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Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery

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Carbon emissions from tropical land‐use change are a major uncertainty in the global carbon cycle. In African woodlands, small‐scale farming and the need for fuel are thought to be reducing vegetation carbon stocks, but quantification of these processes is hindered by the limitations of optical remote sensing and a lack of ground data. Here, we present a method for mapping vegetation carbon stocks and their changes over a 3‐year period in a > 1000 km2 region in central Mozambique at 0.06 ha resolution. L‐band synthetic aperture radar imagery and an inventory of 96 plots are combined using regression and bootstrapping to generate biomass maps with known uncertainties. The resultant maps have sufficient accuracy to be capable of detecting changes in forest carbon stocks of as little as 12 MgC ha−1 over 3 years with 95% confidence. This allows characterization of biomass loss from deforestation and forest degradation at a new level of detail. Total aboveground biomass in the study area was reduced by 6.9 ± 4.6% over 3 years: from 2.13 ± 0.12 TgC in 2007 to 1.98 ± 0.11 TgC in 2010, a loss of 0.15 ± 0.10 TgC. Degradation probably contributed 67% (96.9 ± 91.0 GgC) of the net loss of biomass, but is associated with high uncertainty. The detailed mapping of carbon stock changes quantifies the nature of small‐scale farming. New clearances were on average small (median 0.2 ha) and were often additions to already cleared land. Deforestation events reduced biomass from 33.5 to 11.9 MgC ha−1 on average. Contrary to expectations, we did not find evidence that clearances were targeted towards areas of high biomass. Our method is scalable and suitable for monitoring land cover change and vegetation carbon stocks in woodland ecosystems, and can support policy approaches towards reducing emissions from deforestation and degradation (REDD).
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Quantifying small-scale deforestation and forest
degradation in African woodlands using radar imagery
CASEY M. RYAN*, TIMOTHY HILL*,EMILYWOOLLEN*,CLAIREGHEE*,EDWARD
MITCHARD*, GEMMA CASSELLS*, JOHN GRACE*, IAIN H. WOODHOUSE* and MATHEW
WILLIAMS*
*School of Geosciences, University of Edinburgh, Edinburgh EH9 3JN, UK, The National Centre for Earth Observation, Natural
Environment Research Council, UK
Abstract
Carbon emissions from tropical land-use change are a major uncertainty in the global carbon cycle. In African wood-
lands, small-scale farming and the need for fuel are thought to be reducing vegetation carbon stocks, but quantifica-
tion of these processes is hindered by the limitations of optical remote sensing and a lack of ground data. Here, we
present a method for mapping vegetation carbon stocks and their changes over a 3-year period in a >1000 km
2
region in central Mozambique at 0.06 ha resolution. L-band synthetic aperture radar imagery and an inventory of 96
plots are combined using regression and bootstrapping to generate biomass maps with known uncertainties. The
resultant maps have sufficient accuracy to be capable of detecting changes in forest carbon stocks of as little as
12 MgC ha
1
over 3 years with 95% confidence. This allows characterization of biomass loss from deforestation and
forest degradation at a new level of detail. Total aboveground biomass in the study area was reduced by 6.9 ±4.6%
over 3 years: from 2.13 ±0.12 TgC in 2007 to 1.98 ±0.11 TgC in 2010, a loss of 0.15 ±0.10 TgC. Degradation probably
contributed 67% (96.9 ±91.0 GgC) of the net loss of biomass, but is associated with high uncertainty. The detailed
mapping of carbon stock changes quantifies the nature of small-scale farming. New clearances were on average small
(median 0.2 ha) and were often additions to already cleared land. Deforestation events reduced biomass from 33.5 to
11.9 MgC ha
1
on average
.
Contrary to expectations, we did not find evidence that clearances were targeted towards
areas of high biomass. Our method is scalable and suitable for monitoring land cover change and vegetation carbon
stocks in woodland ecosystems, and can support policy approaches towards reducing emissions from deforestation
and degradation (REDD).
Keywords: agriculture, ALOS PALSAR, backscatter, carbon mapping, carbon stocks, emissions, land-use change, machamba,
radar
Received 5 May 2011; revised version received 19 August 2011 and accepted 26 August 2011
Introduction
Deforestation and other land-use change are major
components of the anthropogenic carbon (C) cycle,
transferring 0.92.2 PgC year
1
from the biota to the
atmosphere (Houghton, 2010). This number is highly
uncertain (Denman et al., 2007; Ramankutty et al., 2007;
Van Der Werf et al., 2009; Houghton, 2010) and esti-
mates often exclude many of the processes leading to
degradation of forest land (Houghton, 2010). Deforesta-
tion is primarily the result of the clearing of land for
agriculture (Geist & Lambin, 2002), both for the large-
scale production of global commodities (Defries et al.,
2010), and, particularly in Africa, for small-scale pro-
duction of food and cash crops (Burgess et al., 2002;
Fisher, 2010). In Africa, land-use change emissions are
thought to be in region of 0.3 ±0.2 PgC year
1
(Houghton & Hackler, 2006; Williams et al., 2007; Ciais
et al., 2011), but the data underlying these estimates
come from extrapolation of outdated, unreliable and
inconsistent national estimates (Grainger, 2008; Kinder-
mann et al., 2008; FAO, 2010).
Woodlands, characterized by an open tree canopy
and a continuous grass layer, cover 36% of vegetated
Africa (Mayaux et al., 2004), and as such represent a
low density, but large, stock of vegetation C (Dewees
et al., 2010). African countries dominated by woodlands
have high population densities, high population
growth rates and high, but uncertain, deforestation
rates (FAO, 2010). As a result, nations dominated by
woodlands contribute around half of Africa’s deforesta-
tion emissions [based on data from Mayaux et al. (2004)
and FAO (2010)]. Woodlands are particularly difficult
Present address: Claire Ghee, The James Hutton Institute,
Invergowrie, Dundee DD2 5DA, UK.
Correspondence: Casey M. Ryan, tel. + 44 131 650 7722,
fax + 44 131 662 0478, e-mail: casey.ryan@ed.ac.uk
©2011 Blackwell Publishing Ltd 1
Global Change Biology (2011), doi: 10.1111/j.1365-2486.2011.02551.x
to monitor with optical/infrared remote sensing due to
inter- and intra-annual changes in tree leaf display
(Grainger, 1999) and the transient presence of a sub-
stantial grass layer which complicates the interpretation
of such satellite imagery (Archibald & Scholes, 2007).
Consequently, degradation emissions from woodlands
are highly uncertain, but are thought to be substantial
(Ahrends et al., 2010).
The standard approach to estimating C emissions
from deforestation is based on estimates of changes in
forest area, aided by increasingly robust estimates of
forest area change (Etter et al., 2006; Achard et al., 2007;
Hansen et al., 2008; Miettinen et al., 2011). Degradation
emission estimates, however, require repeat in situ mea-
surements of carbon density (GOFC-GOLD, 2010)
which are scarce (Ahrends et al., 2010), because they
require a large number of plots and strata to estimate
accurately any changes in mean C density. Recent air-
borne approaches (Asner et al., 2010) have not been
widely deployed to date. As a result, changes in forest
C density resulting from fire (Ryan & Williams, 2011)
or the selective extraction of biomass for fuel or timber
(Nepstad et al., 1999), are rarely assessed and have not
been fully included in emissions estimates (Houghton
& Hackler, 2006; Denman et al., 2007).
Even when combined, the methods outlined above
yield large uncertainties on estimates of changes in veg-
etation C stock, let alone on emissions (Houghton et al.,
2009). Uncertainty stems from three main issues: (i) The
use of arbitrary forest/nonforest thresholds whereas C
stocks are a continuous variable. Such thresholds are a
particular problem in woodlands where distinct edges
are rare, (ii) a lack of in situ measurements of C density,
and the potential artefacts associated with differential
land use on research plots and (iii) land that is defor-
ested or degraded may not be representative of the for-
est type in which it is found (Loarie et al., 2009;
Houghton, 2010). That is, farmers are likely to target
areas for deforestation based on careful consideration
of agricultural potential. Therefore, land with higher
than average soil fertility and/or water availability
might be expected to have a greater probability of con-
version to agriculture. The same areas might also be
expected to have higher than average soil and vegeta-
tion C stocks due to the increased productivity.
Small-scale, often shifting, cultivation exemplifies the
problem of estimating changes in C stocks in forests
and woodlands, producing a mosaic landscape that is
frequently misclassified (Mertz, 2009) and is rarely
well-represented by discrete land cover classes. The
landscape C dynamics (Williams et al., 2008) cannot be
adequately described by changes between categories
such as degraded land, forest or agriculture (Schmidt-
Vogt et al., 2009). Furthermore, the scale at which
small-scale farmers clear forest is constrained by a lack
of mechanical power and transport. This physical limit
results in small farms (scale ~ha) that may not be
recorded on land-use maps or detected with accuracy
by coarse-scale (~km
2
) satellite-based land cover change
analyses.
Thus, there is a clear need for direct measurement at
sub-hectare resolution of changes in C stocks due to
land cover change (LCC, Houghton et al., 2009). Our
first aim is to demonstrate a method for such measure-
ments in African woodlands at a spatial resolution
sufficient to characterize small-scale farming and deg-
radation. This is achieved by the use of 25 m resolution
L-band radar imagery to map C stocks and their
changes through the years 20072010 in an area of cen-
tral Mozambique. Radar imagery has several advanta-
ges for this purpose. First, cloud and atmospheric
effects are largely irrelevant allowing observations at
cloudy sites. Second, L-band (23 cm wavelength) nor-
malized radar cross section (hereafter referred to as
backscatter) has been shown to have a reasonably direct
relationship to woody biomass up to a saturation at
around 50 MgC ha
1
(Le Toan et al., 1992; Rignot et al.,
1994; Magnusson et al., 2007; Karjalainen et al., 2009).
These advantages mean that spaceborne radar imagery
is increasingly used in support of land-use mapping in
the tropics (Van Der Sanden & Hoekman, 1999; Hoek-
man et al., 2010; Rahman & Sumantyo, 2010). Previous
work has shown that L-band radar backscatter is well
correlated to biomass across several African landscapes
(Mitchard et al., 2009).
The resultant C maps allow us to ask several ques-
tions about the nature of the processes of deforestation
and degradation:
How much carbon is lost to deforestation compared
to degradation?
• Are areas of high carbon density preferentially tar-
geted for LCC?
What is the carbon density of changed areas before
and after LCC?
• What size are LCC events, and how are they clus-
tered in space?
Methods
Site description and land-use history
Our study area covers 1160 km
2
in the Gorongosa and Nham-
atanda districts of Sofala province in central Mozambique. It is
primarily dominated by miombo woodland, the most wide-
spread vegetation type in Southern Africa (Frost, 1996). It has
a seasonal wet-dry climate with ~900 mm rain per year, with
82% falling in the 5 months between November and March
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
2C. M. RYAN et al.
(Ryan et al., 2011). The vegetation consists of miombo wood-
land on the well-drained flanks of the Rift valley, grading to
more scattered savanna on the poorly drained valley floor
(Tinley, 1977, 1982). The terrain is gently undulating in most
of the study area, but in the west there are steep slopes associ-
ated with the Pungue and Vunduzi rivers (Fig. S1). Ninety
seven percent of the study area has a slope of <10°(based
on 90 m resolution elevation data from the Shuttle Radar
Topography Mission (SRTM, Farr et al., 2007, http://srtm.
usgs.gov).
The area is undergoing rapid land-use change: in 1992, with
the end of the Mozambican civil war, there were major popu-
lation movements in the area, including resettlement of semi-
abandoned rural areas such as Nhambita (Fig. 1). The only
surfaced road in the area, the EN1, was rebuilt, along with a
bridge over the River Pungue (rehabilitation of both took place
from 1999 to 2002), connecting the study area and Gorongosa
town to the Beira corridor, the major area of economic activity
in central Mozambique. Gorongosa town has grown rapidly
since 1992 (INE, 2010) with the district population increasing
by 50% from 1997 to 2007 to 117, 129. Currently, 69% of the
population is aged below 18 years (INE, 2010). Forest or
woodland loss in the area is primarily the result of: (i) clear-
ance for small-scale agriculture, notably for maize production
on farms of between ~1 and 2 ha and (ii) charcoal production,
involving the selective removal of medium size stems from an
area of ~0.2 ha surrounding temporary kilns. Charcoal is sold
along the EN1 highway for transport south to Inchope and Be-
ira, but there is also some demand for charcoal and fuel wood
in Gorongosa town. In addition, fire is extensively used to
manage the landscape and wild fires are common. Frequent
and intense fires can reduce biomass in these woodlands
(Furley et al., 2008; Ryan & Williams, 2011).
Carbon stock estimation
The basis of our approach is to produce a 3-year time series of
carbon maps of the area. The maps are produced using a com-
bination of satellite radar images and in situ carbon stock
inventories. We deal here only with carbon in the above-
ground woody vegetation pool (AGB, MgC ha
1
), although
more information on belowground biomass at this site can be
found in Ryan et al. (2011).
Radar imagery. Synthetic Aperture Radar (SAR) remote
sensing can provide information on vegetation biomass (Le
Toan et al., 1992), as well as many other characteristics of
the land surface (Woodhouse, 2006a). SAR utilizes an active
sensor aboard a satellite or plane to send out a beam of
energy and measures the intensity of the echoes that return
to the sensor. The parameter of interest is the backscatter
(technically the normalized radar cross section, a unitless
variable (m
2
/m
2
)). Backscatter can loosely be thought of as
the ratio of the power that comes back from a patch of
ground to the power sent to that patch of ground (based on
the arbitrary assumption that the ground is an isotropic scat-
terer). The energy that returns to the sensor varies with the
proportion of the incident energy that is scattered by the
land surface, and the directionality of that scattering. When
energy of an appropriate wavelength is used, it interacts
with the structural elements of the tree canopy (branches
Goron gosa
Mucombeze
Remo te Pa rk
Mbula wa
Nhambita
Sanc tuar y
Vunduzi
2007
0 8 16 24 324
Kilomete rs
Gorongosa
Mucombeze
Remo te Pa rk
Mbulawa
Nhambita
Sanc tuar y
Vunduzi
Change
Goron gosa
Mucombeze
Remote Park
Mbulawa
Nhambita
Sanc tuar y
Vunduzi
2010
AGBtC/ha
60
30
0
MgC/ha
N
(b)(a) (c)
Fig. 1 Estimated carbon stocks in the study area in (a) 2007 and (b) 2010. Each image is derived from the mean of three ALOS PALSAR
scenes from that year’s dry season. (c) shows the areas detected as undergoing abrupt change (red) with a probability greater than 95%
and a reduction in biomass to less than 50% of original biomass. Areas that did not undergo change are indicated in grey, and white
indicates areas with <10 tC ha
-1
. The maps are annotated with the local road network and the ‘sub-areas‘ used in Fig. 7. (GIS data cour-
tesy of ARA-CENTRO).
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
QUANTIFYING FOREST LOSS IN AFRICAN WOODLANDS 3
and trunks): typically, more woody biomass results in more
diffuse scattering and thus more energy being returned to
the sensor and a higher backscatter value is recorded. Back-
scatter is also affected by soil roughness (as it changes the
directionality of the scattering) and moisture (as it changes
the total proportion of scattering), as well as other environ-
mental factors. In this study L-band (~23 cm wavelength)
SAR imagery was used, as it is less affected by soil condi-
tions than shorter wavelengths, and is known to be able to
detect deforestation and to be sensitive to forest biomass
due to its ability to penetrate the forest canopy (Almeida-
Filho et al., 2005; Fransson et al., 2007; Karjalainen et al.,
2009; Mitchard et al., 2009). Ten images were acquired that
covered the study site spanning the dates 23 June 2007 to 1
October 2010 (Table 1). All images were for the dry season
and two or three were available for each year.
The technical details of the imagery and processing now fol-
low. Images were obtained from the phased array L-band syn-
thetic aperture radar sensor aboard the advanced land
observing satellite (ALOS PALSAR) in the Fine Beam Dual
mode (Shimada et al., 2010). All images were acquired on the
ascending pass, have an incidence angle centred on 34.3°, and
were provided at a pixel size of 12.5 m with 4 equivalent
looks, but then averaged to 25 m for 16 equivalent looks. Only
Horizontal-send Vertical-receive data are used here, as previ-
ous studies have shown this polarization to be more sensitive
to biomass than Horizontal-send Horizontal-receive (Le Toan
et al., 1992; Mitchard et al., 2009).
The images were processed using the Alaska Satellite Faci-
lity’s MapReady software v2.3.6 (ASF, 2010), combined with
~90 m elevation data from the SRTM. Each image was
converted from digital numbers to backscatter using the cali-
bration coefficients of Shimada et al. (2009) and a geometric
and radiometric terrain correction was applied. Visual com-
parison with optical imagery (IKONOS and Landsat) showed
that the resulting images were well geolocated (12 pixel
error, i.e. 2550 m) compared to prominent landscape features
such as roads, bridges and an airstrip. In particular, the
images were very closely geolocated to each other: no geoloca-
tion differences were visible between the ten images. Com-
monly, backscatter values are used on the logarithmic dB
scale, but here untransformed values of backscatter are used,
as the backscatter-biomass trend is usually linear in untrans-
formed space, but nonlinear in dB space.
Ground data. For each radar scene, backscatter values were
converted into carbon density using a regression equation
based on inventories of 96 plots in the forest, woodland and
cropland in the south of the study area. These data come from
a range of inventories conducted during 20062009, and
include plots of sizes ranging from 0.1 to 2.2 ha (mean ±SD
0.63 ±0.33 ha, Fig. 2). The inventories include all standing
trees >5 cm diameter at breast height (DBH). Many of the
woodland inventory data are described in Ryan et al. (2011),
as is the site-specific allometric equation for converting stem
diameter to carbon mass for each stem. Briefly, the data set
consist of: fifteen 1 ha square permanent sample plots in the
woodland and savanna (Ryan et al., 2011), eight 0.28 ha plots
used in the fire experiments of Ryan & Williams (2011), thirty
0.57 ha plots in a transect from open savanna to woodland
(Woollen et al. in prep; these had a nested sampling design,
see supplementary information), five 0.5 ha plots in relatively
remote woodland, 37 plots of 0.12.2 ha (mean 0.6 ha) on
cropland (Ghee, 2009) and one dense forest plot consisting of
three 0.04 ha subplots. Details of the plot data are given in the
supplementary information. Every plot is thought to have
avoided abrupt LCC (i.e. deforestation) during the study
period, assessed by repeat ground visits or satellite imagery;
however, we cannot rule out gradual changes due to
aggradation or degradation.
Table 1 Regression statistics for the relationship between radar backscatter (unitless) and aboveground woody biomass (MgC
ha
1
)
Date
RMA slope
(MgC ha
1
)
RMA intercept
(MgC ha
1
) OLS
Calibration
error
Validation
error
Validation
bias
Slope
95%
confidence
interval Intercept
95%
confidence
interval adj R
2
RMSE
(MgC ha
1
)
RMSE
(MgC ha
1
)
Bias
(MgC ha
1
)
23-Jun-07 1562 13151810 20.9 26.6 to 15.1 0.39 10.4 10.9 1.7
08-Aug-07 1268 10821453 13.7 18.0 to 9.5 0.49 9.3 9.9 1.5
23-Sep-07 1329 11531505 11.0 14.5 to 7.5 0.58 8.4 8.7 1.4
10-May-08 1647 14171877 22.6 27.9 to 17.3 0.53 8.9 9.3 1.5
25-Jun-08 1632 13851880 19.9 25.2 to 14.5 0.44 9.8 10.4 1.6
28-Jun-09 1601 13631840 17.5 22.3 to 12.6 0.47 9.6 10.1 1.6
28-Sep-09 1303 11151490 11.2 15.0 to 7.4 0.50 9.2 9.6 1.5
16-May-10 1801 15182083 24.7 31.0 to 18.4 0.41 10.3 10.7 1.7
01-Jul-10 1679 14311926 25.6 31.6 to 19.6 0.48 9.5 9.9 1.6
01-Oct-10 1348 11691526 13.3 17.0 to 9.5 0.58 8.4 8.7 1.4
Mean 1517 12951739 18.0 22.9 to 13.1 0.49 9.4 9.8 1.6
RMA indicates reduced major axis regression; OLS, ordinary least squares regression; RMSE, root mean squared error.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
4C. M. RYAN et al.
Regression and error propagation. To convert backscatter
images into aboveground biomass (AGB in units of
MgC ha
1
), for each timeslice we regress plot mean backscat-
ter against plot AGB, assuming that the plot AGB is consistent
over the observational period. We use reduced major axis
(RMA) regression (Mitchard, 2011) implemented in MATLAB by
Trujillo-Ortiz & Hernandez-Walls (2010). RMA regression
minimizes the errors on both axes (rather than just on the
y-axis as in normal regression), which is appropriate because
there are errors in both data sets and the observer controls nei-
ther (Sokal & Rohlf, 1995).
To estimate error on the predictions from the regression, a
5000 92-fold cross-validation procedure was employed. Half
the dataset was withheld and used to estimate the root mean
squared validation error (RMSE), and bias (B) of predictions
from each regression. These statistics are defined as (Hui &
Jackson, 2007):
B¼PiYi
^
Yi
n
RMSE ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
PiYi
^
Yi

2
n2
v
u
u
t;
where Y
i
is a validation data point (not used in the regression)
and
^
Yiis the prediction from the regression. Band RMSE were
calculated for all 10 time slices 5000 times, each time with a
different random split of the data, and the mean values of B
and RMSE for the 5000 validations are reported. For indicative
purposes the adjusted R
2
of an ordinary least squares regres-
sion is also shown in Table 1.
The temporal covariance of the bias was quantified by look-
ing at the correlation of the bias between time steps for each of
the 5000 validations, i.e. by asking if the regression is biased
high in year x, is it likely to be biased high in subsequent years
x+n?
Regression errors were propagated to the carbon maps with
a bootstrap procedure. Bootstrapping was used as it implicitly
includes any spatial or temporal covariance of the uncertainty
resulting from the AGB-backscatter regressions. For each set
of 10 carbon maps, 30 000 realizations were created each using
a different regression based on randomly selected regression
data (resampled with replacement). Derived quantities such
as carbon stock change, rates of loss, and the deforestation
and degradation totals, were also calculated for each of the
30 000 bootstraps, allowing the uncertainties introduced by
the regression to be estimated. All values are reported as the
mean ±the standard deviation of the 30 000 bootstraps. The
number of bootstraps was chosen after initial analysis showed
that repeat calculation with different random number seeds
using 10 000 bootstraps yielded identical results to three
significant figures.
Change detection algorithm
One of the aims of this article is to estimate parameters of pix-
els before and after LCC. This necessarily involves defining a
threshold of change. Here we focus on detecting abrupt losses
of AGB and define LCC of interest as being in pixels where:
(i) AGB is >20 MgC ha
1
at the start of the study, thus
increasing the likelihood that only forested pixels are exam-
ined, (ii) the AGB of the pixel after the change is reduced to a
minimum percentage (φ, default 50%) of its initial AGB, a
threshold which should exclude most natural changes in for-
est structure, and (iii) the probability that the change in AGB
occurred by chance (given the noise in the data) is <(1a)
(default a=0.95). The use of a ratio to define change is
appropriate with SAR imagery in power space, as ratioing
SAR images is generally preferred to differencing because of
its noise characteristics (Rignot & Van Zyl, 1993; Radke et al.,
2005).
The time series C(t), where Cis the AGB of a pixel over the
t=1,,10 images, provides a rich data source with which to
detect changes, and the 10 time slices allow more confidence
in using noisy data. For change detection, a simple iterative
algorithm locates the time-point of change, τ, at which the
mean of the preceding values of C(t) is most different to the
mean of the remaining part of the time series. The mean AGB
before (C1) and after (C2)τare then estimated. We use a
Monte Carlo procedure to estimate the probability, P,of
obtaining the change in AGB (C1C2) by chance, based on
sampling from the time series and including the RMSE valida-
tion errors from the regression (Table 1). A mathematical
description is given in the supplementary information.
Once changed pixels have been identified, each LCC event
(defined as adjacent changed pixels that have identical τ) was
automatically converted to a polygon in ARCGIS (ESRI, CA,
USA) and the area of each polygon calculated.
Change detection validation
Two tests were used to produce accuracy statistics for the
change detection. To assess successful LCC detection we
delineated 92 agricultural fields formed by clearing woodland
0 10 20 30 40 50 60
0
2
4
6
8
10
12
14
16
Above
g
round biomass, M
g
C ha–1
Number of plots
Fig. 2 Histogram of the aboveground biomass (AGB) of the
plots used to estimate and validate the AGB-backscatter rela-
tionship. Aboveground woody carbon stock is estimated from
diameter at breast height (DBH) measurements using a site-spe-
cific allometric equation. n=96.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
QUANTIFYING FOREST LOSS IN AFRICAN WOODLANDS 5
between March 2007 and June 2009. These conversions were
assessed by visual interpretation of multispectral SPOT 4
(20 m resolution) imagery from 7 March 2007 and IKONOS
(1 m) imagery from 24 June 2009 (an example is shown in
Fig. 6). The edges of the new farms were converted by hand to
polygons using ARCGIS. These areas of known LCC had a
mean area of 1.7 ±1.5 ha and were well distributed across the
study area. Although the AGB of these new farms is
unknown, visual inspection suggests that very few trees
remain and that they have crossed the change threshold out-
lined above. The miss rate is defined as the number of pixels
on these farms that are not detected as changed.
To assess false positives, an area of the Gorongosa National
Park, termed ‘The Sanctuary’ (Fig. 1) that is extremely
unlikely to have undergone human-induced LCC was analy-
sed. The Sanctuary is an electric-fenced area of 5761 ha
designed to retain animals imported to the Park (not including
elephants). Note that this definition of false positive is very
broad and includes both natural changes to the woodland as
well as random errors in detection as ‘false positives’.
The sensitivity of the hit rate and false positive rate to the
parameters used in the change detection algorithm was
assessed: φ,the fraction of AGB remaining on a changed
pixel was varied from 80% to 20%, and a, the threshold P-
value above which a LCC event is considered significant,
from 0.8 to 1.
Assessing preferential selection of high biomass areas
To test whether land with high AGB is preferentially selected
for LCC, compared with the null hypothesis that LCC is ran-
dom with respect to AGB, we compare the observed results to
a pseudodata set for which the null hypothesis is true. The
pseudodata time series of AGB for each pixel, denoted C*(t),is
constructed as follows: for each pixel, a time series is con-
structed with a constant mean equal to the observed AGB in
2007. Then a random 4% of pixels are pseudodeforested at a
time point randomly selected between t=2 and t=9. At this
time point, an abrupt change is imposed and the AGB reduced
to a mean of 10 MgC ha
1
. Noise was added to the time series
by drawing each value from a normal distribution with means
as described above and standard deviations N(t), the RMSE
validation errors from the regressions. The change detection
algorithm was run as described above to identify pixels that
were pseudochanged, estimating the parameters C1* and C2*,
the pseudo-equivalents of C1 and C2. The resulting distribu-
tions of C1* and C1 are compared using a two sample Kol-
mogorov-Smirnov test to test if the distribution of C1 is larger
than C1*.
Biomass loss due to deforestation and degradation
To compare the contributions of deforestation and degrada-
tion to the total carbon loss from the study area, the change
(ΔC) between the mean AGB of the three images in 2007
(C
2007
) and the mean AGB of the three images in 2010 (C
2010
)
was examined. The image is classified into four categories that
follow the conventional definition of de/reforestation and de/
aggradation based on a binary forest/nonforest pixel classifi-
cation in 2007 and 2010. The forest/nonforest classification is
made using a default AGB threshold of 15 MgC ha
1
. How-
ever, the effects of varying the threshold from 5 to 20 MgC
ha
1
were evaluated.
The deforestation AGB loss (Δ
D
) is defined as the net sum of
ΔC for pixels that shift from the forest to nonforest classes.
Reforestation gain (Δ
R
) is the net sum of ΔC for pixels moving
from nonforest to forest. Forest de/aggradation (Δ
GF
) is the
net sum of ΔC for forest-pixels remaining forest, and nonforest
de/aggradation (Δ
GN
) is the net sum of ΔC for nonforest pixels
remaining nonforest. Thus:
C2007 C2010 ¼DC¼DDþDRþDGF þDGN
and net deforestation is:
DD,net ¼DDþDR
Thus for all terms describing a change in biomass stocks,
positive numbers indicate loss of biomass and negative values
denote gains.
Results
Ground data and regression
The 96 plots ranged in AGB from 0 to 56 MgC ha
1
(mean 15 ±12 MgC ha
1
; Fig. 2; ±indicates one stan-
dard deviation throughout). The ordinary least squares
regression of backscatter against AGB gave values of
R
2
from 0.40 to 0.58 (Table 1). For the ten images,
regression slopes ranged from 1272 to 1803 MgC ha
1
0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055
0
10
20
30
40
50
60
Aboveground biomass (MgC ha–1)
−23.01 −20.00 −18.24 −16.99 −16.02 −15.23 −14.56 −13.98 −13.47 −13.01 −12.60
Radar backscatter, (dB)
σHV
0
23−Jun−2007
28−Jun−2009
01−Oct−2010
Radar backscatter, (m2/m2)
σHV
0
Fig. 3 Regression of radar backscatter from ALOS PALSAR
and aboveground biomass. Ten regressions were performed,
one for each image in Table 1, but for clarity only the data and
regression lines from the first (June-2007, open circle, dashed
line), sixth (June-2009, closed circles, light line) and last (Octo-
ber-2010, crosses, heavy line) image are shown. Each line is fit to
minimize the errors on both axes (RMA regression).
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
6C. M. RYAN et al.
(Table 1; Fig. 3) and this translated into moderate vari-
ation in estimated carbon stocks through the study per-
iod. For instance, backscatter values for a pixel over a
patch of protected forest ranged from 89% to 107% of
the first image, which translated to a range of 47
61 MgC ha
1
(Fig. 4).
The validation procedure estimated RMSE validation
errors of 8.710.9 MgC ha
1
for the different time slices
(mean error 9.8 ±0.7 MgC ha
1
), and mean absolute
bias of 1.6 ±0.1 MgC ha
1
(Table 1). The worst-case
scenario for change detection is that these biases are
random between each scene, but the covariance of these
biases through time was high (Table S1), with, for
example, r>0.86 between the bias in the three scenes
from 2007, and r>0.44 for bias between years.
Carbon stocks and changes
The carbon stocks in the study area exhibit an east-west
gradient related to the topography, with AGB
~60 MgC ha
1
in the undisturbed areas to the west of
the Vunduzi river falling to ~30 MgC ha
1
in the centre
of the study area (Fig. 1). East, towards the floor of the
Rift valley, substantial biomass is restricted to the river
lines and high points. Imposed on this topographic
pattern, the effects of human disturbance are obvious,
with almost no large blocks of woodland remaining to
the south of the River Pungue and along the west side
of the highway. However LCC is less apparent to the
east of the highway, an area that is part of the buffer
zone of the Gorongosa National Park. The town of
Gorongosa marks the epicentre of a zone of reduced
biomass.
The study area contained AGB of 2.13 ±0.12 TgC
in 2007 and 1.98 ±0.11 TgC in 2010, a loss of 0.15
±0.10 TgC, or 6.9 ±4.6% of the 2007 AGB over 3 years.
Pixels that lost >9tCha
1
contributed half the total loss
in AGB, with the remaining loss being in pixels that lost
<9 tC.
Spatial patterns of carbon stock change
Looking at the spatial patterns of change in C stocks
(Fig. 5), there were several areas of increasing AGB
mainly in the Park and Sanctuary (A; letters refer to
points marked on Fig. 5; names to the sub-areas delin-
eated with black lines in Fig. 1) as well as decreases (B).
Losses were observed all along the highway, but partic-
ularly to the west of the road; in comparison, to the east
of the road in the buffer zone of the Park there are
fewer areas of biomass loss (C). New farms were
opened in the area between the Vunduzi river and the
road, and a cluster of new farms can be seen in the inac-
cessible region to the west of the river (D). A new
power line built in the Park is visible along the bound-
ary of the Nhambita sub area (E). An area of private
land, in which charcoal production and agriculture are
not present (Eng. A. Serra, pers. comm. 2011) stands
out clearly from the surrounding decrease in AGB in
Mbulawa (F). In Nhambita a string of new farms along
a previously high AGB river line is visible (G).
The probability distribution functions (PDFs) of car-
bon stocks in the sub areas (Fig. 7) provide further
insight into the AGB stocks and changes. In the inhab-
ited areas (Gorongosa town, Nhambita, Mucombeze
and Mbulawa) the LCC is evident in the difference
between the 2007 and 2010 PDFs. In contrast, in the
Sanctuary, the 2010 PDF is shifted to higher biomass
values compared to 2007, indicating regrowth. The
Remote Park shows no change in AGB over the study
period. Vunduzi, an almost undisturbed, well wooded
area, is the only sub-area with a normal PDF. The bimo-
dal PDF of Gorongosa appears to consist of a defor-
ested PDF similar to Mucombeze combined with a
woodland PDF centred on 40 MgC ha
1
.
Deforestation and degradation
The loss of AGB across the study area over the 3 years
was 149 ±101 GgC. Using the default threshold of
forest being any pixel with AGB >15 MgC ha
1
, loss
01−Jan−2007 01−Jan−2008 01−Jan−2009 01−Jan−2010 01−Jan−2011
−10
0
10
20
30
40
50
60
70
Aboveground biomass, MgC ha–1
Fig. 4 Example time series for a single pixel over (a) an undis-
turbed riverine forest (solid line, filled circles) and (b) an area of
woodland converted to a farm (dashed line, open circles). The
lines indicate the parameters as estimated by the change detec-
tion algorithm. Over the protected forest no change was
detected (mean aboveground biomass (AGB) of 55.0 ±4.5 MgC
ha
1
). The new farm (illustrated in Fig. 6) was created between
June 2008 and June 2009. AGB before the change point was
42.7 ±1.8 and 5.3 ±5.3 MgC ha
1
afterwards. The probability
of this change being observed by chance is <0.001. The points
representing the forest pixel have been shifted forward by
10 days for clarity.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
QUANTIFYING FOREST LOSS IN AFRICAN WOODLANDS 7
of AGB due to deforestation was 92 ±11 GgC
(Table 2). This was offset by a reforestation gain in
AGB of 36 ±4.5 GgC, leading to a net deforestation
loss of 55 ±12 GgC. This can be compared to forest
degradation loss of 42 ±73 GgC and nonforest degra-
dation of 53 ±43 GgC, so that total degradation is
94 ±90 GgC. Thus most of the uncertainty in ΔC comes
from the degradation term.
Thus, the best guess of the percentage of net biomass
loss that can be attributed to degradation is 67% (med-
ian of the bootstraps). This result varied only slightly
with different thresholds of forest/nonforest: from 69%
with 10 MgC ha
1
as the threshold to 65% when using
20 MgC ha
1
as the threshold. This proportion of bio-
mass loss attributed to degradation is highly uncertain,
with an 80% confidence interval of 2180% (Fig. S2).
Fig. 5 Carbon stock change in the study area. The image shows the aboveground biomass (AGB) in 2010 as a percentage of AGB in
2007. Values greater than 100% indicate areas of biomass gain (blue) and below 100% areas of biomass loss (red). Features lettered A-F
are described in the main text. Areas with AGB <10 tC ha
1
are shown in white. The red box indicates the area shown in Fig 6. The red
line shows the EN1 highway. Rivers are marked in blue.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
8C. M. RYAN et al.
There is a 79% chance that degradation was >50% of
the net loss of AGB.
Characteristics of land cover change events
The change detection algorithm was able to detect per-
pixel abrupt changes of >12 MgC ha
1
, a threshold that
is a function of the noise associated with the measure-
ments of AGB, the number of observations and the
detection algorithm parameters aand φ. Using the
default parameters (a=0.95 and φ=50%) abrupt LCC
was detected in 2.61% of the study area.
In total, 3029 ha were detected as changed in 6761
events (mean size =0.45 ±0.80 ha, median =0.19 ha).
Events larger than the median contributed 88% of the
total changed area; events >0.5 ha, 70%; and events
>1 ha contributed 49% of the total changed area. The
mean event size (assumed to be false positives) in
the Sanctuary (0.14 ha) was smaller than the events in
the inhabited sub-areas (means 0.340.52 ha). In the
inhabited areas, event size was smallest in Mucombeze
(mean 0.34 ha), where forested land is very scarce (26%
of land was forested in 2007) and largest (mean 0.52 ha)
in Vunduzi where all land was forested in 2007. The
other inhabited sub-areas have intermediate event sizes
and forest cover.
The mean change in AGB when an event occurred
was for the area to lose 21.3 ±5.5 MgC ha
1
, being
reduced from C1 =33.5 ±9.8 to C2 =11.9 ±18.4
MgC ha
1
. The reduction in AGB ranged from 12.2
56.9 MgC ha
1
. With the exception of the Vunduzi sub-
area, the mean pre-change AGB of changed pixels (C1)
was never more than 0.7 MgC ha
1
higher than if LCC
was random with respect to AGB (C1*), although the
differences were significant to P<0.001 (Fig. 7). This
suggests that the hypothesis that high AGB pixels are
preferentially subject to LCC is not true to a substantial
extent. However, in the Vunduzi sub-area, C1
(53.6 MgC ha
1
) was 4.7 MgC ha
1
higher (P<0.001)
than C1* (48.9 MgC ha
1
), suggesting a modest prefer-
ence towards high AGB land, but the number of chan-
ged pixels is relatively small (n=345).
Events were significantly clustered (P<0.001)
according to the nearest neighbour distance. The
observed mean distance from one event to the nearest
was 87 m compared to 206 m if the events were evenly
spaced. Many cleared areas are the result of small
expansions to the frontiers of already cleared land
(example in Fig. 6).
Change detection validation and sensitivity
Using our default parameters, LCC was detected in
1646 of the 2611 validation pixels, a miss rate of 35%.
Generally, the missed pixels were at the fringes of the
events only in 10 of the 90 validation events was no
LCC detected implying that the misses will affect
the size of detected events more than the number.
The false positive rate was 0.016% in the Sanctuary,
equal to 0.005% year
1
. The sensitivity analysis
(Fig. 8) showed that it is possible to suppress the false
positive rate further, but only at the expense of an
increase to the miss rate. A zero false positive rate
can be achieved in the Sanctuary by reducing φto
20% (for a>0.95), but this gives a miss rate of 74%.
Conversely, a lower miss rate can be achieved, say
20%, with φ=60% and a=0.9, but this gives a false
positive rate of 0.07%.
Discussion
Can L-band radar accurately measure changes in
biomass?
Our method was able to detect a loss (6.9 ±4.6%) of
biomass from the landscape over a relatively short
observation period of 3 years. This change in biomass
(ΔC) is equivalent to a reduction in C density from 18.4
to 17.1 MgC ha
1
across the whole study area, a change
that would be difficult to detect without very accurate
stratification and intensive ground-based sampling.
The relative error on the standing stock estimates is 6%,
but this increases to 67% for ΔC, the change in stocks
(Table 2). Without the high covariance of the regression
Table 2 Change in aboveground biomass (AGB) stocks classified by forest/nonforest transitions over the period 20072010
Transition type Area (km
2
) AGB loss over 3 years (GgC)
Deforestation (forest ?nonforest) 75 92 ±11
55 ±12 (33%)Reforestation (nonforest ?forest) 48 36 ±4.5
Forest degradation (forest ?forest) 530 42 ±73
94 ±90 (67%)Nonforest degradation (nonforest ?nonforest) 507 53 ±43
All 1160 149 ±101
The threshold for forest is AGB>15 MgC ha
1
. Negative changes indicate a gain in biomass stocks.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
QUANTIFYING FOREST LOSS IN AFRICAN WOODLANDS 9
errors through time (Table S1) the error on the change
in stocks would have been higher.
When the net changes in biomass are split between
deforestation and degradation, it is clear that most of
the uncertainty in ΔC comes from degradation. It is
much easier to detect change in the small area (75 km
2
)
that was deforested and which lost 92 ±11 GgC, com-
pared to the very large (1037 km
2
) area that was
degraded, which probably lost a comparable amount of
AGB (94 ±90 GgC) (Table 2). The nature of the chal-
lenge is illustrated by a best guess assessment of the
change in C density for the forest-remaining-forest area
from 30.3 to 29.4 MgC ha
1
.
However, at a pixel scale, abrupt change (including
degradation) is detectable at 95% confidence as long as
it exceeds 12 MgC ha
1
. Per pixel change detection
rates were 65%, and in 89% of change events some
change was detected. These rates are comparable to
another application of L-band SAR to detect >2 ha clear
cuts in Sweden (hit rate =76% (Pantze et al., 2009)).
However it is clear that we are at the limit of the resolu-
tion of this technique the ALOS imagery detects new
Radar-derived change
detection
Optical/NIR imagery
(NIR:R:G)
08-Aug-07
23-Sep-07
10-May-08 28-Sep-09 01-Oct-10
28-Jun-09 01-Jul-10
25-Jun-08 16-May-10
Kilometers
0 0.5 1 2
Jan07
Jan08
Jan09
Jan10
Jan11
SPOT-4 (20m)IKONOS (1m)LANDSAT-5 (30m)
2007
2009
2010
Fig. 6 Detail of Fig. 5, showing the detected land cover change (LCC) events. The temporal evolution of forest clearance as detected by
the radar imagery is shown from 2007 to 2010 (left panels). The right hand panels show false-colour optical imagery of the same area
for comparison. The outlines of the change events up to July 2009 are overlaid on the middle right image.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
10 C. M. RYAN et al.
farms as having a bare centre and then a gradient of
biomass into the surrounding woodland, but on-the-
ground observations show that the farm/woodland
boundary is normally abrupt. Morphological detection
techniques have the potential to improve the very sim-
ple per-pixel change detection used here and to reduce
0 20 40 60 80
0
0.02
0.04
0.06
0.08
0.1
Mucombeze
0 20 40 60 80
0
0.01
0.02
0.03
0.04
0.05
Nhambita
0 20 40 60 80
0
0.01
0.02
0.03
0.04
Sanctuary
Frequency
0 20 40 60 80
0
0.02
0.04
0.06
0.08
0.1
Remote park
0 20 40 60 80
0
0.01
0.02
0.03
0.04
0.05
Mbulawa
0 20 40 60 80
0
0.02
0.04
0.06
0.08
Vunduzi
0 20 40 60 80
0
0.01
0.02
0.03
0.04
Gorongosa
AGB (M
g
C ha–1)
2007
2010
Pre−change AGB of deforested pixels (C1)
Pre−change AGB of pseudodeforested pixels (C1*)
(where probability of deforestation is random wrt to AGB)
(a) (b)
(c) (d)
(e)
(g)
(f)
Fig. 7 Probability density functions of aboveground biomass in 2007 (black solid line) and 2010 (grey solid line). Also shown is the
aboveground biomass (AGB) of changed pixels prior to change (dashed lines). The observed values (grey dashed lines), and
the expected value if deforestation was random with respect to biomass (black dashed line) are shown. The 2007 and 2010 data are the
mean of three images. The boundaries of each sub-area (ag) are shown in Fig. 1. No data are shown for the changed pixels in the
Sanctuary as the number of changed pixels (n=10) was to few to produce a meaningful probability distribution function.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
QUANTIFYING FOREST LOSS IN AFRICAN WOODLANDS 11
false positives. Such techniques may also be more accu-
rate in determining the size of each LCC event.
Backscatter-biomass relationships for spaceborne
L-band backscatter in ‘difficult’ conditions (i.e. mixed
age stands of multiple species) are reported with similar
statistics to that found here (e.g. R
2
=0.53 (Karjalainen
et al., 2009)) and slightly better results have been found
in even-aged stands (RMSE =30% of biomass (Magnus-
son et al., 2007)), or when considering a very wide range
of biomass (Mitchard et al., 2009). In comparison, air-
borne LiDAR has regression statistics between the
LiDAR metrics and AGB of RMSE of 23.5 MgC ha
1
,
R
2
=0.85 in an example from Peru (Asner et al.,2010).In
the present study the comparable figures were
RMSE =8.7 MgC ha
1
and R
2
=0.49, but the difference
in RMSE is probably due to the lower AGB of our study
area.
Williams et al. (2008) found that regrowing woodland
at this site can accumulate between 0.4 and
0.9 MgC ha
1
year
1
AGB, and we expect that changes
in more mature woodland will be less than this. How-
ever, in two parts of the study area (Fig. 5A) rapid
increases in AGB beyond this rate are observed. Obser-
vations in this area show rapid woody encroachment,
probably the result of fire exclusion in the Sanctuary.
Stem numbers have approximately doubled in 4 years
(data not shown), but biomass has not increased by that
rate because the new stems are small saplings. This
unrealistic sensing of biomass accumulation raises the
issue of which (combination of) plot characteristics L-
band backscatter is most strongly related to stem
number, basal area or biomass (Woodhouse, 2006b)? In
mixed age, diverse sites this question has not yet been
resolved (Woodhouse, 2006b). If the biomass-backscat-
ter relationship is not direct, but is mediated by other
stand characteristics (Lucas et al., 2010), then the site-
specificity of the regression parameters needs to be
evaluated (Mitchard et al., 2009).
How much carbon is lost to deforestation compared
to degradation?
The large uncertainty on the degradation loss makes
it difficult to estimate the fraction of AGB losses attrib-
utable to degradation. However, a best guess is that
total degradation was 67% of total net losses of AGB.
This analysis thus provides tentative support for the
idea of a large and presently un-quantified loss of bio-
mass due to degradation in African woodlands and for-
ests. However, we caution against extrapolating this
change in AGB to emissions, because, firstly, it is likely
that many of the areas that are degraded are subse-
quently deforested (Ahrends et al., 2010), and secondly,
the time period over which a change in standing stocks
translates into a flux to the atmosphere is currently
unknown in this ecosystem.
Loss of AGB in our study area (0.43 MgC ha
1
yr
1
)
was lower than has been reported from plot data in
the surrounds of Dar es Salaam (0.8 MgC ha
1
yr
1
(Ahrends et al., 2010)). This is expected given that our
study site is ~100 km by road from the nearest city,
0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1
0
20
40
60
80
100
Miss rate, %
0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1
0
0.02
0.04
0.06
0.08
0.1
P value
False positive rate, %
20%
40%
50%
60%
80%
Fig. 8 Miss rates and false positives for the land cover change (LCC) detection algorithm under a variety of definitions of change. The
P-value shown on the x-axis is the threshold probability above which a change event is considered real. The various lines indicate φ,
the fraction of biomass remaining after change, below which the pixel’s biomass needs to be reduced for the change to be detected. A
P-value of 0.95 and φ=50% were used as default values.
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
12 C. M. RYAN et al.
Chimoio, which has a much smaller population and
thus pressure for resource extraction is likely to be
lower.
The distinction between deforestation and degrada-
tion is logical in the context of the area multiplied by C
density approach to C stock estimation. However, the
advent of high resolution maps of C stocks and their
change, where the resolution is similar to the area of a
single tree canopy, means that the distinction becomes
ambiguous in effect degradation is just very small-
scale deforestation. It may be more useful to character-
ize a LCC regime in terms of the frequency, area (and
shape), and intensity of biomass change, as these
parameters might be more easily related to the extent of
different land uses, which is normally the information
policy makers require.
• Are areas of high carbon density preferentially tar-
geted for LCC?
The difference between the AGB of land that under-
went LCC and the mean of comparable surrounding
woodland was <1 MgC ha
1
, suggesting there is no
substantial preference for LCC to be undertaken in
high biomass areas (Fig. 7). There are some indica-
tions that this may not be true in all areas (such as
Vunduzi), but for now, the hypothesis that emissions
might be substantially higher as a result of preferen-
tial deforestation of high AGB areas has not been sup-
ported. This suggests that the other criteria for the
location of new farms dominate (such as proximity to
existing farms, roads or dwellings) or that AGB is not
correlated to the agricultural suitability of the land.
The latter might be true even if potential AGB and soil
fertility are correlated because of past land use or
other disturbance to AGB.
What is the carbon density (MgC ha
1
) of changed
areas before and after LCC?
After a change event, land was not reduced to zero
AGB, but instead averaged 11.9 ±18.4 MgC ha
1
. This
result fits with observations on the ground, where large
trees are often left in newly cleared areas to provide
shade, or because of the disproportionate effort needed
to remove them (Ghee, 2009). In addition, trees on
deforested land are often ringbarked or otherwise
killed, but left standing, and as they dry out will scatter
radar waves less as their dielectric properties change.
However, there is evidence of non-linearity in the AGB-
backscatter regression at low values of backscatter, so
these measurements of very low AGB values may be
subject to additional uncertainties. This may be because
at very low biomass, backscatter will be strongly influ-
enced by the properties of the ground surface, includ-
ing surface roughness and soil moisture.
• What size are LCC events, and how are they clus-
tered in space?
In Central Mozambique, farmers have an arable area
of 1.4 ha on average (Simler et al., 2004). However,
most LCC events detected in this study were much
smaller than this (mean 0.45 ha), indicating that farms
are built up in size by repeated small clearances
(Fig. 6). The significant spatial clustering of LCC events
supports this. This ‘death by a thousand cuts’ type of
woodland clearance poses a stiff challenge for rapid
monitoring of LCC: a detection system that ignored
events <1 ha would miss the areal majority of LCC in
this study area, and so might need to operate over long
time scales to be effective.
Limitations
Despite only using dry season imagery, there is consid-
erable variation through time in the backscatter values
of the ALOS PALSAR imagery, and the slope of the
backscatter-AGB regression. This is presumably due to
variations in environmental conditions such as soil
moisture, which is well known to influence L-band
backscatter (Rignot et al., 1994; Pulliainen et al., 1999;
Magnusson et al., 2007; Pantze et al., 2009; Lucas et al.,
2010), changes to understory vegetation, or sensor cali-
bration drift. This variation suggests that accurate bio-
mass change detection with this sensor will require
either: invariant features to be present in each scene;
correction for e.g. the effects of soil moisture with ancil-
lary data sets and models; or recalibration to ground
data at each time point. We adopted the latter approach
here, but with an inventory that was not repeated each
year, and thus an assumption that plot biomass has not
changed during the study period.
Furthermore, there are potentially significant uncer-
tainties associated with the stem diameter-biomass
allometry (Chave et al., 2004) even with the use of a
site-specific model. This can introduce bias of ~17%
(Ryan, 2009). Although the regression procedure used
here accounts for the random error, the bias will
remain and influence estimates of AGB stocks.
It should also be noted that the L-band biomass-back-
scatter response is known to saturate at the level of bio-
mass typically found in closed canopy forests. Studies
report saturation at between 30 and 100 MgC ha
1
(Mitchard et al., 2009; Lucas et al., 2010), although this
appears to depend on vegetation structure (Lucas et al.,
2010). Thus the method presented here which utilizes
L-band will be most useful in low C density woodlands
rather than higher density forests. The proposed
BIOMASS mission would employ P-band radar in an
effort to overcome this limitation (Le Toan et al., 2011).
©2011 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2011.02551.x
QUANTIFYING FOREST LOSS IN AFRICAN WOODLANDS 13
Conclusions
• Multi-temporal L-band radar imagery can be effec-
tive in detecting small-scale deforestation, but may
fail to detect small levels of degradation over large
areas. Abrupt changes of more than 12 MgC ha
1
are detectable at 95% confidence on a 25 925 m
pixel scale.
In this area of Mozambique, LCC events are mostly
small (median = 0.2 ha) but range from 0.06 to 18 ha
and reduce aboveground carbon from 33.5 to
11.9 MgC ha
1
on average. They are strongly clus-
tered together, and many events are the expansion of
previously cleared land.
There was no evidence that the areas that were defor-
ested had a higher biomass than the average sur-
rounding woodland.
Degradation losses are likely to be substantial, with a
best guess that they represent 67% of the net biomass
loss. This number is extremely uncertain however.
Acknowledgements
The Nhambita community and staff of Envirotrade facilitated
fieldwork. Staff of the Gorongosa national park allowed
access to plots within the park. The GeoEye Foundation pro-
vided the IKONOS image. ESA and JAXA provided the
ALOS imagery (C1P.7493). CR was supported by the UK
NERC Carbon Fusion project, the Mpingo Conservation &
Development Initiative (http://tinyurl.com/mpingo) under
their REDD Pilot Project funded by the Royal Norwegian
Embassy in Tanzania, and the EU FP7 iREDD+project. EM is
funded by Gatsby Plants. We also thank Iain Cameron for
his advice and help. We thank the two anonymous reviewers
for their helpful comments.
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QUANTIFYING FOREST LOSS IN AFRICAN WOODLANDS 15
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