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Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in Côte d’Ivoire, close to the Taï National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user’s accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in Côte d’Ivoire.
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Agroforest Syst (2023) 97:109–119
https://doi.org/10.1007/s10457-022-00791-2
Modelling thespatial distribution oftheclassification error
ofremote sensing data incocoa agroforestry systems
DanKanmegneTamga · HoomanLatifi·
TobiasUllmann· RolandBaumhauer·
MichaelThiel· JulesBayala
Received: 29 November 2021 / Accepted: 19 October 2022 / Published online: 28 October 2022
© The Author(s) 2022
vegetation indices from Sentinel-1 and -2 data respec-
tively, to train a random forest algorithm. A classified
map with the associated probability maps was gener-
ated. (ii) Shannon entropy was calculated from the
probability maps, to get the error maps at different
thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the gen-
erated error maps were analysed using a Geographi-
cally Weighted Regression model to check for spatial
autocorrelation. From the results, a producer accuracy
(0.88) and a user’s accuracy (0.91) were obtained. A
small threshold value overestimates the classification
error, while a larger threshold will underestimate it.
The optimal value was found to be between 0.3 and
0.4. There was no evidence of spatial autocorrelation
except for a smaller threshold (0.2). The approach dif-
ferentiated cocoa from other landcover and detected
encroachment in forest. Even though some informa-
tion was lost in the process, the method is effective
for mapping cocoa plantations in Côte d’Ivoire.
Keywords Cocoa mapping· Geographically
weighted regression· Sentinel-1· Sentinel-2·
Shannon entropy· Spatial error assessment
Introduction
The Cocoa tree (Theobroma cocoa) is a major cash
crop in the forest region of West Africa. It is grown
for cocoa beans which are known to be used as input
in the agro-industrial sector (chocolate and beverage
Abstract Cocoa growing is one of the main activi-
ties in humid West Africa, which is mainly grown in
pure stands. It is the main driver of deforestation and
encroachment in protected areas. Cocoa agroforestry
systems which have been promoted to mitigate defor-
estation, needs to be accurately delineated to support
a valid monitoring system. Therefore, the aim of this
research is to model the spatial distribution of uncer-
tainties in the classification cocoa agroforestry. The
study was carried out in Côte d’Ivoire, close to the Taï
National Park. The analysis followed three steps (i)
image classification based on texture parameters and
D.KanmegneTamga(*)· H.Latifi· M.Thiel
Department ofRemote Sensing, Institute forGeography
andGeology, Julius-Maximilians-University ofWürzburg,
Oswald-Külpe-Weg 86, 97074Würzburg, Germany
e-mail: dan.kanmegne_tamga@uni-wuerzburg.de
H.Latifi
Department ofPhotogrammetry andRemote Sensing,
Faculty ofGeodesy andGeomatics Engineering,
K. N. Toosi University ofTechnology, P.O. Box,
15433-19967Tehran, Iran
T.Ullmann· R.Baumhauer
Department ofPhysical Geography, Institute
forGeography andGeology, Julius-Maximilians-
University ofWürzburg, Am Hubland, 97074Würzburg,
Germany
J.Bayala
Centre forInternational Forestry Research (CIFOR) -
World Agroforestry (ICRAF), CIFOR-ICRAF, Sahel 06,
BP 9478, Ouagadougou06, BurkinaFaso
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production), but also in cosmetic and pharmaceutical
products. Cocoa beans and cocoa food preparations
are the first exported commodities of the region. They
represent about 5% of the revenues from exports,
more than precious stones (3%) and their primary
destinations are the Americas (40%), Europe (28%),
and Asia (16%) (ECOWAS 2016). At a microeco-
nomic level, cocoa farming is the basis of the liveli-
hood of about two million farmers in the West Afri-
can cocoa belt (Boeckx etal. 2020). Despite the fact
that revenues from cocoa stricto sensu are not suf-
ficient to reach descent living income (only 33% of
farmers in Ghana has a sufficient income from cocoa
Boeckx etal. 2020), and the burning question of child
labour in the cocoa value chain (Busquet etal. 2021;
ILO 2017; International Anti-slavery 2004), the sus-
tainable intensification of cocoa production could be
a solution to poverty alleviation (Boeckx etal. 2020).
The demand for cocoa will increase since the global
cocoa beans market size is projected to expand by
7.3% in 2025, mainly due to the rapid growing of
chocolate industries in emerging economies such as
China and India (GVR 2019).
Côte d’Ivoire is the world largest producer of
cocoa beans producer, with over 2 million tons in
2020, which makes up for approximately 39% of the
world’s cocoa production (Shahbandeh 2021). For
that performance, 47 000 ha of forest were cleared
in 2020 (Mighty Earth 2021). Smallholder farmers
take advantage of soil fertility and land availability
to establish new cocoa farms at the expense of forest
(Ruf etal. 2014). Encroachments in protected areas
where reported, with alarming consequences such as
habitat degradation and loss of wildlife -especially for
primate species-, carbon emissions and soil degrada-
tion (Abu etal. 2021; Dean 2019; Bitty et al. 2015).
Cocoa farming is the main driver of deforestation,
responsible of the loss of more than 80% of the forest
cover in Côte d’Ivoire between 1961 and 2000 (Sabas
etal. 2020). Here the term cocoa and cocoa planta-
tion will be used alike to refer to cocoa farming.
Certification of cocoa, which includes environ-
mental standards, was introduced to limit deforesta-
tion. These standards include sustainable farming and
reduced deforestation, mainly to ensure that cocoa
farms are established outside protected area (ProLand
2020). Since its introduction in Côte d’Ivoire in 2005,
certification has not consistently prevented deforesta-
tion. One reason is that it is voluntary, but the main
reason is the gap in the geographic coverage. Weak-
ness in traceability was reported as the most critical
factors due to a lack of accurate maps of cocoa, which
results in poor auditing and difficulty for the enforce-
ment of standards (ProLand 2020). Efforts have been
made to limit cocoa extension, by improving old
cocoa farm productivity through the introduction of
forest trees in cocoa plantations; but also by advance-
ment of surveillance around the Comoé and Taï
National parks. (GIZ 2020; Pye-Smith etal. 2016).
However, these efforts have not been geographically
evaluated due to the lack of a robust method to gener-
ate accurate maps.
Freely-available remote sensing imagery from the
Sentinel mission, provided by the European Space
Agency (ESA) has been used for the detection and
mapping of cocoa plantations in West Africa. Abu
et al. (2021) detected cocoa and its impact on pro-
tected areas in Côte d’Ivoire. With the same data-
set processed with deep learning, it was possible
to separate full sun cocoa and cocoa agroforestry
(cocoa associated with forest trees) in the same region
(Ashiagbor et al. 2020). However, cocoa farms are
difficult to map, especially in the regions where they
share the landscape with other spectrally overlap-
ping crops such as rubber and palm oil. This create
a heterogenous landscape also referred to as complex
landscape where the classified map will have a dis-
proportionate number of misclassified pixels (Fillela
2018). To ensure image classification of good qual-
ity and with high resolution imagery could be used
in combination with open-source data. This solution
was used to successfully delineate cocoa agroforests
in Cameroon, and to classify different agroforestry
systems (AFS) in Mali (Aguilar etal. 2018; Numbisi
etal. 2019). Such high-quality map comes at a cost
in terms of the price, geographic restriction, and the
difficulty to integrate it in a near-real time monitoring
system.
Another solution is to use freely available imagery
for the classification of cocoa farms while present-
ing a spatial assessment of the classification error
alongside the classification. It is acknowledged that
the confusion matrix is not informative enough to
evaluate a classification, since it does not provide
any spatial information on the error. Therefore, we
introduced a spatial error assessment workflow as a
supplement for the evaluation of a classified map
(Comber etal. 2020; Narayan etal. 2021; Roodposhti
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et al. 2019). The spatial evaluation of the classifi-
cation is a post classification method that is used
to identify pixels with a high probability of being
misclassified. It is based on the analysis of the het-
erogeneity at each pixel based on the prediction prob-
abilities (Roodposhti et al. 2019). It also evaluates
the spatial correlation of the error to identify local
conditions that affect the classfication (Comber etal.
2020). The application of this classification workflow
is still missing in West Africa. Therefore, the aim of
this study is to model the spatial distribution of error
on the classified map to provide reliable cocoa maps
in Côte d’Ivoire. The study neither assesses the per-
formance of classification techniques nor evaluates
the type of input data, as it rather aims at detecting
and mapping misclassified pixels. The hypotheses
are: (i) the analysis of the heterogeneity of each pixel
could reveal misclassified pixels; and (ii) spatial error
assessment could reduce false detection of cocoa
encroachment from non-protected into protected area;
(iii) the distribution of the classification error is spa-
tially correlated and could be explained by satellite
derived indices.
Methods
Study area
The study was conducted in the southern part of Côte
d’Ivoire. The area was a forest region associated
with the transitional equatorial climate. Agriculture
was the main activity to support the economic life
in the region. Within the region, available lands are
promptly converted into plantations of cash crops
such as cocoa (Theobroma cocoa), rubber (Hevea
brasiliensis) and oil palm (Elaeis guineensis), but
also for food crops like yam (Dioscorea alata), rice
(Oriza sativa), and cassava (Manihot esculenta).
Agricultural plantations are located next to two pro-
tected forests in the study area, including the northern
part of Taï National Park and a portion of the wildlife
reserve of N’Zo. These are fully protected areas, with
no human intervention.
A field campaign was conducted in March 2020,
in the administrative subdivisions of Gueyo and Buyo
(Fig. 1). The data acquisition consisted of collect-
ing georeferenced points and polygons in AFS, using
sampling plots of 25 × 25m, which were used in the
image classification workflow. Four AFS were con-
sidered: (i) cocoa plantations (full sun cocoa or cocoa
agroforestry) with at least two production cycles; (ii)
Rubber and (iii) oil palm plantations as monoculture
planted in rows, often established on formal agricul-
tural plots; and (iv) agricultural farms dedicated to
food production including cotton (Gossypium sp).
In total, 139 referenced polygons were collected: 30
plots of cocoa, 31 plots of rubber, 16 plots of palm
and 62 agricultural farms.
Overview of methodology
The data processing was organized in three steps:
(1) supervised image classification from which two
outputs were generated: a classification map and
the probability maps of each class. (2) the Shannon
entropy at pixel level was calculated using the prob-
ability maps, and different thresholds were specified
to model the spatial distribution of the classification
error (Roodposhti etal. 2019). (3) A geographically
weighted regression was used to evaluate the spatial
autocorrelation of the classification error (Comber
etal. 2020).
Data pre-processing
Sentinel‑1
The Sentinel-1 (S1) C-band Synthetic Aperture Radar
data provided by the ESA was used in combination
with optical data to deal with clouds in the region of
interest. The selected acquisition mode was the Inter-
ferometric Wide swath (IW) with VV and VH polari-
sations, which is the main acquisition mode over land
and satisfies most current service requirements such
as preserving revisit performance (Copernicus 2022).
With Google Earth Engine (GEE), a composite image
(median pixel value) was created between 2017 and
2020, which is corresponding to the period of expo-
nential expansion of cash crops in the area (Yao etal.
2020). The median image was used to be able to com-
bine S1 with Sentinel-2 (S2). A mosaic image of the
study area was finally downloaded at the spatial reso-
lution of 10m.
The texture information was retrieved from the VV
and VH bands using the Grey Level Co-occurrence
Matrix (GLCM) (Table1). Texture parameters from
GLCM provide valuable information on the spatial
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relationship between pixels on the image, and also
significantly improve the classification accuracy,
especially for heterogenous landscapes with open
canopy like cocoa plantations (Mishra et al. 2019).
To generate the GLCM texture parameters, a window
size of 5 by 5 pixels, and 32 number of grey levels
were used as suggested in the literature (Mishra etal.
2019; Numbisi etal. 2019).
Sentinel‑2
The S2 archive provided by ESA was used in combi-
nation with S1. Five bands were used: red (R), green
(G), blue (B), near-infra-red (NIR) and red-edge (RE)
at the spatial resolution of 10 m. The images were
acquired using GEE, which uses the Sen2Cor module
for the preprocessing of S2 images. Since the area is
cloudy throughout the year, the cloud percentage per
pixel was set to 5%, and for each band to get a use-
ful image. The composite image (median image) was
generated between 2017 and 2020. Seven vegetations
indices (NDVI, GLI, EVI, SAVI, MSAVI, TCARI,
VARI, see Table 2) were generated to provide
Fig. 1 Study area in Southern Côte d’Ivoire. Land cover mask represents the area corresponding to water forest, and urban areas.
Gueyo and Buyo are the administrative subdivisions where the field work was carried out
Table 1 GLCM texture parameters (Hall-Beyer 2017)
Texture measures Abbreviation (for
VV polarization)
Formula
Entropy VV_entro ∑−ln(Pij)Pij
Contrast VV_contr ∑Pij(i−j)2
Variance VV_var ∑Pij(i−µi)2
Correlation VV_cor ∑Pij[(i−µi)(j−µi)/σ2]
Mean VV_mean ∑Pij/N
Homogeneity VV_homo ∑Pij/(1 + (i−j))2
Dissymmetry VV_diss ∑(∑(|i−j|Pij))
Second moment VV_sec ∑(∑(Pij)2)
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additional information on the biophysical properties
of the vegetation in the study area.
Data analysis
Random forest classification
Random Forest (RF) is a commonly used machine
learning algorithm for classification, which combines
the output of multiple decision trees to reach a single
classification result. It could predict a feature class,
or the probability of belonging to a class. RF reduces
the risk of overfitting, provides flexibility (as it is not
affected by data distribution or missing values) and
gives information on the feature importance.
To classify the image features from S1 and S2, the
data were split in train (75%) and test (25%) sets. A
10-folds cross validation with 10 repetitions was used
to train the model using the train set, and the optimal
variables were randomly sampled at each split. The
Overall Accuracy (OA), the Producer and User Accu-
racies (PA and UA), and the area under the curve
were generated as evaluation metrics for the classifi-
cation (Abu etal. 2021). The trained model was used
to predict AFS classes on the image, and to generate
the probability map of each feature class. To reduce
the salt and pepper effect on the map, a probability
pass filter of 3 by 3 pixels was applied on the clas-
sified map. The spatial resolution was evaluated and
integrated in the area analysis.
Spatial error assessment
The probability map of each feature class, generated
from RF algorithm, was used to generate an entropy
map using the Shannon entropy (Eq.1). The entropy
values range from 0 to 1, where high entropy values
are associated with high probability of classification
error (Roodposhti etal. 2019).
where H(x) is the entropy value for pixel (x), and Pi
is the probability value of the pixel (x) for the AFS
class i.
The pixels of the entropy map were grouped into
two classes (error and good classification) using pre-
defined entropy threshold’s value. This was referred
to as error map. Different threshold values were
evaluated: (1) a single entropy threshold value for the
entire map (Roodposhti etal. 2019). Here four values
were evaluated (0.2; 0.3, 0.4; 0.5). (2) Entropy thresh-
old value corresponding to each feature class. To get
the value for each class, field polygons were used to
extract entropy pixel values for each class. Under
normal distribution, the mean value was selected as
threshold for the given class. The extracted values
were cocoa = 0.32; palm = 0.52; farm = 0.3 and r ub-
ber = 0.43. The error maps were then used to remove
error pixels from the classified map. Since the error
map from (2) was based on field plots, it was used as
reference with which other error maps were compared
to evaluate under- and overestimation for each class.
Geographically weighted regression (GWR)
A GWR model (2) was implemented to evaluate
non-stationarity on the error maps. Non-stationarity
occurs when a global model such as ordinary least
square model fails to capture the spatial relationship
(1)
𝐇
(𝐱)=−
P𝐢𝐥𝐨𝐠𝟐P
𝐢
Table 2 Vegetation indices and formulae
Bands: (B blue, G green, R red, RE red-edge, NIR near-infrared)
Vegetation indices Formula References
1. Normalized difference vegetation index (NDVI) (NIR–R)/(NIR + R) Rouse etal. (1973)
2. Green leaf index (GLI) (2 * G–R–B)/(2 * G + R + B) Gobron etal. (2000)
3. Enhanced vegetation index (EVI) 2.5 * (NIR–R)/(NIR + 6 * R–7.5 * B + 1) Huete (1999)
4. Soil adjusted vegetation index (SAVI) (1 + L) * (NIR–R)/(NIR + R + L), L = 0.5 Huete (1988)
5. Modified soil adjusted vegetation index (MSAVI) 0.5 x (2 * NIR + 1–sqrt((2 * NIR + 1)2–8 * (NIR–R)) Qi etal. (1994)
6. Transformed chlorophyll absorption in reflec-
tance index (TCARI)
3 * ((RE–R)- 0.2 * (RE-G) * (RE/R)) Haboudane etal. (2002)
7. Visible atmospherically resistance Index (VARI) (G–R)/(G + R–B) Gitelson etal. (2002)
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between variables (Narayan Mishra et al. 2021).
GWR is described as a spatial and statistical tech-
nique for modelling heterogeneous processes when
the relationship between the dependent and the inde-
pendent variables varies as a function of spatial loca-
tion on the map (Brunsdon etal. 1996).
where yi is the classification (error of good classifica-
tion) at location i; βi0 is the intercept variable at loca-
tion i; βik: represents the local regression coefficient
for variable k at location i. and Xik the independent
variable k at location i and εj represents the residual
error at location i.
To implement GWR, 500 points were randomly
distributed over the study area to extract pixel values
from the independent variables (satellite data) and
the dependent variable (error maps). The relevant
features were selected based on the Akaike Informa-
tion Criterion (AIC) in stepwise logistic regression,
and the residuals were evaluated for spatial autocor-
relation (Comber et al. 2020). No evidence of spa-
tial autocorrelation would suggest a stationary map,
therefore a GWR model is not applicable. However,
if there is evidence of spatial autocorrelation, a Mul-
tiscale-GWR model (MS-GWR) would be applied
to determine the spatial scale at which each predic-
tor varied. If the residuals from the MS-GWR model
are spatially autocorrelated, then a specific variant of
GWR (MS-GWR or Mixed-GWR) will be applied
to calculate the coefficient of the variable across the
map (Comber etal. 2020). This analysis was done in
R using the packages Raster, GWmodel and ggplot2.
Results
Image classification
Texture parameters and vegetation indices generated
from S1 and S2 data, respectively, were used to train
a RF model for the classification of four AFS in the
region. The classification suggested a high perfor-
mance of OA = 0.94 (Kappa = 0.92), with the small-
est producer’s accuracy for cocoa (PA = 0.88 and
UA = 0.91). The vegetation over the protected area
was classified mainly as cocoa and rubber planta-
tions (Fig. 2). When looking at the field plots, the
(2)
𝐲
𝐢=𝛃𝐢𝟎+
𝛃𝐢𝐤𝐗𝐢𝐤 +𝛆
𝐢
classification showed some confusion between fea-
tures classes. In Fig.1a, rubber and farm pixels were
falsely classified as cocoa. Moreover, pixels cor-
responding to cocoa are falsely classified as palm
or rubber (Fig. 1c, d respectively). As suggested by
the AUC, cocoa and rubber plantations were more
difficult to distinguish (AUC = 0.95) compared to
farm and palm plantation (AUC = 0.98 and 0.99
respectively).
Spatial error assessment
To model the spatial distribution of the error, an
entropy map was generated based on the probabil-
ity of each feature class, where different thresholds
were used to produce the error maps (Fig. 3). The
figure indicated that higher entropy threshold values
(thr = 0.5 for example) resulted in smaller area of the
classification error (38.5% compared to 58.3% for
thr = 0.2), which could result in underestimation of
the classification error for larger threshold values or
overestimation for smaller values. For each threshold,
the protected areas were labelled as error, with some
points corresponding to an AFS within their bound-
ary. The area of each AFS at different thresholds
was evaluated against the reference, which showed
that there was an overestimation of the error with
thr = 0.2 and thr = 0.3, corresponding to ca. 1 865.5
km2 and 645.3 km2, respectively. In addition, thr = 0.4
and thr = 0.5 showed an underestimation of the error
(376.3 km2 and 1 141.5 km2, respectively). The dif-
ference in the cocoa area (134.5 km2) between the
error map with thr = 0.3 and the reference error map
(based on field plots) was small compared to the other
classes.
The spatial distribution of the classification error
(reference error map) was used to remove errone-
ous pixels from the classified AFS map (Fig.4). The
result showed that the method was efficient to deal
with the classification error as illustrated by Fig.4c.
However, removal of the erroneous pixels also partly
removed classified pixels (Fig. 4d). The area sta-
tistics indicated that 72.8% (7 425 ha) of the area
is allocated to cocoa plantation, 15.3% to rubber (1
555ha), 7.9% to agriculture (804ha) and 4.09% for
to palm (417ha). Based on the results, there was evi-
dence of encroachment in the protected areas, where
the detected feature classes included cocoa (8.1%:
197ha), rubber (8.1%: 16ha) and farm (1ha).
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Geographically weighted regression (GWR)
The stepwise regression indicated that 12 features
were significant to model the distribution of the
classification error across the map (R2 = 0.78). The
residual analysis of the regression showed that there
was no evidence of spatial autocorrelation (p = 0.66).
However, there was evidence of spatial autocorre-
lation in the scenario with the overestimated error
(thr = 0.2), as indicated in Table3.
Discussion
Cocoa farms are established in small scale planta-
tions, which resulted in a salt and pepper effects
that are difficult to interpret. Therefore, a smooth
filter was used to correct this effect. The analysis
detected 88% of the cocoa in the region, of which
91% were accurately classified. This performance
was superior to a comparable study in Côte d’Ivoire
(PA = 82.9 and UA = 62.2) carried out by Abu etal.
(2021). This could be explained by the size of the
study area: Abu etal. (2021) worked on the entire
country level, whereas our study area was limited
to the cocoa production zone. In central Africa,
an accuracy of 89.76% was obtained in Cameroon
(Numbisi etal. 2019). This study used higher reso-
lution images to delineate cocoa agroforestry from
secondary forest. In Côte d’Ivoire, cocoa planta-
tions are established as full sun (with few to no
forest trees), thus their structure looks like rubber
plantations, whereas in Cameroon, cocoa is grown
under trees and are infeasible to be separated from
secondary forest. This difference in context justifies
the difference in methodology and in the results. An
accuracy of 97.6% was obtained for the classifica-
tion of a cocoa plantation in Brazil using S1 & S2
and deep learning (Filella 2018). The study reported
a high level of prediction error in the final map,
leading to the conclusion that the spatial and spec-
tral resolutions of S2 is inappropriate to map cocoa
agroforests. In fact, classifying cocoa agroforests
requires high resolution imagery, which is not the
Fig. 2 Classification of agroforestry systems in the south of Côte d’Ivoire. Zoom on the field plots is presented in the right and left
side of the classification. White dots on the image represented the areas that have been masked out
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case for full sun cocoa, for which S1 and S2 have
been successfully used (Abu etal. 2021).
To reduce the error in the classification, Shan-
non entropy was added to the classification work-
flow to model the classification error. Here, entropy
refers to the likelihood of a pixel to be a particular
feature class. It is used in classification workflow as
a weighted uncertainty metric to compare different
classification models, or to compute the uncertainty
of classification at pixel level (Numbisi et al. 2019;
Roodposhti etal. 2019). A threshold value of 0.4 was
used by Numbissi etal. (2019) for the classification
of cocoa agroforestry in central Africa, where optimal
threshold value was found between 0.3 and 0.4 with
0.4 slightly underestimating the map error. Rood-
poshti etal. (2019) found that the optimal threshold
value was 0.2 for homogenous farming landscapes
like corn, grass, and soybean. Thus, depending on
final use of the map, a threshold of 0.3 or 0.4 could
be used. The value at 0.3 would be a rigorous choice
to limit probability of false detection for monitoring
cocoa extension around protected areas. The spectral
confusion between cocoa, rubber, palm, and forest
was used to detect encroachment. Consequently, high
entropy values were associated with pixels in the pro-
tected areas, so that only footprints of encroachment
could be detected.
The expansion of cocoa plantation is acknowl-
edged as the main driver of deforestation in Côte
d’Ivoire, and evidence of encroachment in pro-
tected areas has been reported (Abu etal. 2021). We
detected encroachment of cocoa in the Taï National
Park. The distribution of the plantations within the
park were located around the limit of the study area
with a pattern that was like artefacts. This could be
because a composite image was used as input layer
since the use of single layers was infeasible due to
presence of clouds in the region throughout the year.
Therefore, validation would only be possible via
fieldwork, interpretation of aerial photography or
Fig. 3 Error maps for different entropy thresholds of agroforestry systems in south Côte d’Ivoire
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UAV data. Evaluation of the spatial autocorrelation
on the image was necessary to identify local drivers
of the error on the map. The study reported that the
spatial distribution of the classification error was not
related to any local condition. However, signs of local
conditions associated with errors revealed when the
error was highly overestimated (thr = 0.2).
The proposed methodology provides a frame-
work for more reliable and accurate classification,
particularly for AFS established within complex
landscapes. It shows a pathway to model the spa-
tial distribution of the classification error on the
map and improve the classification by showing pix-
els which are accurately classified. This is neces-
sary for the monitoring of cocoa expansion around
protected areas to limit the risk of false detection.
In addition, an accurate map is required to deliver
accurate estimation of carbon stocks in each land-
scape for carbon estimation studies.
Fig. 4 Improved classification of agroforestry systems in southern Côte d’Ivoire using entropy value threshold derived from field
plot
Table 3 Summary of the GWR
Threshold values Number of
significant
features
Logistic
regression
(R2)
Moran’ I (p value) Conclusion GWR Moran’I (p
value)
Appropriate GWR
method
Thr = 0.2 13 0.8 6.09 E−6 Evidence of Spatial
autocorrelation
0.005 MS-GWR
Thr = 0.3 12 0.79 0.33 No spatial autocor-
relation
Not applicable
Thr = 0.4 13 0.77 0.61
Thr = 0.5 11 0.77 0.63
Thr = field plot 12 0.78 0.66
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Conclusion
This study aimed at modelling the spatial distribu-
tion of the classification error in cocoa AFS in Côte
d’Ivoire. The proposed model integrates the Shan-
non entropy in the classification framework to cap-
ture the classification error on the map. It was found
that the entropy threshold value should be selected
between 0.3 and 0.4 for the classification of full sun
cocoa or cocoa agroforestry, respectively. Addition-
ally, the model was able to detect encroachment in
protected areas and could be used for monitoring
the expansion of cocoa. This proposed methodol-
ogy could also be used for image classification in
presence of classes that are difficult to be separated.
S1 and S2 could be used to the near-real time moni-
toring of cocoa plantation in Côte d’Ivoire, and
the classification on the map could be corrected
using other techniques like spectral-spatial super
resolution image reconstitution, in combination
with advanced classification approaches like deep
learning.
Acknowledgements Authors acknowledge the support from
the German Federal Ministry for Education and Research
(BMBF) via the project carrier at the German Aerospace
Center (DLR Projektträger)through the research project:WAS-
CAL-DE-Coop (FKZ: 01LG1808A).
Funding Open Access funding enabled and organized by
Projekt DEAL.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Crea-
tive Commons licence, and indicate if changes were made. The
images or other third party material in this article are included
in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit
http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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