Introduction
There are large uncertainties in the spatial and temporal patterns of forest
loss and associated fluxes of carbon in the tropical ecosystems (Grainger,
2008; Hansen et al., 2010; Malhi, 2010; Pan et al., 2011). Forest loss can
be either natural, for example due to wind-throw or natural fires, or
anthropogenic, usually labelled deforestation. Deforestation carbon
emissions are a significant but declining fraction of total anthropogenic
CO2 emissions (van der Werf et al., 2009). In Amazonia, tropical
deforestation was the main source of carbon emissions (Morton et al., 2008),
at least during their 2003 to 2007 study period. More than half of the total
forest carbon is stored in tropical intact forests, of which more than 50 %
is stored in living biomass, about a third in the soil, with the remaining
carbon being stored in dead wood and litter (Pan et al., 2011). In South
America, deforestation is mainly caused by the expansion of agriculture and the area
used for cattle ranging (FAO, 2006; Fearnside, 2005; Geist and Lambin,
2002), and the continent is responsible for almost half of the tropical
deforestation emissions (Harris et al., 2012; Pan et al., 2011). Over the
last 30 years soybean production has expanded rapidly in Amazonia, partly
driven by improved yield-increasing and labour-saving technologies (Grau et
al., 2005; Naylor et al., 2005).
Historically, the widely used data sets for forest area changes and timber
harvesting in the 1980s and 1990s are the forest resource assessments (FRAs), as
reported by countries to the United Nations Food and Agriculture
Organization (UN FAO) (FAO, 2006), but which are known to suffer from issues
regarding consistency (Grainger, 2008). Satellite observations overcome some
of the issues found in earlier FAO data sets, because they systematically
monitor in space and time. Over the last three decades several
satellite-based deforestation data sets have been developed. Landsat
satellite imagery is the longest operative option for monitoring vegetation.
From 1972 through January 1999 the Landsat Multispectral Scanner (MSS)
provided continuous data at a relatively high spatial resolution of 90 m.
For 1982 onwards the Landsat (Enhanced) Thematic Mapper ((E)TM) provides
vegetation cover at an even higher spatial resolution of 30 m, with a
16-day revisit time. However, the effective temporal resolution is much
lower because of issues related to cloud cover, which often persists not only in the
wet season but also during the dry season between June and November in the
Amazon basin south of the Equator (Costa and Foley, 1998). Therefore, these
observations are mostly used in annual or multi-year analyses, but there is
a need for alternative non-optical data techniques to provide time series on
a monthly or higher temporal resolution (Asner, 2001). Other widely used
satellite data sets for vegetation are the Normalized Difference Vegetation
Index (NDVI), often derived from the Advanced Very High Resolution
Radiometer (AVHRR). NDVI is sensitive to canopy greenness (Anyamba and
Tucker, 2005; Tucker et al., 2005; Zhu et al., 2013). This data set has a
higher temporal, but coarser spatial resolution than Landsat, and is also
sensitive to aerosols and cloud cover. Other vegetation data sets that can
capture vegetation dynamics are for example the observations based on
long-wavelength radar backscatter (Joshi et al., 2015), where deforestation,
forest degradation and the follow-up vegetation cover could be captured, and
those based on observations from the SeaWinds Ku-band scatterometer
(Frolking et al., 2012), which have been shown to capture gross forest loss in
the Tropics. Also lidar data can be used to estimate forest biomass, and can
thus capture vegetation dynamics (Mitchard et al., 2012). Data availability
for radar and lidar data sets is usually from 1998 onwards.
Over the past years, the number of data sets quantifying vegetation dynamics,
carbon stocks, and other relevant vegetation quantities on both the global and
regional scales has thus increased substantially, often using Landsat and
AVHRR data but also other data sources including the Moderate-resolution
Imaging Spectroradiometer (MODIS, launched in 1999 on board of Terra and in
2002 on Aqua), Medium Resolution Imaging Spectrometer (MERIS, 2002–2012), and
Satellite Pour l'Observation de la Terre Vegetation Program (SPOT VGT, from
1986 onboard different satellites) (Achard et al., 2014; Baccini et al.,
2012; Broich et al., 2011; Ernst et al., 2013; Eva et al., 2012; Frolking et
al., 2012; Jones et al., 2011; de Jong et al., 2013; Kim et al., 2015; Koh
et al., 2011; Mayaux et al., 1998; Morton et al., 2005; Potapov et al.,
2012; Saatchi et al., 2011; Verbesselt et al., 2012; Verhegghen et al.,
2012; Wasige et al., 2012).
One of the regions most closely monitored is the Brazilian Legal Amazon,
where the Brazilian National Institute for Space Research (INPE) developed
the Program for Deforestation Assessment in the Brazilian Legal Amazon with
Satellite Imagery (PRODES). PRODES estimates annual deforestation since 1988
based on a multi-data approach mostly based on Landsat data but also the
China–Brazil Earth Resource Satellite (CBERS-2B) and UK-DCM2 from the
Disaster Monitoring Constellation International Imaging (DMCii) (Shimabukuro
et al., 1998). Other efforts include the recently published global maps of
global forest gain and loss for the 2001–2012 period also using Landsat data
(Hansen et al., 2013).
In addition to the previously mentioned data sets mostly based on visible and
infrared wavelengths, passive microwave observations can also be used to
characterize vegetation dynamics. Vegetation optical depth (VOD) is a
vegetation attenuation parameter in the microwave domain. This parameter was
first described by Kirdiashev et al. (1979) in a zero-order radiative
transfer model for vegetation canopies. VOD is primarily sensitive to the
vegetation water content and also captures information about vegetation
structure (Jackson and Schmugge, 1991; Kerr and Njoku, 1990; Kirdiashev et
al., 1979).
The longer wavelengths of passive microwave enables sensitivity of VOD not
only to the leafy part, but also to woody parts of vegetation (Andela et
al., 2013). Therefore VOD yields information about both the photosynthetic
and non-photosynthetic parts of aboveground vegetation, based on the water
content (Jones et al., 2011; Shi et al., 2008). VOD is shown to be highly
correlated with aboveground biomass (Liu et al., 2011a; Owe et al., 2001)
and thus yields information about the net forest loss – the balance between
decreases in forest loss due to deforestation and degradation and increases
in forest extend due to regrowth or thickening. Furthermore, the advantage
of low-frequency (< 20 GHz) microwave remote sensing is that
aerosols and clouds have a negligible effect on the observations, so even
areas with regular cloud cover are observed frequently, which makes it
suitable to use for global vegetation monitoring at daily time steps.
Comparing AVHRR NDVI and passive microwave-based VOD data sets with a record
longer than 20 years, Liu et al. (2011a) showed that both data sets had
similar seasonal cycles. VOD however, also showed interannual variations in
regions with water stress, which correspond for a large part to variations
in precipitation. VOD was more sensitive to changes in woody vegetation
compared to NDVI, whereas NDVI was more sensitive to herbaceous changes
(Andela et al., 2013). This is the result of NDVI being more sensitive to
canopy greenness (Myneni et al., 1995) and VOD being more sensitive to water
content, relatively speaking. Thus, when forest is converted to large-scale
cropland, the canopy greenness does not necessarily drop, whereas the total
water content of the aboveground biomass decreases (Liu et al., 2011a).
The main disadvantage of these low-frequency passive observations is that a
large footprint is needed to yield an observable signal, making this data set
most suitable for large regional and continental-scale studies. These
retrievals therefore have a relatively coarse resolution, compared to
observations in the visible and near-infrared parts of the spectrum.
Furthermore, the presence of open water regions affects the signal. This, in
combination with the large footprint of the gridded product, may lead to
underestimation of VOD when grid cells are close to large open waters (Jones
et al., 2011). VOD is retrieved from several satellite sensors. The
observations retrieved from the Advanced Microwave Scanning Radiometer
(AMSR-E) and Special Sensor Microwave Imager (SSM/I) have been merged to one
data set with a spatial resolution of 0.25∘, based on Cumulative
Distribution Function (CDF) matching. This merged VOD data set has been used
to study vegetation dynamics in different ecosystems on both global and
regional scales (Andela et al., 2013; Liu et al., 2012, 2013, 2015; Poulter
et al., 2014; Zhou et al., 2014). Guan et al. (2012) compared QuickScat
Ku-band backscatter coefficients (dB) with VOD and NDVI and noted that the
three data sets are comparable, but that dB shows abnormal high values when
more bare soil is present in the pixel.
This paper aims to estimate large-scale forest loss in South America. We
show how the merged VOD product can be used to estimate forest loss for
South America on a country-level scale, but we also point towards
limitations of our approach and the data set. The main novelty of our
approach is the relatively long (1988–2011) time series based on a
consistent data stream. We detail how we translated the VOD signal to forest
loss area by calibrating our results to the Global Forest Change maps of
Hansen et al. (2013), which are subsequently compared to the Landsat-derived
PRODES data set. We provide a country-level analysis of the newly derived
maps, and zoom in on Brazil to present a state-level analysis of forest loss
over the 1990–2010 period. This time period is somewhat shorter than the
time span of the VOD data set due to the requirements of the change detection
algorithm we developed.
Data sets
In this section we describe the data sets we used in our analysis. First, we
give more information on the VOD data set that is used for our estimation of
forest loss (Sect. 2.1), followed by describing the two data sets we used for
comparison: the Global Forest Change (GFC, Sect. 2.2), which besides being
used for comparing the spatio-temporal variability is also used to translate
our results to area estimates, and the PRODES data set (Sect. 2.3).
Vegetation optical depth
Forest loss estimates in this paper are based on VOD, which is derived
from passive microwave remote sensing. Passive microwave remote sensing
differs from active microwave remote sensing (radar) in the sense that radar
transmits a long-wavelength microwave signal through the atmosphere and then
records the amount of energy backscattered, whereas passive systems record
electromagnetic energy that was reflected or emitted from the surface of the
Earth. VOD was first introduced by Kirdiashev et al. (1979), and then
modified to be used in the well-known omega-tau model (Mo et al., 1982).
Kirdiashev et al. (1979) already described the relationship between VOD and
vegetation water content. This relationship was further simplified by
Jackson and Schmugge (1991) where the vegetation water content was directly
related to VOD. The algorithm of the VOD data set we used here is based on
the land parameter retrieval model (LPRM) (Meesters et al., 2005; Owe et
al., 2001, 2008). LPRM is based on a radiative transfer model and solves
simultaneously for soil moisture and VOD. It can be applied to passive
microwave sensors and has been used in numerous studies (see de Jeu et al.,
2014). VOD can be used to estimate biomass (Liu et al., 2015), and changes
therein correspond to net forest loss (equals the net sum of deforestation,
degradation, and regrowth) in a 0.25∘ grid cell.
The VOD time series used here is based on merging observations from two
sensors (Liu et al., 2011a). The different observations come from SSM/I
(1988–2007) and AMSR-E (July 2002–September 2011). These two sensors have
different specifications regarding wavelength, viewing angle, and spatial
footprint and therefore the absolute values of the retrieved VOD values
differ. Their relative dynamics, however, are similar (Liu et al., 2011a).
In the merging procedure the AMSR-E retrievals were used as a reference,
because this product has the higher accuracy due to its relatively low
frequency. The cumulative distribution frequency (CDF) matching technique
was used for rescaling SSM/I to match AMSR-E. For the period July 2002
through September 2011 AMSR-E data are used. Before July 2002, SSM/I
observations are used. Full details on the merging process can be found in
Liu et al. (2011a, b). In this study, we used monthly values, which were
derived from the merged VOD data set (version January 2015) by averaging the
daily data fields, and were resampled to 0.25∘. VOD observations
are dimensionless and their values range from 0 to 1.5. At a certain point,
when VOD values exceed 0.8, the vegetation becomes so dense that the soil
component in the radiative transfer becomes very small. This is a gradual
process and when VOD values are higher than 0.8 additional checks are
necessary before using the values in vegetation studies. When VOD exceeds
1.2, smaller-scale variations in the vegetation canopy cannot be captured
anymore (Owe et al., 2001).
Global Forest Change (GFC)
Hansen et al. (2013) released early 2014 the Global Forest Change (GFC)
project gridded data set, which is probably the most data-rich and computer-intensive production of global forest change maps. It contains annual maps
over the time period 2001–2013 at a 30 m resolution. The maps are based
on the 30 m Landsat 7 Enhanced Thematic Mapper Plus (ETM+) scenes,
which were resampled and normalized to create a gridded data set of
cloud-free image observations. Forest loss is defined in GFC as a change
from forest to non-forest state, comprising deforestation and degradation.
In our analysis, we used the annual forest loss data set and reprocessed
these to the 0.25∘ resolution of our analysis by summing the
30 m values. While regrowth is detected and reported, we focused on the
forest loss data when we used GFC for comparison; regrowth is thus not
included in our analysis of GFC. We did not include the 2000 forest cover
map as mask for forested areas to avoid omitting areas that were deforested
before 2000.
PRODES deforestation
The Brazilian space agency INPE provides annual gross deforestation maps of
the Brazilian Legal Amazon within the Program for Deforestation Assessment
in the Brazilian Legal Amazonia (PRODES). INPE defines deforestation as the
gross deforestation rate of the conversion of intact forests (old growth
forest) to a different land use such as agro-pasture, wood exploration areas,
and silviculture. Degradation and deforestation of regenerating secondary
forests are not monitored by PRODES (INPE, 2013).
Grid cells that were excluded from our analysis: VOD avg: grid
cells with an average VOD that is either above 1.2 or below 0.6 and thus
outside the usable range for our study. GLWD: grid cells containing more
than 50 % open water, which makes the VOD signal unreliable.
Both: grid cells containing more than 50 % open water and where VOD is
outside the usable range.
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Although PRODES covers a relatively long time period, the method of
detection of deforestation has changed over time. For the time period
1988–2002 the detection of deforestation polygons was done by visual
interpretation of Landsat 5 and Landsat 7 scenes. More recently these
polygons were manually digitized in the PRODES Analog project (INPE, 2013).
After 2002, PRODES started to use digital image processing and visual
interpretation of Landsat bands 3, 4, and 5, creating and interpreting images
of soil, shade, and vegetation fractions (INPE, 2013; Shimabukuro et al.,
1998). Deforestation is reported once per year in August based on changes
over the previous 12-month period. Deforestation within PRODES is defined as
clear-cut areas of primary forests exceeding 6.25 ha. Because of this
threshold in detection omitting deforestation smaller than 6.25 ha, INPE
reports that underestimation of deforestation occurs. Furthermore, there may
be unobserved areas due to cloud cover in the Landsat images during the time
period of visual interpretation until 2005 (INPE, 2013).
Methods
In this section we will first explain the pre-processing of the data (Sect. 3.1), and then describe the methodology used to detect forest loss
(Sect. 3.2). Finally, we will explain how the detected changes were converted
to forest loss area (Sect. 3.3)
Example 0.25∘ grid cell in the Brazilian state of Mato
Grosso. A: Observed monthly VOD signal and 19-month moving average
(VODMovingAVG). B: Interyearly difference (IYD), whether it met the t test
criteria, and annually summed IYD values taking only negative values into
account. For comparison the corresponding GFC values are also given.
![]()
Data selection
We aimed to estimate gross forest loss for each 0.25∘ pixel on an
annual basis, which will be explained in Sect. 3.2. We first filtered the
available data to circumvent false detections related to the use of
microwave data. The excluded grid cells are shown in Fig. 1, and the data
exclusion was based on two criteria:
Average VOD values should be below 1.2. This is to prevent false detection
in densely vegetated areas without clear forest loss. The value was based on
Owe et al. (2001), who stated that VOD values larger than 1.2 cannot be used
to detect significant vegetation changes. When vegetation is very dense, the
VOD signal becomes noisy and potential changes in forest cover cannot be
detected anymore. These pixels are mainly found in the middle of the Amazon
forest, where forest loss rates are low. In addition, we excluded grid cells
where VOD values were on average below 0.6 to maintain a focus on forested
grid cells. Also when forest loss occurs in the early stages of the time
series, the average VOD value will not be below this limit of 0.6. This
value was based on the comparison between VOD and MODIS-based Vegetation
Continuous Fields (VCF), which provides information about the fraction tree
cover in a pixel. Our VOD threshold of 0.6 corresponds to 10 % tree cover
for two-thirds of the pixels, a percentage sometimes used to define forest
(Saatchi et al., 2011; UNFCCC, 2006) although there is no consensus about
this definition.
Large open water should be avoided. Open water affects microwave emissions
and can lead to underestimation of VOD (Jones et al., 2011). Therefore
0.25∘ grid cells, which contain more than 50 % open water based
on the Global Lakes and Wetlands Database (GLWD, Lehner and Döll, 2004),
were masked out.
We excluded these grid cells also from GFC and PRODES data when we compared
the results. Therefore, total South American forest loss over 2001–2010 for
GFC reported here are on average 4 % lower than without the data
exclusion, which also gives an indication of our underestimation due to
masking out of these grid cells.
Detection of forest loss
Our method is a change detection method based on the principle that VOD is
directly related to the aboveground living biomass. Therefore persistent
changes in VOD over time are related to changes in biomass (Liu et al.,
2015), for example when forest is converted to non-forest. Basically we
track the full time series and inspect whether there are sudden drops in the
signal that could be the result of forest loss. Our approach is based on four
steps and explained using an example grid cell located in the Brazilian
state of Mato Grosso, where forest loss has been high during the 2000–2005
interval according to Hansen et al. (2010).
As a first step we deseasonalized the time series based on a 19-month moving
average of VOD (VODMovingAVG, Fig. 2a):
VODMovingAVG(lat, long,m)=AverageVODobslat, long, m-9:m+9
where lat, long, m is the latitude (lat), longitude (long) and month (m). With m-9:m+9 we refer to
all data points 9 months before until 9 months after the specific month.
This approach was preferred over taking out the seasonal cycle based on the
average of all cycles because the seasonal cycle from forest and non-forest
is different. In addition, a longer moving average masks part of the signal
due to droughts or anomalous wet periods which also influence VOD. We also
tested longer averaging windows (see Sect. 4.5 for details about the tested
windows), but the results were relatively insensitive to this and it
decreased the numbers of years over which we could report. In the example
grid cell VODMovingAVG decreased most strongly during 2002–2005 (Fig. 2a).
To estimate where forest loss potentially occurred and how this was
partitioned over different year(s), in the second step we calculated the
difference of VODMovingAVG with the same variable 12 months earlier, and
label this the inter-yearly-difference (IYD, Fig. 2b).
IYD(lat, long,m)=VODMovingAVG(lat, long,m)-VODMovingAVG(lat, long,m-12)
When the IYD was below 0, this specific month was detected as a possible moment
for forest loss. In the third step, we tested using a two-sided t test
whether IYD was negative because of forest loss, or because of other reasons,
for example due to natural interannual variability related to rainfall. The
first group of the t test consisted of all VOD observations preceding the
month where IYD was negative. The second group consisted of all other VOD
observations from that moment until the end of the time series. When the
p value was smaller than 0.05, we flagged the grid cell and month as forest
loss (Fig. 2b). These three steps were done for every grid cell and month
from October 1989 until January 2011.
In the fourth and final step, we calculated the sum of the absolute IYD values,
which we will refer to as VODoutliers in the rest of this paper. This
was done from 1990 through 2010 to get annual values (Fig. 2b).
Conversion to area forest loss
Our method yields the number of VODoutliers per year for each grid cell,
which is related qualitatively to the amount of forest loss and may thus
yield insight into the spatial and temporal dynamics of forest loss.
However, to go one step further and convert our results to the area of
forest loss we calibrated our results to the gross forest loss estimates of
GFC. Because of the large differences in spatial resolution (30 m for
GFC and 0.25∘ for VOD) and because our data set is most useful for
large-scale assessments, we calibrated the conversion of the
VODoutliers to area based on a country-level approach for the overlapping
time period (2000–2010). In general, our method yields net forest loss
per grid cell within one year, because we considered decreases in VOD, which
is the net result of deforestation, forest degradation, and regrowth within a
grid cell per year.
Because VOD and biomass are not linearly related, we binned VOD into five groups
comprising the average VOD values between 0.6 and 1.2 (0.6–0.7, 0.7–0.8,
0.8–0.9, 0.9–1.0 and 1.0–1.2). The last bin was larger to arrive at more
robust regression outcomes, because there are fewer grid cells with VOD
above 1.0. For every bin we performed a Pearson regression (Pearson
performed preferably, compared to Spearman) forced through the origin, with
all VODoutliers per year related to the same GFC values. Based on the
linear regression, we obtained a slope for each VOD bin, which was used to
convert VODoutliers to gross forest loss area per 0.25∘ grid
cell.
VODareaforestloss(year)=∑bin=15VODoutliers( year, bin)×slope (bin)
Forest loss extent based on the VODoutliers for the 5-year
epochs. Grey areas are masked out (Fig. 1).
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Results
Spatial extent
The largest feature over our study period is the well-known arc of
deforestation along the Southern edge of the Amazon basin (Fig. 3), showing
high forest loss in every period. Highest forest loss was observed in the
Brazilian states Mato Grosso, Pará, and Maranhão. However, forest
loss rates were not uniform in space and time, Fig. 3 shows that forest loss
rates have fluctuated with lowest forest loss observed during the 1995–1999
period and highest forest loss observed in the 2000–2004 period.
While forest loss in South America is most often associated with this arc of
deforestation, also other regions experienced forest loss. One is the region
extending from northern Argentina to Bolivia via Paraguay (Fig. 3a, label 1), also known as the Chaco region, showing high forest loss over the full
time period. Forest loss in this region is expanding and increasing in
intensity over time. Another region extends from the southeastern part of
Paraguay into Brazil along the border of the Brazilian state Mato Grosso do
Sul (Fig. 3a, label 2). During the 1995–1999 period forest loss was on the
rise here and increased to a maximum during the 2000–2004 period, but
decreased during the 2005–2009 epoch.
Statistics for the comparison between GFC forest loss
(km2 yr-1) and IYD (yr-1). This was done for all grid cells
and when aggregating the grid cells in a country-level analysis. The
coefficient of variation (CV in %) was based on the Root Mean Square
Error (RMSE in km2) between both data sets.
VOD bin
Based on all grid cells
Country level
Slope
r2
CV (%)
RMSE (km2)
r2
CV (%)
RMSE (km2)
0.6–0.7
22.4
0.63
804
15.7
0.63
203
666
0.7–0.8
34.8
0.52
163
3.7
0.84
122
586
0.8–0.9
61.7
0.80
147
5.0
0.84
83
567
0.9–1.0
79.4
0.72
134
4.7
0.88
92
684
1.0–1.2
82.7
0.72
253
3.2
0.96
53
366
Finally, the region north of Manaus in the Brazilian states of Roraima and
Amazonas (Fig. 3a, label 3) which partly consists of wooded savanna, also
showed high forest loss. Here the forest loss increased and expanded during
the 1990s with the biggest change between the first and second half of the
1990s. Forest loss stayed relatively stable during the first half of the
2000s. During the 2005–2009 time window some areas with intense forest loss in
previous periods did not show up anymore, for example large parts of the arc
of deforestation. Besides these three large regions, several smaller
fluctuations occurred. These can mostly be seen in the southeastern
Brazilian state Minas Gerais.
Calibration with GFC
We converted the summed VODoutliers to a forest loss area according to Eq. (3),
where the slopes varied between the five different bins (Table 1). The
Pearson correlation on a grid-scale was lowest (r2= 0.52) for the bin
with the average VOD from 0.6–0.7. The other four bins had correlations ranging
from r2= 0.63 to 0.80 (Table 1). The largest errors are found in the
regions with dense vegetation and relatively little forest loss (Figs. 4 and 5). The RMSE on a grid-cell scale shows that the bin with the lowest
average VOD values (0.6–0.7) has the highest error compared to GFC (Table 1).
Error estimates for each grid cell. The error is defined as VOD
minus GFC forest loss area expressed as a percentage of GFC for the
overlapping time period. White indicates that both data sets had no forest
loss.
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Error as a function of mean GFC forest loss, where the error is
defined as VOD minus GFC forest loss area as a percentage of GFC for the
overlapping time period.
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Country-level forest loss estimates (total area, contribution to
total South American forest loss, contribution of forest loss as a
percentage of the masked-country area, as well as absolute and relative
trends) for VOD and GFC for the overlapping time period (2001–2010).
Asterisks indicate the significance, where ∗=p > 0.25; ∗∗=p < 0.25; ∗∗∗=p < 0.05.
Average forest loss 2001–2010
Slope 2001–2010
Absolute (km2 yr-1)
Percentage of total
Percentage of masked
Absolute (km2 yr-2)
Relative
forest loss area
country area (%)
(Absolute/Average)
(Absolute/Total) (%)
(%)
VOD
GFC
VOD
GFC
VOD
GFC
VOD
GFC
VOD
GFC
Argentina
4517
3329
11.73
8.29
0.61
0.53
79∗
358∗∗
1.68
11.00
Bolivia
3045
2338
8.07
5.89
0.39
0.33
21∗
166∗∗∗
0.75
7.84
Brazil
21 926
27 317
55.18
67.81
0.32
0.39
-1385∗∗
-1530∗∗
-6.47
-5.55
Chile
173
408
0.50
1.04
0.12
0.30
35∗∗
17∗∗∗
18.62
4.19
Colombia
1899
1861
4.95
4.75
0.20
0.21
-2∗
65∗∗
-0.13
3.46
Ecuador
450
305
1.24
0.79
0.18
0.15
-63∗∗
19∗∗
-14.19
6.21
Fr. Guiana
115
17
0.33
0.04
0.16
0.02
13∗∗
0∗
11.08
1.18
Guyana
288
50
0.75
0.13
0.16
0.03
-3∗
0∗
-1.24
-0.61
Peru
1077
1047
3.06
2.69
0.12
0.13
52∗
84∗∗∗
4.46
8.24
Paraguay
3030
2556
7.68
6.49
1.05
0.98
115∗
213∗∗∗
3.93
8.78
Surinam
276
29
0.75
0.08
0.25
0.03
34∗∗∗
2∗∗
12.57
8.69
Uruguay
868
122
2.28
0.31
0.77
0.12
131∗
18∗∗∗
13.61
15.43
Venezuela
1322
658
3.46
1.70
0.21
0.11
-148∗∗∗
20∗
-13.65
3.12
Total
38 987
40 038
100.00
100.00
-1121∗
-568∗
-2.94
-1.42
On a country scale, the correlations per bin were higher with the lowest
(r2= 0.63) again for the bin with the lowest average VOD (0.6–0.7), and
the four other bins had increasing correlations from r2= 0.84 to 0.96
(Table 1). The country-level comparison of our VODoutliers with GFC forest
loss had a Pearson linear agreement of r2= 0.90 (p < 0.001). In
Fig. 6 the country-level VOD and GFC forest loss area estimates are plotted
against each other along with the 1:1 line. Most data points were reasonably
close to this line, although VOD overpredicted forest loss towards the lower
end of the spectrum. Especially in the countries with the lowest forest
loss, including Surinam, Uruguay, French Guiana, and Guyana, our method
yielded more forest loss than GFC. As a percentage of the available area per
country (Table 2) Uruguay (0.65 %), Surinam (0.22 %), French Guiana
(0.14 %), and Guyana (0.13 %) also showed higher average forest loss over
the overlapping time period based on VOD. Chile is on the other hand the
country where VOD provides lower forest loss estimates for the overlapping
time period (-0.18 %) compared to GFC. The country with the largest
relative forest loss is Paraguay for both VOD (1.05 %) and GFC (0.98 %).
In Fig. 7 we show these derived annual forest loss data from VOD for the full
time period, along with GFC for 2001 through 2010. Obviously the average
forest loss area for the overlapping period agrees between both data sets
because our approach was tuned to match GFC, but the spatial and temporal
variability can be different, potentially yielding new insights.
Country-level comparison of calibrated VOD and GFC forest loss
based on annual totals (2001–2010). The inset shows the same data on a
linear scale. The red lines depict the 1:1 line.
![]()
Country-level time series of annual totals of forest loss
according to GFC (2001–2010) and VOD (1990–2010). VOD data are
unreliable for 1991 as a result of the eruption of Mount Pinatubo.
![]()
The main differences between VOD and GFC are thus that VOD estimates higher
forest loss for the countries Uruguay, Paraguay and Chile compared to GFC.
Furthermore, although VOD and GFC agreed on Brazil being the main driver of
South American forest loss (54 % for VOD and 68 % for GFC), VOD
estimates show higher interannual variability in this. This is mainly the case in
2001, 2006 and 2009, where VOD estimated 36–41 % less Brazilian forest
loss compared to GFC (Table 2).
The main feature in the GFC time series is the peak in 2004 (with values of
49 000 and 58 000 km2 yr-1 for GFC and VOD respectively). VOD also
shows this peak, but indicates that the two preceding years were high as
well, making for a broader peak (2002–2004) with comparable values. The
higher VOD values in 2002 and 2003 than GFC were mainly the result from
higher estimated forest loss in Argentina and Paraguay. From 2005 onwards
both data sets agreed on the decreasing forest loss rates and the
interruptions in 2007, 2008, and 2010, although the exact patterns differed.
Following Brazil, the countries with the highest forest loss were Argentina,
Bolivia, Colombia, and Paraguay, each responsible for 5–8 % of total South
American forest loss. The difference between VOD and GFC in relative
contribution of each country to the total South American forest loss is on
average 2 %, with the maximum difference of 13 % for Brazil (all absolute
differences, see Table 2).
Country-level trends
2001–2010
To further compare VOD with GFC, we also calculated the trends per country,
based on linear regression, over the 2001–2010 period in absolute values and
as a percentage relative to their average forest loss over that time period
(Table 2). It should be noted that not all the trends are statistically
significant, partly because of the large interannual variability (Fig. 7,
Table 2). The overall trend for all South American forest loss over the
overlapping time period is negative for both data sets with a relative slope
of -2.9 and -1.4 % yr-2, for VOD and GFC respectively, which in
absolute terms corresponds to -1121 and -568 km2 yr-2. For individual countries in general both data sets agreed
and these trends were highly variable (Table 2).
1990–2010
Focusing on the full time series, Fig. 7 indicates that totals of forest loss in
South America were not stable or monotonically in- or decreasing. Instead,
they appear to be highly dynamic – at least from a VOD perspective –
especially during the first few years of our study period (1990–1994). After
that, forest loss was fluctuating without a clear trend until about 2001,
with 1991, 1995 and 1999 being high forest loss years. After this
fluctuating stage a period with relatively high forest loss started, with
2002–2005 being four consecutive years with high forest loss. After 2005 forest
loss decreased, with interruptions in 2007 and 2010 (Fig. 7).
Trends in forest loss based on VOD for the whole time period
(1990–2010) and the decades 1990–2000 and 2000–2010. Absolute values
indicate the slope based on Pearson linear regression and the relative
values are the absolute values relative to the average forest loss for that
country over the full 21-year time period. Asterisks indicate the
significance, where ∗=p > 0.25; ∗∗=p < 0.25; ∗∗∗=p < 0.05.
Slope 1990–2010
Slope 1990–2000
Slope 2000–2010
Difference 2000s–1990s
km2 yr-2
%
km2 yr-2
%
km2 yr-2
%
km2 yr-2
%
Argentina
170∗∗∗
4.58
182∗∗
5.76
109∗
3.43
-73
-2.32
Bolivia
49∗∗
1.92
92∗
0.75
72∗
0.59
-20
-0.16
Brazil
-59∗
-0.27
1078∗
9.79
-765∗
-6.95
-1843
-16.74
Chile
9∗∗
5.23
35∗∗∗
3.34
23∗∗
2.21
-12
-1.13
Colombia
-36∗
-1.88
-197∗∗
-16.69
10∗
0.88
208
17.57
Ecuador
-12∗
-2.67
-42∗∗
-14.85
-35∗
-12.58
6
2.27
Fr. Guiana
0∗
-0.31
-8∗
-3.76
13∗∗∗
6.34
21
10.10
Guyana
-8∗∗
-2.72
-16∗
-2.12
4∗
0.50
20
2.61
Peru
-23∗
-1.79
-85∗
-4.55
45∗∗
2.39
130
6.94
Paraguay
98∗∗
3.99
32∗
2.35
12∗
0.86
-21
-1.49
Surinam
5∗
2.25
-421∗∗
-4.03
31∗∗∗
5.91
53
9.94
Uruguay
60∗∗∗
6.99
130∗∗∗
11.91
-23∗
-2.08
-152
-13.99
Venezuela
-50∗∗∗
-3.97
-57∗
-0.30
-80∗∗
-0.42
-23
-0.12
Total
204∗
0.55
1122∗
3.01
-584∗
-1.57
-1706
-4.58
Time series of deforestation (PRODES) and forest loss area (VOD)
for the Brazilian states in the Amazon (1990–2010). PRODES deforestation
data are missing for 1993. VOD data are unreliable for 1991 as a result of the
eruption of Mount Pinatubo.
![]()
We calculated the linear trends over the whole time period and the two
decades 1990–2000 and 2000–2010 separately (Table 3). Over 1990–2010 Uruguay
showed a clear relative increasing trend of almost 7 % yr-2 (in
absolute values 60 km2 yr-2). Over the same time period also
Argentina, Chile, Paraguay, and Venezuela showed substantial in- or
decreasing trends larger than 3 % yr-2. When investigating the
decades 1990–2000 and 2000–2010 separately, additional patterns emerged.
During the 1990s Argentina, Brazil, Colombia, Ecuador, and Uruguay had trends
exceeding 5 % yr-2. During the 2000s, Brazil, Ecuador, and Surinam
showed trends below -5 % yr-2. The strongest differences per decade
were found in Brazil (where the forest loss trend changed from +9.8 % yr-2
in the 1990s to -7 % yr-2 in the 2000s) Colombia (-16.7 to 0.88 % yr-2),
and in Uruguay (+11.9 to
-2.1 % yr-2) (Table 3). Other countries with substantial different
trends between the two periods were Argentina 5.8 to 3.4 % yr-2), French Guiana (-3.8 to 6.3 % yr-2), Peru
(-4.6 to 2.4 % yr-2), and Surinam (-4 to
5.9 % yr-2).
Brazilian state-level comparison with PRODES
In addition to a comparison on the country scale, we also compared our results
for the Brazilian states within the legal Amazon using the PRODES data set
(Fig. 8). PRODES covers a longer period than GFC, but provides only data for
the Legal Amazon. We do not expect PRODES and our data set to compare
perfectly given that PRODES detects only deforestation of primary forests
and VOD detects deforestation, degradation, and regrowth including forest
loss of secondary forest. Nevertheless, the Pearson's r2 over the full
21-year time period between these two data sets was 0.60 (p < 0.001)
with a RMSE of 1.6 × 103 km2 yr-1 on a state level.
Our results show for the Brazilian states a highly dynamic pattern with no
steadily in- or decreasing trend (Fig. 8). The most notable difference
between both data sets is that VOD suggest that 1991, 1999, 2002, and 2010
were high forest loss years, which PRODES did not show. Furthermore, PRODES
showed increasing deforestation from 2002 until a peak in 2004, whereas VOD
peaked in 2005. While there are substantial differences in the temporal
variability in the VOD and PRODES data sets, they do agree on where most
forest loss occurred: Pará and Mato Grosso. Combined, these two states
were responsible for 69 and 61 %, for PRODES and VOD respectively, of
all Brazilian Legal Amazon deforestation (PRODES) and forest loss (VOD). The
total average forest loss in the Legal Amazon from 1990 through 2010
(excluding 1993, which is missing in PRODES) was 16.6 × 103
and 15.2 × 103 km2 yr-1 for PRODES and VOD respectively. The states
with largest relative differences between VOD forest loss and PRODES
deforestation are Amazonas and Roraima, with 1307 and 499 km2 yr-1 respectively. These regions have little forest loss. The
gridded errors for these states for VOD compared with GFC for the
overlapping time period are relatively large: 705 and 399 % for
Amazonas and Roraima respectively (Fig. 4, Table 4).
Average error for the Brazilian states. The error is defined as the
VOD minus GFC forest loss area expressed as a percentage of GFC forest loss
for the overlapping time period per state in the Legal Amazon.
State
(VOD-GFC) /
GFC (mean % yr-1)
Acre
17
Amapá
50
Amazonas
399
Maranhâo
17
Mato Grosso
35
Pará
94
Rondônia
37
Roraima
705
Tocantins
2
Sensitivity Analysis
Our forest loss detection approach was based on several assumptions, and we
tested how sensitive our results are to two main assumptions. First we
tested whether the way we used the t test (i.e. group 1 consists of all data
until IYD is negative and group 2 consists of all data after this moment) is
valid, or whether a fixed or smaller time period would capture forest loss
better. The main reason to test this is that based on our method, group
sizes in the t test are not equal and group 2 could become so large, that
recovery of vegetation could have taken place. Therefore we performed the
same detection method, but now with the t test group sizes fixed to 12, 24,
or 36 months. This implies that the detectable time period changed to
1990–2010, 1991–2009, and 1992–2008 for the three different group sizes. The
results showed for both the country-level analysis and the state-level
analysis that our original method (without a fixed time period) yielded the
highest correlations with GFC and PRODES. In general we found that
correlation decreased with decreasing group sizes.
Besides the t test group sizes, we also tested whether excluding grid cells
that were not normally distributed would make a difference. This was done
because a t test requires normally distributed data. We tested three
scenarios.
The standard scenario, where we excluded grid cells where the total average
VOD was either larger than 1.2 or below 0.6, and GLWD was larger than
50 %.
As scenario 1, but we also excluded grid cells that were not normally distributed
(p= 0.10).
As scenario 1, but we also excluded grid cells that were not normally distributed
(p= 0.05)
Excluding these not-normally distributed grid cells in scenarios 2 and 3
implied that respectively 25 and 32 % of the total South American
forest loss based on GFC would be missed. However, the Pearson's r2 for
all three scenarios stayed at 0.90. Based on these results we assumed that
excluding the not-normally distributed points did not have an effect on the
large-scale country-level analysis and we used all grid cells based on
scenario 1 in our analysis.
Discussion
Our results indicated that the patterns of forest loss changed over both
space and time, although the well-known arc of deforestation remained the
single largest feature in South America over our full study period. Our
results agree with earlier work showing that forest loss area, and probably
also carbon emissions, declined after peaking in the year 2004 (Food and
Agriculture Organization of the United Nations, 2010; Macedo et al., 2012;
Malhi et al., 2008; Nepstad et al., 2009). This decrease in forest loss is
observed mainly because Brazil reduced forest loss through a combination of
conservation policies (law enforcement, expansion of the governmental
protection of the Amazon area, and strict control of these enforcement by
suspension of credit to landowners violating the rules) and because of
changes in prices of agricultural outputs from 2005 onwards (Nepstad et al.,
2009).
While forest loss in the arc of deforestation, the region around the
southern border of Mato Grosso do Sul (Fig. 3a, label 2) and the region
around Manaus (Fig. 3a, label 3), declined after 2004, in the Gran Chaco
region (Fig. 3a, label 1) it increased over the time, as shown earlier by
Chen et al. (2013). In this region the observed forest loss is in areas
where deciduous broadleaf forest (> 10 m tall) with closed
canopy is converted to shorter (< 10 m) Chacoan woodlands and
agricultural areas (Steininger et al., 2001) and could be related to soy
bean production in this region (Boletta et al., 2006; Gasparri and Grau,
2009; Zak et al., 2004). This is in line with our trends and time series
(Fig. 7, Table 2) in which both VOD and GFC show an increasing trend for
Argentina over 2001–2010, whereas a decreasing trend over that time period
occurred in Brazil (Table 2). One explanation could be the relocation of
agricultural hotspots because of the strict forest law and effective forest
law enforcement within Brazil (Dobrovolski and Rattis, 2014).
The spatial pattern of forest loss in Northern Brazil in the states of
Amazonas and Roraima (Fig. 3, label 3) can partly be explained by forest
fires (Fearnside, 2000); the peak during the 1995–2000 time period for
example could be caused by the El Niño drought fire events during 1997
and 1998 (Barbosa and Fearnside, 1999). This is supported by fire emissions
estimates for this region derived from the Global Fire Emissions Database
(van der Werf et al., 2010). During these droughts, man-made fires destroyed
millions of hectares of fragmented and natural forest (Laurance, 1998). This
increase that continued during the 2000s in Amazonas and Roraima is not seen
anymore in the country-level time series (Fig. 7), because these changes are
relatively small compared to the changes in the arc of deforestation.
In the country-level analysis between VOD and GFC the latter indicates
higher average South American forest loss, with a difference of 3126 km2 yr-1 or 7.6% yr-1 of average VOD forest loss. The
country with the largest absolute contribution in both data sets is Brazil.
In GFC Brazil had a 10 % larger contribution to the South American total
forest loss than in VOD. This could be caused by the difference in what both
GFC and VOD measure. GFC measures gross forest loss while, due to our
methodology, VOD yields net forest loss. In areas with much regrowth, VOD
will therefore underestimate forest loss compared to GFC. This also has the
consequence that VOD is most reliable in areas where deforestation is the
dominant change. Another reason could be the different spatial resolutions
of the satellite products that the data sets are based on. GFC is based on
Landsat, which has a spatial resolution of 30 m and can capture many
small-scale forest loss events which will be missed in our data set based on
VOD with its much coarser 0.25∘ resolution. The difference in
spatial resolution could also be the reason why other countries, such as
Chile, show less forest loss and higher interannual variability in VOD than
in GFC, and why countries with relatively little forest loss, such as
Uruguay, Surinam, French Guiana, and Guyana had more forest loss based on VOD
(Fig. 6). In Uruguay many forest plantations occur (Fig. 1 in the Supplement of Achard
et al., 2014) and the result of these plantations is that forest loss is
often of small scale. This, in combination with the overestimation of VOD
with smaller scale forest loss, could explain why Uruguay shows so much
higher values at the country level, although additional research is required
to better understand these differences. While we would in general favour GFC
over VOD during the overlapping periods for the reasons mentioned above, the
temporal resolution of VOD is superior to any other data set for our study
period from 1990–2010. For areas with frequent cloud cover where Landsat may
have difficulties in acquiring reliable data, VOD may be in a better
position to map forest loss.
We also compared our results for the whole time period from 1990 through
2010 with PRODES data in a state-level comparison, and they had a Pearson
r2 of 0.66. As mentioned earlier, to some degree the comparison is one
of apples and oranges because PRODES provides annual estimates of
deforestation in pixels where no deforestation has occurred before, whereas
the VOD data set will give information about deforestation and degradation
and potentially regrowth. Although forest loss based on VOD includes
degradation and regrowth, PRODES shows on average over the whole time period
1451 km2 yr-1 (9.6 % yr-1 of the total average legal Amazon
forest loss according to VOD) more deforestation than VOD. This could be
caused by the differences in methodology and spatial resolution of both
data sets we mentioned before, but also potential inconsistencies in PRODES
could play a role; until 2002 PRODES was based on visual interpretation,
after which PRODES digital was used. On a state-level VOD overestimates
forest loss area in the states of Amazonas and Roraima, which is mostly
related to the relatively low and small-scale forest loss in these states
(Fig. 4, Table 4).
One of the most striking differences between VOD and PRODES were the years
1991, 1999 and 2010 when VOD was much higher than PRODES. The underlying
reasons may not be directly related to forest loss. In 1991 this difference
could be explained by the eruption of Mount Pinatubo, which had the result
that led to increased VOD in the Tropics (Kobayashi and Dye, 2005; Liu et
al., 2011a). The peak in 1999 in VOD was mainly caused by an increase in the
state of Amazonas. During 1999 heavy flooding occurred in this region (Chen
et al., 2010). Since VOD is sensitive to large waters, the VOD signal could
have been influenced by this event. Finally, the peak in 2010 could be caused
by the drought that hit the Amazon that year (Lewis et al., 2011). Amazon
forests are sensitive to increasing moisture stress and this could affect
aboveground biomass (Phillips et al., 2009). This supports the findings of
Liu et al. (2012), who noticed that VOD responded to interannual variability
in precipitation for tropical regions. However, this 2010 peak in forest
loss was also detected by GFC. PRODES did not show this peak, partly because
it was related to secondary forest degradation and deforestation, which is
not captured by PRODES (Fanin and van der Werf, 2015). This indicates the
need to better reconcile the differences between these various estimates and
not rely on one single data set.