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A global map of mangrove forest soil carbon at 30 m spatial resolution

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With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids250m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m−-3). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon (C) density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86-729 Mg C ha−-1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30-122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products from this work is intended to serve nations seeking to include mangrove habitats in payment-for-ecosystem services projects and in designing effective mangrove conservation strategies.
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Environ. Res. Lett. 13 (2018) 055002
A global map of mangrove forest soil carbon at 30 m
spatial resolution
Jonathan Sanderman1,21 , Tomislav Hengl2, Greg Fiske1, Kylen Solvik1, Maria Fernanda Adame3, Lisa
Benson5,6, Jacob J Bukoski7, Paul Carnell8, Miguel Cifuentes-Jara9, Daniel Donato19, Clare Duncan4,8,
Ebrahem M Eid10,20, Philine zu Ermgassen17,18, Carolyn J Ewers Lewis8, Peter I Macreadie8, Leah Glass5,
Selena Gress11, Sunny L Jardine12, Trevor G Jones5,13, Eug´
ene Ndemem Nsombo14, Md Mizanur Rahman15,
Christian J Sanders16, Mark Spalding17 and Emily Landis17
1Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540, United States of America
2ISRIC — World Soil Information, Wageningen, The Netherlands
3Australian Rivers Institute, Griffith University, Nathan, QLD, Australia
4Institute of Zoology, Zoological Society of London, Outer Circle, Regents Park, London NW1 4RY, United Kingdom
5Blue Ventures Conservation, London, United Kingdom
6Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, United Kingdom
7Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, United States of America
8Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Burwood, VIC, Australia
9Forests, Biodiversity and Climate Change Program, CATIE 7170, Turrialba, Costa Rica
10 Botany Department, Faculty of Science, Kafr El-Sheikh University, Kafr El-Sheikh, Egypt
11 School of Applied Sciences, Edinburgh Napier University, Edinburgh, Scotland
12 School of Marine and Environmental Affairs, University of Washington, Seattle, WA, United States of America
13 Department of Forest Resources Management, University of British Columbia, Vancouver, BC, Canada
14 Institute of Fisheries and Aquatic Sciences, University of Douala, Doula, Cameroon
15 Graduate School of Agriculture, Kyoto University, Kyoto, Japan
16 National Marine Science Centre, Southern Cross University, Coffs Harbour, NSW, Australia
17 The Nature Conservancy, Arlington, VA, United States of America
18 School of Geosciences, University of Edinburgh, Edinburgh, United Kingdom
19 Washington State Department of Natural Resources, Olympia, WA United States of America
20 Biology Department, College of Science, King Khalid University, Abha 61321, Saudi Arabia
21 Author to whom any correspondence should be addressed.
29 June 2017
10 April 2018
13 April 2018
30 April 2018
Original content from
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under the terms of the
Creative Commons
Attribution 3.0 licence.
Any further distribution
of this work must
maintain attribution to
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Keywords: blue carbon, carbon sequestration, land use change, machine learning
Supplementary material for this article is available online
With the growing recognition that effective action on climate change will require a combination of
emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon
sinks have become political priorities. Mangrove forests are considered some of the most
carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for
mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of
mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer
scale variability that would be required to inform local decisions on siting protection and restoration
projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove
soil carbon measurements and developed a novel machine-learning based statistical model of the
distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model,
which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to
capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of
10.9 kg m−3). Of the local variables, total suspended sediment load and Landsat imagery were the
most important variable explaining soil carbon density. Projecting this model across the global
mangrove forest distribution for the year 2000 yielded an estimate of 6.4 PgC for the top meter of soil
with an 86–729 Mg C ha−1 range across all pixels. By utilizing remotely-sensed mangrove forest cover
change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30–122 TgC
with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products
© 2018 The Author(s). Published by IOP Publishing Ltd
Environ. Res. Lett. 13 (2018) 055002
from this work are intended to serve nations seeking to include mangrove habitats in payment-for-
ecosystem services projects and in designing effective mangrove conservation strategies.
1. Introduction
Mangrove forests, occupying less than 14 million ha
(Giri et al 2011), just 2.5% of the size of the Ama-
zon rainforest, provide a broad array of ecosystem
services (Barbier et al 2011). Mangroves are critical
nursery habitats for fish, birds and marine mammals
(Mumby et al 2004, Nagelkerken et al 2008), act as
effective nutrient filters (Robertson and Phillips 1995),
buffer coastal communities from storm surges (Gedan
et al 2011) and support numerous rural economies
(Spalding et al 2014, Temmerman et al 2013). These
ecosystem service benefits have been valued at an aver-
age of 4200 US$ha−1 yr−1 in Southeast Asia (Brander
et al 2012). Despite these ecosystem service bene-
fits, mangroves are highly threatened by both urban
expansion and other higher valueland uses because
of their close proximity to major human settlements.
There are no reliable estimates of original mangrove
cover, but some authors have suggested that 35% or
more of original cover may have been lost and wider
areas have been degraded (Valiela et al 2001, Spald-
ing et al 2010). Loss rates have slowed dramatically
in the past 10–20 years in most areas, however they
remain considerable, with rates up to 3.1% annu-
ally in some countries (Hamilton and Casey 2016).
The major drivers of loss are conversion for aquacul-
ture, especially shrimp farming, agriculture and urban
development (Alongi 2002, Valiela et al 2001, Spald-
ing et al 2010, Richards and Friess 2016) but loss due
to extreme climatic events are also becoming more
common (Duke et al 2017).
With the growing recognition that effective action
on climate change will require a combination of
emissions reductions and removals (Rockstr¨
om et al
2017), protecting, enhancing and restoring natural car-
bon sinks have become political priorities (Boucher
et al 2016, Grassi et al 2017). Mangrove forests can
play an important role in carbon removals; in addition
to being some of the most carbon-dense ecosystems
in the world (Donato et al 2011), if kept undisturbed,
mangrove forest soils act as long-term carbon sinks
(Breithaupt et al 2012). As such, there is strong interest
in developing policy tools to protect and restore man-
groves through payment for ecosystem services (Friess
et al 2016, Howard et al 2017).
Mangroves can store significant amounts of car-
bon in their biomass (Hutchison et al 2014); however,
the vast majority of the ecosystem carbon storage is
typically found in the soil (Donato et al 2011, Mur-
diyarso et al 2015, Sanders et al 2016). For example,
Kauffman et al (2014) found that within the same estu-
ary, soil carbon contributed 78% of total ecosystem
carbon storage in tall mangroves but 96%–99% of
total ecosystem carbon in medium and low stature
mangrove stands. Importantly, Kauffman et al (2014)
found that conversion of these mangrove forests to
shrimp ponds resulted in the loss of 90% of this car-
bon from the top 3 m of soil (612–1036 Mg C ha−1).
In addition to avoided emissions, many mangrove for-
est soils are accreting as sea level rises (Krauss et al
2014), providing continual carbon sequestration on
the order of 1.3–2.0 Mg C ha−1 yr−1 (Breithaupt et al
2012, Chmura et al 2003). Clearly, there can be a
major climate benefit to halting or even slowing the rate
of mangrove conversion, with a rough potential esti-
mated to be 25–122 Tg C yr−1 (Pendleton et al 2012,
aki et al 2012). For nations with large mangrove
holdings, protection and restoration can make major
contributions to meeting climate mitigation targets
(Herr and Landis 2016).
While many mangrove forests do accumulate large
quantities of soil carbon, others do not. There can be
significant variability in soil carbon stocks across dif-
ferent mangrove forests (Jardine and Siikam¨
aki 2014)
but also within the same mangrove forest (Adame
et al 2015, Kauffman et al 2011). Understanding the
distribution of soil carbon in mangrove forests will be
very important in prioritizing protection and restora-
tion efforts for climate mitigation. The controls on soil
carbon stocks are diverse and are likely scale dependent;
however, some generalizations can be made. Man-
grove forests, no matter how productive, will struggle
to have high soil carbon stocks in the upper meter
of soil if they receive large annual sediment loads.
Mangrove forests in river deltas, such as the Sundar-
bans (Banerjee et al 2012) and the Zambezi river delta
in Mozambique (Stringer et al 2016), typically only
contain a few percent organic carbon throughout the
soil profile. These locations may still have very high
carbons stocks, but the density of carbon is low due
to the high allocthonous input of mineral sediments.
Conversely, forests with moderately low productivity
can accumulate large amounts of soil carbon if they
are in an isolated hydrogeomorphic setting (Ezcurra
et al 2016). Within the same mangrove forest there
are typically steep hydrogeomorphic gradients from
the seaward to landward extent of the forest which
results in zonation of both vegetation (Snedaker 1982)
and soil carbon storage (Kauffman et al 2011, Ouyang
et al 2017, Ewers Lewis et al 2018) but not necessarily
for the same reasons. Within a similar hydrogeomor-
phic position, forest productivity and soil edaphic
conditions (e.g. redox potential, pH, salinity) driving
decomposition rates are often the dominant controls
on soil carbon density. Consideration of this nested
Environ. Res. Lett. 13 (2018) 055002
hierarchy of controls will be necessary to successfully
capture the variability in soil carbon at both local and
global scales.
Accurate estimates and an understanding of the
spatial distribution of mangrove soil carbon stocks
are a critical first step in understanding climatic and
anthropogenic impacts on mangrove carbon stor-
age and in realizing the climate mitigation potential
of these ecosystems through various policy mecha-
nisms (Howard et al 2017). Previous global estimates
(Atwood et al 2017, Jardine and Siikam¨
aki 2014),
do not capture enough of the finer scale spatial
variability that would be required to inform local deci-
sions on siting protection and restoration projects.
To close this information gap, we have: (1) com-
piled and published a harmonized global database of
the profile distribution of soil carbon under man-
groves, (2) used this database to develop a novel
machine-learning based data-driven statistical model
of the distribution of carbon density using spatially
comprehensive data at an 30 m resolution, (3) pro-
jected the model results across global mangrove habitat
for the year 2000 (Giri et al 2011), and (4) over-
laid estimates of mangrove forest change between
2000 and 2012 (Hamilton and Casey 2016) to esti-
mate potential soil carbon emissions from recent forest
2. Methods
2.1. Mangrove soil carbon database
A harmonized globally representative database (avail-
able at: 10.7910/DVN/OCYUIT) was compiled from
peer-reviewed literature, grey literature and from con-
tributions of unpublished data from a number of
researchers and organizations. Details of database
development and a statistical summary of the data
are given in the supplemental information available
2.2. Spatial modelling of soil organic carbon
In order to maximize the utilization of available soil
carbon data, we developed a machine learning-based
model of organic carbon density (OCD) which models
OCD as a function of depth (d), an initial estimate of
the 0–200 cm organic carbon stock (OCS) from the
global SoilGrids 250 m model (Hengl et al 2017), and
a suite of spatially explicit covariate layers (X𝑝):
OCD(𝑥𝑦𝑑) = 𝑑+ OCSSG +𝑋1(𝑥𝑦)
+𝑋2(𝑥𝑦) + ...𝑋𝑝(𝑥𝑦)
where OCSSG is the aggregated organic carbon stock
estimated for 0–200 cm depth using global SoilGrids
250 m approach down-sampled from 250 m–30 m res-
olution, and xyd are the 3D coordinates northing
easting and soil depth (measured to center of a hori-
zon). Note here that we model spatial distribution
of OCD in three dimensions (soil depth used as a
predictor) using all soil horizons layers at different
depths, which means that a single statistical model
can be used to predict OCD at any arbitrary depth.
This 3D approach to modeling OCD reduces the need
for making complex assumptions about the downcore
trends in OCD, and maximizes the use of collected
The derived spatial prediction model is then used
to predict OCD at standard depths 0, 30, 100, and
200 cm, so that the organic carbon stock (OCS) can
be derived as a cumulative sum of the layers down
to the prediction depth for every 30 m pixel identi-
fied as having mangrove forest in the year 2000 (Giri
et al 2011). Importantly, we found that there is a spatial
mismatch between the global mangrove forest dis-
tribution (GMFD) of Giri et al (2011) and satellite
imagery (figure S2). To best resolve this spatial mis-
match, we have adjusted the GMFD by growing all
vectors by one pixel (30 m) and then filtering out any
pixel that falls over water by using Landsat NIR band
(see SI for more details).
Environmental covariates have been compiled to
represent the postulated major controls on OCS in
soils generally (McBratney et al 2003) and specifi-
cally for mangrove ecosystems (Balke and Friess 2016).
Covariates included:
1. Vegetation characteristics including percent
forest cover (Hansen et al 2013) and Landsat
bands 3 (red), 4 (near infrared), 5 (shortwave
infrared) and 7 (shortwave infrared) for the year
2000 (Hanson et al 2013) retrieved from http://
2. Digital elevation data, which at or near sea-level
approximately follows forest canopy height (Simard
et al 2006), from the shuttle radar topography mis-
sion (SRTM GL1; NASA, 2013) was retrieved from, maintained by the NASA
EOSDIS Land Processes Distributed Active Archive
Center (LP DAAC) at the USGS/Earth Resources
Observation and Science (EROS) Center, Sioux
Falls, South Dakota;
3. Long-term averaged (1990–2010) monthly sea sur-
face temperature (SST) averaged into four seasons
were generated in Google Earth Engine from NOAA
AVHRR Pathfinder Version 5.2 Level 3 Collated
data (Casey et al 2010) and downscaled to 30 m
resolution using bicubic resampling;
4. The M2 tidal elevation amplitude product
(FES2012) from a global hydrodynamic tidal model
which assimilates altimetry data from multiple plat-
forms was used to represent tidal range at each
location. The FES2012 product was produced by
Noveltis, Legos and CLS Space Oceanography Divi-
sion and distributed by Aviso, with support from
Cnes (;
Environ. Res. Lett. 13 (2018) 055002
5. Averaged (2003–2011) monthly total suspended
matter (TSM) averaged into four seasons estimated
from MERIS imagery collected by the European
Space Agencys Envisat satellite. Processed and vali-
dated TSM data was retrieved from the GlobColour
project (
6. A mangrove typology map delineating mangroves
into estuaries and then either organogenic or
mineralogenic based on an analysis of TSM and
tidal amplitude data (Zu Ermgassen, unpublished
Sea surface temperature, tidal amplitude and TSM
are 4 km resolution ocean products and needed to
be extrapolated to each pixel containing mangrove
forest. Missing values in the sea surface tempera-
ture, tidal amplitude and TSM were first filled-in
using spline interpolation in SAGA GIS, then down-
scaled to 30 m resolution using bicubic resampling in
GDAL. Including SoilGrids and depth, there were a
total of 20 covariates used in building the mangrove
OCD model.
The ability of the training points to represent
the entire covariate space of the global mangrove
domain was assessed by conducting a principal com-
ponents analysis (PCA) on 15 000 randomly selected
points and the 1613 points used in the spatial
model. Spatial variables were detrended and centered
by subtracting the mean and dividing by the stan-
dard deviation (s.d.) before entering into the PCA
Soil carbon typically varies in highly non-linear
ways with depth and across the landscape and as
such the ability of standard parametric models to cap-
ture this variation is limited (Jardine and Siikam¨
2014, Hengl et al 2017). Here we model the spatial
(xyd) distribution of OCD using a machine learn-
ing random forest model implemented in the ranger
package (Wright and Ziegler 2015) in the R environ-
ment for statistical computing (R Core Team 2000).
Given the clustered nature of the point data, we have
implemented a spatially balanced random forest model
design. Model performance was assessed with a 5
fold (Leave-Location-Out) cross-validation procedure
where 20% of complete locations were withheld in each
model refitting (Gasch et al 2015). The relative impor-
tance of using SoilGrids as a covariate was assessed by
implementing the cross-validation procedure with and
without this variable.
Finally, prediction error of OCS for 0–1 m depth
was derived for ±1 s.d. based on the quantile regression
approach of Meinshausen (2006) and implemented
in R via the ranger package. This procedure is rela-
tively computationally demanding so a random subset
of approximately 15 000 points were selected to cal-
culate prediction errors. All modeling was run on
ISRIC High Performance Computing servers with
48 cores of 256 GB RAM.
2.3. Data analysis
Soil carbon stocks were calculated for the global extent
of mangroves for the year 2000 by summing the OCS in
each pixel for 1 and 2 m depths. Country level carbon
stocks were also calculated for the same depths. Given
the fringing nature of mangroves, a global spatial vector
data layer was built that allocated the offshore area for
each country where mangrove forests can be found. It
was derived from the Exclusive Economic Zone for each
country. This layer was then dissolved with the onshore
areas for each associated country and subsequently used
to quantify mangrove OCS tonnage and areal extent.
Potential loss of OCS due to mangrove habitat
conversion was calculated between 2000 and 2015 by
summing the OCS in mangrove forest pixels which
were identified to be deforested. While this analysis
cannot distinguish between natural and anthropogenic
disturbance, human-driven land use change is believed
to be by far the dominant driver of deforestation
in mangrove ecosystems (Alongi 2002, Murdiyarso
et al 2015). We define deforested using the Global
Forest Change dataset (Hansen et al 2013) available
online from:
science-2013-global-forest. We chose to use this
approach for estimating deforestation instead of using
the derived mangrove tree cover loss data produced
by Hamilton and Casey (2016) as used by Atwood
et al (2017) because the Hamilton and Casey (2016)
analysis only considered forested area as area actually
covered by trees (i.e. if a 100 ha forest has 80% tree
cover then it is counted as 80 ha of forest). In our opin-
ion, this definition mischaracterizes forest area extent.
Next, an estimate of the soil carbon emissions associ-
ated with land use conversion is needed. The amount
of OCS lost can be highly variable and depends on
the new land use (Kauffman et al 2014,2016b, Jones
et al 2015) and probably on soil properties. Pendle-
ton et al (2012) used a 25%–100% loss range. Donato
et al (2011) used a low estimate of 25% of the OCS
in top 30 cm and 75% in top 30 cm +35% from
deeper layers as a high estimate. Expanding on ear-
lier work, Kauffman et al (2017) found that on average
54% of belowground carbon (soil +roots) to 3 m was
lost after conversion to shrimp ponds and pastures.
Given the limited number of studies comparing soil
OCS change with land use change, in this work we
adopt the same 25%–100% range as used by Pendleton
et al (2012) applied to the first meter of soil. Finally,
country level statistics for OCS loss were calculated as
described above. All global and country level analy-
ses were performed on the 30 m resolution dataset in
Google Earth Engine (Gorelick et al 2017).
3. Results
3.1. Model results
The random forest model was successful in captur-
ing the major variation in OCD across the mangrove
Environ. Res. Lett. 13 (2018) 055002
Figure 1. Model fitting results: the corresponding 3D Random Forest model for soil organic carbon density (a) with cross-validation
results in (b), and relative variable importance plot (c). TSM = total suspended matter, SST = sea surface temperature (numbers
following TSM and SST refer to quarter of the year), NIR = Near Infrared, SW1 = Short wave mid infrared.
database (figure 1(a)) with an R2of 0.84 and a root
mean square error (RMSE) of 6.9 kg m−3 compared to
the mean OCD value of 29.6 kgm−3. Cross-validation
results yield an R2of 0.63 and an RMSE of 10.9 kg m−3
(figure 1(b)), which is the de-facto mapping accu-
racy to be expected on the field. Low OCD values
were slightly over-predicted and high OCD values
were under-predicted (figure 1(b)). The initial OCS
prediction from SoilGrids 250 m was the most impor-
tant variable explaining mangrove OCD. Running
the 5 fold cross-validation without and with Soil-
Grids indicated that this single variable explained
improved model performance by nearly 50% (R2
increased from 0.42–0.63). Seasonal total suspended
matter (TSM), depth of sample, mangrove tree cover,
Landsat Red band, sea surface temperature (SST), and
tidal range were the next ten most important vari-
ables, respectively (figure 1(c)). Quantile regression
analysis indicated that the full uncertainty (±1 s.d.)
about a mean prediction of carbon stocks to 1 m depth
averaged 40.4% of the mean OCS with lowest rel-
ative uncertainty in the most carbon-rich mangrove
forests (figure S7).
3.2. Mangrove soil carbon storage
Projection of the mangrove OCD model to global
mangrove forests revealed the distribution of soil car-
bon storage in these ecosystems (figure 2). The mean
(±1 s.d.) OCS to 1 m depth was 361 ±136 Mg C ha−1
with a range of 86–729 Mg C ha−1 . At the national
level (table S1), Bangladesh had the lowest per ha
stocks, averaging just 127 Mg C ha−1 followed by China
and the nations bordering the Persian Gulf and Red
Sea with an average OCS of 214 and 233 Mg C ha−1 ,
respectively. The highest per ha stocks were found
in many of the pacific island nations, averaging
505 Mg C ha−1 with much of Southeast Asia ranking
well above the global mean.
While the national level comparisons are reveal-
ing, by modeling at a 30 m resolution much richer
details of potential within forest variation in OCS are
seen (figure 2). Mangrove forests dominated by sed-
iment laden fluvial inputs typically have consistently
low OCS as seen in the Sundarbans and Madagascar
(figures 2(a) and (e)). In non-deltaic mangroves, the
model appears to have captured the large zonal vari-
ation in OCS that is often observed in field studies
(figures 2(b) and (c).
3.3. Soil carbon loss due to habitat loss
Utilizing the Hanson et al (2013) global deforesta-
tion analysis (figure S8), we found that 278049 ha
(1.67% of total) of area identified as mangrove habitat
in the year 2000 was deforested resulting in the com-
mitted emission of 30.4–122 Tg C (111–447 Tg CO2)
from mangrove forest soils due to land use change
between 2000 and 2015 (figure 3). The relative rank of
nations in terms of loss of mangrove forest area and
OCS were often not the same (table S1). Indonesia
alone was responsible for 52% of this global loss with
Malaysia and Myanmar representing another 25% of
the global total loss (figure 3(c)). When visualized as
a percent loss from year 2000 stocks, a slightly differ-
ent pattern emerged (figure 3(d)). Guatemala had the
highest percent loss of mangrove OCS (0.9%–6.8%)
followed by several southeast Asian nations, but high
percent losses were also found in several Caribbean
island nations as well as the United States and
several west African countries.
4. Discussion
4.1. Amount and distribution of Mangrove SOC
Our new estimate of global mangrove OCS of
6.4 Pg C in the upper meter and 12.6 Pg C to 2 m is
largely consistent with past efforts to calculate this
value (Donato et al 2011, Jardine and Siikam¨
2014, Sanders et al 2016). However, our estimate is
double that of Atwood et al (2017) primarily due to
their use of the Hamilton and Casey (2016) estimate of
mangrove extent instead of Giri et al (2011). Impor-
tantly, by using an environmental covariate model,
Environ. Res. Lett. 13 (2018) 055002
Figure 2. Global distribution of mangrove soil carbon stocks for the top meter of soil (hex bin area 19 000 km2) and detailed maps
(30 m resolution) for selected mangrove regions of the world: (1) Sundarbans along the India/Bangladesh border, (2) Bah´
ıa de los
Muertos, Pacific coast of Panama, (3) southwest coast of Papua, Indonesia, (4) Hinchinbrook Island, Queensland, Australia, (5)
Ambaro Bay, Madagascar, and (6) Guinea-Bissau and Guinea along the West African coast. In top panel, data presented as mean stock
(Mg C ha−1) for mangrove forest area only within each hex bin.
we have been able to make plausible estimates for
regions where no sampling has taken place instead of
relying on global mean values (i.e. Atwood et al 2017).
The total amount of soil carbon was similar in
our analysis and the most comparable analysis, that
of Jardine and Siikam¨
aki (2014), but the spatial distri-
bution of carbon-rich versus carbon-poor mangroves
varied substantially. For example, we found much
higher OCS levels in West Africa than in East African
nations (figure 2and table S1) but the reverse was
found by Jardine and Siikam¨
aki (2014). Large dis-
crepancies were also found for Colombia, Sri Lanka
and many of the countries bordering the Red Sea.
These differences were most likely driven by lack of
data in those regions at the time of the analysis by
Jardine and Siikam¨
aki (2014) given that nearly half
the data in our database was collected after their
study was published. Additionally, our analysis sug-
gested a much larger range in OCS (86–729 Mg C ha−1 )
compared to 272–703 Mg C ha−1 in the analysis of
Jardine and Siikam¨
aki (2014). This difference in
range was likely due to the inclusion of more data
from sub-tropical and temperate mangroves (figure
The depth trend analysis (figure S6) and ran-
dom forest variable importance (figure 1(b)) both
indicated that depth should be considered in calcu-
lation of OCS. For locations that were either stable
peat domes or located in estuaries receiving large
annual sediment loads, a stable OCD profile distri-
bution would be expected and this was found for many
sites (figures S6(a) and (e)). However, where man-
groves are growing in a mineral matrix that is receiving
only low sediment loads, a decline in OCD may be
expected as the carbon inputs from the productive
mangrove forest would be concentrated in the sur-
face horizons (figure S6(b)). Still in other cases (figure
S6(c)), changes in hydrologic/sediment regimes can
lead to irregular depth patterns or even an increase in
OCD with depth.
Environ. Res. Lett. 13 (2018) 055002
Figure 3. Top 20 nation rankings for (a) total mangrove area lost between the years 2000 and 2012, (b) area loss as a percent of year
2000 mangrove area, (c) total soil organic carbon stocks, (d) carbon loss as a percent loss of year 2000 soil carbon stock. Range in
values for (c) and (d) come from 25%–100% loss of carbon in upper meter of soil in pixels identified as being deforested between the
years 2000 and 2015.
While total area of mangroves was a key deter-
minant of total soil carbon storage, amongst the top
25 mangrove OCS holding nations, there was a nearly
even split between nations with smaller area of high
soil carbon density forests and those nations with lots
of low soil carbon density forests (figure 4). Indone-
sia was the clear exception to this trend with the
largest mangrove holdings which also contain rich car-
bon stocks resulting in Indonesia alone holding nearly
25% of the worlds mangrove OCS.
Compared to terrestrial carbon pools, mangrove
forests rank low due to their limited spatial extent. For
example in the upper meter of soil, permafrost affected
soils are estimated to store 472 ±27 Pg C (Hugelius
et al 2014), tropical forests contain 188 Pg C, and
soils under permanent cropping contain 150 Pg C
(table 1). However, on an equal area basis, man-
grove forests on average store more soil carbon than
most other ecosystems (table 1). Importantly, our
analysis has demonstrated mangrove soil carbon is
highly variable and many mangroves actually store
fairly modest amounts of carbon in the upper one
or two meters of soil. While not the focus of this
analysis, it is important to point out that while some
mangrove forests store modest levels of OCS in the
upper meter of soil, they can have high sequestration
rates and conversely carbon-dense mangroves can have
low annual sequestration rates (Lovelock et al 2010,
MacKenzie et al 2016).
4.2. Drivers of soil carbon storage
Our spatial modelling framework, in which global pre-
dictions were combined with local high resolution
images, was successful as the general patterns of car-
bon variation from SoilGrids 250 m were maintained,
while the spatial detail was significantly improved by
moving from 250 m–30 m spatial resolution. The ini-
tial SoilGrids 250 m OCS prediction (Hengl et al 2017)
Environ. Res. Lett. 13 (2018) 055002
Table 1. Soil organic carbon stocks (mean with 5th-95th percentile in parentheses) and total storage for different terrestrial ecosystems
compared to mangrove forests.
1 m soil organic carbon stock
Land cover category (IGBP code)aArea (106ha) Pg C Mg C ha−1
Mangrove forestb16.6 6.4 361 (94–628)
Gelisols (permafrost soils)c1878 472.0 389 (178–691)
Evergreen Needleleaf forest (1)d286 60.0 210 (121–346)
Evergreen Broadleaf forest (2) 1248 188.4 151 (85–271)
Deciduous Needleleaf forest (3) 116 29.3 253 (163–412)
Deciduous Broadleaf forest (4) 165 22.1 134 (83–223)
Mixed forest (5) 771 152.8 198 (93–343)
Closed shrublands (6) 56 6.2 110 (39–223)
Open shrublands (7) 1933 325.8 169 (49–329)
Woody savannas (8) 1179 185.8 158 (82–274)
Savannas (9) 1010 112.9 112 (52–201)
Grasslands (10) 1810 280.1 155 (56–289)
Permanent wetlands (11)e104 25.1 241 (114–474)
Croplands (12) 1177 149.6 127 (60–200)
Cropland/Natural veg. mosaic (14) 868 117.7 136 (58–238)
adata for MODIS-based IGBP land cover classes (Friedl et al 2010) extracted from 1 m OCS map for the year 2010 produced by Sanderman
et al (2017).
bmangrove area and OCS data from this study.
cpermafrost area from Tarnocai et al (Tarnocai et al 2009), OCS data from Hugelius et al (2014).
dsome overlap between class 1 (evergreen Needleleaf forest) and gelisols.
eclass 11 (permanent wetlands) likely has overlap with mangrove area.
Figure 4. Rank of nations by mangrove area plotted against
rank by soil carbon density for all nations containing
>30 Tg C. Bubble size is proportional to total carbon stock
(Tg C) within each nation.
was based upon machine learning algorithms using
237 covariates that covered the main state factors of
soil formation (Jenny 1994)—climate, relief, living
organisms (vegetation) and parent material—and was
four times more important in predicting mangrove
OCD than any of the local covariates. However,
the local covariates allowed for a much more refined
picture of the spatial variation in OCS within man-
grove forests (figure 2) which was not captured in the
250 m resolution SoilGrids 250 m prediction. Covari-
ates related to hydrogeomorphology (TSM and tidal
range), as hypothesized, were important predictors of
local variation in OCD. Both TSM and tidal range
were strongly negatively correlated with OCD suggest-
ing that locations with either high sediment loads or
strong tidal flushing do not accumulate large carbon
stocks. The mangrove typology ended up being unin-
formative likely because most of the data that went into
this classification was already captured in the model.
Covariates related to mangrove biomass (SRTM
elevation and Landsat bands) were also important in
explaining the local distribution of OCD (figure 1(b)).
However, as pointed out by Bukoski et al (2017), it is
unclear whether the importance of these vegetation-
related data are causal drivers of differences in OCD
or they just happen to co-vary in the same way as
OCD. To further explore the relationship between
forest biomass carbon and soil carbon storage, we
extracted aboveground biomass data from Hutchison
et al (2014) and compared it to our OCS results (fig-
ure 5). While a clear positive trend was found between
biomass and soil carbon storage (R2= 0.26), there is
clearly a lot of variance especially at lower biomass levels
where nearly the full range in OCS can be found.
Depth below the soil surface was an important
covariate in modeling OCD distribution (figure 1(b)).
This finding was supported by the database depth
trend analysis (figure S6) which indicated that a flat
depth distribution of OCD would be an incorrect
assumption 37%–64% of the time. These findings sug-
gest that the simple scaling performed in figure S5
and in several previous assessments (Bukoski et al
2017, Jardine and Siikam¨
aki 2014, Atwood et al 2017)
may not be an accurate estimation of total OCS
especially when extrapolating from a surface horizon
sample alone.
4.3 Soil carbon loss due to land conversion (2000–
Our analysis suggests that mangrove soils have lost
or are at least committed to losing 30.4–122 Tg C due
to the land use conversion that occurred between the
Environ. Res. Lett. 13 (2018) 055002
Figure 5. Comparison of aboveground biomass carbon (Hutchison et al 2014) with organic carbon stocks (OCS) for the top meter
of soil. Points were generated by casting 10 000 random points into mangrove areas and extracting values from both maps. Linear
regression R2= 0.26.
years 2000 and 2015 (figure 3). Given that at the global
level the rate of mangrove forest lost was consistent
over this time period (Hamilton and Casey 2016),
we estimated an annual soil carbon emission of 2.0–
8.1 Tg C yr−1. This value is significantly lower than
previous estimates (Donato et al 2011, Pendleton
et al 2012) for two reasons. First, we use remote
sensing-based measurements of actual mangrove loss
instead of applying a large range of annual conver-
sion rates, which are notoriously variable according to
their source (Friess and Webb 2011). Second, we have
summed the actual OCS values for each of the pix-
els where land conversion has taken place (i.e. figure
S8) instead of applying a conversion rate to a mean
OCS value.
The three nations of Indonesia, Malaysia and
Myanmar contributed 77% of global mangrove OCS
loss for this time period (figure 3). Despite similar area
loss (figure 3(a)), Malaysia lost approximately twice as
much soil carbon as Myanmar due to the large dif-
ferences in carbon density between these two nations
(mean OCS = 485 ±57 versus 245 ±63 Mg C ha−1,
respectively). This comparison highlights the impor-
tance of using local OCS values for estimating carbon
emissions attributed to mangrove conversion.
Not all land use conversions result in equal loss
of OCS. Conversion of mangrove forest to shrimp
ponds results in a rapid and near complete loss of car-
bon in the upper meter of soil (Kauffman et al 2014),
as well as losses deeper in the soil profile (Kauffman
et al 2017). Conversion to other agricultural uses such
as pasture for beef production (Kauffman et al 2016b)
and cereal crops (Andreetta et al 2016) also appear
to result in large soil carbon emissions. However,
mangrove degradation and loss due to over harvest-
ing for fuelwood (Jones et al 2015) or due to natural
disturbance (Cahoon et al 2003) likely leads to more
moderate emissions as decomposition and erosion
exceed new plant carbon inputs.
It is important to note that nearly all available
data on OCS loss due to conversion to other land
uses come from organogenic mangrove forests. In
a mineral-dominated mangrove systems with only a
few percent sediment OCC, we would not expect the
same level of carbon loss as when peat deposits are
drained or removed. In fact, reclamation of deltaic sed-
iments for paddy rice cultivation can lead to increases
in OCS (Kalbitz et al 2013), although methane emis-
sions would be expected to increase. Additionally, if
mangrove habitat is lost due to deforestation with-
out a change in hydrologic regime, mineral-dominated
mangroves can continue to accrete carbon, but at
a lower rate than in a system that has additional
organic matter inputs from the mangroves themselves
erez et al 2017).
4.4. Limitations and uncertainties
While we endeavored to ensure that the model input
data was of the highest quality possible, there undoubt-
edly remain unknown errors in the database which
are contributing to model error. Machine learning
models are particularly sensitive to outlier values
and extrapolation (Murphy 2012). Various research
groups use different methods for determining the
organic carbon concentration (OCC) of a sample
with not all publications reporting whether or not
results were corrected for occurrence of inorganic car-
bon or whether or not roots were excluded before
further processing. Bulk density (BD) is a difficult
parameter to measure accurately in many soils, and
based on our analysis of BD versus OCC (figure S1)
some reported data are unlikely to be accurate. We
Environ. Res. Lett. 13 (2018) 055002
developed a procedure to correct potential BD errors,
but a pedotransfer function gives only an approxi-
mation of the true value. Given the importance of
depth in our models (figure 1(b)), it was unfortu-
nate that so many investigations only report OCS for
large depth increments. We suggest that future studies
using the common practice of collecting subsamples
within larger horizon increments (e.g. Kauffman and
Donato 2012, Kauffman et al 2016a) report the spe-
cific depth increment of the sample rather than that of
the entire core. This additional level of transparency in
the data would allow mass-preserving splines (Bishop
et al 1999) to be fit through the distinct measurement
intervals, resulting in unbiased estimates of OCS.
The largest uncertainty in the input data likely
resulted from imperfect information on plot location.
Whether, accidental or purposeful (i.e. not wanting
to identify exact locations), spatially misplaced data in
publications are of limited utility in geospatial appli-
cations. All of the covariate data for each mangrove
point were selected from spatial layers resulting in
the potential for a mismatch between the recorded
OCS and the spatial predictors. In this study, we visu-
ally inspected all coordinates against Google Earth
imagery and our adjusted mangrove domain, and then
contacted many authors to seek further information
and manually adjusted coordinates when we were
confident that the adjustments lead to better spatial
location. In the final analysis, 199 soil profiles had to
be excluded from analysis because we could not confi-
dently locate these plots within the adjusted mangrove
spatial domain.
5. Conclusions
This work has produced three resources which we
hope to be of significant value to the blue carbon
research and management communities: 1) a large har-
monized database of soil carbon data from mangrove
ecosystems; 2) high-resolution (30 m) predictions
with error of soil carbon stocks across all mangrove
forests globally; 3) estimates of potential soil carbon
losses due to mangrove habitat loss between 2000
and 2015. By using a statistical data-driven model,
we have been able to produce credible estimates
OCS for the numerous mangrove regions where no
field data exist. We found that mangrove OCS is
highly variable (86–729 Mg C ha−1 in the top meter)
but that much of the variability could be captured
using spatially-comprehensive predictors in a machine-
learning framework. Of the 6400 Tg C in the upper
meter of soil, 30–122 Tg have likely been lost due to
deforestation since the year 2000 with 77% of this
loss attributed to Indonesia, Malaysia and Myanmar.
These spatially-explicit estimates of mangrove soil car-
bon storage and loss will provide a practical first step
for enabling nations to prioritize mangrove protection
as part of their climate mitigation and adaptation plans.
We would like to thank A Andreetta, M Osland, J
M Smoak, A DelVecchia, M E Gonneea, R K Bho-
mia and J Kelleway for providing additional data
from their publications. S-T Kang for translating
and extracting data from Chinese language papers.
R K Bhomia acknowledges CIFOR SWAMP project,
USFS International Program and USAID for fund-
ing. M Rahman acknowledges support from USAID
for funding. PM and CJS acknowledge support from
the Australian Research Council (DE130101084 and
LP160100242) and (DE160100443 and DP150103286),
respectively. CD acknowledges support from the Ruf-
ford Foundation and Darwin Initiative for funding.
JS, TH, GF and KS were supported by an anonymous
gift to The Nature Conservancy. MFA was sup-
ported by funding from the Queensland Government
through the Advance Queensland Fellowship. ISRIC
is a non-profit organization primarily funded by the
Dutch government.
Data availability
The mangrove soil carbon database and model
outputs can be downloaded from Harvard dataverse at
Jonathan Sanderman
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... Estimates of global mangrove carbon stocks have been improved over the years [13][14][15][16]. Due to the peculiarity of having forest stands of known age, the MMFR has been the centre of many studies: Ong et al. [28] were the first to investigate the aboveground biomass of 5-, 10-, 15-, and 25-year-old trees using allometric equations (ranging from 16 to 300 Mg ha −1 ), followed by Putz and Chan [29] (62-462 Mg ha −1 ). ...
... Soil carbon data were estimated from Adame et al. [35] who measured the carbon content in 1 m soil cores at different forest ages (5,15,30, and 40 years and a clear-felled site). Mean carbon values were extrapolated for ages ranging 0 to 30 years and each forest patch of a certain age was associated with its soil carbon value, using the same procedure described above for the AGB. ...
... On this basis, 304.85 Mg out of 396 Mg greenwood in 2.2 ha is used for charcoal production and 91. 15 Mg as firewood which ultimately delivers 82.08 Mg of charcoal. After considering the amount of wood used in charcoal production and its yield, along with the number of workers involved and the kilometres travelled for transport, the carbon emission estimates were found to be ranging from 0.001 Mg C for boat transport to 278 Mg C for greenwood burning inside the kilns for a total of 336.17 Mg C ( Figure 4). ...
Matang Mangrove Forest Reserve (MMFR) in peninsular Malaysia has been managed for pole and charcoal production from Rhizophora stands with a 30-year rotation cycle since 1902. The aim of this study is to estimate the carbon budget of the MMFR by considering the carbon stock of the forest, evaluated from remote sensing data (Landsat TM and ETM+, JERS-1 SAR, ALOS PALSAR, ALOS-2 PALSAR-2, SRTM, TANDEM-X, and WorldView-2) for aboveground carbon and field data for belowground carbon. This was investigated in combination with the emissions from the silvicultural activities in the production chain, plus the distribution and consumer-related activities covering the supply chain, estimated with appropriate emission factors. The aboveground biomass carbon stock of the productive forest was of 1.4 TgC, while for the protective forest (not used for silviculture) it was at least equal to 1.2 TgC. The total soil carbon of ca. 32 TgC shows the potential of the MMFR as a carbon sink. However, the commercial exploitation of mangroves also generates greenhouse gasses with an estimate of nearly 152.80 Mg C ha−1 during charcoal production and up to 0.53 Mg C ha−1 during pole production, for a total emission of 1.8 TgC. Consequently, if the productive forest alone is considered, then the carbon budget is negative, and the ongoing silvicultural management seems to be an unsustainable practice that needs a reduction in the exploited area of at least 20% to achieve carbon neutrality. However, even with the current management, and considering the protective forest together with the productive zones, the MMFR carbon budget is slightly positive, thus showing the importance of mangrove conservation as part of the management for the preservation of the carbon stock.
... Additionally, mangrove forests, part of coastal ecosystems, are among the most carbon-rich ecosystems on Earth and have significant carbon storage potential per unit area (Donato et al., 2011;Sanderman et al., 2018). If healthy and sustainably managed, mangrove forests could also alleviate flooding for over 18 million people and avoid damages amounting to 82 billion USD globally (Beck et al., 2018). ...
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Scientific studies show that fast actions to reduce near-term warming are essential to slowing self-reinforcing climate feedbacks and avoiding irreversible tipping points. Yet cutting CO 2 emissions only marginally impacts near-term warming. This study identifies two of the most effective mitigation strategies to limit near-term warming beyond CO 2 mitigation, namely reducing short-lived climate pollutants (SLCPs) and promoting targeted nature-based solutions (NbS), and comprehensively reviews the latest scientific progress in these fields. Studies show that quickly reducing SLCP emissions, particularly hydrofluorocarbons (HFCs), methane, and black carbon, from all relevant sectors can avoid up to 0.6 C of warming by 2050. Additionally, promoting targeted NbS that protect and enhance natural carbon sinks, including in forests, wetlands, grasslands, and agricultural lands, can avoid emissions of 23.8 Gt of CO 2 e per year in 2030, without jeopardizing food security and biodiversity. Based on the scientific evidence, we provided a series of policy recommendations on SLCPs and NbS, including: 1) implementing the Kigali Amendment to reduce HFC emissions; 2) deploying cost-effective, sector-based measures to reduce methane and black carbon emissions; and 3) implementing targeted NbS to protect and enhance existing carbon sinks and shifting away from forest-burning bioenergy. These fast-acting strategies on SLCPs and NbS will play a key role in securing the most avoided warming in the near-term and help countries meet their mid-century carbon neutrality goals. Finally, we proposed future research topics, including: improving measurement and monitoring systems and techniques for SLCP emissions; developing and improving assessments of marginal abatement costs for SLCP mitigation in different sectors; better quantifying the avoided warming potential from protecting different types of natural carbon sinks by 2030, 2050, and over longer periods; and identifying whether there are any biomass types for energy sources that are consistent with the United Nations Environment Assembly's 2022 resolution adopting a definition of NbS. Further research in these areas could help address barriers to adoption and assist countries with better integrating the most effective SLCP and NbS strategies into their climate policies.
... Meskipun hutan mangrove sangat penting bagi kehidupan manusia, namun hutan mangrove kehilangan luasnya lebih cepat dibandingkan hutan hujan tropis [16]. Hutan mangrove mengalami penurunan luas sebesar 20-35% dari total area keseluruhan pada periode 1980 hingga 2015 [27,28] dan hilangnya mangrove global diperkirakan sebesar 0,22% per tahun [29]. Hilangnya mangrove disebabkan oleh faktor antropogenik [30], and naturogenik [31][32][33][34]. ...
Taman Nasional Baluran merupakan taman konservasi yang mengalami degradasi mangrove. Upaya restorasi mangrove perlu dilakukan untuk mendukung Peraturan Daerah pada Kabupaten Situbondo No 6 tahun 2014. Penelitian ini bertujuan untuk menghitung luasan perubahan kawasan hutan mangrove setiap tahun dan pada tahun prediksi. Penelitian ini menggunakan model terintegrasi Markov Chain danCellular Automata untuk menyimulasikan perubahan penggunaan lahan periode 2000 dan 2020 dan memprediksi penggunaan lahan mangrove periode 2030. Teknologi penginderaan jauh digunakan untuk menganalis penggunaan lahan melalui citra satelit Landsat (tahun 2000, 2010, dan 2020). Hasil penelitian menunjukkan bahwa penutupan lahan mangrove mengalami penurunan sebesar 0,5% pada tahun 2000 – 2010 dan mengalami peningkatan sebesar 3,5% pada tahun 2010-2020. Luasan mangrove terus mengalami peningkatan pada tahun 2020 – 2030 yaitu sebesar 9,3% atau 122 Ha. Penerapan model CA-Markov dalam memprediksi penutupan lahan menunjukan nilai kstandard 0,8 yang dapat diartikan bahwa pemodelan dapat diterima secara ilmiah. ABSTRAK Taman Nasional Baluran is a conservation park that is experiencing mangrove degradation. Mangrove restoration efforts need to be carried out to support the Regional Regulation of Situbondo Regency No. 6 of 2014. This study aims to calculate the extent of changes in mangrove forest areas every year and in the predicted year. This study used an integrated Markov Chain and Cellular Automata model to simulate land use change for the period 2000 and 2020 and predict mangrove land use for the period 2030. Remote sensing technology was used to analyze land use through Landsat satellite imagery (2000, 2010, and 2020). The results showed that mangrove land cover decreased by 0.5% in 2000 – 2010 and increased by 3.5% in 2010 – 2020. Mangrove area continues to increase in 2020 – 2030, which is 9.3% or 122 Ha. The application of the CA-Markov model to predict land cover shows a standard value of 0.8 which means that the modeling is scientifically accepted.
... The majority of mangrove exosystem carbon storage is located in the soil [8]. Sanderman et al. [9] revealed between 2000 to 2015 up to 122 million tons of mangrove soil carbon were released associated with the mangrove forest loss. ...
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Mangroves grow in the tidal zone and have many benefits for the ecosystem and human life. Mangrove loss monitoring is important information to know the condition and status of mangrove forests. Along with the development of computer technology, machine learning and satellite imagery has widely used for mangrove mapping. The goal of this study is to monitor two decades (2000–2020) of mangrove loss using a random forest (RF) algorithm with Landsat-7 and Landsat-8 data in East Luwu, Indonesia. East Luwu has a high mangrove deforestation rate based on the previous study. More detailed mangrove loss monitoring in this area is needed to know the annual mangrove deforestation rate in this area. This study used an RF model to produce mangrove maps in the whole study area from 2000 to 2020. According to the large computing and storage capabilities of time-series satellite data, this study used Google Earth Engine (GEE) platform as the cloud computing process. A total of 2500 independent testing points were collected to calculate the evaluation assessment of produced mangrove maps. Based on the evaluation assessment, the average overall score of produced mangrove map is 0.966, while the average UA score of mangrove class is 0.936. In general, this study revealed the total area of mangroves in East Luwu from 2000 to 2020 has a decreased trend. The highest annual rate of mangrove loss happened from 2000 to 2005 with a loss rate of −14.11% (2477.39 Ha). The main factor of mangrove loss in this area is caused by the aquaculture ponds. In addition, we found an increase in mangrove areas from 2016 to 2020 by +1.04% (87.96 ha).
... Furthermore, the carbon sequestration function of mangrove ecosystems has received attention from the perspective of climate change. Many reports suggest that mangrove soils contain a large amount of carbon (Donato et al. 2011;Atwood et al. 2017;Inoue 2018;Sanderman et al. 2018). Because the growth rates of mangrove plants, such as leaf and root production rates, are not that high compared with those of other terrestrial woody plants (Lin and Sternberg 1993;Farnsworth et al. 1996;Ball et al. 1997;Ellison and Farnsworth 1997;Lόpez-Hoffman et al. 2006;Inoue et al. 2022), the high carbon content of the soils may be related to the low rate of organic matter decomposition in the flooded saline soils (Miyajima and Hamaguchi 2018). ...
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Purpose There is increased recognition of the importance of mangroves worldwide, with efforts being made to sustainably manage these ecosystems for forestry and fishery use. Although successive monitoring of mangrove growth after planting has been conducted in some afforestation stands, measurements of soil environmental changes accompanying plant growth have not been made in most stands. In this study, we observed the interactive relationship between the underground root biomass of mangrove, Rhizophora stylosa, and soil chemical properties at an afforestation stand on Tarawa atoll, Kiribati. Methods We first estimated underground root biomass in the stand. Next, we measured the concentrations of dissolved phosphorus, nitrogen, and other ions (Br⁻, Ca²⁺, K⁺, Na⁺, Cl⁻, and SO4²⁻) in soil pore water, as well as the isotopic ratio of leaf carbon and nitrogen in mangrove patches of different ages. Results Estimated underground root biomass was positively related with phosphate and nitrate concentrations in soil pore water, indicating the formation of a rhizosphere environment. Leaf δ¹⁵N analysis suggested that the discrimination of nitrogen isotopes during nitrification and/or uptake of NH4⁺ and NO3⁻ occurs in accordance with plant growth. Differences in salt stress among the patches were reflected in leaf δ¹³C, suggesting it would be a good indicator of the physiological response of mangrove plants to salinity. Conclusions Our findings revealed the changes that occur on a yearly basis in the chemical properties of mangrove leaves and soil pore water after mangrove plantation. These data help to improve our understanding of environmental succession during the formation of mangrove ecosystems.
... Wave climate can induce coastal risk (for example, erosion 1,3 and flooding 16,17 ) and contributes to the balance of mass and energy fluxes in coastal ecosystems 18 . The general characteristics of a coastal environment, such as the distribution of ecosystems (for example, coral reefs 19 , seagrasses 20 and mangroves 21 ) and coastal features (for example, carbon soil mangroves 22 and sedimentary environments 23 ), vary based on the major climate regions (planetary areas delimited by atmospheric circulation patterns and similar climate conditions) where they are found. In addition to changes in storminess, spatial and temporal changes in climate regions (for example, from tropical to subtropical, from subtropical to extratropical) can cause substantial changes to the prevailing wave climate (for example, significant wave height H s and mean wave direction) and severely alter nearshore processes. ...
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Wave climate is a primary driver of coastal risk, yet how climate change is altering wave climate is not fully understood. Here we identify transitional wave climate regions, coastlines with a future change in the occurrence frequency of a wave climate, with most of the regions located in south-western and eastern ocean basins. Analysis of the spatio-temporal changes in the atmosphere-driven major wave climates (the easterlies, southerlies and westerlies) under 2 emission scenarios for 2075–2099 and 2081–2099 shows increases in frequency from 5 to 20% for the easterly and southerly wave climates. The projected changes in these regions, in addition to sea-level rise and changes in storminess, can modify the general patterns of the prevailing wave climates and severely alter their coastal risks. Consequently, transitional wave climate regions should be recognized as areas of high coastal climate risk that require focus for adaptation in the near term.
... In the marine realm, sensitive ecosystems like coastal marine vegetation that support a vast diversity of marine life and important ecosystem services, such as fisheries and carbon sequestration are at a high to very high risk of climate negative impacts (Edgar et al. 2000;Li et al. 2018;Sanderman et al. 2018). One of the most important coastal marine vegetation ecosystems are kelp forests, large brown seaweed of the Laminariales order. ...
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Mangrove restoration projects are now widely established, aiming to regain the carbon benefit of the mangrove ecosystem that is essential for climate change mitigation. This study aimed to investigate mangrove litter as the source of carbon in restored mangrove forests in Perancak Estuary, Bali, Indonesia, which previously experienced substantial mangrove loss due to shrimp aquaculture development. We assessed the production and decomposition of mangrove litter and associated macrozoobenthic biodiversity in restored forests with plantation age ≥14 years and intact mangrove forests as the reference. The monthly production of three groups of mangrove litter (leaf, reproductive, and wood) was assessed over 12 months. A leaf litter decomposition experiment was performed to inspect the interspecific and disturbance history variation in organic matter formation among four major mangrove species: Rhizophora apiculata, Bruguiera gymnorhiza, Avicennia marina, and Sonneratia alba. Our results showed that annual litterfall production from restored and intact mangroves in Perancak Estuary were 13.96 and 10.18 Mg ha−1 year−1, which is equivalent to approximately 6282 and 4581 kg C ha−1 year−1 of annual litterfall carbon sink, respectively. Although restored mangroves had significantly higher plant litterfall production than intact mangroves, no significant difference was detected in leaf litter decomposition and macrozoobenthic biodiversity between these forest types.
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Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.
As Canada’s vast Boreal Plains are extensively managed, predictive soil mapping could be used as an effective tool to generate high-resolution soil information for the region to inform sustainable resource management. This study aimed to investigate the use of multi-temporal remote sensing data and terrain derivatives to map soil types in the region. A method of constraining sub-group and great-group soil type predictions based on the predictions at higher-order levels (great-group and order, respectively) was tested. Sentinel time series median values obtained using Google Earth Engine were tested in combination with first- and second-order digital elevation model derivatives for use as predictor variables in the predictive models. A recursive feature selection process was implemented to reduce the number of predictor variables used in model training. Soil classes were predicted at the order, great group, and subgroup levels and two approaches were tested. In the first approach, models were unconstrained based on previous predictions. In the second approach, models were constrained to predict only soil great group classes that occur within the predicted soil order for a given location and similarly predict only soil subgroup classes that occur within the predicted soil great group for a given location. Determined through independent validation testing, the most probable predicted soil maps had overall accuracies ranging from 42 to 68% and Kappa scores ranging from 0.33 to 0.48. Overall, the constrained models had the best performance of the approaches tested.
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Human appropriation of land for agriculture has greatly altered the terrestrial carbon balance, creating a large but uncertain carbon debt in soils. Estimating the size and spatial distribution of soil organic carbon (SOC) loss due to land use and land cover change has been difficult but is a critical step in understanding whether SOC sequestration can be an effective climate mitigation strategy. In this study, a machine learning-based model was fitted using a global compilation of SOC data and the History Database of the Global Environment (HYDE) land use data in combination with climatic, landform and lithology covariates. Model results compared favorably with a global compilation of paired plot studies. Projection of this model onto a world without agriculture indicated a global carbon debt due to agriculture of 133 Pg C for the top 2 m of soil, with the rate of loss increasing dramatically in the past 200 years. The HYDE classes "grazing" and "cropland" contributed nearly equally to the loss of SOC. There were higher percent SOC losses on cropland but since more than twice as much land is grazed, slightly higher total losses were found from grazing land. Important spatial patterns of SOC loss were found: Hotspots of SOC loss coincided with some major cropping regions as well as semiarid grazing regions, while other major agricultural zones showed small losses and even net gains in SOC. This analysis has demonstrated that there are identifiable regions which can be targeted for SOC restoration efforts.
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Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.
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Mangrove soils represent a large sink for otherwise rapidly recycled carbon (C). However, widespread deforestation threatens the preservation of this important C stock. It is therefore imperative that global patterns in mangrove soil C stocks and their susceptibility to remineralization are understood. Here, we present patterns in mangrove soil C stocks across hemispheres, latitudes, countries and mangrove community compositions, and estimate potential annual CO2 emissions for countries where mangroves occur. Global potential CO2 emissions from soils as a result of mangrove loss were estimated to be ~7.0 Tg CO2e yr⁻¹. Countries with the highest potential CO2 emissions from soils are Indonesia (3,410 Gg CO2e yr⁻¹) and Malaysia (1,288 Gg CO2e yr⁻¹). The patterns described serve as a baseline by which countries can assess their mangrove soil C stocks and potential emissions from mangrove deforestation.
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Scientists have the difficult task of clearly conveying the ecological consequences of forest and wetland loss to the public. To address this challenge, we scaled the atmospheric carbon emissions arising from mangrove deforestation down to the level of an individual consumer. This type of quantification represents the " land-use carbon footprint " , or the amount of greenhouse gases (GHGs) generated when natural ecosystems are converted to produce commodities. On the basis of measurements of ecosystem carbon stocks from 30 relatively undisturbed mangrove forests and 21 adjacent shrimp ponds or cattle pastures, we determined that mangrove conversion results in GHG emissions ranging between 1067 and 3003 megagrams of carbon dioxide equivalent (CO 2 e) per hectare. There is a land-use carbon footprint of 1440 kg CO 2 e for every kilogram of beef and 1603 kg CO 2 e for every kilogram of shrimp produced on lands formerly occupied by mangroves. A typical steak and shrimp cocktail dinner would burden the atmosphere with 816 kg CO 2 e. This is approximately the same quantity of GHGs produced by driving a fuel-efficient automobile from Los Angeles to New York City. Failure to include deforestation in life-cycle assessments greatly underestimates the GHG emissions from food production.
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‘Blue carbon’ ecosystems—seagrasses, tidal marshes, and mangroves—serve as dense carbon sinks important for reducing atmospheric greenhouse gas concentrations, yet only recently have stock estimates emerged. We sampled 96 blue carbon ecosystems across the Victorian coastline (southeast Australia) to quantify total sediment stocks, variability across spatial scales, and estimate emissions associated with historical ecosystem loss. Mean sediment organic carbon (Corg) stock (±SE) to a depth of 30 cm was not significantly different between tidal marshes (87.1 ± 4.90 Mg Corg ha⁻¹) and mangroves (65.6 ± 4.17 Mg Corg ha⁻¹), but was significantly lower in seagrasses (24.3 ± 1.82 Mg Corg ha⁻¹). Location (defined as an individual meadow, marsh, or forest) had a stronger relationship with Corg stock than catchment region, suggesting local-scale conditions drive variability of stocks more than regional-scale processes. We estimate over 2.90 million ± 199,000 Mg Corg in the top 30 cm of blue carbon sediments in Victoria (53% in tidal marshes, 36% in seagrasses, and 11% in mangroves) and sequestration rates of 22,700 ± 5510 Mg Corg year⁻¹ (valued at over $AUD1 million ± 245,000 year⁻¹ based on the average price of $AUD12.14 Mg CO2 eq⁻¹ at Australian Emissions Reduction Fund auctions). We estimate ecosystem loss since European settlement may equate to emissions as high as 4.83 million ± 358,000 Mg CO2 equivalents (assuming 90% remineralization of stocks), 98% of which was associated with tidal marsh loss, and what would have been sequestering 9360 ± 2500 Mg Corg year⁻¹. This study is among the first to present a comprehensive comparison of sediment stocks across and within coastal blue carbon ecosystems. We estimate substantial and valuable carbon stocks associated with these ecosystems that have suffered considerable losses in the past and need protection into the future to maintain their role as carbon sinks.
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This study records and documents the most severe and notable instance ever reported of sudden and widespread dieback of mangrove vegetation. Between late 2015 and early 2016, extensive areas of mangrove tidal wetland vegetation died back along 1000 km of the shoreline of Australia’s remote Gulf of Carpentaria. The cause is not fully explained, but the timing was coincident with an extreme weather event; notably one of low precipitation lacking storm winds. The dieback was severe and widespread, impacting more than 7400 ha or 6% of mangrove vegetation in the affected area from Roper River estuary in the Northern Territory, east to Karumba in Queensland. At the time, there was an unusually lengthy period of severe drought conditions, unprecedented high temperatures and a temporary drop in sea level. Although consequential moisture stress appears to have contributed to the cause, this occurrence was further coincidental with heat-stressed coral bleaching. This article describes the effect and diagnostic features of this severe dieback event in the Gulf, and considers potential causal factors.
Although the Paris Agreement's goals (1) are aligned with science (2) and can, in principle, be technically and economically achieved (3), alarming inconsistencies remain between science-based targets and national commitments. Despite progress during the 2016 Marrakech climate negotiations, long-term goals can be trumped by political short-termism. Following the Agreement, which became international law earlier than expected, several countries published mid-century decarbonization strategies, with more due soon. Model-based decarbonization assessments (4) and scenarios often struggle to capture transformative change and the dynamics associated with it: disruption, innovation, and nonlinear change in human behavior. For example, in just 2 years, China's coal use swung from 3.7% growth in 2013 to a decline of 3.7% in 2015 (5). To harness these dynamics and to calibrate for short-term realpolitik, we propose framing the decarbonization challenge in terms of a global decadal roadmap based on a simple heuristic—a “carbon law”—of halving gross anthropogenic carbon-dioxide (CO2) emissions every decade. Complemented by immediately instigated, scalable carbon removal and efforts to ramp down land-use CO2 emissions, this can lead to net-zero emissions around mid-century, a path necessary to limit warming to well below 2°C.
Forest-based climate mitigation may occur through conserving and enhancing the carbon sink and through reducing greenhouse gas emissions from deforestation. Yet the inclusion of forests in international climate agreements has been complex, often considered a secondary mitigation option. In the context of the Paris Climate Agreement, countries submitted their (Intended) Nationally Determined Contributions ((I)NDCs), including climate mitigation targets. Assuming full implementation of (I)NDCs, we show that land use, and forests in particular, emerge as a key component of the Paris Agreement: turning globally from a net anthropogenic source during 1990–2010 (1.3 ± 1.1 GtCO2e yr⁻¹) to a net sink of carbon by 2030 (up to −1.1 ± 0.5 GtCO2e yr⁻¹), and providing a quarter of emission reductions planned by countries. Realizing and tracking this mitigation potential requires more transparency in countries’ pledges and enhanced science-policy cooperation to increase confidence in numbers, including reconciling the ≈3 GtCO2e yr⁻¹ difference in estimates between country reports and scientific studies.