Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation

Abstract

Despite sustained declines in Amazon deforestation, forest degradation from logging and fire continues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arc of deforestation. Limited data on the magnitude of carbon losses and rates of carbon recovery following forest degradation have hindered carbon accounting efforts and contributed to incomplete national reporting to reduce emissions from deforestation and forest degradation (REDD+). We combined annual time series of Landsat imagery and high-density airborne lidar data to characterize the variability, magnitude, and persistence of Amazon forest degradation impacts on aboveground carbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of the carbon stocks in intact forests, and differences persisted even after 15 years of regrowth. In comparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD. Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a history of one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD, respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires, selective logging before burning did not explain additional variability in modeled ACD loss and recovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure that can aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recovered faster than attributes of canopy structure that are critical for biodiversity in tropical forests, including the abundance of tall trees. We provide the first comprehensive look-up table of emissions factors for specific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon loss and recovery trajectories provide an important foundation for assessing the long-term contributions from forest degradation to regional carbon cycling and advance our understanding of the current state of frontier forests.

Full text

Environ. Res. Lett. 13 (2018) 065013 https://doi.org/10.1088/1748-9326/aac331
LETTER
Quantifying long-term changes in carbon stocks and
forest structure from Amazon forest degradation
Danielle I Rappaport1,6, Douglas C Morton2,MarcosLongo
3,4, Michael Keller3,4,5,RalphDubayah
1
and Maiza Nara dos-Santos3
1Department of Geographical Sciences, University of Maryland, College Park, MD, United States of America
2NASA Goddard Space Flight Center, Greenbelt, MD, United States of America
3Embrapa Agricultural Informatics, Brazilian Agricultural Research Corporation (EMBRAPA), Campinas, SP, Brazil
4NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States of America
5USDA Forest Service, International Institute of Tropical Forestry, San Juan, Puerto Rico
6Author to whom any correspondence should be addressed.
OPEN ACCESS
RECEIVED
2 January 2018
REVISED
26 April 2018
ACCEPTED FOR PUBLICATION
9 May 2018
PUBLISHED
7 June 2018
Original content from
this work may be used
under the terms of t he
Creative Commons
Attribution 3. 0 licence.
Any further distribution
of this work must
maintain attrib ution to
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citation and DOI.
E-mail: drappap@umd.edu
Keywords: aboveground biomass, forest structure, habitat, understory fires, carbon cycling, airborne lidar, REDD+
Supplementary material for this article is available online
Abstract
Despite sustained declines in Amazon deforestation, forest degradation from logging and fire
continues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arc
of deforestation. Limited data on the magnitude of carbon losses and rates of carbon recovery
following forest degradation have hindered carbon accounting efforts and contributed to incomplete
national reporting to reduce emissions from deforestation and forest degradation (REDD+). We
combined annual time series of Landsat imagery and high-density airborne lidar data to characterize
the variability, magnitude, and persistence of Amazon forest degradation impacts on aboveground
carbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of the
carbon stocks in intact forests, and differences persisted even after 15 years of regrowth. In
comparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD.
Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a history
of one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD,
respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires,
selective logging before burning did not explain additional variability in modeled ACD loss and
recovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure that
can aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recovered
faster than attributes of canopy structure that are critical for biodiversity in tropical forests, including
the abundance of tall trees. We provide the first comprehensive look-up table of emissions factors for
specific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon loss
and recovery trajectories provide an important foundation for assessing the long-term contributions
from forest degradation to regional carbon cycling and advance our understanding of the current
state of frontier forests.
Introduction
ChangesinAmazonforestcarbonstocksareasignifi-
cant source of greenhouse gas emissions from human
activity (van der Werf et al 2009,Panet al 2011,Aguiar
et al 2016). Understanding the long-term response of
Amazon forests to land use and climate is essential
for balancing the global carbon budget and improving
climate projections (e.g. Gatti et al 2014,Friedling-
stein et al 2014). Although annual deforestation rates
in the Brazilian Amazon have declined by 80% since
2004 (Hansen et al 2014,INPE2015), forest degrada-
tion from fire and logging remains a threat to forest
carbon stocks across the Amazon arc of deforestation
(Morton et al 2013). The magnitude of carbon losses
from forest degradation is large (Longo et al 2016),
© 2018 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 13 (2018) 065013
but the long-term consequences of fire and logging
on forest structure and composition remain uncertain
(Andrade et al 2017).
Decades of Amazon frontier expansion have left a
mosaic of degraded forests along the Amazon arc of
deforestation (Asner et al 2005,Mortonet al 2013).
Nearly 3% of southern Amazonia burned between
1999–2010, and the persistence of burned frontier
forests (Morton et al 2013) underscores the importance
of considering fire separately from deforestation for
complete forest carbon accounting. Selective logging
is also widespread across the leading edge of frontier
expansion. In 2009 alone, 14.2 million m3of round
wood was extracted from the largest logging centers
in the Brazilian Legal Amazon (Pereira et al 2010).
Canopy damage in logged forests can increase vulner-
ability to additional disturbances, including fire (Uhl
and Vieira 1989,HoldsworthandUhl1997), but the
feedbacks and synergies among disturbance agents, as
well as the long-term impacts of degradation, are still
largely unresolved.
The scarcity of large-scale, long-term studies on
fire and logging impacts has undermined efforts to
quantify emissions from Amazon forest degradation
for global carbon accounting (Le Quer´
eet al 2016)
and climate mitigation efforts (Andrade et al 2017).
Reducing land-use emissions is one cost-effective cli-
mate mitigation pathway (e.g. Canadell and Raupach
2008,Griscomet al 2017), including efforts to reduce
emissions from deforestation and forest degradation
(REDD+) under the United Nations Framework Con-
vention on Climate Change. To be eligible for REDD+
performance-based payments, countries must be able
to monitor, report, and verify (MRV) reductions in
carbon emissions from degradation or deforestation.
However, because of large uncertainties regarding net
carbon emissions from fire and logging, degradation
has remained poorly integrated within the REDD+
accounting framework (Mertz et al 2012,Goetzet al
2015) and excluded from national reporting (e.g. Brazil
2014).
The challenge to quantify degradation emissions
stems from the heterogeneity and time-dependence of
degradation impacts (Longo et al 2016,Andradeet al
2017). The variability in degradation impacts may result
from regional differences in underlying biomass dis-
tributions (Avitabile et al 2016,Longoet al 2016),
forest resilience to fire (Brando et al 2012,Floreset al
2017), and land use (Arag˜
ao and Shimabukuro 2010).
Discrepancies in emissions estimates also stem from
methodological differences among studies. Field-based
studies provide valuable context for understanding the
long-term impacts of degradation (e.g. Berenguer et al
2014), but forest inventory measurements typically
have limited spatial and temporal coverage due to cost
constraints. By contrast, experimental studies control
for much of the variability in degradation history but
may be limited in their capacity to simulate the diver-
sity of degradation impacts (e.g. Brando et al 2014).
Consequently, existing estimates for committed car-
bon emissions from Amazon understory fires vary by
an order of magnitude, ranging from ∼20 Mg C ha−1
(Brando et al 2014) to 263 Mg C ha−1 (Alencar et al
2006). Airborne lidar provides the spatially exten-
sive and structurally detailed information on forest
structure and aboveground carbon stocks needed to
reconcile previous estimates of degradation emissions
and quantify co-benefits of avoided degradation (Goetz
et al 2015,Longoet al 2016,Satoet al 2016).
Here, we used a purposeful sample of high-density
airborne lidar to capture a broad range of degraded
and intact forest conditions in the southern Brazilian
Amazon. For each forest stand, we combined degra-
dation history information from annual time series of
Landsat data with airborne lidar data to characterize
canopy structure and estimate aboveground carbon
density (ACD) using a lidar-biomass model specifically
developed for frontier forests in the Brazilian Amazon
(Longo et al 2016). Our large-area lidar coverage and
sampling chronosequence addressed two questions: (1)
What are the trajectories of loss and recovery of for-
est carbon stocks and habitat structure following fire
and logging in frontier Amazon forests? (2) How do
degradation type, frequency, and severity contribute to
variability in degraded forest carbon stocks and habitat
structure over time? Our study directly targets a lin-
gering data gap for REDD+(Andrade et al 2017)by
quantifying the ratesof ACD recovery over 1- to 15-year
time horizons following a broad range of degrada-
tion pathways, including sequential impacts of logging
and burning. These time-varying emissions estimates,
or emissions factors, can be combined with activity
data on the extent of forest degradation to establish
REDD+baselines; confirm the relative contributions
from fire, logging, and regeneration to regional net for-
est carbon emissions; and estimate the consequences
to mitigation targets if degradation remains omitted
from greenhouse gas accounting. Airborne lidar also
provides detailed, quantitative information on habitat
structure that may support an improved understand-
ing of the biodiversity co-benefits of reducing forest
degradation—an integral, but poorly formalized com-
ponent of REDD+MRV.
Methods
Study area
The study area covers approximately 20 000 km2at
the southern extent of closed-canopy Amazon forests
in the Brazilian state of Mato Grosso (figure 1).
Mean annual precipitation (1895 mm) and tempera-
ture (25 ◦C) support tropical forests and a diversity
of land uses (Souza et al 2013). A four-month dry
season (figure S1 available at stacks.iop.org/ERL/13/
065013/mmedia) and periodic drought events (Chen
et al 2011) contribute to the extent, duration, and
severity of understory forest fires in the study region
2

Environ. Res. Lett. 13 (2018) 065013
Brazil
Figure 1. Degraded and intact forest stands were distrib uted across 20 000 km2in the Brazilian state of Mato Grosso (top inset).
In the false-color composite image (2014 Landsat, bands 543), forest appears green, deforested areas appear pink, and wetland and
open water appear purple. Circles indicating the centroid of forest stands with lidar coverage are color-coded by degradation history
(U—undisturbed; L—logged; LB—logged and burned; B—burned). Airborne lidar data sampled frontier forests on private lands and
within the Xingu Indigenous Park (light blue outline) and along a degradation gradient (bottom inset).
(Morton et al 2013,Brandoet al 2014). Additionally,
decades of agricultural expansion and selective logging
(e.g. Asner et al 2005,Souzaet al 2005, Matricardi
et al 2007)haveleftapatchworkoffragmentedand
degraded forestsin the study area, with few intact forests
remaining outside of the Xingu Indigenous Reserve or
Rio Ronuro Ecological Station (figure 1).
Data and analysis
We combined Landsat time series and airborne lidar
data to quantify variability in forest structure and ACD
across gradients of degradation type, frequency, sever-
ity, and timing. Degradation history for areas with
lidar coverage was characterized using a two-tiered
classification approach. First, the annual occurrence
of logging, understory fires, and deforestation was
mapped based on spatial, spectral, and temporal infor-
mation derived from annual time series of cloud-free
Landsat mosaics for the early dry season mont hs (June–
August) of 1984–2016 (figure S2; text S1). Understory
fires and deforestation events were identified based on
multi-year patterns of damage and recovery in Land-
sat Normalized Difference Vegetation Index (NDVI)
(Morton et al 2011,Mortonet al 2013). Logged
forests were identified with an automated detection
approach based on the spatial distribution of log
landing decks (Asner et al 2004, Keller et al 2004).
Mutually exclusive classification rules for the magni-
tude, duration, size, and shape of deforestation and
degradation events avoided double counting errors
common with the integration of independent products
(figure S2; text S1) (Morton et al 2011,Bustamante
et al 2016). Second, forest stands of uniform degra-
dation history were manually delineated within the
extent of lidar coverage and visually validated to con-
firm the extent and timing of degradation events.
Logging roads visible in multiple years of Land-
sat data were excluded from logged forest stands to
control for the impact of logging infrastructure on
estimated carbon stocks and recovery trajectories.
Airborne lidar data were used to estimate ACD
in intact and degraded forest types stratified by
degradation history. High-density airborne lidar data
(minimum of 14 returns per m2) were collected
as part of the Sustainable Landscapes Brazil project
across a range of intact and degraded forests in
a space-for-time substitution sampling design (table
S1, data available from: www.paisagenslidar.cnptia.
embrapa.br/webgis/). Based on the classification
3

Environ. Res. Lett. 13 (2018) 065013
approach described above, the 2891.25 ha of lidar
coverage were stratified into 58 forest stands (4.50–
498.50 ha; table S2).
A lidar-biomass model based on mean top of
canopy height (TCH, m) (Longo et al 2016)wasused
to estimate ACD (kg C m−2)inforeststandsat0.25ha
resolution:
ACDTCH =0.054 (±0.012)TCH1.76(±0.07) (1)
where the parenthetical values are the standard errors
of the parameters. Equation (1) assumes a biomass-
to-carbon conversion factor of 0.5, following Baccini
et al (2012). We selected the TCH model because
of its simplicity, sensitivity to the lower range of
the ACD distribution, and accurate representation of
ACD in burned forests (Longo et al 2016). Equa-
tion (1) was developed using inventory and lidar data
from intact and degraded Amazon forests. Here, we
applied the model to a new set of lidar data sampled
from the same regional context in which the Longo
et al (2016) model was calibrated; about 8% of the
lidar data set overlapped with the data used in model
development.
Pixel-based uncertainty associated with modeled
ACD was calculated from three sources of statisti-
cal uncertainty following the methods described in
Longo et al (2016). A Monte Carlo approach with
10 000 iterations was used to propagate the pixel-
based uncertainty to the stand level by adjusting each
biomass pixel with randomly distributed noise propor-
tionate to its uncertainty before aggregating data at the
stand level. The stand-level standard error was derived
from the standard deviation of the simulated stand-
level means.
Given the importance of canopy structure for
wildlife habitat in tropical forests (Bergen et al 2009),
we also calculated two lidar-based measures of habitat
structure. First, residual canopy cover was calculated
using 1 m resolution lidar canopy height models
(CHMs) as the proportion of the forest stand greater
than or equal to the mean canopy height in intact forests
(21 m). Second, clusters of one or more canopy trees
(≥21 m) were identified using the 1 m CHMs with a
maximum search radius of 10 m using a 3 ×3pixel
moving window (Silva et al 2015). These metrics pro-
vided complementary information on changes in forest
structure from degradation and recovery processes to
assess the drivers of ACD variability and the time-
varying recovery of both carbon and habitat structure
in degraded forests.
We used multiple linear regression to model the loss
and recovery trajectories of ACD and canopy struc-
ture based on the chronosequence of lidar samples.
Four least squares models were fit using the lm func-
tion in R version 3.3.0 (www.R-project.org). Model
1 estimated median ACD in degraded forest stands
based on degradation type (burned or logged-only),
timing (years since last degradation event), and fire
frequency. Median ACD was selected as the mea-
sure of central tendency for each stand because of the
skewed ACD distributions in degraded forests. Model
2 further stratified once-burned forests by fire sever-
ity, visible as rings of high- and low-severity canopy
damage, based on the relative difference between
the pre-fire and post-fire Landsat dry-season NDVI
(RdNDVI). A fixed threshold of mean minus the stan-
dard deviation of RdNDVI was only used to stratify
low and high-severity fire damages in once-burned
stands because the spatial variability of fire damages
was not well preserved following recurrent fire events.
Models 3 and 4 used degradation type, timing, and fre-
quency to predict residual canopy cover and density
of canopy tree clusters, respectively. In all four mod-
els, the variable for time since last degradation event
was log-transformed to satisfy assumptions of normal-
ity and homoscedasticity (Vargas et al 2008, Becknell
et al 2012). Additionally, to isolate the effect of for-
est recovery from the long-term impacts of logging
infrastructure, logged forest stands were adjusted to
exclude secondary roads and log landing decks. Inter-
actions between degradation history (type, frequency,
severity) and degradation timing were evaluated for sig-
nificance and model performance in all four models.
Lastly, differences across degradation strata were evalu-
ated using pairwise Wilcoxon tests to accommodatethe
diversity of non-normal data distributions.
Consistent with recommendations from the Inter-
governmental Panel on Climate Change (Penman et al
2003), an additional Monte Carlo procedure was used
to propagate the effect of ACD uncertainty on model
parameters and predictions by performing 10 000
realizations of the model fit on adjusted stand-level
medians with normally distributed noise proportional
to the stand-level standard error, or the standard devi-
ation of the stand-level Monte Carlo aggregations.
Results
Degradation type, frequency, timing, and severity
contributed to ACD variability in frontier forests.
Lidar-based estimates of ACD in 58 Amazon for-
est stands varied by nearly two orders of magnitude
between the most heavily degraded forest stand
(median: 4.5 Mg C ha−1), a stand that had been logged
and burned three times, and the most carbon-dense
intact forest stand (median: 114.3 Mg C ha−1 ;tableS2).
At the pixel scale, median carbon density in degraded
forests (51.2 Mg C ha−1) was less than half of ACD
in intact forests (113.5 Mg C ha−1 ). Degraded ACD
was also more heterogeneous than intact ACD (coef-
ficient of variation: 68.4% and 16.7% for degraded
(2638.00 ha) and intact forest pixels (253.25 ha),
respectively).
The variability in ACD following degradation could
not be constrained by degradation type alone. ACD in
pixels with a history of fire (median: 20.4 Mg C ha−1 ;
4

Environ. Res. Lett. 13 (2018) 065013
Fire frequency
ACD normalized to intact forest (%)
Figure 2. Forests affected by multiple fires had the largest differences in aboveground carbon density (ACD) compared to median
ACD in intact forests (red line). Additionally, ACD distributions between burned and logged-and-burned forests were similar for
once-burned forests. The violin plots summarize ACD distributions as a function of fire frequency and degradation class. The median
and interquartile range of each group are indicated with the black circle and line. The tails of the violins are trimmed to the range of
data, and all violins have the same area prior to trimming the tails.Distinct letters indicate significant differences among distributions
from a pairwise Wilcoxon test with a Holm correction procedure to adjust 𝛼for multiple testing. See figure S4(a) for a comparison
across burn frequency groups that also accounts for stand age.
1605.75 ha) was significantly lower (p<0.05)
than ACD in logged-only pixels (77.8 Mg C ha−1;
1032.25 ha); however, ACD varied broadly within both
degradation classes. At the stand level, there was con-
siderable overlap between the ranges of median ACD in
burned forests (4.5–95.2 Mg C ha−1) and logged-only
forests (39.0–117.3 Mg C ha−1,tableS2).
Degradation timing was a critical factor for fur-
ther differentiating ACD between and within logged
andburnedforestclasses(figureS3;table1). Within
two years of recovery, median ACD in burned pix-
els was 9.5 Mg C ha−1 , compared to 68.4 Mg C ha−1 in
logged-only pixels. Following 10 to 15 years of recovery,
neither class recovered its estimated pre-disturbance
ACD, and median ACD in burned pixels remained
considerably lower than in logged pixels (difference:
17.5 Mg C ha−1).
Fire frequency governed both the magnitude and
the spatial pattern of residual forest carbon stocks (fig-
ures 2and 3;table1). Repeated burning resulted in a
non-linear decline in ACD, irrespective of logging his-
tory, with lowest ACD in forests subjected to three
or more fires (figure 2). Forests affected by a sin-
gle fire (n= 10) retained 67.0 Mg C ha−1 (interquartile
range [IQR] ±26.4 Mg C ha−1 ). Twice-burned forests
(n= 5) contained less than half the carbon stocks in
once-burned forests (31.6 ±21.1 Mg C ha−1 ). Forests
burned three to five times (n= 13) retained few trees
from the pre-fire forest stand; ACD was only one-
sixthofthatofonce-burnedforests(10.3MgCha
−1),
with the narrowest IQR of all burn frequencies
(±10.5 Mg C ha−1). Importantly, the observed decrease
in IQR with increasing fire frequency indicated a r educ-
tion in structural complexity from repeated burning
(figures 2and 3).
Unliketheimpactofsuccessivefires,therewas
no significant long-term impact on ACD recovery
attributable to prior logging after controlling for fire
frequency (figure S4). Because the distinction between
burned and logged-and-burned forests was not a sta-
tistically significant predictor of degraded forest ACD,
nor did it improve model fit, logged-and-burned
and burned forest stands were combined to model
post-fire recovery of ACD.
Fire frequency and the time since the last degra-
dation event explained the greatest variability in
degraded ACD recovery (Model 1; adjusted R2= 0.89;
F-statistic = 106.5 figure 4(a); table S3). The immedi-
ate reduction in ACD differed significantly for each
degradation pathway (regression intercept; table S3).
In the year following degradation, the modeled ACD
for forests that had been logged, once-burned, twice-
burned, and subjected to three or more burns was 62.3,
52.0, 19.4, and 11.0 Mg C ha−1, respectively. However,
the rate of ACD recovery was similar for all classes,
as interaction effects between fire frequency and time
since degradation event were not statistically significant
(table S3). Given these initial differences and the slow
recovery in degraded forest ACD, the legacy of forest
degradation was still evident 15 years following fire and
logging (table 2).
Initial fire severity was a statistically significant
predictor of ACD recovery in once-burned forests
(Model 2; Adjusted R2= 0.88; F-statistic = 87.87; fig-
ure 4(b); tables 2and S3). In the year following fire,
estimated high- and low-severity damages differed by
16% of intact ACD (table 2). Modeled differences in
ACD resulting from initial fire severity were preserved
through time, with once-burned forests recovering
between 57.6% and 73.9% of intact ACD after 15 years
of recovery, depending on initial fire severity (figure
4(b); table 2). Covariation of ACD with Landsat and
lidar metrics of canopy density in burned forests pro-
vided additional insights into the contribution of fire
5

Environ. Res. Lett. 13 (2018) 065013
Table 1. Forest degradation from logging and fire alters ACD and stand structure relative to neighboring intact forests. Lidar-based estimates of the fraction of original canopy cover, number of canopy tree clusters, and the distribution
of ACD in degraded forests. Degraded forests were partitioned along three axes of variability—degradation type, frequency, and timing. The lower, middle (median) and upper quartile of aboveground biomass density (Mg C ha−1)are
shown as ACD25 ,ACD
50,andACD
75, respectively.
Intact Logged
(1–2 yrs)
Logged
(4–5 yrs)
Logged
(10–11 yrs)
Logged
(14–15 yrs)
Logged
(18–20 yrs)
Fire 1x
(1–2 yrs)
Fire 1x
(4–5 yrs)
Fire 1x
(10–11 yrs)
Fire 1x
(14–15 yrs)
Fire 2x Fire 3x+
%Originalcanopy 100 46.9 60.1 61.6 76.7 83.3 21.7 47.0 58.5 58.4 20.1 5.3
Num. canopy clusters 170 79 104 111 127 145 34 78 92 99 31 8
ACD25 102.1 52.3 64.9 80.4 83.8 86.4 53.0 55.5 58.4 83.3 22.3 6.6
ACD50 113.5 68.4 76.8 89.7 98.8 105.5 64.3 65.6 74.0 91.0 31.6 10.3
ACD75 125.1 84.0 88.8 99.8 111.6 121.0 72.2 76.6 89.8 100.2 43.4 17.1
6

Environ. Res. Lett. 13 (2018) 065013
c.b.a.
3 Fires2 Fires1 Fire
- 121.6 - 1
- 87.0
- 58.9
- 30.9 - 0
ACD NDVI
Figure 3. Ring patterns in burned forests indicate diurnal differences in fire line intensity, and increasing fire frequency results in a
progressive loss of forest biomass and structural diversity. Lidar-based estimates of aboveground carbon density (ACD, Mg C ha−1)at
0.25 hectare resolution for 5000 ×200 m transects are overlaid on post-fire Landsat NDVI for once-burned (a)twice-burned(b)and
thrice-burned (c) forest stands. See tables S1 and S2 for additional profile information associated with each stand (stand IDs from left
to right: 26, 13, and 8).
Table 2. Estimates based on the multiple linear regression models of aboveground carbon density predicted at four standard reporting
periods following the most common logging and fire pathways. For each degradation class, modeled ACD and 95% confidence interval (in
parentheses) are shown as the percentage of the intact forest reference (113.5 Mg C ha−1). The confidence interval was calculated based on the
mean of 10 000 confidence intervals generated from the Monte Carlo linear regressions, which were iteratively fit to the stand-level biomass
estimates adjusted with noise proportionate to the stand-level standard errors. Model predictions for low- and high-severity fires are derived
from model 2; all other predictions presented here are derived from model 1 (see table S3).
Logged Burned 1x (Average) Burned 1x (Low) Burned 1x (High) Burned 2x Burned 3x+
Y1 54.8 (49.4–60.3) 45.8 (38.0–53.6) 48.6 (41.1–56.1) 32.2 (24.1–40.3) 17.1 (8.5–25.8) 9.7 (4.5–14.9)
Y5 71.0 (67.5–74.5) 61.9 (56.3–67.6) 63.6 (58.0–69.3) 47.3 (41.0–53.6) 33.3 (25.2–41.4) 25.8 (20.0–31.7)
Y10 77.9 (73.6–82.3) 68.9 (63.1–74.7) 70.1 (64.4–75.8) 53.8 (47.4–60.1) – – – –
Y15 82.0 (76.9–87.1) 73.0 (66.8–79.1) 73.9 (67.9–80.0) 57.6 (51.0–64.2) – – – –
severity to ACD variability within a single fire (figures
S6 and S7).
Changes in canopy structure from logging and fire
were also persistent after 15 years of forest recovery (fig-
ure S5; table 1). Degradation timing and fire frequency
explained the greatest variability in the recovery trajec-
tory of residual canopy (Model 3; adjusted R2= 0.74;
F-statistic = 38.85) and density of canopy trees (Model
4; adjusted R2= 0.76; F-statistic = 36.3; figure S5; table
S4). Understory fires resulted in the largest reduction
of canopy tree clusters, particularly following recur-
rent fires. Logged forests retained more than twice as
many canopy tree clusters (46.5%) as once-burned
forests (20.0%) when measured within 1–2 years of
the degradation event. Forests burned three or more
times retained only 4.7% the number of canopy tree
clusters found in intact forests. After 14–15 years of
regrowth, once-burned forests recovered only 80%
of the canopy tree clusters present in logged forests.
Further, these impacts to forest structure may persist
even after ACD in degraded forests returns to pre-
degradation levels. For example, after 14–15 years of
regrowth, once-burned forests recovered a larger frac-
tion of intact-forest ACD (80.2%) than canopy tree
clusters (58.2%).
Discussion
Amazon forest degradation from logging and fire has
a lasting impact on forest carbon stocks and canopy
structure. The slow recovery of degraded forests under-
scores the need to address drivers of degradation to
ensure the retention of carbon stocks and preserve
complex canopy structure in frontier Amazon forests.
Using a large sample of intact and degraded forests,
we provide the first comprehensive look-up table of
degradation emissions factors for Amazon forests to
guide the incorporation of forest degradation within
REDD+MRV (tables 1and 2). Our findings illus-
trate the persistence of degradation impacts beyond
the time scales for REDD+MRV and even REDD+
baselines (typically 10 years), providing the foundation
for further investigations into the relativ e contributions
from fire and logging to regional land-use emissions.
ACD in degraded forests varied by two orders of
magnitude across the study area (table S2), provid-
ing clear support for the creation of multiple classes
of forest degradation within REDD+or other car-
bon accounting frameworks based on degradation
frequency, severity, and timing. Overall, understory
fires led to larger and more persistent changes in ACD
and forest structure than logging, consistent with pre-
vious findings from Longo et al (2016). Our results
further demonstrate how fire severity and fire fre-
quency contribute to non-linear declines in ACD and
homogenization of degraded forest structure (figure 2,
tables 1and 2). Collectively, these results address key
data gaps that have hindered MRV of Amazon forest
degradation.
Lidar-based estimates of carbon losses from fire
were much larger than previous reports from exper-
imental studies and forest inventories. For example,
the reduction in ACD one year following a single burn
7

Environ. Res. Lett. 13 (2018) 065013
Logged
Burned 1x (low severity)
Burned 1x (high severity)
Burned 2x
Burned 3x+
Logged
Burned 1x
Burned 2x
Burned 3x+
0
30
60
90
120
0 5 10 15 20
Years since last degradation
ACD Mg C ha−1
0
30
60
90
120
0 5 10 15 20
Years since last degradation
ACD Mg C ha−1
Figure 4. Patterns of aboveground biomass recovery following forest degradation highlight the magnitude and duration of ACD
accumulation following fire. (a) Relationship between ACD and stand age for forests that were logged, burned once, burned twice,
and burned three or more times. (b) Initial fire severity in once-burned forests further explains the heterogeneity in residual carbon
stocks. Points correspond to estimated stand-level medians, error bars correspond to stand-level standard errors derived from 10 000
Monte Carlo stand-level aggregations, and the shaded bands represent the mean 95% confidence interval from 10000 Monte Carlo
simulations of the model fit. Model details are presented in table S3.
in this study (54.2%) was approximately three times
larger than from experimental fires in the southeast-
ern Amazon (Brando et al 2014). This discrepancy
may reflect the improved capacity to characterize the
heterogeneity of wildfire damages using airborne lidar
or the difficulty for prescribed fires in experimental
studies to replicate the emergent properties of wild-
fires, such as fire front intensity. Field studies have
also reported smaller relative losses in ACD following
fire (13.7%; Berenguer et al 2014). These differences
may reflect the confounding influence of different
age classes and burn frequencies, the challenges of
8

Environ. Res. Lett. 13 (2018) 065013
capturing the length scales of spatial variability (see
figure 3) using typical inventory plots (0.25–1.0 ha), or
regional variability in fire intensity from climatic and
forest-type specific responses to fire (e.g. Flores et al
2017). These broad discrepancies reinforce the need
for large-scale studies of additional frontier landscapes
to support emissions mitigation programs, including
REDD+MRV.
Reducing the incidence and frequency of under-
story forest fires would preserve both carbon stocks and
habitat structure in frontier landscapes. The marginal
carbon cost of recurrent fire events in this study suggests
that avoiding just one additional fire in a previously
burned forest would retain carbon stocks equivalent
to one-third of the intact reference ACD. Notably,
not all degradation sequences have the same cumu-
lative impact. We contrast the non-linear impact of
recurrent burns with the effect of selective logging
before fire. In the case of recurrent burns, each fire
leads to a greater proportional loss. However, log-
ging before fire did not amplify the long-term carbon
losses from fire, after accounting for fire frequency; nor
was logging a significant predictor of carbon recov-
ery, regardless of fire history. These findings suggest
that the distribution of fine litter (e.g. Balch et al
2008) may be a more important determinant of fire
damage than large woody debris or canopy openings
from logging.
The slow recovery of degraded ACD suggests that
the continued omission of degradation from carbon
accounting may result in substantial underreporting
of forest carbon emissions. Relative to baseline peri-
ods, the frequency and severity of Amazon droughts
(Boisier et al 2015, Duffy et al 2015) are projected to
increase degradation risk in coming decades (Nobre
et al 2016,LePageet al 2017). The look-up table
of proportional losses between degraded and intact
forests developed in this study may facilitate the inte-
gration of carbon losses from fire and logging into
REDD+monitoring and reporting protocols. Further,
accounting for carbon emissions from forest degra-
dation may also reduce uncertainties in the Amazon
carbon budget. Previous studies have either excluded
a post-disturbance recovery term (Arag˜
ao et al 2014)
or have combined secondary and degraded forests
(Houghton et al 2000,Panet al 2011), despite the
diversity of loss and recovery pathways among degra ded
and secondary forest types (Poorter et al 2016).
Parallel ACD recovery curves in years 1–5 fol-
lowing logging and fire may reflect common site
constraints, distinct mechanisms of forest growth, and
model calibration. For example, different mechanisms
of vegetation recovery and canopy closure may gener-
ate similar changes in estimated ACD, such as small
gains in mean canopy height in logged forests and
fast height growth of shorter resprouting or surviv-
ing trees in burned forests. Additionally, given that
logging intensity is the single best predictor of ACD
recovery time (Rutishauser et al 2015), evidence for
greater extracted wood volume of low-value species
in frontier forests (Richardson and Peres 2016)than
in interior forests and experimental logging sites may
explain differences with previous estimates of ACD
recovery in logged forests (e.g. Chambers et al 2004,
Putz et al 2012,Andradeet al 2017). Further, mois-
ture availability is a critical constraint on regeneration
rates (Poorter et al 2016,Wagneret al 2016); mois-
ture stress from the seasonality of the study site
may limit recovery rates in both logged and burned
forests. Additional observations in repeatedly burned
forests are needed to constrain long-term estimates
of recovery patterns (>5 years) in the more heavily
degraded sites.
Airborne lidar captures details about 3D forest
structure needed to quantify aboveground carbon
stocks and advance quantitative reporting on biodi-
versity safeguards and other co-benefits of REDD+.
Individual tree and plot-level data from airborne lidar
provide insights into the mechanisms driving biomass
variability and habitat impacts from forest degrada-
tion. The residual density of large canopy trees, which
can be directly quantified using high-density airborne
lidar, is an important driver of ACD variability in
degraded forests (Slik et al 2013), and closely corre-
sponds to the spatial patterns of fire-induced canopy
mortality (figure S7). In addition to ACD, the loss
of canopy trees may also alter the forest microme-
teorology, aerodynamic roughness, and successional
success of grasses and lianas (Ray et al 2005,Silv
´
erio
et al 2013). These changes, in turn, can increase vul-
nerability to windthrow and repeated fires, especially
during drought years (e.g. Balch et al 2015). Canopy
trees also serve as biodiversity refugia; the slower
recovery of canopy tree clusters than carbon stocks
in this study may suggest a more persistent impact
of degradation on biodiversity than biomass in the
first decades following logging or fire, consistent with
findings from Martin et al (2013). Characterizing the
time-integrated effects of avoided degradation on for-
est structure is clearly an important step for policies and
management that aim to promote the retention of both
biomass and biodiversity. Measurement and monitor-
ing capabilities to support REDD+commitments to
safeguard biodiversity and promote other co-benefits
are not yet operational (Goetz et al 2015). This work
highlights the potential of airborne lidar to advance
REDD+MRV for both carbon and non-carbon
objectives.
Our findings provide a detailed characterization
of the carbon and habitat changes following Ama-
zon forest degradation, but additional measurements
are needed to assess regional variability in degrada-
tion impacts. Additional lidar samples across gradients
in land use, forest type, and climate may identify
important differences in degradation impacts and ACD
recovery. For example, previous work suggests that
transitional forests along the southern extent of the
Amazon may be more resilient to mortality from a
9

Environ. Res. Lett. 13 (2018) 065013
single, low-severity fire during average weather con-
ditions (Brando et al 2012) than interior forests. By
contrast, forests in Central Amazon floodplains have
exposed roots during dry periods, thin bark, and lack
the ability to resprout, rendering them more vul-
nerable to fire-induced dieback (Flores et al 2017).
Additionally, multi-temporal observations are needed
to unequivocally attribute ACD losses to degrada-
tion, characterize delayed mortality, and investigate the
potential for arrested succession (Barlow et al 2003).
Multi-temporal studies may also help constrain inter-
annual variability in fire damages (Brando et al 2014),
consistent with the ∼15% difference in ACD observed
in this study between low and high-severity damages
within a single fire. Complementary field measure-
ments may help characterize key aspects of degraded
forest structure that are not well captured by airborne
lidar, such as the species distribution of regeneration
from seeds or sprouts and the selective impact of de gra-
dation on mean wood density (Bunker et al 2005,
Longo et al 2016). Lastly, the strong correspondence
between changes in Landsat surface reflectance and
lidar-derived estimates of forest structure and ACD
in burned forests may support regional estimates of
carbon losses from understory fires using Landsat or
similar moderate resolution imagery.
Conclusion
Forest degradation is ubiquitous in frontier Amazon
forests, and damages from logging and fire were larger
and longer lasting than previously reported for our
southern Amazon study region. Combining the look-
up table of emissions estimates from this study with
activity data from satellite monitoring programs may
allow for regional estimates of combined emissions
from deforestation and forest degradation for REDD+.
Understory fires—particularly, repeated burns—pose
the greatest risk to forest carbon stocks and canopy
structure along the Amazon arc of deforestation. Thus,
avoiding additional fires in frontier landscapes may
have an outsized benefit for carbon retention and
habitat. Routine monitoring of frontier forests with
airborne lidar may provide additional insights regard-
ing the direct impacts of forest degradation on both
carbon stocks and forest structure, including potential
interannual variability from climate controls on fire
severity or market influences on logging removals. Our
approach to disentangle the complex legacy of degra-
dation by combining forest inventory, airborne lidar,
and Landsat time series offers a blueprint to generate
degradation emissions factors in other geographies and
regional circumstances.
Acknowledgments
This work was partially supported by a National Science
Foundation Doctoral Dissertation Research Improve-
ment Grant (grant 1634168), a NASA Earth and Space
Science Fellowship (D Rappaport), and NASA’sCar-
bon Monitoring System program. Additional support
was provided by the Brazilian National Council for Sci-
entific and Technological Development (CNPq, grant
457927/2013-5) and Science Without Borders program
(D Morton), and the S˜
ao Paulo State Research Foun-
dation (FAPESP, grant 2015/07227-6, M Longo). M
Keller was supported as part of the Next Generation
Ecosystem Experiments-Tropics, funded by the US
Department of Energy, Office of Science, Office of
Biological and Environmental Research. Support for
data acquisition and M.N. dos-Santos was provided by
the Sustainable Landscapes Brazil project, a collabora-
tion of the Brazilian Agricultural Research Corporation
(EMBRAPA), the US Forest Service, USAID, and the
US Department of State. We gratefully acknowledge
the assistance from Hyeungu Choi with lidar data pro-
cessing.
ORCID iDs
Danielle I Rappaport https://orcid.org/0000-0001-
9122-9684
Douglas C Morton https://orcid.org/0000-0003-
2226-1124
Marcos Longo https://orcid.org/0000-0001-5062-
6245
Michael Keller https://orcid.org/0000-0002-0253-
3359
MaizaNarados-Santos https://orcid.org/0000-0003-
2720-2393
References
Aguiar A P D et al 2016 Land use change emission scenarios:
anticipating a forest transition process in the Brazilian
Amazon Glob. Change Biol. 22 1821–40
Alencar A, Nepstad D and Diaz M C V 2006 Forest understory fire
in the Brazilian Amazon in ENSO and Non-ENSO years: area
burned and committed carbon emissions Earth Interact. 10
1–17
Andrade R B, Balch J K, Parsons A L, Armenteras D, Roman-Cuesta
R M and Bulkan J 2017 Scenarios in tropical forest
degradation: carbon stock trajectories for REDD+Carbon
Balance Manage. 12 6
Arag˜
ao L E O C and Shimabukuro Y E 2010 The incidence of fire in
Amazonian forests with implications for REDD Science 328
1275–8
Arag˜
aoLEOC,PoulterB,BarlowJB,AndersonLO,MalhiY,
Saatchi S, Phillips O L and Gloor E 2014 Environmental
change and the carbon balance of Amazonian forests Biol. Rev.
89 913–31
Asner G P, Keller M, Pereira R Jr, Zweede J C and Silva J N 2004
Canopy damage and recovery after selective logging in
Amazonia: field and satellite studies Ecol. Appl. 14
280–98
Asner G P, Knapp D E, Broadbent E N, Oliveira P J, Keller M and
Silva J N 2005 Selective logging in the Brazilian Amazon
Science 310 480–2
Avitabile V et al 2016 An integrated pan-tropical biomass map
using multiple reference datasets Glob. Change Biol. 22
1406–20
10

Environ. Res. Lett. 13 (2018) 065013
Baccini A et al 2012 Estimated carbon dioxide emissions from
tropical deforestation improved by carbon-density maps Nat.
Clim. Change 2182–5
Balch J K, Nepstad D C, Brando P M, Curran L M, Portela O, De
CARVALHO O and Lefebvre P 2008 Negative fire feedback in
a transitional forest of southeastern Amazonia Glob. Change
Biol. 14 2276–87
Balch J K et al 2015 The susceptibility of southeastern Amazon
forests to fire insights from a large scale burn experiment
BioScience 65 893–905
Barlow J, Peres C A, Lagan B O and Haugaasen T 2003 Large tree
mortality and the decline of forest biomass following
Amazonian wildfires Ecol. Lett. 66–8
Becknell J M, Kissing Kucek L and Powers L 2012 Aboveground
biomass in mature and secondary seasonally dry tropical
forests: a literature review and global synthesis Forest Ecol.
Manage. 276 88–95
Berenguer E, Ferreira J, Gardner T A, Arag˜
ao L E O C, De Camargo
P B, Cerri C E, Durigan M, Oliveira R C D, Vieira I C G and
Barlow J 2014 A large-scale field assessment of carbon stocks
in human-modified tropical forests Glob. Change Biol. 20
3713–26
Bergen K M, Goetz S J, Dubayah R O, Henebry G M, Hunsaker C T,
Imhoff M L, Nelson R F, Parker G G and Radeloff V C 2009
Remote sensing of vegetation 3-D structure for biodiversity
and habitat: Review and implications for lidar and radar
spaceborne missions J. Geophys. Res. Biogeosci. 114
Boisier J P, Ciais P, Ducharne A and Guimberteau M 2015
Projected strengthening of Amazonian dry season by
constrained climate model simulations Nat. Clim. Change 5
656–60
Brando P M, Nepstad D C, Balch J K, Bolker B, Christman M C,
Coe M and Putz F E 2012 Fire-induced tree mortality in a
neotropical forest: theroles of bark traits, tree size, wood
density and fire behavior Glob. Change Biol. 18 630–41
Brando P M et al 2014 Abrupt increases in Amazonian tree
mortality due to drought–fire interactions Proc. Natl Acad.
Sci. 111 6347–52
Brazil 2014 Brazil’s submission of a Forest Reference Emission
Level (FREL) for reducing emissions from deforestation in the
Amazonia biome for REDD+results-based payments under
the UNFCCC
Bunker D E, DeClerck F, Bradford J C, Colwell R K, Perfecto I,
Phillips O L, Sankaran M and Naeem S 2005 Species loss and
aboveground carbon storage in a tropical forest Science 310
1029–31
Bustamante M et al 2016 Toward an integrated monitoring
framework to assess the effects of tropical forest degradation
and recovery on carbon stocks and biodiversity Glob. Change
Biol. 22 92–109
Canadell J G and Raupach M R 2008 Managing forests for climate
change mitigation Science 320 1456–7
Chambers J Q, Higuchi N, Teixeira L M, Dos Santos J, Laurance S G
and Trumbore S E 2004 Response of tree biomass and wood
litter to disturbance in a Central Amazon forest Oecologia 141
596–611
Chen Y, Randerson J T, Morton D C, DeFries R S, Collatz G J,
Kasibhatla P S, Giglio L, Jin Y and Marlier M E 2011
Forecasting fire season severity in South America using sea
surface temperature anomalies Science 334 787–91
Duffy P B, Brando P, Asner G P and Field C B 2015 Projections of
future meteorological drought and wet periods in the Amazon
Proc. Natl Acad. Sci. 112 13172–7
FloresBM,HolmgrenM,XuC,vanNesEH,JakovacCC,
Mesquita R C G and Scheffer M 2017 Floodplains as an
Achilles’heel of Amazonian forest resilience Proc. Natl Acad.
Sci. 114 4442–6
Friedlingstein P, Meinshausen M, Arora V K, Jones C D, Anav A,
Liddicoat S K and Knutti R 2014 Uncertainties in CMIP5
climate projections due to carbon cycle feedbacks J. Clim. 27
511–26
Gatti L V et al 2014 Drought sensitivity of Amazonian carbon
balance revealed by atmospheric measurements Nature 506
76–80
Goetz S J, Hansen M, Houghton R A, Walker W, Laporte N and
Busch J 2015 Measurement and monitoring needs,
capabilities and potential for addressing reduced emissions
from deforestation and forest degradation under REDD+
Environ. Res. Lett. 10 123001
Griscom B W et al 2017 Natural climate solutions Proc. Natl Acad.
Sci. 114 11645–50
Hansen A J, Phillips L B, Dubayah R, Goetz S and Hofton M 2014
Regional-scale application of lidar: variation in forest canopy
structure across the southeastern US Forest Ecol. Manage. 329
214–26
Holdsworth A R and Uhl C 1997 Fire in Amazonian selectively
logged rain forest and the potential for fire reduction Ecol.
Appl. 7713–25
HoughtonRA,SkoleDL,NobreCA,HacklerJL,LawrenceKT
and Chomentowski W H 2000 Annual fluxes of carbon from
deforestation and regrowth in the Brazilian Amazon Nature
403 301–4
INPE 2015 Amazon program - monitoring the Brazilian Amazon
by satellite: the Prodes, Deter, Degrad and Terraclass systems
(www.inpe.br)
Keller M, Asner G P, Silva J M N and Palace. M 2004 Sustainability
of selective logging of upland forests in the Brazilian Amazon:
carbon budgets and remote sensing as tools for evaluation of
logging effects Working Forests in the American Tropics:
Conservation Through Sustainable Management? ed D J
Zarin, J Alavalapati, F E Putz and M Schmink (New York:
Colombia University Press) pp 41–63
Le Page Y, Morton D, Hartin C, Bond-Lamberty B, Pereira J M C,
Hurtt G and Asrar G 2017 Synergy between land use and
climatechangeincreasesfuturefireriskinAmazonforests
Earth Syst. Dyn. 81237
Le Qu´
er´
e C, Andrew R M, Canadell J G, Sitch S, Korsbakken J I,
Peters G P, Manning A C, Boden T A, Tans P P and Houghton
R A 2016 Global carbon budget 2016 Earth Syst. Sci. Data 8
605
Longo M, Keller M M, dos-Santos M N, Leitold V, Pinag´
eER,
Baccini A, Saatchi S, Nogueira E M, Batistella M and Morton
D C 2016 Aboveground biomass variability across intact and
degraded forests in the Brazilian Amazon Glob. Biogeochem.
Cycles 30 1639–60
Martin P A, Newton A C and Bullock J M 2013 Carbon
pools recover more quickly than plant biodiversity
in tropical secondary forests Proc. R. Soc. B280
20132236
MatricardiEAT,SkoleDL,CochraneMA,PedlowskiMand
Chomentowski W 2007 Multi-temporal assessment of
selective logging in the Brazilian Amazon using Landsat data
Int. J. Remote Sens. 28 63–82
Mertz O et al 2012 The forgotten D: challenges of addressing
forest degradation in complex mosaic landscapes under
REDD+Geografisk Tidsskrift-Danish J. Geogr. 112
63–76
Morton D C, DeFries R S, Nagol J, Souza Jr. C M, Kasischke E S,
Hurtt G C and Dubayah R 2011 Mapping canopy damage
from understory fires in Amazon forests using annual time
series of Landsat and MODIS data Remote Sens. Environ. 115
1706–20
Morton D C, Page Y L, DeFries R, Collatz G J and Hurtt G C 2013
Understorey fire frequency and the fate of burned forests in
southern Amazonia Phil.Trans.R.Soc.BBiol.Sci.368
20120163
NobreCA,SampaioG,BormaLS,Castilla-RubioJC,SilvaJSand
Cardoso M 2016 Land-use and climate change risks in the
Amazon and the need of a novel sustainable development
paradigm Proc. Natl Acad. Sci. 113 10759–68
Pan Y et al 2011 A large and persistent carbon sink in the world’s
forests Science 333 988–93
11

Environ. Res. Lett. 13 (2018) 065013
Penman J et al 2003 Good Practice Guidance for Land Use,
Land-Use Change and Forestry (Kanagawa: IPCC National
Greenhouse Gas Inventories Programme, Institute for Global
Environmental Strategies)
PereiraD,SantosD,VedovetoM,Guimar
˜
aes J and Ver´
ıssimo A
2010 Fatos Florestais (Imazon: Bel ´
em, PA)
Poorter L et al 2016 Biomass resilience of Neotropical secondary
forests Nature 530 211–4
Putz F E et al 2012 Sustaining conservation values in selectively
logged tropical forests: the attained and the attainable Conserv.
Lett. 5296–303
Ray D, Nepstad D and Moutinho P 2005 Micrometeorological and
canopy controls of fire susceptibility in a forested Amazon
landscape Ecol. Appl. 15 1664–78
Richardson V A and Peres C A 2016 Temporal decay in timber
species composition and value in Amazonian logging
concessions PLoS ONE 11 e0159035
Rutishauser E et al 2015 Rapid tree carbon stock recovery in
managed Amazonian forests Curr. Biol. 25 R787–8
Sato L Y, Gomes V C F, Shimabukuro Y E, Keller M, Arai E,
dos-Santos M N, Brown I F and e Cruz de Arag˜
ao L E O 2016
Post-fire changes in forest biomass retrieved by airborne
LiDAR in Amazonia Remote Sens. 8839
Slik J W F et al 2013 Large trees drive forest aboveground biomass
variation in moist lowland forests across the tropics Glob. Ecol.
Biogeogr. 22 1261–71
Silva C A, Crookston N L, Hudak A T and Vierling L A 2015
rLiDAR: An R package for reading, processing and visualizing
LiDAR (Light Detection and Ranging) data, version 0.1
Silv´
erio D V, Brando P M, Balch J K, Putz F E, Nepstad D C,
Oliveira-Santos C and Bustamante M M C 2013 Testing the
Amazon savannization hypothesis: fire effects on invasion of a
neotropical forest by native cerrado and exotic pasture grasses
Phil.Trans.R.Soc.BBiol.Sci.368 20120427
Souza C M, Roberts D A and Monteiro A 2005 Multitemporal
analysis of degraded forests in the southern Brazilian Amazon
Earth Interact. 91–25
Souza A P, Mota L L, Zamadei T, Martin C C, Almeida F T and
Paulino J 2013 Classificac¸˜
ao clim´
atica e balanc¸o h´
ıdrico
climatol ´
ogico no estado de Mato Grosso Nativa 134–43
Uhl C and Vieira I C G 1989 Ecological impacts of selective logging
in the Brazilian Amazon: a case study from the Paragominas
region of the state of Par´
aBiotropica 98–106
van der Werf G R, Morton D C, DeFries R S, Olivier J G, Kasibhatla
P S, Jackson R B, Collatz G J and Randerson J T 2009 CO2
emissions from forest loss Nat. Geosci. 2737–8
Vargas R, Allen M F and Allen E B 2008 Biomass and carbon
accumulation in a fire chronosequence of a seasonally dry
tropical forest Glob. Change Biol. 14 109–24
Wagner F H et al 2016 Climate seasonality limits leaf carbon
assimilation and wood productivity in tropical forests
Biogeosci. Katlenburg-Lindau 13 2537–62
12