated with primarily field-based sampling approaches, and consider alternatives leading to more effective forest conservation and climate change mitigation.
Full-text available
Remote sensing products can provide regular and consistent observations of the Earth´s surface to monitor and understand the condition and change of forest ecosystems and to inform estimates of terrestrial carbon dynamics. Yet, challenges remain to select the appropriate satellite data source for ecosystem carbon monitoring. In this study we examine the impacts of three attributes of four remote sensing products derived from Landsat, Landsat-SPOT, and MODIS satellite imagery on estimates of greenhouse gas emissions and removals: (1) the spatial resolution (30 vs. 250 m), (2) the temporal resolution (annual vs. multi-year observations), and (3) the attribution of forest cover changes to disturbance types using supplementary data. With a spatially-explicit version of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), we produced annual estimates of carbon fluxes from 2002 to 2010 over a 3.2 million ha forested region in the Yucatan Peninsula, Mexico. The cumulative carbon balance for the 9-year period differed by 30.7 million MgC (112.5 million Mg CO 2e ) among the four remote sensing products used. The cumulative difference between scenarios with and without attribution of disturbance types was over 5 million Mg C for a single Landsat scene. Uncertainty arising from activity data (rates of land-cover changes) can be reduced by, in order of priority, increasing spatial resolution from 250 to 30 m, obtaining annual observations of forest disturbances, and by attributing land-cover changes by disturbance type. Even missing a single year in the land-cover observations can lead to substantial errors in ecosystems with rapid forest regrowth, such as the Yucatan Peninsula.
Full-text available
Halving carbon emissions from tropical deforestation by 2020 could help bring the international community closer to the agreed goal of <2 degree increase in global average temperature change and is consistent with a target set last year by the governments, corporations, indigenous peoples organizations and non-governmental organizations that signed the New York Declaration on Forests (NYDF). We assemble and refine a robust dataset to establish a 2001-2013 benchmark for average annual carbon emissions from gross tropical deforestation at 2.270 Gt CO2 y(-1) . Brazil did not sign the NYDF, yet from 2001-2013 Brazil ranks first for both carbon emissions from gross tropical deforestation and reductions in those emissions - its share of the total declined from a peak of 69% in 2003 to a low of 20% in 2012. Indonesia, an NYDF signatory, is the second highest emitter, peaking in 2012 at 0.362 Gt CO2 y(-1) before declining to 0.205 Gt CO2 y(-1) in 2013. The other 14 NYDF tropical country signatories were responsible for a combined average of 0.317 Gt CO2 y(-1) , while the other 86 tropical country non-signatories were responsible for a combined average of 0.688 Gt CO2 y(-1) . We outline two scenarios for achieving the 50% emission reduction target by 2020, both emphasizing the critical role of Brazil and the need to reverse the trends of increasing carbon emissions from gross tropical deforestation in many other tropical countries that, from 2001 to 2013, have largely offset Brazil's reductions. Achieving the target will therefore be challenging, even though it is in the self-interest of the international community. Conserving rather than cutting down tropical forests requires shifting economic development away from a dependence on natural resource depletion toward recognition of the dependence of human societies on the natural capital that tropical forests represent, and the goods and services they provide. This article is protected by copyright. All rights reserved.
Full-text available
Tropical forests could offset much of the carbon released from the declining use of fossil fuels, helping to stabilize and then reduce atmospheric CO2 concentrations, thereby providing a bridge to a low-fossil-fuel future.
Disturbances are a major determinant of forest carbon stocks and uptake. They generally reduce land carbon stocks but also initiate a regrowth legacy that contributes substantially to the contemporary rate of carbon stock increase in US forestlands. As managers and policy makers increasingly look to forests for climate protection and mitigation, and because of increasing concern about changes in disturbance intensity and frequency, there is a need for synthesis and integration of current understanding about the role of disturbances and other processes in governing forest carbon cycle dynamics, and the likely future of this and other sinks for atmospheric carbon. This paper aims to address that need by providing a quantitative review of the distribution, extent and carbon impacts of the major disturbances active in the US. We also review recent trends in disturbances, climate, and other global environmental changes and consider their individual and collective contributions to the US carbon budget now and in the likely future. Lastly, we identify some key challenges and opportunities for future research needed to improve current understanding, advance predictive capabilities, and inform forest management in the face of these pressures.