Oil seed crops, especially oil palm, are among the most
rapidly expanding agricultural land uses, and their expansion is known to
cause significant environmental damage. Accordingly, these crops often
feature in public and policy debates which are hampered or biased by a lack
of accurate information on environmental impacts. In particular, the lack of
accurate global crop maps remains a concern. Recent advances in deep-learning and remotely sensed data access make it possible to address this
gap. We present a map of closed-canopy oil palm (Elaeis guineensis) plantations by typology
(industrial versus smallholder plantations) at the global scale and with
unprecedented detail (10 m resolution) for the year 2019. The
DeepLabv3+ model, a convolutional neural network (CNN) for semantic
segmentation, was trained to classify Sentinel-1 and Sentinel-2 images onto
an oil palm land cover map. The characteristic backscatter response of
closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn
spatial patterns, such as the harvest road networks, allowed the distinction
between industrial and smallholder plantations globally (overall accuracy =98.52±0.20 %), outperforming the accuracy of existing regional
oil palm datasets that used conventional machine-learning algorithms. The
user's accuracy, reflecting commission error, in industrial and smallholders
was 88.22 ± 2.73 % and 76.56 ± 4.53 %, and the producer's accuracy,
reflecting omission error, was 75.78 ± 3.55 % and 86.92 ± 5.12 %, respectively. The global oil palm layer reveals that
closed-canopy oil palm plantations are found in 49 countries, covering a
mapped area of 19.60 Mha; the area estimate was 21.00 ± 0.42 Mha (72.7 %
industrial and 27.3 % smallholder plantations). Southeast Asia ranks as
the main producing region with an oil palm area estimate of 18.69 ± 0.33 Mha or 89 % of global closed-canopy plantations. Our analysis
confirms significant regional variation in the ratio of industrial versus
smallholder growers, but it also confirms that, from a typical land development
perspective, large areas of legally defined smallholder oil palm resemble
industrial-scale plantings. Since our study identified only closed-canopy
oil palm stands, our area estimate was lower than the harvested area
reported by the Food and Agriculture Organization (FAO), particularly in West Africa, due to the omission of
young and sparse oil palm stands, oil palm in nonhomogeneous settings, and
semi-wild oil palm plantations. An accurate global map of planted oil palm
can help to shape the ongoing debate about the environmental impacts of oil
seed crop expansion, especially if other crops can be mapped to the same
level of accuracy. As our model can be regularly rerun as new images become
available, it can be used to monitor the expansion of the crop in
monocultural settings. The global oil palm layer for the second half of 2019 at a spatial resolution of 10 m can be found at 10.5281/zenodo.4473715 (Descals et al.,
2021).
Introduction
Crops that produce vegetable oils, such as soy, rapeseed, oil palm, and
sunflower, take up ca. 6 % of all agricultural land and ca. 2.3 % of the
total global land area and are among the world's most rapidly expanding
crop types (OECD, 2018). Demand for vegetable oils is increasing
with one estimate foreseeing an increase from 205 Mt in 2019 (OECD,
2018) to 310 Mt in 2050 (Byerlee et al., 2017). This has created a
need to optimize land use for vegetable oil production in order to minimize
environmental impacts and maximize socioeconomic benefits. One of the
requirements for this is accurate global maps for all oil-producing crops.
The most comprehensive maps available (International Food Policy Research Institute, 2019) map these crops
by disaggregating crop statistics identified at national and subnational
units for the year 2005 to 5 arcmin grid cells, which is a relatively
coarse spatial resolution. Direct identification of crops from satellite
imagery is likely to result in more accurate maps that delineate where
different crops have been planted. One of the most extensively mapped crops
is oil palm (Elaeis guineensis) because of societal concerns about the
associated environmental impacts on tropical forests and social disruption.
However, only the global extent of industrial plantations is reasonably well
known, while the more heterogeneous plantings at smallholder scales remain
largely unmapped (Meijaard et al., 2018).
A global map of oil palm at each production scale provides critical insights
into the current debate about the social and environmental sustainability of
the crop (Meijaard et al., 2018,
2020b). What would allow for a more accurate determination of the environmental
impacts from oil palm expansion, for example, is assessing the deforestation
that preceded oil palm development and the related carbon emissions, as well as
the impacts on species' distributions, key biodiversity areas, and
socioeconomic impacts. As total and local production volumes of palm oil
are reasonably well known, a comparison to the total planted area would
allow more accurate average yield estimates and regional variations in
yield. Similarly, accurate maps of planted oil palm can determine the extent
to which oil palm development has displaced other food crops, an important
element in the policy debate in the European Union regarding the use of palm
oil in biofuels (Meijaard and Sheil, 2019). Such information is
important for comparing oil palm to other vegetable oil crops, such as soy,
rapeseed, sunflower, groundnut, and coconut, once global maps for these
crops become available. The challenge is thus to develop a method to
accurately map large industrial plantations, as well as smallholder oil palm
areas.
Previous studies have demonstrated the usefulness of radar imagery for the
detection of closed-canopy oil palm stands. Palm-like trees have a
characteristic backscatter response which consists of a low vertical
transmit and vertical receive (VV) and high vertical transmit and horizontal
receive (VH) in Sentinel-1 or a high horizontal transmit and vertical
receive (HV) and low horizontal transmit and horizontal receive (HH) in
PALSAR imagery (Miettinen and Liew, 2011). This characteristic backscatter
response is a consequence of the canopy structure of palm-like trees and
allows for the detection of closed-canopy palm plantations, particularly oil
palm. Several studies have taken advantage of this characteristic
backscatter response for mapping oil palm at the local and the regional
scale (Koh et
al., 2011; Lee et al., 2016; Nomura et al., 2019; Oon et al., 2019) and
similarly for using supervised classification models
(Descals et al., 2019; Shaharum et al., 2020; Xu et
al., 2020).
The mapping of oil palm plantations by typology (smallholder versus
industrial) with remotely sensed data presents a more challenging
classification problem than the detection of only closed-canopy oil palm. In
addition to the backscatter response of radar data, texture analysis also
offers a complementary method to distinguish between smallholders and
industrial-scale plantations (Descals et al., 2019). Contextual
information, such as the presence and shape of harvesting road network and
drainage structures, can be included as predictive variables for the
classification of industrial and smallholder plantations.
Deep learning, in particular semantic segmentation, is a subfield of machine
learning with characteristics suitable for the distinction of smallholder
and industrial oil palm plantations. Deep learning employs a series of
models for computer vision that excel in very complex classification
scenarios (LeCun et al., 2015), and, in particular, convolutional
neural networks (CNNs) have recently been embraced by the remote-sensing
community due to the ability to recognize intricate patterns in the images
(Ma et al., 2019). To date, there are no studies that consider CNNs
for the land use classification of oil palm plantations at regional or
global scales. One study used deep learning for object detection, focusing
on the identification of single palm trees (Li et al., 2017).
The aim of this study is to (i) present an up-to-date map of oil palm
plantations by typology (industrial versus smallholder plantations) at the
global scale and with unprecedented detail (10 m resolution) for the
year 2019 and (ii) show the suitability of deep learning in remote sensing
for complex classification scenarios in which contextual information may be
useful.
MethodsOverview
The classification model for oil palm plantations used the Sentinel-1 and
Sentinel-2 half-yearly composites as input images (Fig. 1). The maps
presented in this study correspond to the second half of 2019. We used
a deep-learning model that was trained with 296 images of 1000 × 1000 pixels
distributed throughout the main oil-palm-producing regions and applied over
Sentinel-1 and Sentinel-2 composites in the potential area (Fig. 2) where
oil palm can grow. Table 1 shows the geospatial data used in the study. The
links to the datasets appear in Sect. 6. The processing steps depicted in
Fig. 1 were implemented in different computing environments (Appendix
Fig. A1) depending on the convenience of the processing. The annual
compositing of Sentinel-1 and Sentinel-2 images was done in Google Earth
Engine (GEE) (Gorelick et al., 2017) since a cloud-processing
platform was suited for this task considering the high amount of satellite
data required in the compositing. The visual interpretation of training and
validation data was also done in GEE. The training of the CNN and the
classification of images, however, was performed with a local computer using
Matlab 2019a since the implementation of the CNN model was less feasible in
GEE. The CNN model can also be trained and used for the prediction of images
with Python (code accessible through Sect. 5). The Sentinel-1 and
Sentinel-2 images taken in 2019 are the only data necessary to reproduce the
results of the global oil palm map. The rest is auxiliary data used for the
identification of the oil palm distribution, the visual interpretation of
oil palm plantation, and the comparison with other oil palm maps.
Diagram of the algorithm used to generate the global oil palm
layer. The input images, Sentinel-1 and Sentinel-2 half-yearly composites,
were obtained from Google Earth Engine on a grid of 100 × 100 km. The
Sentinel-1 and Sentinel-2 tiles were classified with a convolutional neural
network (CNN). The CNN model was trained with labeled images with constant
size (1000 × 1000 pixels). The output classification layer was validated
with 13 495 points that were randomly distributed.
Localization map of the grid cells where the convolutional
neural network (CNN) was applied for the classification of industrial and
smallholder plantations. The grid cells cover a potential distribution area
(blue line) over seven tropical regions of the world where oil palm can grow:
Central and South America, Central and West Africa, South and Southeast
Asia, and the Pacific. Cells in red depict the areas where there is the presence
of industrial oil palm plantations in the IUCN layer. Cells filled with
green signify areas where closed-canopy oil palm was detected by the CNN.
Data sources used in the study.
SourceBand/inputSpatialUsage*ReferenceresolutionSentinel-1 GRDVV and VH10 m1, 3Torres et al. (2012)Sentinel-2 Level-2AB410 m1, 3Drusch et al. (2012)IUCN industrial layerLand cover (oil palm map)30 m2, 4Meijaard et al. (2018)Oil palm layer SumatraLand cover (oil palm map)10 m2Descals et al. (2019)Oil palm layer IndonesiaLand cover (oil palm map)30 m2Gaveau et al. (2021)Oil palm layer in SE AsiaLand cover (oil palm map)100 m2Xu et al. (2020)WorldClim V1 Bioclim19 bioclimatic variables30 arcsec4Hijmans et al. (2005)DigitalGlobe imageryRGB orthoimages<1 m3Google Earth Engine (2020)FAOSTATOil palm harvested areaCountry-level statistics2FAO (2020)
* The column describes how the data were used in the study: (1) input of the
convolutional neural network (CNN), (2) used for comparison with the results
of the CNN, (3) base layers for the visual interpretation of oil palm
plantations, and (4) used for the identification of the potential
distribution of oil palm.
Potential distribution of oil palm
The classification of oil palm plantations was restricted to those areas
where the climatic conditions were favorable for oil palm growth. In order
to delimit the potential distribution of oil palm, we used climate data and
an existing global oil palm dataset. The climate dataset was obtained from
WorldClim V1 Bioclim (Hijmans et al., 2005), which provides 19 gridded variables at a spatial resolution of 30 arcsec that
are generated from monthly temperature and precipitation. This study's
existing oil palm layer was obtained from the International Union for Conservation of Nature (IUCN; Meijaard et
al., 2018) and shows the industrial oil palm plantations at the global scale
(link to the IUCN layer is available in Sect. 6). This map was derived
from a compilation of all published spatial data on oil palm combined with
the manual digitization of characteristic spatial signatures of industrial-scale
oil palm using cloud-free Landsat mosaics acquired in 2017 and created in
GEE.
The potential area where oil palm can grow was estimated with the climate
variable range in the IUCN layer. We estimated the histogram of the 19
bioclimatic variables in the areas that were classified as industrial oil
palm plantations in the IUCN layer. Appendix Table A1 shows the minimum and
maximum of each bioclimatic variable for the industrial plantations. A pixel
in the WorldClim dataset was considered favorable for oil palm growth when
at least 17 out of the 19 bioclimatic variables fell within the
climate range observed in the IUCN layer (Appendix Fig. A2). The resulting
potential oil palm distribution map encompasses similar areas as used in
previous studies (Pirker et al., 2016; Strona et al.,
2018; Wich et al., 2014). The classification of oil palm plantations was
processed in a grid of 100 × 100 km that covers the area with
favorable conditions for oil palm growth (Fig. 2).
Sentinel-1 and Sentinel-2 preprocessing
The CNN classifies radar and optical images collected by Sentinel-1 (C-band)
(Torres et al., 2012) and Sentinel-2 (multispectral)
(Drusch et al., 2012) satellites, respectively, both of which missions were
launched by the European Space Agency and were part of the Copernicus Programme
(https://www.copernicus.eu, last access: 17 March 2021). The images were preprocessed and downloaded from GEE
(code is available in Sect. 5, Descals, 2021). We used the Sentinel-1 synthetic aperture radar (SAR) Ground Range
Detected (GRD), which has a temporal resolution of 12 d, in both
ascending and descending orbits. We used the Interferometric Wide Swath
images processed at a spatial resolution of 10 m. The scenes were
processed with the local incident angle (LIA) correction, and then the median
value was computed over the second half of 2019 for the ascending and
descending scenes separately. The final composite is the average of the two
orbit composites.
We also used Band 4 (red band; central wavelength = 665 nm) of Sentinel-2
Level 2A (surface reflectance). Different feature selection algorithms
highlighted the relevance of Band 4 for predicting industrial and oil palm
plantations in a previous study (Descals et al., 2019). Band 4
is the 10 m resolution band that best shows the roads in industrial
plantations because of the high contrast in terms of reflectance between the
road and the surrounding oil palm. The high light scattering of vegetation
in the near-infrared spectrum makes the recognition of roads less feasible
in the 10 m near-infrared band (Band 8). The Sentinel-2 images were
masked with the quality flag provided in Level 2A, which is produced by the
ATCOR algorithm and provides information about the clouds, cloud shadows,
and other non-valid observations (Drusch et al., 2012). The
images were aggregated for the second half of 2019 using the normalized
difference vegetation index as the quality mosaic. The 5 d revisit time of
Sentinel-2 allowed for the generation of cloud-free composites over the study
area.
Image labeling
Semantic segmentation models require input images with a constant size for
both training and prediction. The size of the input images in this study was
set to 1000 × 1000 pixels, which corresponds to an area of 10 × 10 km in a
10 m resolution image. We set an input size of 10 km because it captures
the contextual spatial information necessary for identifying smallholders
and industrial plantations (e.g., harvesting road network). Consequently,
the model was trained with Sentinel-1 and Sentinel-2 half-yearly annual
composites of 10 × 10 km. The oil palm plantations that were present within
the Sentinel composites were labeled by visual interpretation. We digitized
the oil palm plantations also by interpreting the very high-resolution
DigitalGlobe images that are displayed as the base layer in GEE. The
DigitalGlobe images have a sub-meter spatial resolution and are displayed as
true-color composites in GEE. These images are updated regularly, and the
date depends on the location, but usually the images were taken during the
past 1 to 2 years. The DigitalGlobe images were used as complementary
data to the Sentinel-1 and Sentinel-2 composites in the visual interpretation. We
used the geometry editing tool in GEE for labeling smallholder and
industrial plantations. Once the training areas were labeled, we downloaded
the truth images from GEE along with the Sentinel-1 and Sentinel-2
composites for the second half of 2019. The image labeling was carried
out in 84 different regions of the world where oil palm is cultivated
(Appendix Fig. A3) and resulted in 200 training images.
Deep-learning algorithms require large amounts of data to ensure good
performance, and data augmentation is a technique used to improve the
performance of the models when the size of the training data is small
(Shorten and Khoshgoftaar, 2019). Data augmentation aims to generate
a more diverse training dataset with certain affine transformations applied
to the original training data. Data augmentation techniques have been used
in remote-sensing studies (Yu et al., 2017), in which affine
transformations, such as flips, translations, and rotations, have improved the
accuracy results of deep-learning models. We used the rotation of images (90∘ clockwise) as the data augmentation technique for this study
(Appendix Fig. A4). The rotation was applied only to the training images
that presented more than 10 % of the pixels labeled as smallholders in
order to reduce the class imbalance between industrial and smallholder
plantations. We also clipped the central area of 4 × 4 blocks of labeled
images and rotated them by an angle of 45∘. This process
resulted in 96 additional images that were added to the 200 original
training images.
Definition of industrial and smallholder plantations
Definitions of smallholders and industrial plantations differ per country,
and many variations within each of these classes exist
(Bronkhorst et al., 2017; Glenday and Gary, 2015; Meijaard
and Sheil, 2019). For the current study, we used the following generalized
classifications. An industrial oil palm plantation typically covers several thousand hectares
of land and is very well structured and homogeneous in tree age. It consists
of an area bounded by long linear, sometimes rectangular boundaries. It has
a dense trail and a road and/or canal network. Roads in industrial plantations are
developed at the start of plantation development and, therefore,
equidistantly placed for optimal harvesting. In flat surface plantations,
the harvesting trails are usually built in straight lines and thus form a
rectilinear grid (Fig. 3a). In contrast, the industrial plantations that
are constructed over steep terrain usually present curvy trails (Fig. 3b).
A smallholder oil palm plantation must be typically smaller than 25 ha to be recognized as “small” by the
Indonesian government. These definitions vary by country, with Malaysia using
a 4 ha cut-off, while in Cameroon, this varies from 8 to 40 ha (for an
overview, see Table 2 in Meijaard et al., 2018). Compared to an
industrial plantation, a smallholder plantation tends to be less structured in
shape and more heterogeneous in tree age. Smallholder plantations tend to
form a landscape mosaic composed of small plantations of varying shape and
size mixed with other types of land cover (e.g., idle land or other
plantation types) (Fig. 3d). When smallholder plantations form a large
homogenous cluster, this cluster has a less dense trail network than
industrial plantations (Fig. 3a, c).
Examples of industrial and smallholder oil palm
plantations seen by a 10 m resolution Sentinel-1 and Sentinel-2
composite (red channel: VV; green channel: VH; and blue channel: Band 4). The VV and VH bands were
transformed and stretched so that the closed-canopy oil palm appears in
green. (a) An industrial plantation on a flat surface in Brazil with
harvesting trails built in straight lines and thus forming rectilinear
grids. (b) An industrial plantation on hilly terrain in Indonesia, with curvy
harvesting trails. (c) Smallholder plantations forming a large homogeneous
cluster in Indonesia. (d) Smallholder plantations of varying shape, size, and
tree age in Côte d'Ivoire (image source: Copernicus Sentinel data 2019).
Accuracy assessment of the global oil palm layer for the
second half of 2019 and comparison of the global layer with the results of a
previous study (Descals et al., 2019) which used a random forest in Sumatra
for the same year. The accuracy metrics of the global layer were estimated
with 10 816 points randomly distributed in the main oil-palm-producing areas
in the world, while the comparison used only the validation points that were
located in Sumatra (2463 points). The reported metrics are the overall
accuracy (OA), the user's accuracy (UA), and the producer's accuracy (PA).
The accuracy metrics are reported with a confidence interval (95 %
confidence level).
Image segmentation is the subfield of deep learning that aims to link each
pixel of an image to a class label. Thus, semantic segmentation is the
analog of the standard pixel-wise machine-learning algorithms that are used
in remote sensing for image classification (Ma et al., 2019). The
difference is that semantic segmentation, as any model based on a CNN,
automatically learns and exploits the spatial patterns within the image by
tuning the parameters of different convolutional operations.
This study employed the classification model DeepLabv3+
(Chen et al., 2017, 2018) with the MobileNetV2
(Sandler et al., 2018) as a backbone network. DeepLab has a series
of versions for semantic segmentation. DeepLabv3+ is the latest version
(link to the code in Sect. 5). The model uses an encoder–decoder
architecture in which the image is downsampled with max-pooling layers
during the encoder part and spatial information is retrieved during the
decoder part. A characteristic of DeepLabv3+ is that the CNN uses atrous
convolutions which enhance the field of view of filters to incorporate a
larger spatial and informational context. The second-last layer of the CNN
shows the probability that a pixel belongs to a certain class, and the last
operation of the CNN assigns the class with the maximum value in the
probability layers, resulting in the final classification layer.
Validation
The accuracy of the global oil palm classification layer was evaluated with
10 816 reference points: 544 points were industrial plantations, 305 were
smallholders, and 9967 were other types of land uses. The points were
randomly distributed using a simple random sampling, which means that each
pixel on the map had an equal chance of being selected, and were distributed
in the 100 × 100 km cells where the IUCN oil palm layer showed the presence
of industrial plantations (cells outlined in red in Fig. 2). This sample
method led to a high imbalance between the points labeled as “Other land
uses” and the points labeled as oil palm, both industrial and smallholder,
since oil palm plantations present a rare occurrence in the study area. The
rare occurrence of oil palm implied that the probability of randomly
selecting an oil palm plantation was also low. This low representation of
oil palm plantations in the simple random sampling resulted in a high
uncertainty in the oil palm area estimates at the regional and country
level. For this reason, we included 2679 points that were distributed with
a stratified random sampling in order to achieve a minimum sample size in
the industrial and smallholder oil palm classes. The size of each stratum was
977 points in the class industrial oil palm and 802 in the class smallholder
oil palm, and 900 were other types of land uses. The 2679 stratified points
were merged with the 10 816 simple random points, making a total of 13 495
points that were used to calculate the oil palm area estimates.
Since the study aims to classify closed-canopy oil palm against other land
uses, we included young oil palm and plantations that have not reached the
full canopy coverage in the class “Other land uses”. The points were
visually interpreted using the Sentinel-1 and Sentinel-2 annual composites of the
year 2019 (See Sect. 2.3) and the DigitalGlobe orthoimages (<1 m spatial resolution) that are displayed as the base layer in the GEE
code editor.
The accuracy metrics that we reported were the overall accuracy (OA), the
user's accuracy (UA), and the producer's accuracy (PA) (Olofsson
et al., 2014). The OA is the proportion of reference points that have been
correctly classified and is calculated by summing the number of correctly
classified points and dividing by the total number of points. The OA
represents the probability that a randomly sampled pixel is correctly
classified. The PA results from dividing the number of correctly classified
points in each class by the number of visually interpreted points for each
class. The PA is the complement of the omission error: PA = 100 % –
omission error. Thus, the PA for the classes “industrial” and
“smallholder” is a relevant accuracy metric that shows the rate at which
the oil palm plantations were missed in the classification image. On the
other hand, the UA results from dividing the number of correctly classified
points in each class by the number of points classified in each class. The
UA is the complement of the commission error: UA = 100 % – commission
error). The UA for the classes “industrial” and “smallholder” indicates
the rate at which land uses have been incorrectly classified as oil palm
plantations.
The accuracy metrics were evaluated following the good practices for
estimating area and assessing accuracy reported by Olofsson et
al. (2014). The practices explain the post-stratified estimation of the OA,
PA, and UA with a confidence interval. Olofsson et al. (2014) also describe
the formulation for the area estimation for the classes that are present in
the land cover map. The area estimates are also calculated with a confidence
interval (here, a 95 % confidence interval for both accuracy
metrics and area estimates was utilized). Here, we used the term “area mapped” for the total area
classified as a given class and the term “area estimate” for the estimation of the actual
area and the associated uncertainty following the practices in Olofsson et
al. (2014). The area mapped is subject to the good accuracy of the
classification; for instance, a high omission rate in the class “industrial
closed-canopy oil palm” would potentially lead to a small area mapped, which
would represent an underestimate of the actual industrial oil palm area. The
area estimate and its confidence interval, however, cover the actual area
with a given confidence level.
Owing to the high imbalance in the validation dataset, we tested whether the
overall accuracy of the CNN was higher than the no-information rate. The
no-information rate was computed as the overall accuracy obtained if all
pixels were classified as the major class, which is the class “Other land
uses” in our study. The hypothesis test evaluates whether the overall
accuracy obtained in the CNN classification is significantly higher than the
no-information rate with a 95 % confidence level. If the null hypothesis
is rejected (OA > no-information rate), we can be assured that the
CNN did better than predicting indiscriminately all pixels with the class
“Other land uses”.
Comparison with other oil palm datasets
The accuracy of the CNN classification was compared with existing oil palm
maps of Sumatra for the year 2019 (Descals et al., 2019) and
Southeast Asia for the year 2016 (Xu et al., 2020). Also, we
compared our oil palm area estimates with the oil palm harvested area
included in Food and Agriculture Organization of the United Nations (FAOSTAT) data at the country level and with the area estimates
obtained from an oil palm map developed in Gaveau et al. (2021) over Indonesia for the year 2019. The oil palm maps in Descals et al. (2019) and Xu et al. (2020) were generated with a random forest classification,
while the map developed by Gaveau et al. (2021) was generated
by digitizing the oil palm plantations in Landsat and SPOT6 images.
In order to compare the current results with our previous study in Descals
et al. (2019), we reclassified the young oil palm classes in this existing
dataset to the class “Other land uses”. We also kept only the validation
points that cover Sumatra; this resulted in 2463 points out of the 13 495
total points. For the comparison with Xu et al. (2020), we used our CNN model
to classify Sentinel-1 and Sentinel-2 composites for the second half of 2016. Moreover, we reclassified the smallholders and industrial plantations
as a single class since the oil palm map in Xu et al. (2020) does not make
distinctions between oil palm typology (industrial versus smallholder
plantations). We also removed the validation points that were placed in
young plantations because the temporal analysis in Xu et al. (2020) aimed to
detect young oil palm and the plantations that had been clear-cut in the
previous years. Note that the dataset of Xu et al. (2020) includes a 100 m multi-year
classification for the years 2001–2016 and that we only compared the last year
(2016) to ensure data availability in Sentinel-1 and Sentinel-2 over the study area.
Results
The global map of industrial and smallholder plantations reveals the
importance of high-resolution images (10 m) for the accurate
delimitation of smallholder plantations. Figure 4 shows the degree of detail
of the classification image obtained with Sentinel-1 and Sentinel-2
composites. The figure also exemplifies the classification of industrial
plantations with the characteristic road network and the surrounding
smallholder plantations. Appendix Fig. A5 shows examples of landscape
types of oil palm plantations that were successfully detected and others
that were omitted.
Example of the global oil palm layer in Côte d'Ivoire.
Panel (a) shows a Sentinel-2 true color image. Panel (b) shows the resulting
classification image obtained with a convolutional neural network (CNN). The
classification image depicts an industrial plantation (red) surrounded by
smallholder plantations (purple). The CNN learns contextual information, such
as the rectilinear road network in the industrial plantation, which is
noticeable in the Sentinel-2 composite. Panel (c) shows the probability of
closed-canopy oil palm. The probability layer was generated from the second-last layer of the CNN which reflects the probability of each class (image source: Copernicus Sentinel data 2019).
We estimated the global area of planted closed-canopy oil palm at 21.00 ± 0.42 Mha, of which 15.26 ± 0.40 Mha (72.7 %) was industrial
plantations and 5.72 ± 0.22 Mha (27.3 %) was smallholders. The map
confirms that Southeast Asia is the highest-producing region in the world
(Fig. 5) with a total surface area of 18.69 ± 0.33 Mha. It is
followed by South America (0.91 ± 0.06 Mha), West Africa (0.79 ± 0.11 Mha), Central America (0.52 ± 0.04 Mha), Central Africa
(0.21 ± 0.6 Mha), and the Pacific (0.14 ± 0.00 Mha). Oil palm
plantations were found in 49 tropical countries (see Appendix Table A2).
However, the estimated oil palm area varies greatly among countries, with
Indonesia and Malaysia representing the bulk of the total surface area,
while most other countries have a plantation area below 2 Mha (Fig. 6).
Density maps generated with the global oil palm layer.
Panels (a, b, c) show the density maps of industrial oil palm
plantations, and panels (d, e, f) show the density maps for smallholder
plantations. The maps have a spatial resolution of 10 km and represent the
surface of closed-canopy oil palm, in hectares, in an area of
109 hectares. The values on the map were obtained by
dividing the area of the oil palm within the 10 km pixel by the total
area covered in the pixel.
The region with the highest percentage of smallholder oil palm was West
Africa (68.7 % of total plantings; Appendix Fig. A6). Elsewhere, the
percentage of smallholders varied from 14.5 % in Central Africa to
26.8 % in the Pacific. As Fig. 6 illustrates, however, countries in the
same region might show different proportions of smallholders and industrial
plantations. For instance, Thailand showed the highest proportion of
smallholders (71.5 %), which differed from the low ratio in neighboring
Malaysia (15.4 %). Countries in Southeast Asia also showed the highest oil
palm surface per total land area, followed by smaller countries that
allocate the majority of their cropland to oil palm production (Guatemala,
Honduras, Costa Rica, and São Tomé and Príncipe).
Oil palm plantation area per typology (industrial versus
smallholder) for the second half of 2019 in the 10 first countries
with the largest oil palm area. The figure reflects the area mapped
(asterisk mark), which resulted from the classification of Sentinel-1 and
Sentinel-2, and the area estimate with a confidence level of 95 %.
The accuracy metrics obtained with the 10 816 points show an OA of 98.52 ± 0.20 % (Table 2) for the global oil palm map (Appendix Table A3
shows the confusion matrix). This OA is significantly higher than the
no-information rate (92.00 ± 0.51 %), and, thus, we can be assured that
the CNN classification did better than assigning the major class to all the
validation points. The UA and PA were lower in industrial and smallholder
plantations than the same accuracies obtained in the class “Other land
uses”. Smallholder plantations showed the lowest UA (76.56 ± 4.53 %), while the industrial plantations showed the lowest PA (75.78 ± 3.55 %). The UA and PA accuracies were lower when evaluated only
in Sumatra (smallholder UA = 63.27 ± 7.82 % and industrial PA = 69.15 ± 4.62 %). However, these accuracies were considerably lower
in Descals et al. (2019), which presented a UA = 45.85 ± 6.84 % for
smallholders and PA = 54.26 ± 4.42 % for industrial plantations.
The state-of-the-art methodology using CNN also showed a higher overall
accuracy than the random forest classification for the case study in Sumatra
(91.31 ± 0.97 % compared to the 94.02 ± 0.89 % in the
current study).
Our results (OA = 96.59 ± 0.50 %) performed better than the
classification image (OA = 91.35 ± 0.69 %) of Xu et al. (2020) for 2016 (Appendix
Table A4). The producer's accuracy for industrial plantations in the
results (PA = 76.41 ± 3.08 %) of Xu et al. (2020) is higher than our results (PA = 73.65 ± 2.94 %), although this difference is not significant for a
confidence level of 95 %. The main difference between the datasets,
however, was found in the user's accuracy for smallholders, in which our
results excelled (UA = 96.60 ± 0.51 % compared to 57.36 ± 3.76 % in the dataset of Xu et al., 2020). The comparison with the data of Xu et al. (2020), however, only
reflects the accuracies for closed-canopy oil palm plantations; the
multi-annual analysis in Xu et al. (2020) also included the detection of
disturbances in the time series to classify young plantations (Fig. 7).
Similar to the data of Xu et al. (2020), the dataset produced in Gaveau et al. (2021) also mapped young oil palm and areas that were clear-cut for oil palm
plantation in Indonesia. For this reason, our closed-canopy oil palm area
estimate was 12.05 ± 0.23 Mha in Indonesia – area mapped was 11.54 Mha with 7.71 Mha (66.8 %) industrial and 3.83 Mha (33.2 %) smallholder – but, by comparison, Gaveau et al. (2021) found a higher oil palm area for Indonesia for
the same year: 16.26 Mha. Despite this difference, Gaveau et al. (2021) found a similar
ratio between industrial and smallholder plantation extent: 10.33 Mha
industrial (64 %) and 5.93 Mha smallholder (36 %).
Comparison of the classification image obtained with the
convolutional neural network (CNN) and the last year of the multiannual
analysis presented in Xu et al. (2020). Panel (a) shows a Sentinel-1 composite
(VV-VH-VV) for the second half of 2016 in Riau province (Indonesia).
The VV and VH bands were transformed and stretched so that
closed-canopy oil palm appears in green. Panel (b) shows the
classification image that results from the CNN using the Sentinel-1 and
Sentinel-2 composites for 2016. Panel (c) shows the oil palm layer presented
in Xu et al. (2020) for the year 2016 (image source: Copernicus Sentinel data 2019).
The comparison with inventories from FAOSTAT is also evidence of a large omission
of oil palm plantations in West Africa (Appendix Fig. A7). The total
surface reported as harvested area in FAOSTAT is 4.16 Mha in West Africa,
while our oil palm area estimate was 0.79 ± 0.11 Mha and the area
mapped 0.42 Mha. The country with the highest difference is Nigeria with an
area estimate of 3.02 Mha reported by FAOSTAT that contrasts with the 0.01 Mha classified by the CNN and the 0.25 ± 0.07 Mha total closed-canopy
oil palm area estimate.
Discussion
The results confirm previous findings on the suitability of radar satellite
data for mapping closed-canopy oil palm plantations at the regional scale
(Miettinen and Liew, 2011) and the improved accuracies obtained with
the combined use of radar and optical data for mapping smallholder and
industrial oil palm plantations (Descals et al., 2019). Our
study further shows that these plantations can be mapped globally and by
typology at high spatial resolution (10 m). The results obtained with
the CNN outperformed previous studies and provide evidence that deep
learning is more suitable than standard machine-learning algorithms, such as
random forests, when contextual information is required for class
prediction. Overall, the results show a high accuracy (OA = 97.46 ± 0.26 %). The accuracy assessment and the comparison with other products
also provide evidence of uncertainties associated with the oil palm definition: young
plantations, plantations that have open canopies, and plantations mixed with
non-palm tree species, such as semi-wild oil plantations in Africa.
We compared our findings with three studies, Descals et al. (2019), Xu et
al. (2020), and the dataset developed by Gaveau et al. (2021).
Our CNN model applied to Sentinel-1 and Sentinel-2 classified closed-canopy
oil palm stands with higher detail (10 m spatial resolution) than
existing datasets, although, at a coarser resolution (100 m spatial
resolution), the temporal analysis used in Xu et al. (2020) aimed to detect
disturbances (e.g., land clearing and burning) that may or may not result in
the development of oil palm plantations. Thus, Xu et al. (2020) classified
open-canopy plantations that remained undetected in our classification.
Accordingly, the omission error for oil palm was lower in the case of Xu et al. (2020),
although this difference was not significant. However, Xu et al. (2020) detected
much more than oil palm plantation, including scrubs and grasslands, and,
therefore, the commission error for oil palm was significantly higher in the map of Xu et al. (2020) (UA = 57.36 ± 3.76 % compared to 96.55 ± 1.92 % in
our study).
Gaveau et al. (2021) did not directly measure planted areas, but instead,
they identified areas that were “cleared to develop plantations”. An area
may have been cleared for oil palm and left idle because of several
constraints, or the area may have been planted, but the plantation may have
failed. The comparison with the maps of Xu et al. (2020) and Gaveau et al. (2021) provides evidence for an important
shortcoming with our method: the classification of oil palm with radar data
can only detect closed-canopy oil palm stands and, thus, excludes areas
cleared for oil palm that have been left idle or where oil palm trees died.
Moreover, oil palm must be at least 3 years old (Descals et al., 2019) to
reach the full canopy closure. Therefore, it is likely that our maps missed
young oil palm plantations developed after 2016. The dataset developed in
Gaveau et al. (2021) is more suited to verify the impacts of the oil palm
industry on forests, while our method is more suited to map the productive
planted area, i.e., closed-canopy oil palm stands >3 years old.
In contrast, the dataset of Gaveau et al. (2021) was produced mostly by the visual interpretation
and manual delimitation of oil palm development, while our method consisted
of a supervised learning algorithm; our trained CNN can automatically
classify remotely sensed data into oil palm maps for future land cover
monitoring.
Since our method classified only closed-canopy oil palm, it also struggled
to detect oil palm in nonhomogeneous settings (e.g., oil palm mixed with
other crops), plantations with low canopy coverage, and naturally occurring
and semi-wild oil palm trees, known as feral oil palm, that are present in
Africa. These semi-wild oil palm plantations explain the large difference
with the harvested area reported by FAOSTAT in West Africa. This means that
our global estimate of total planted oil palm areas (21.00 ± 0.42 Mha)
is an underestimate which considers only closed-canopy oil palm
plantations. It is difficult to say by how much we underestimate the total
planted area if considering young, nonhomogeneous settings and sparse oil
palm plantations, but, assuming constant planting rates and an average palm age of
25 years before replanting, we could miss 3/25 = 12 % of the
total planted area.
Despite the caveats regarding the total areas of planted oil palm, our
findings of the ratio between smallholders and industrial-type plantings are
still relevant as both types of oil palm are similarly affected by omission
in our analysis (except the oil palm we miss in agroforestry-type settings,
which is mostly smallholders). Evidence of this is that the ratios reported
by Gaveau et al. (2021) are similar to our estimates in
Indonesia. Globally, our data indicate that 72.7 % of planted oil palm is
under industrial-scale management and 27.3 % is managed by smallholders.
These percentages diverge from the commonly stated claim that 40 % of the
palm oil produced globally is from smallholders (Meijaard et
al., 2018). Not only is the land managed in smallholder-type settings less
than 40 %, but there is also a significant yield gap between
industrial-scale and smallholder-scale operators. Smallholder yields are
often 40 % or lower than yields in industrially managed plantations
(Woittiez et al., 2017), which suggests that the overall
contribution of smallholders to global palm oil production is about 18 %
rather than 40 %. Industrial-scale operators thus appear to produce about
82 % of the global palm oil. We note that this excludes the locally
produced palm oil in agroforestry-type settings in the African tropics,
where oil palm is traditionally produced for local consumption.
Our findings on the ratio between smallholder- and industrial-scale oil palm
are different from those reported by various governments. For example, the
government of Indonesia estimates that 40.8 % of the country's oil-palm-planted area is developed under smallholder licenses, whereas our analysis
of the typical characteristics of planted crops indicate that this ratio is
66.8 % industrial and 33.2 % smallholder for the country. To qualify as
a “smallholder farmer” in Indonesia, according to the government, farms must
be less than 25 ha. Those that cultivate less than 25 ha of oil palm are
required to apply for a plantation registration certificate (STD-B), while
those producers cultivating more than 25 ha require a plantation business license (IUP-B; Jelsma et al., 2017). The latter involves more
complex procedures and regulatory requirements, such as an environmental
impact assessment (Paoli et al., 2013). Those with an STD-B are
exempted from most of these requirements (Jelsma et al., 2017). This creates
an incentive for producers to classify their plantations as nonindustrial
scale because the paperwork and licensing involve fewer hurdles. This
mismatch between land occupancy (de facto) and legal allocation (de jure)
was also noted by Gaveau et al. (2017) in Sumatra, who noted
unregistered medium-sized landowners operating like companies in terms of
their approach to oil palm development but without formal company status.
Missing young plantations cannot explain the large difference we noticed in
Indonesia between our planted area estimate and FAO harvested area because
expansion has gone down in recent years (Gaveau et al., 2019).
In important producing regions (Riau, Sumatra), only 15 % of all
agricultural land parcels have a national-level registration, and 26 % of
all oil palm plantations were only registered at the village level
(Meijaard et al., 2018). Unregistered plantations explain why
we found more plantations than FAOSTAT. Discrepancies between this study's
findings and those of various governments on the ratio between smallholder-
and industrial-scale oil palm could result from underestimations by
authorities as identified by Oon et al. (2019). As with Indonesia,
it indicates how difficult it is to accurately map smallholder oil palm
because of the heterogeneous characteristics of this land use, the lack of
legal registration of smallholder lands, and potentially vested interests in
running large-scale operations under smallholder-type licenses (Appendix
Fig. A8).
The CNN model trained for the year 2019 is planned to be used for follow-up
monitoring once a year to generate global oil palm maps. The shortcomings of
deep learning include the high computational cost for training the models
and the high cost for gathering labeled data compared to the standard
machine-learning algorithms commonly used in remote sensing, such as random
forest. In this study, 296 images of 1000 × 1000 pixels were used as a
training dataset, consisting of 200 labeled images and 96 augmented images,
and the computing time for training a pretrained DeepLabv3+ was nearly 8 d with an office computer. Despite this, the computing time and the size
of our training dataset were considerably lower than state-of-the-art deep-learning studies in computer vision (i.e., more than 200 000 labeled images
in the Common Objects in Context, COCO, dataset) in which the number of
classes and complexity of the classification problem surpasses the current
study.
Code availability
The code that generates the Sentinel-1 and Sentinel-2 composites can be
found at: 10.5281/zenodo.4617748 (Descals, 2021).
The original code of the semantic segmentation model DeepLabv3+ can be
found at: https://github.com/tensorflow/models/tree/master/research/deeplab GitHub (2021).
Data availability
The dataset presented in this study is freely available for download at
https://doi.org/10.5281/zenodo.4473715 (Descals
et al., 2021). The dataset contains 634 100 × 100 km tiles covering areas
where oil palm plantations were detected. The file grid.shp contains the grid that
covers the potential distribution of oil palm. The file
grid_withOP.shp shows the 100 × 100 grid squares with the presence of oil palm plantations. The
classified images (oil_palm_map folder, in GeoTIFF format) are the output of the
convolutional neural network based on Sentinel-1 and Sentinel-2 half-yearly
composites. The images have a spatial resolution of 10 m and contain
three classes: (1) industrial closed-canopy oil palm plantations, (2)
smallholder closed-canopy oil palm plantations, and (3) other land
covers and/or uses that are not closed-canopy oil palm. The file
Validation_points_GlobalOilPalmLayer_2019.shp includes the 13 495 points that were used to validate the product. Each
point includes the attribute “Class”, which is the labeled class assigned
by visual interpretation, and the attribute “predClass”, which reflects the
predicted class by the convolutional neural network. The “Class” and
“predClass” values are the same as the raster files: (1) industrial
closed-canopy oil palm plantations, (2) smallholder closed-canopy oil palm
plantations, and (3) other land covers/uses that are not closed-canopy oil
palm.
The data can be visualized online at the BIOPAMA application portal:
https://apps.biopama.org/oilpalm/ (last access: 18 March 2021). The BIOPAMA application
portal also includes the probability layer, which shows the probability
(from 0 to 100) that a pixel is a closed-canopy oil palm plantation. The data
can also be visualized on the Google Earth Engine (GEE) experimental app:
https://adriadescals.users.earthengine.app/view/global-oil-palm-map-2019 (last access: 18 March 2021).
The global oil palm map is hosted in GEE as an Image Collection: https://code.earthengine.google.com/?asset=users/adriadescals/shared/OP/global_oil_palm_map_v1 (last access: 18 March 2021).
The Sentinel-1 SAR GRD and Sentinel-2 Level-2A used in this study (scenes
taken in the second half of 2019 in the tropics and second half of 2016 in Sumatra) are available at https://scihub.copernicus.eu/ (last access: 18 March 2021) and can be retrieved in GEE. When using GEE,
the Sentinel-1 and Sentinel-2 data are hosted and accessed in the Earth Engine data
catalog (the links to the data are https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD, last access: 18 March 2021, Earth Engine Data Catalog, 2014, and https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR, last access: 18 March 2021, Earth Engine Data Catalog, 2017, respectively). Despite the fact that the data are hosted by GEE,
these satellite data are the same as those accessed via the official portal
(Copernicus Open Access Hub: https://scihub.copernicus.eu/, last access: 18 March 2021);
data ingested and hosted in GEE are always maintained in their original
projection, resolution, and bit depth (Gorelick et al., 2017).
The WorldClim bioclimatic variables (WorldClim V1 Bioclim)
(Hijmans et al., 2005) were also accessed through GEE: https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO (last access: 18 March 2021). The data can be accessed in the official portal at
https://www.worldclim.org/data/v1.4/worldclim14.html (last access: 18 March 2021).
The IUCN industrial oil palm layer (Meijaard et al., 2018) can
be found at https://doi.org/10.5061/dryad.ghx3ffbn9 (Meijaard and Gaveau, 2021). The oil
palm layer of Indonesia and Malaysia for the year 2016 (Xu et al., 2020) can
be found at https://doi.org/10.5281/zenodo.3467071 (Xu et al., 2019). The oil
palm layer of Sumatra for the year 2019, developed with the same methodology
as in Descals et al. (2019) for Riau province (Indonesia), is
hosted as a GEE asset at https://code.earthengine.google.com/?asset=users/adriadescals/shared/Sumatra_oilPalm_L2_Descals_et_al_2019 (last access: 18 March 2021).
Very high-resolution images (spatial resolution <1 m) from
DigitalGlobe can be visualized in the GEE code editor or
Google Maps (i.e., https://www.google.com/maps/@-3.969372,105.048514,782m/data=!3m1!1e3, last access: 18 March 2021; Google, 2021).
The country-wide harvested area of oil palm was extracted from the FAOSTAT
database (accessed on 10 Jun 2020): http://www.fao.org/faostat/en/ (FAO, 2020).
Conclusions
This study presents the first global map of oil palm plantations for the
year 2019 derived from remotely sensed data with a spatial resolution of 10 m. We classified Sentinel-1 and Sentinel-2 data onto a map that
discriminates between smallholders and industrial oil palm plantations. We
obtained high accuracies with user's and consumer's accuracy generally
above 80 % thanks to the use of cutting-edge deep-learning algorithms.
The method is deployable and can generate yearly maps for oil palm
monitoring in a cloud processing environment and based on freely available
satellite imagery.
Our global oil palm map makes an important contribution to the palm oil
debate. It will be useful to solve or at least clarify a range of social and
environmental debates. We know that oil palm plantations are a major cause
of deforestation in Indonesia and Malaysia (Austin et al.,
2019; Gaveau et al., 2019), but the share of oil-palm-driven deforestation
to global tropical forest loss is not known. This map will help to inform
the debate on oil-palm-driven deforestation globally. Forest clearing for
oil palm is associated with negative socioeconomic impacts on
forest-dependent communities (Santika et al., 2019). This map
validates a novel approach to mapping where oil palm is grown by
smallholders who generate direct income or consumption from their own
plantations as opposed to industrial-scale oil palm (or industrial-scale
plantings disguised as smallholdings) where plantations provide labor
opportunity, but profits are primarily channeled to company owners and the
government (through taxes). The data can thus guide better planning for
maximizing socioeconomic benefits from oil palm. The global oil palm layer
also assists in the discussion about environmental impacts of oil palm,
including on biodiversity (Fitzherbert et al., 2008;
Meijaard et al., 2018) and regional climate (McAlpine et al.,
2018). These negative impacts are real but need to be considered in the
light of meeting the global demand for vegetable oil through the optimal
allocation of land not just to oil palm but to all major oil-producing
crops. This requires high-resolution spatial data for all oil seed crops
(Meijaard et al., 2020a) so that informed decisions can be made
about land use based on yield differences, past environmental and social
impacts of different crops, and the different characteristics of oils from
different crops and their particular end uses. Finally, and relevant to the
current COVID-19 pandemic, our global map can help localize areas where
zoonotic diseases can originate from, especially in areas where oil palm
expansion was associated with recent deforestation. Such insights are
essential for the health of people and the economy (Wardeh et al.,
2020).
Range of climate values in industrial plantations. These ranges
represent the minimum and the maximum values of the WorldClim bioclimatic
variables observed in the industrial oil palm plantations of the IUCN layer. The variable names bio05 and bio06 represent the maximum temperature of the warmest month and the minimum temperature of the coldest month.
Bio-variablesMin. valuesMax. valuesUnitsAnnual mean temperature18.5028.90∘CMean diurnal range6.0014.50∘CIsothermality57.0095.00%Temperature seasonality1.1922.19∘CMax. temperature of warmest month24.1036.50∘CMin. temperature of coldest month12.9024.20∘CTemperature annual range (bio05-bio06)7.1018.30∘CMean temperature of wettest quarter18.7029.00∘CMean temperature of driest quarter18.3029.00∘CMean temperature of warmest quarter18.9029.60∘CMean temperature of coldest quarter18.2028.10∘CAnnual precipitation987.005032.00mmPrecipitation of wettest month134.00831.00mmPrecipitation of driest month1.00274.00mmPrecipitation seasonality9.00101.00Coef. of variationPrecipitation of wettest quarter386.002069.00mmPrecipitation of driest quarter7.00911.00mmPrecipitation of warmest quarter107.001795.00mmPrecipitation of coldest quarter8.001955.00mm
List of countries where we confirmed the presence of oil
palm plantations with the global oil palm layer for the second half of 2019.
North and Central AmericaSouth AmericaWest AfricaCentral AfricaSouth and Southeast AsiaPacificDominican RepublicEcuadorTogoAngolaIndonesiaPapua New GuineaMexicoVenezuelaGhanaBurundiSingaporeSolomon IslandsEl SalvadorColombiaCôte d'IvoireRwandaBruneiVanuatuGuatemalaPeruGuineaUgandaPhilippinesPanamaBrazilGuinea-BissauTanzaniaMalaysiaCosta RicaSierra LeoneCameroonThailandNicaraguaLiberiaEquatorial GuineaBurmaHondurasBeninSão Tomé & PríncipeCambodiaBelizeNigeriaGabonVietnamRep. of the CongoIndiaCentral African Rep.Sri LankaDem. Rep. of the Congo
Confusion matrix of the global oil palm layer (second half of 2019) validated with 10 816 points. Columns represent the mapped
classes, and rows are the true label.
Comparison of the classification image obtained with the
convolutional neural network (CNN) and the classification image presented in
Xu et al. (2020) for 2016. In order to compare both methodologies, we applied
the CNN to Sentinel-1 and Sentinel-2 composites of the second half of 2016, which
corresponds to the last year of the multi-annual analysis in the dataset of Xu et al. (2020).
We used 5199 points randomly distributed in the study area, which covers
Malaysia, Sumatra, and Borneo. The accuracy metrics are reported with a
confidence interval (95 % confidence level).
Global OP year 2016 (SE Asia)Xu et al. (2020) for 2016OA (%)96.59 (96.10, 97.09)91.35 (90.65, 92.04)Other96.60 (96.09, 97.11)97.37 (96.91, 97.84)UA (%)Industrial + smallholder96.55 (94.63, 98.47)57.36 (53.60, 61.11)Other99.65 (99.46, 99.84)92.79 (92.20, 93.38)PA (%)Industrial + smallholder73.65 (70.71, 76.58)79.49 (76.41, 82.57)
Diagram of the workflow for the generation of the oil
palm map. The color of the square that surrounds the processing steps
depicts the programming environment used. The classification of satellite
images was done with Matlab 2019a, but alternatively, the images can be
classified with the code distributed in Python.
Maps generated for the estimation of the study area. The
upper map (a) shows the number of WorldClim bioclimatic variables that fall
within the range observed in the industrial oil palm plantations (IUCN
layer). The middle map (b) shows the potential area for oil palm growth, which
represents the pixels with more than 17 bioclimatic variables out of 19 falling within the range observed in the IUCN layer. The lower map (c)
reflects the grid used to cover the study area.
Location of areas where Sentinel-1 and Sentinel-2 was collected
for training the convolutional neural network.
Data augmentation used in the training data. Sentinel-1
and Sentinel-2 were labeled in images 1000 × 1000 pixels in size. These training
images were rotated 90∘ clockwise to increase the size and quality
of the training dataset. We also applied a rotation of 45∘ in
labeled images 2000 × 2000 pixels in size (image source: Copernicus Sentinel data 2019).
Ratio of smallholder and industrial plantations at the
sub-continental scale. The ratio was obtained with the global oil palm layer
for the second half of 2019.
Oil palm surface per country generated with the global
oil palm map (2019) and extracted from the FAOSTAT “harvested area” for the year 2018. The error bar in the global oil palm map shows the confidence interval
with a confidence level of 95 %.
Smallholder oil palm planted right up to the
river edge in Sumatra. These large areas of industrial-scale oil palm
plantings are operated under smallholder licenses, potentially making it
easier to bypass environmental legislation to prohibit the planting of oil palm within 50 m of river banks (Foto: Erik Meijaard).
Author contributions
The concept for this work originated from ZS
during scientific project discussions with SW, EM, and SP for the goal of
providing the scientific and policy-making community with up-to-date and
repeatable monitoring capabilities of oil palm plantations. AD and ZS
designed the study, AD collected the training data, and AD and SW collected
the validation points. AD implemented the data processing workflow. AD, ZS,
SW, EM, DLAG, and SP were involved in the analysis, writing, and the revision of the manuscript, and AD generated the figures and tables.
Competing interests
Adrià Descals performed remote-sensing
consulting work for the ICT & GIS department at PT Austindo Nusantara
Jaya Tbk. Serge Wich received research funding from PT Austindo Nusantara
Jaya Tbk, is a member of the IUCN Oil Palm Task Force, and has done oil-palm-related work for this task force. Erik Meijaard chairs and has received
funding from the IUCN Oil Palm Task Force, and he has done work paid for by palm
oil companies and the Roundtable on Sustainable Palm Oil. David Gaveau is a
member of the IUCN Oil Palm Task Force, a group tasked by the IUCN to
investigate the sustainability of palm oil, and he has done oil-palm-related
work for this task force and Greenpeace.
Disclaimer
All features and data are provided “as is” with no
warranties of any kind. The views expressed are purely those of the writers
and may not in any circumstance be regarded as stating an official position
of the European Commission.
Acknowledgements
The authors would like to thank the BIOPAMA
(European Commission, Joint Research Centre) technical developer team
members' effort with the online visualization tool (Luca Battistella, Martino Boni,
James Davy). The authors also thank Aaron McKinnon (Copernicus Emergency
Management Service and Global Land Service – Communication Manager) for
proofreading the paper.
Financial support
This study was supported by the Biodiversity and
Protected Areas Management (BIOPAMA) program, an initiative of the Organization of African, Caribbean, and Pacific Group of States financed by the 10th and
11th European Development Funds of the European Union and co-managed by the
European Commission Joint Research Centre and the International Union for
Conservation of Nature.
Review statement
This paper was edited by David Carlson and reviewed by two anonymous referees.
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