Estuarine, Coastal and Shelf Science 59 (2004) 219e230
Mapping land use/cover in a tropical coastal area using
satellite sensor data, GIS and artificial neural networks
J.F. Mas
Instituto de Geografı´a - UNAM, Unidad acadámica Morelia, Aquiles Serdán 382, Colonia Centro,
C.P. 58000, Morelia, Michoacán, Mexico
Received 15 January 2003; accepted 25 August 2003
Abstract
A common problem when classifying remotely sensed images in order to map land use/cover is spectral confusion: different land
use/cover classes present similar spectral signatures and are misclassified. This paper presents a procedure for mapping land use/
cover combining the spectral information from a recent image and data about spatial distribution of land use/cover types obtained
from outdated cartography and ancillary data. Two fuzzy maps, which indicate the membership of each land use/cover class, were
generated from the ancillary and spectral data, respectively, using an artificial neural networks approach. The combination of both
maps was obtained using fuzzy rules. In comparison with spectral classification, this procedure allowed a statistically significant
increase of accuracy of land use/cover classification (from 67% to 79%). The advantages of this procedure for combining spectral
and ancillary data, with regard to others previously published in the literature, are that it allows one to take into account previous
mapping efforts and to establish relationships between land use/cover and environmental variables specific to the mapped area.
Ó 2003 Elsevier Ltd. All rights reserved.
Keywords: coastal land covers; mapping; remote sensing; Landsat; geographical information systems; artificial neural networks; Mexico
1. Introduction
In many regions, such as Mexico, coastal vegetation is
being destroyed at an alarming rate by urban and
agricultural development (Dugan, 1988; Loa Loza,
1994). To monitor changes efficiently over large areas,
accurate and inexpensive mapping techniques are required. Digital processing of remotely sensed imagery,
such as Landsat ETM+, has many advantages over
traditional photo-interpretation mapping. However, the
accuracy of the map is dependent upon the spectral
signature of the various land cover features, and the
ability of the classification procedures to discriminate
between them. Land cover classes vary spectrally,
especially where land covers present high diversity and
spatial complexity. These classes often lack unique
signature, and different land covers can present very
Tel.: +52-443-317-94-23; fax: +52-443-317-94-25.
E-mail address: [email protected]
0272-7714/04/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ecss.2003.08.011
similar spectral features. This interclass confusion introduces errors into the resulting spectral classification.
In visual classification, an interpreter evaluates several
characteristics such as tone, texture, size, pattern, location
and association and his own knowledge about the land
cover distribution in order to identify the components of
the image. The majority of these characteristics are not
used in conventional digital image classification. Attempts based upon different approaches, such as the use of
texture (Gong and Howarth, 1990; Palubinskas et al.,
1995; Franklin et al., 2000), object-oriented approaches
(Blaschke et al., 2000; Mansor et al., 2002) and the use of
ancillary information (Hutchinson, 1982; Kontoes et al.,
1993; Long and Skewes, 1996; Mas and Ramı́rez, 1996;
Srinivasan and Richards, 1990), have been made in order
to increase the accuracy of spectral classifications.
However, because of the statistical assumption of the
more common algorithms such as maximum likelihood,
ancillary information cannot be used directly during the
classification process (Hutchinson, 1982). The use of
Boolean rules in a geographic information system (GIS)
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J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
context is the most common way to use ancillary information. However, complex relationships between ecological factors and land cover distribution can hardly be
expressed by deterministic decision rules (Hutchinson,
1982; Mas and Ramı́rez, 1996) and, this approach
requires the knowledge of these relationships for the
study area. The rising use of artificial neural networks in
image classification can provide new ways to combine
spectral and ancillary information into the classification
process (Civco, 1993; Foody, 1995; Atkinson and Tatnall,
1997).
An artificial neural network (ANN) is an information-processing paradigm inspired by the way the
densely interconnected, parallel structure of the brain
processes information. ANNs are mathematical models
that emulate some of the observed properties of biological nervous systems and draw on the analogies of
adaptive biological learning. The key element of the
ANN paradigm is the structure of the informationprocessing system which is composed of a large number
of highly interconnected processing elements that are
analogous to neurons and are tied together with
weighted connections that are analogous to synapses.
Advantages of the ANN approach include the ability to
handle non-linear functions, to perform model-free
function estimation, to learn from data relationships
that are not otherwise known, and to generalize unseen
situations. ANNs have been shown to be highly flexible
function approximators for any type of data. Therefore,
ANNs make powerful tools for models, especially when
the underlying data relationships are unknown (Lek and
Guégan, 1999; Lek et al., 1996). In the past decade,
ANNs have seen an explosion of interest and have been
successfully applied across a large range of domains such
as medicine, molecular biology, ecological and environmental sciences and image classification (Atkinson and
Tatnall, 1997; Lek and Guégan, 1999).
Research into ANNs has led to the development of
various types of neural networks, suitable to solve different kinds of problems. Nowadays, one of the most
popular ANN is the multi-layer feed-forward neural
network or multiplayer perceptron (MLP) (Atkinson
and Tatnall, 1997; Bishop, 1995). The MLP is based on a
supervised procedure, i.e. the network constructs a
model based on examples of data with known outputs.
The training is done solely from the examples presented,
which are together assumed to implicitly contain the
information necessary to establish the relation. An MLP
is a powerful system, capable of modelling complex
relationships between variables. It allows prediction of
an output object for a given input object or a set of input
objects.
The architecture of the MLP is a layered feed-forward
neural network, in which the non-linear elements
(neurons) are arranged in successive layers, and the
information flows unidirectionally, from the input layer
to the output layer, through the hidden layer(s) (Fig. 1).
The neurons receive and send signals through these
connections. Connections are given a weight, which
modulates the intensity of the signal they transmit. When
the network is executed, the input variable values are
placed in the neurons of the input layer, which activate,
successively, the neurons of the hidden and the output
layers. Each neuron calculates its activation value by
taking the weighted sum of the outputs of the units in the
preceding layer. The activation value is passed through
the activation function to produce the outputs of the
neuron. When the entire network has been executed, the
outputs of the output layer act as the output of the entire
network and the case is allocated to the most highly
activated output.
The learning procedure is based on a relatively simple
concept: if the network gives the wrong answer, then the
weights are corrected so the error is lessened so future
responses of the network are more likely to be correct.
Training data is presented iteratively in order to adjust
the connection weights and obtain the best fit between
expected and observed values. The best-known example
of a neural network-training algorithm is backpropagation. In this algorithm, a training pattern is presented to
Fig. 1. Schematic illustration of a three-layered perceptron, with one input layer, one hidden layer and one output layer. In this example X1, X2,., X6
are six input variables (e.g. spectral bands); Y1 and Y2 are two output variables (e.g. land use/cover classes).
J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
the network and the signals are fed forwards as
described above. Then, the network output is compared
with the desired output and the error is computed. The
error is then back-propagated through the network and
the weights of the connections are altered according to
what is known as the generalized delta rule (Rumelhart
et al., 1986; Bishop, 1995):
Duij ðt þ 1Þ ¼ hðdj oi Þ þ aDuij ðtÞ
ð1Þ
where uij(t) is the connection weight from input i to
neuron j at time t, h the learning rate, dj the error at
processing unit j, and a is the momentum parameter.
The learning rate controls the size of weight changes
made by the algorithm. The addition of momentum
causes the backpropagation algorithm to ‘‘pick up
speed’’ if a number of consecutive steps change the
weights in the same direction. This process of feeding
forward signals and back-propagating the error is
repeated iteratively until the error of the network as
a whole is minimized or reaches an acceptable magnitude.
As the network is trained to minimize the error on the
training set, a major issue is over-learning or over-fitting
to the training data. A network with more weights models
a more complex function, and is therefore prone to overfitting (Bishop, 1995; Foody and Arora, 1997). In order to
avoid over-learning, cross-verification is used: some of the
training cases (verification set) are not actually used for
training but to keep an independent check on the progress
of training. As training progresses, the training error
naturally drops. If the verification error stops dropping,
or starts to rise, this indicates that the network is starting
to over-fit the data, and training should stop.
In a classification problem, an output unit’s task is to
output a strong signal if a case belongs to the class, and
a weak signal if it does not. Therefore, the activation
value may also be considered as a fuzzy membership
value (Civco, 1993; Foody, 1995), which can be perceived
as a measure of certainty with regard to belonging to the
class. When the ANNs were used to map land use/cover
as a function of spectral or environmental variables they
produced a membership value, ranging from zero to one,
depending on their degree of closeness to the class for
each class used in the training process.
This study aims at developing a simple procedure
able to map land use/cover using spectral and ancillary
information based upon using an artificial neural
network approach.
2. Study area
The study area is situated in the region of the Lagoon
of Términos, in the State of Campeche, located in the
south eastern part of Mexico between 18(02# and
221
19(10# North and 91(01# and 92(29# West (Fig. 2)
and covers about 19,200 km2. The study area consists of
a mosaic of natural grasslands, pasture lands, croplands,
mangroves, wetlands (dominated by Cyperus sp. and
Typha latifolia) and remnants of tropical forests. Soils
are largely dominated by gleysol types (80% of the area);
there are also solonchak, rendzina, regosol and vertisol
soils, which represented 7, 6, 3 and 2% of the study area,
respectively. Relief is flat, 80% of the study area is above
5 m below sea level. The south east section of the study
area presents some moderate elevations which reach
130 m above sea level. The spatial distribution of the
vegetation is determined by the topography, the soils,
and the distance from the coast line. The conversion of
natural into man-made cover is dependent upon factors
such as the distance from the roads, the elevation and
the types of soils (Mas and Puig, 2001). Rates of land
use/cover changes in the region are high; annual rates of
deforestation reached 2.2 and 5.3% during 1974e1986
and 1986e1991, respectively. Much of the land surrounding the lagoon has been deforested for cattle
ranching and rice farming (R. Isaac-Márquez, pers.
commun., 1993; Mas, 1999; Mas and Puig, 2001).
3. Materials and methods
A Landsat ETM+ image (path 21, row 47) dated
April 3, 2000 was registered and resampled to a UTM
projected output image composed of 30 m!30 m pixels
with an RMS error of less than 1.0 pixel. Resampling
was done by the nearest neighbour method, which set
the radiometric value of the output pixel equal to the
nearest input pixel in the original geometry, in order
to preserve the original values of the image. A digital
model of elevation along with soil, land use/cover, and
road network digital maps with a scale of 1:250,000, was
obtained from the National Institute of Geography,
Statistics and Informatics (INEGI). The land use/cover
map (INEGI, 1984) which was derived from the visual
interpretation of aerial photographs dated 1972 and
1980 in addition to intensive field work. There is not
a statistical accuracy assessment of this map but it is
generally considered by the users as accurate but largely
outdated due to the rapid land use/cover changes in the
region. Additional spatial variables, such as the shortest
distance to the nearest road, and to the coast line, were
generated because they were considered a priori as
factors which can control the pattern of distribution of
land use/cover in the region. Binary maps were derived
for each soil type from the map of soils and indicated the
presence/absence of a given soil. As the digitalizing
process of the map can generate errors in the position of
the boundary between soil types (in addition to error or
fuzziness of the delimitation of soil units during the
elaboration of the map), the limits between the different
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J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
Fig. 2. Localization of the study area.
types of soils were ‘‘fuzzyfied’’ applying a low pass filter
which, for each cell, calculated the mean of the value
within a neighbourhood (a circle of 200 m radius in this
case) and sent it to the corresponding cell location on
the output grid. The application of the filter transformed
the hard boundary into a narrow band of pixels which
show increasing membership value when going inside
the soil unit (Fig. 3). The Landsat ETM+ image and the
digital maps were integrated into a GIS database in
raster format using a common UTM projection.
A critical issue when carrying out a supervised
classification with both spectral and ancillary information is that the training data represent the entire variation of each class with regard to the spectral and
J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
223
Fig. 3. Boolean and fuzzy representation of the boundary between two parches. The black and the white tones indicated a total membership to one
class. In the fuzzy representation the grey tones represent partial membership to both classes.
ancillary variables. In order to ensure it, a new and
simple way of incorporating the ancillary information
was used. For this, the general framework of the
classification procedure was simple and included two
parallel classification procedures. The first classifier
‘‘learned’’ the distribution of land use/cover types from
establishing spatial relationships between the land use/
cover (outdated) map and the ancillary data (e.g. soils,
elevation.). This approach avoided a partial or biased
training data because the map used for training covered
the entire study area. It allowed the production of
a digital fuzzy map which portrayed, via each pixel, the
possibility of the presence of each land use/cover type.
The second classifier produced another ‘‘fuzzy’’ map
using a spectral classification of the recent remotely
sensed image based upon standard training areas. Thus,
for each pixel, the two fuzzy maps indicated a membership value which expressed the possibility of the
presence of a cover type from its environmental conditions and its spectral features, respectively. Fuzzy
operators such as AND and OR can be used to derive
a new class membership from two memberships derived
from different fuzzy classifications (Zadeh, 1978;
Palubinskas et al., 1995). In order to combine the two
fuzzy maps, the AND operator was calculated as the
minimum of the two membership values. The use of the
AND operator ensures that the most stringent requirement for the class selection was met. For example, a pixel
which was located upland and presented a spectral
signature analogous to tropical and mangrove forest
(both have a similar spectral response) has a high membership value related to these two covers in the spectral
classification. However, in the classification based on the
ancillary information, the membership value to the
mangrove class was low because the classifier learned
from the land use/cover map that no mangroves were
found at high elevations. Thus, after combining both
spectral and ancillary classifications, the final membership of this pixel to the class mangrove was low and it
was classified as tropical forest (Fig. 4).
In order to carry out the classification based upon
the ancillary data, each spatial variable was overlaid on
the land use/cover map from INEGI to establish the
relationship between land use/cover and the variables.
The overlay operation allowed for the construction of
a tabular database which indicated, for each pixel, the
value of the spatial variables and the type of land use/
cover. These data were used to train the first MLP aimed
at classifying land use/cover from the environmental
variables.
The accuracy of a supervised classification depends
upon the representativeness of the estimates of the
Fig. 4. Combination of the fuzzy memberships by the AND operator.
The columns represent the membership of a given pixel to three cover
classes (T.F.: tropical forest, M: mangrove and P: pasture) derived
from spectral, ancillary data and the combination of both. The
combined membership values are the minimum of spectral and
ancillary values for each class.
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J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
number and the nature of the spectral classes present in
the image data. When insufficient observational or
documentary evidence of the nature of the land cover
types is available, an exploratory unsupervised classification can be carried out. However, the identification of
the spectral classes picked out by the classifier in terms
of information classes is achieved whatever information
is available to the analyst. The use of unsupervised
classification techniques is a method of ensuring that the
training area has been well chosen to represent a spectral
class (Mather, 1999). In the present study, the spectral
classification was carried out following two steps in
order to obtain representative training areas. First, an
unsupervised spectral classification was carried out using
the isodata algorithm, which iteratively clusters pixels
using minimum distance techniques and groups the
pixels with similar radiometric values into the same
cluster (Tou and Gonzalez, 1974). This unsupervised
classification allowed the selection of training sites
which represented the whole spectral variety a class
could represent. For example, in case of a land use/cover
class corresponding to various clusters, the training sites
of this class were chosen in order to include these
various clusters (i.e. to represent the entire spectral
variability of the class). A tabular database which
indicated, for each pixel of the training sites, its land
use/cover class and the digital number value indicating
the reflectance in the different bands, was used to train
the second MLP.
A backpropagation training algorithm was used in
the MLP training processes. Data were divided into
three sections: the training set, the verification set, and
the test set following the proportion 1/2, 1/4 and 1/4,
respectively. The verification set was used to track the
network’s error performance, to identify the more
efficient networks, and to stop training if over-learning
occurred. The test set was not used in training at all, and
gave an independent assessment of the network’s
performance when the entire network design procedure
was completed. A key design decision was the question
of how many input variables and hidden units to include
in the network. The network configuration was determined empirically by testing various possibilities and
evaluating the accuracy of the classification of the test
set. In order to select the input variables, a sensibility
analysis was carried out. This analysis rates variables
according to the deterioration in performance that
occurs if that variable is no longer available to the
model. It indicates which input variables are considered
most important by that particular network and allows to
prune out the input variables with low sensibility.
Among the MLP architectures which presented good
performance (test set classification accuracy over 70%),
the simplest were chosen: MLPs based upon less input
variables and less nodes in the hidden layer(s) were
preferred because of their better ability to generalize and
classify unseen pixels accurately (Bishop, 1995; Rosin
and Fierens, 1995; Kavzoglu and Mather, 2000). The
output from each MLP was an activation value which
expressed the membership to each land use/cover class.
The result was then two fuzzy land use/cover maps that
portrayed gradations of the possibility of each class. The
combination of the two maps allowed the generation of
a fuzzy land use/cover map which took into account
both ancillary and spectral data. A final hard (not fuzzy)
map was finally obtained labelling each pixel into the
class with the highest fuzzy membership value in order
to obtain a ‘‘standard’’ map and assess accuracy.
In order to assess the accuracy of the classified
images, a random reference sample of 488 points of
verification was selected. The land use/cover classes of
the surrounding area of these points were checked by
visual interpretation using high resolution digital aerial
photographs dated October 2000 and March 2002 (pixel
size about 1.5 m). An error matrix, which showed the
number of points correctly and incorrectly identified,
was constructed. Overall accuracy (proportion of the
points correctly identified) was computed and, commission errors (erroneously including a point from a class),
omission errors (erroneously excluding a point from
a class), producer’s accuracy (proportion of verification
points of a category correctly classified) and user’s
accuracy (proportion of points classified into the class
which are correctly identified) were calculated for each
class (Stehman, 1997; Stehman and Czaplewski, 1998).
The interval of confidence of the estimate of the
accuracy was determined using the following equation
(Dicks and Lo, 1990):
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
pð1 pÞ
d¼t
n
ð2Þ
where d is the half width of the confidence interval,
t ¼ 1:96 the standard normal deviate for the two-sided
confidence level, p the accuracy and n is the number of
verification points.
In order to quantify the improvement obtained by the
incorporation of the ancillary data, a first classification
was carried out using only the spectral information and
another one using the spectral and the ancillary
information (‘‘ancillary-improved’’ classification). The
accuracy of both classifications was assessed using the
same reference sample. Even though the two maps were
constructed independently, when the verification sample
used for the comparison is the same, the test statistic
employed for the hypothesis test should take this lack of
independence into account. Therefore, when only a
single reference sample is used, some type of paired
comparison is appropriate, and a test statistic based on
an assumption of two independent samples represents at
best an approximation to the correct statistical test
(Stehman, 1997). In this study, the statistical significance
J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
of the difference in accuracy between the spectral
classification and the ancillary-improved classification
was done using a t-test approach. A paired-samples
t-test was used for global and producer’s accuracies
which were based upon the same reference sample
exactly. In the case of user’s accuracies, because the
reference sample is split using the classified image,
different samples were used to assess the same class in
the two classified images. Therefore, an independentsamples t-test was used.
4. Results
The image was classified into six land use/cover
classes: (1) tropical forest; (2) mangroves; (3) wetlands;
(4) agriculture (grasslands, pasture lands and croplands); (5) water; and (6) urban areas. Because of the
large amount of data (for example, the training sites on
the multispectral image have about 480,000 pixels),
between 4000 and 8000 cases for each class were
randomly selected to generate the input data for the
spectral classification MLP, except the class ‘‘urban
area’’ which only has 1000 pixels. In total the spectral
training set presented 28,700 cases. The ancillary input
data presented similar proportion for each class (5000
for the first five classes and 300 for the urban area) and
a total of about 25,000 cases. Among the MLP
architectures, which presented good performance, the
simplest were chosen. The MLP for classifying ancillary
data presented nine inputs (six soils type, elevation,
distance to roads and distance to the coast) and one
hidden layer with six nodes. The MLP for spectral
classification had five inputs (bands 2, 3, 4, 5 and 7) and
two hidden layers with three and four nodes, respectively (Fig. 5). For both MLPs, the output units have
linear activation functions while the hidden units have
logistic sigmoid activation functions. The MLPs for
classifying spectral and ancillary data were trained by
225
backpropagation (learning rate 0.1, momentum 0.3) with
50 and 40 epochs (iterations of the entire training data)
and were able to classify correctly 82 and 73% of the test
set, respectively. Fig. 6 shows the spectral and environmental variables used in the classification procedure.
A preliminary classification was obtained using only
the spectral information, classifying each pixel into the
class which presented the higher membership value in
order to assess the improvement obtained by using
ancillary data. Table 1 shows the matrix error of such
classification. Overall accuracy was 74%. However,
without taking water into account, which presented
22% of the points of verification, the overall accuracy
was only 67%. Some classes presented important errors
such as crops and pasture lands which presented an
error of omission of 52% and wetlands which had an
error of commission of 52%. The main confusions were
between crop/pasture lands, tropical forest, mangroves
and wetlands which presented similar spectral signatures. These errors affect the spatial representation and
also the statistics of area of land use/cover types derived
from the classified image. For example, less than 50% of
the points of verification identified as crop/pasture lands
on the photographs are correctly mapped (error of
omission of 52%). On the other hand, 29% of the points
of verification mapped as crop/pasture lands belonged
to this class (error of commission). Therefore, the total
area of this class was subestimated in the map.
As a follow-up step, the spectral fuzzy maps were
combined with the ancillary maps. Fig. 7 shows the
fuzzy maps of the wetlands class. It can be observed that
large areas of tropical forest and mangroves present
high membership to the wetlands class. The combination of the spectral fuzzy map with the ancillary fuzzy
map allowed an avoidance of confusion in an important
part of these areas. The accuracy of the resulting map
was assessed with the same verification data. As shown
in Table 2, overall accuracy increased to 82% (79%
without taking into account the water class). Table 3
shows the global and class accuracies obtained by the
Fig. 5. MLPs used for ancillary and spectral classification.
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J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
Fig. 6. Input components in the data set. The top six maps show the spectral inputs and the map of soils, the bottom three maps show the elevation,
the distance to roads and the distance to the coast. In each map, except the soil map, a dark pixel corresponds to a low value of the input variable.
spectral classification and the ancillary-improved classification along with the statistical significance of the
difference in accuracy. The use of ancillary data allowed
a significant increase in the accuracy of almost all the
classes: both commission and omission errors decreased
below 25% except for the error of commission of the
crop/pasture land and both commission and omission
errors of urban area which remained the same. All the
classes, except urban area, showed a significant increase
of user’s accuracy (wetlands) or producer’s accuracy
Table 1
Error matrix of the classification based upon spectral information only (overall accuracy ¼ 72:54%G3:96). The matrix shows the number of
verification points. Errors, accuracy and confidence interval are expressed in %
Verification points
Map
Crop/pasture
Water
Mangrove
Tropical forest
Wetlands
Urban area
Total
Error of omission
Producer’s accuracy
d (1/2 Interval of confidence)
Crop/
pasture
47
Water
Mangrove
1
107
2
1
57
7
12
3
82
30.49
69.51
9.96
2
7
43
99
52.53
47.47
9.84
1
109
1.83
98.17
2.52
Tropical forest
4
Wetlands
5
68
22
10
1
2
8
71
99
31.31
68.69
9.14
92
22.83
77.17
8.58
Urban area
2
1
4
7
42.86
57.14
36.66
Total
66
109
66
90
149
8
488
Error of
commission
User’s
accuracy
d
28.79
1.83
13.64
24.44
52.35
50.00
71.21
98.17
86.36
75.56
47.65
50.00
10.92
2.52
8.28
8.88
8.02
34.65
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J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
Fig. 7. Fuzzy maps for the wetlands class derived from ancillary (a) and spectral (b) information.
(crop/pasture, water, mangrove and tropical forest). The
class urban area was not improved by the ancillary data
because there was only one polygon labelled as urban
area (Ciudad del Carmen City) in the land use/cover
map used in the ‘‘learning’’ of the relationship between
land use/cover and ancillary data. Thus, the MLP
learned that the characteristics of this town, located on
an island (near the coast line, at very low elevation) were
the general characteristics of all urban areas, thereby
causing a misclassification. This point will be discussed
further when analysing the limitations of the approach
used in this study.
5. Discussion
In order to pinpoint the limitations of the procedure, the incorrectly classified verification points were
displayed upon the spatial variables. For each point,
ancillary and spectral fuzzy membership values were
examined. About 30% of the points considered as
incorrectly classified are ambiguous cases, for which the
land use/cover class determined by photo-interpretation
may be questioned because the point was located in the
transition between different covers, in a fragmented area
where pixels were composed of different covers (mixed
pixels) or corresponded to transition covers between two
covers considered in the classification scheme such as
fallow and secondary vegetation which are intermediate
between agriculture covers and tropical forest. These
pixels may be better represented by the fuzzy map
derived from the combination of the ancillary and
spectral fuzzy maps rather than the hard classification
map (Foody, 1992; Wang, 1990). In order to avoid the
difficulty in choosing a single correct class from the
reference photographs, the ambiguous reference points
were interpreted again assigning a primary and an
alternate interpreted class (Khorram et al., 2000;
Table 2
Error matrix of the classification based upon the combination of spectral and ancillary information (overall accuracy ¼ 81:55G3:44). The matrix
shows the number of verification points. Errors, accuracy and confidence interval are expressed in %
Verification points
Map
Crop/pasture
Water
Mangrove
Tropical forest
Wetlands
Urban area
Total
Error of omission
Producer’s accuracy
d (1/2 Interval of confidence)
Crop/
pasture
79
1
8
11
99
20.20
79.80
7.91
Water
3
103
2
1
109
5.50
94.50
4.28
Mangrove
2
64
10
4
2
82
21.95
78.05
8.96
Tropical forest
Wetlands
13
15
3
77
6
2
4
71
99
22.22
77.78
8.19
92
22.83
77.17
8.58
Urban area
3
4
7
42.86
57.14
36.66
Total
115
103
72
99
92
7
488
Error of
commission
User’s
accuracy
d
31.30
0.00
11.11
22.22
22.83
42.86
68.70
100.00
88.89
77.78
77.17
57.14
8.48
0.00
7.26
8.19
8.58
36.66
228
J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
Table 3
Comparison between accuracy indices obtained by the spectral classification and the ancillary-improved classification. For user’s accuracies,
comparison was obtained by an independent-samples t-test (with equal variance not assumed), for producer’s and global accuracies by a pairedsamples t-test
Classification based upon
t-Test for equality of accuracies
Class
Spectral only Spectral þ ancillary Difference t
User’s accuracy
Crop/pasture
Water
Mangrove
Tropical forest
Wetlands
Urban area
71.21
98.17
86.36
75.56
47.65
50.00
68.70
100.00
88.89
77.78
77.17
57.14
2.51
1.83
2.53
2.22
29.52
7.14
Producer’s accuracy
Crop/pasture
Water
Mangrove
Tropical forest
Wetlands
Urban area
47.47
98.17
69.51
68.69
77.17
57.14
79.80
94.50
78.05
77.78
77.17
57.14
32.33
3.67
8.54
9.09
0.00
0.00
Global accuracy
72.54
81.55
9.01
95% Interval confidence of the difference
df
Sig
Lower
Upper
0.354
1.421
0.446
0.359
4.906
0.258
137.88
108.00
131.93
183.92
217.21
12.74
0.724
0.158
0.656
0.720
0.000
0.800
16.55
0.72
8.87
10.00
6.01
52.75
11.52
4.39
13.72
14.45
17.66
67.03
5.846
2.028
2.400
2.566
98
108
81
98
0.000
0.045
0.019
0.012
21.35
7.26
1.46
2.06
43.29
0.83
15.61
16.12
4.79
487
0.000
5.32
12.71
Woodcock and Gopal, 2000). For about 84% of the
ambiguous points, the primary or the alternate class
matched the classified class. In order to explore further
the fuzziness representation given by the fuzzy classification, the primary and the alternate reference classes
were compared with the two classes which presented the
two highest fuzzy membership values in the classified
image. In 61% of the cases, both pairs of classes
coincided which indicates that the fuzziness membership
gave a good representation of the transition or the
mixture between two classes. With the method used to
combine spectral and ancillary information, a pixel
which presents a high membership value to two or more
classes must present high membership values derived
from both spectral and ancillary data, i.e. this pixel must
present a similar spectral signature to the different
classes but also be located in the overlap between the
spatial distribution of these different classes.
About 9% of the incorrectly classified points seem to
be associated with the limits of the polygons of the map
of soils. Thus, these errors can be attributed to the
imprecision of the delimitation of the soil units or to the
vagueness of the real limits and the difficulty of representing transition zones of soils and cover in a Boolean
representation (one pixel, one land use/cover class). The
remaining confusion occurred between cover classes
which presented a similarity in their spectral response
and spatial distribution. In order to improve the discrimination of these classes, additional variables should be
taken into account during the classification procedure.
Thirty-four percent of the erroneously classified points
were correctly classified taking into account ancillary
membership alone. Therefore, many misclassifications
were due to the fact that the candidate pixel presented
a high membership value to the correct class in the
ancillary fuzzy map but was discarded because it presented a very low membership value in the spectral fuzzy
map. Thus, an increase of accuracy could be obtained by
increasing the overlap between class signatures, which
leads to an augmentation of the membership values to
various class, for example using fuzzy training sites
(Eastman and Laney, 2002). Means of the final membership values to the winning class were 0.64 and 0.27
for correctly and incorrectly classified pixels, respectively.
Therefore, these values are related to the probability of
a pixel to be correctly classified and can be used to
examine classification uncertainty and elaborate maps
which indicate areas where classification is doubtful.
The incorporation of ancillary data in an ANNs
approach allowed a significant improvement of the accuracy of the majority of the land use/cover classes. The
advantages of this approach are varied:
It allows to take into account previous mapping
efforts which were based upon aerial photographic
interpretation and intensive field work. This was
only allowed through the visual interpretation of the
satellite sensor imagery in order to update the
previous maps (Mas et al., 2002). The use of this
method, or a more sophisticated one based upon the
same approach, allowed the classifier to learn from
the outdated map and to classify using this
knowledge and recent spectral data.
This approach allowed the establishment of relationships between land use/cover and environmental
variables which are site dependent, e.g. specific to the
mapped area. These site-specific relationships are
more susceptible to allow an accurate classification
than regional relationships. For example, in this
mapped region, the mangroves are located relatively
J.F. Mas / Estuarine, Coastal and Shelf Science 59 (2004) 219e230
near the coast line while, in the Petenes region which is
located 100 km north, mangroves can be found 15 km
from the coast (Rico-Gray, 1982).
However, this approach presents some limitations.
First, it depends on the accuracy of the previous land use/
cover map. In the event that this map were greatly
inaccurate, the MLP could learn wrong relationships
between cover and environmental variables. The same
problem can arise when the pattern of distribution of the
covers has changed significantly between the elaboration
of the land use/cover map and the updating procedure.
Thus, this problem can be more important in highly
dynamic areas such as coastal regions or when using very
old maps. However, it is worth noting that even though
the study area presented important land use/cover
changes between the elaboration of the land use/cover
map and the date of acquisition of the Landsat image, the
relationships between land use/cover distribution and
environmental variables did not change importantly. The
problem of erroneous relationships between land use/
cover and environmental variables affected only the
urban area class because of the reduced area of this class.
A way to avoid these problems is the visualization of the
fuzzy maps, which indicate the membership value of each
location in a given class, in order to detect abnormal
membership values in some areas. It also depends on the
availability and accuracy of ancillary data. The increasing
availability of digital spatial information will reduce part
of this problem. However, the quality of this information
is also a critical task because attribute errors or position
errors (such as the limits between two soil types for
example) lead to errors in the classification. In some cases,
the low precision of data does not allow for the discrimination of some classes. For example, in the study area,
lowland flooded forest is associated with topographic
depressions which are not represented in the digital model
of elevation derived from 10 m elevation curves.
ANNs present a promising mode to improve classification of remotely sensed images. Many authors reported
larger accuracy when classifying spectral images with an
ANN approach than with a statistical method such as
maximum likelihood (Paola and Showengerdt, 1995;
Atkinson and Tatnall, 1997). However, a more important
contribution of the ANNs is their ability to incorporate
additional data into the classification process. In the
present study, only ancillary data were used using a perpixel classification. However, additional improvement
may be expected using information such as the texture, or
the shape and the size of the objects in the case of an
object-oriented classification procedure.
Acknowledgements
The author wishes to thank the two reviewers who
provided helpful suggestions for improving this manu-
229
script. Aerial photographs were obtained from project
N011 Actualización del mapa de uso del suelo y vegetación
del Área Protegida ‘‘Laguna de Te´rminos’’ y elaboración
de una base cartográfica digital (Conabio, PEMEX, epomex-University of Campeche) and the National Forest
Inventory 2000 (UNAM, SEMARNAT, INEGI). The
spatial database was elaborated in project N011. The
study was carried out under the project CONACyTSEMARNAT reference number 2002-C01-0075.
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