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International Journal of Advanced Biological Science and Engineering, Vol. 1, Issue 2 (2014), Pages 120-133.
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Land use/cover change detection using remotely sensed imagery in
Arak, Iran
Mozhgan Ahmadi Nadoushan1, Alireza Alebrahim2, Alireza Soffianian3, Hadi Radnezhad4
1
Young Researchers and Elite Club, Majlesi Branch, Islamic Azad University, Isfahan, Iran
2
Department of Agriculture, Payame Noor University
3
Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran
4
Department of Agriculture, Khorasgan (Isfahan) Branch, Islamic Azad University

Corresponding author at: Young Researchers and Elite Club, Majlesi Branch, Islamic Azad
University, Isfahan, Iran.
Phone: +98 9131697106.
E–mail address: [email protected] (M.Ahmadi).
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ABSTRACT
Arak is one of several cities in Iran which have undergone swift urban expansion during recent
decades due to rapid industrialization and population growth. In this study to detect land
use/cover changes near Arak, aerial photos from 1956 and 1972 and Landsat TM and IRS-P6
LISS-III images acquired in 1990 and 2006 were used. Land use/cover maps with four classes
(urban areas, vegetation cover, barren lands and mountains) were generated from the visual
interpretation of aerial photos and an artificial neural network classification method for satellite
images. Both classification methods resulted in land use/cover maps with overall accuracy over
95%. The land use/cover maps of 1956 and 2006 were compared using the post-classification
comparison method for detecting changes between these years. The results of change detection
revealed significant urban expansion, vegetated land degradation, barren land losses and stability
in mountainous areas during 1956-2006 as urban area increased by 3893 ha. Bare lands and
vegetation cover including natural vegetation and farmlands around the city of Arak in 1956
were converted to houses and a variety of factories and companies during the 50 year period.
Keywords: Land use/cover; neural network classification; remote sensing; Arak; Iran
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1. Introduction
The ‘land cover describes the physical appearance of the natural resources. It is a desired
input for many earth’s surface, while land use is a land right related agricultural, geological,
hydrological and ecological category of economically using the land’ (Nagarajan and
Poongothai, 2012) . Land use/cover change has become a central and important component in
current strategies for managing natural resources and monitoring environmental changes. Land
use/cover change is a dynamic process taking place on the bio-physical surfaces that have taken
place over a period of time and space is of enormous importance in natural resource studies.
Land use/cover change dynamics are important elements for monitoring, evaluating, protecting
and planning for earth resources. Change detection in land use and land cover can be performed
on a temporal scale such as a decade to assess landscape change caused due to anthropogenic
activities on the land (Rawat et al., 2013).
Investigating the landscape structure and its change is a prerequisite to the study of
ecosystem functions and processes, sustainable resource management and effective land use
planning (Matsushita et al., 2006) .
Digital change detection is the process of determining and/or describing changes in land
cover and land-use properties based on co-registered multi-temporal remote sensing data (Abdi
et al., 2013a; Shalaby and Tateishi, 2007). Remote sensing is cost effective and technologically
reliable, and is therefore, increasingly used for the analysis of urban sprawl. The use of satellite
images will assist us in identifying the spatial and temporal patterns of urban land expansion
from the urban core, and in detecting land-use change in urban fringes especially in what
concerns the relation between urban and agricultural land uses. GIS provides a flexible
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environment for displaying, storing and analyzing digital data necessary for change detection
(Abdi et al., 2014a; Zanganeh Shahraki et al., 2011).
One of the most widely used change detection methods is the post-classification comparison
method (Abdi et al., 2014b; Lin et al., 2009; Wu et al., 2006), which uses separate classifications
of images acquired at different times to produce difference maps from which ‘‘from–to’’ change
information can be generated. Although the accuracy of the change maps is dependent on the
accuracy of the individual classifications and thus the results are subject to error propagation, the
storage and classification of imagery from a sequence of dates builds a historical series that can
be easily updated and is flexible enough to be used for applications other than change detection
(Abdi and Karimi, 2014a; Yuan et al., 2005). The principal advantage of post-classification
comparison lies in the fact that the two dates of imagery are separately classified; thereby
minimizing the problem of radiometric calibration between dates (Abdi and Karimi, 2014b;
Coppin et al., 2004).
Wu et al (2006) used satellite remote sensing and GIS to monitor and predict land use
change in Beijing, China. A Maximum likelihood classifier was applied for land use
classification and the overlay method was used for detecting land use change during a fifteenyear period stretching from 1986 to 2001(Wu et al., 2006).
Fan et al (2008) explored land use and land cover changes in the Core corridor of the Pearl
River Delta (China) from 1998 to 2003 using TM and ETM+ images and the post-classification
method (Abdi and Navidbakhsh, 2012; Abdi et al., 2013b; Fan et al., 2008; Sadraie et al., 2014).
The main objective of this study is to detect land use/cover changes of the city of Arak and
its periphery using the post-classification method and land cover maps derived from aerial
photographs and satellite images.
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2 . Materials and Methods
2.1. Study Area
The study area is located between latitudes 34º 02' 40"–34 º08' 01" N and longitudes 49º 37'
31"–49 º47' 45" E in the center of Iran and has an area of about 15800 hectares (Fig. 1). This
region has an elevation of about 1800 m above sea level and contains Arak, the capital city of
Markazi Province, and its surrounding area. The average annual temperature of Arak is 13.8º C
and average annual rainfall is 316 mm. Arak has experienced rapid expansion due to population
growth and industrialization during recent decades. The population of Arak increased from just
under 59000 in 1956 to 446760 in 2006 (Iran census information center).
Figure 1. The study area
2.2.Data
Remotely sensed data were used as the primary data source to generate input for change
detection and prediction. A time series of remote sensing data including aerial photos and
satellite images spanning 5 decades was used to generate land use/cover maps.
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Aerial photographs from 1956 and 1972 at a scale of 1:50000 and 1:10000 were employed
for producing land use/cover maps. Landsat TM and IRS-P6 LISS III images (path 68, row 46)
for the years 1990 and 2006 were also used in this study. A Digital Elevation Model (DEM) was
produced from the digital topographic maps at a scale of 1:25.000. Finally, an IRS-1C PAN
image was used to enhance the spatial resolution of IRS-P6 LISS-III image.
2.3.Data preprocessing
To prepare data for mapping land use/cover and detecting its changes, following procedures
were performed.
Geometric correction: All images and aerial photographs were rectified to UTM zone 39 N
with at least 25 well-distributed ground control points. At first geometric correction was carried
out using topographic maps at a scale of 1:25000 to geocode the aerial photos and the 2006 IRS1C PAN image. This rectified image was then employed to register the 2006 LISS-III image.
Geometric correction of Landsat TM image of 1990 was carried out using the IRS-P6 LISS-III
image. Finally, a first-order polynomial model was applied and all data were resampled to a 30 m
pixel size using the nearest neighbor method.
After geometric correction of aerial photos, all photos for each year were mosaicked to prepare
one image for land use/cover mapping.
Topographic correction: A topographic correction was applied to all satellite images due to the
mountainous condition of the study area. The solar azimuth and elevation were read from the
satellite images’ metadata files. Terrain attributes including slope and aspect were derived from
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the Digital Elevation Model (DEM). Topographic correction was carried out on images based on
the Lambertian method.
Image enhancement: The goal of image enhancement is to improve the visual interpretability of
an image by increasing the distinction among its features (Shalaby and Tateishi, 2007) . In this
study, two false color composites (FCC) were produced for selecting training samples. Image
fusion was also performed to increase spatial resolution of the LISS-III image. The LISS-III
image was fused with IRS-1C PAN image to generate an image with a high spatial resolution.
2.4. Land use/cover mapping
For generating land use/cover maps from aerial photos and satellite images, four land use
classes were identified based on field work, false color composite images, and images derived
from the fusion process. The area was classified into these main classes: urban areas, vegetation
cover, bare lands and rocky and mountainous areas. For the current study, urban areas include
residential, commercial, industrial, educational, recreational establishments and transportation
systems and vegetation cover encompasses all green spaces, farmlands and natural vegetation.
2.4.1.Aerial photos interpretation
The land cover pattern was interpreted visually on black and white aerial photographs and
simultaneously digitized with the Arc map software. Identifying features in aerial photos were
based on tone, texture, pattern, size and shape.
2.4.2. Image classification and accuracy assessment
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All land use/cover maps were generated through an artificial neural network classifier. To
achieve the best possible classification accuracy, various artificial neural network structures were
applied and finally the neural network structure with the highest accuracy was selected.
In order to get precise classification results, the training samples were selected from false
color composite (FCC) images and topographic maps.
To map land cover information, three-layer-perceptron neural networks were employed
consisting of one input layer, one hidden layer and one output layer. The input layer included
spectral bands and training samples and the output layer had four nodes. Experiments were
conducted to select the optimum number of nodes in the hidden layer to maximize classification
accuracy. The number of nodes in the hidden layer was selected equivalent to the number of
nodes in the input layer. The parameters of momentum and learning rate were adjusted to 0.5 and
0.2 based on experimental results.
The overall accuracy of land cover maps was calculated from error matrices. The Ground
truth data were derived from GPS, topographic maps and false color composite images and an
error matrix was generated for each land cover map.
2.5. Land use/cover change detection
For performing land use/cover change detection, a post-classification comparison was
employed. Cross-tabulation analysis was carried out for 1956-1972, 1972-1990, 1990-2006 and
1956-2006 and change maps and matrices were generated.
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3.Results and Discussion
In preparing data for this study, an attempt was made to find and select data which cover five
decades from 1956 to 2006. Because of the unavailability of satellite images in 1956, aerial
photos were used.
The root mean square errors (RMSE) for all aerial photographs were between 0.2 and 0.7
pixels. RMSE for PAN, LISS III and TM images were determined to be 0.42, 0.48 and 0.58
pixels, respectively.
Image fusion was done and resulted in an image with high spatial resolution of 5.8 meters.
The fused image enhanced the capability of identifying features and selecting training samples.
Land use/cover mapping was done through the visual interpretation of aerial photos and
neural network classification of satellite images. Both methods resulted in land use/cover maps
with high accuracies. The overall accuracy of the land use/cover maps for 1956, 1972, 1990 and
2006 were 95.03%, 99.05%, 95.53% and 95.53%, respectively. The Kappa index for the 1956,
1972, 1990 and 2006 land use/cover maps were found to be 0.93, 0.98, 0.92 and 0.93.
Information on land cover changes for 1956-1972, 1972-1990, 1990-2006 and 1956-2006
were derived using post-classification analysis of land use/cover maps. The post-classification
approach provided “from-to” change information. Results of post-classification comparison are
shown in Table 1 through Table 4.
The total urban area between 1956 and 1972 increased by 170 %, growing from 500 ha to
1353 ha, or an increase of about 53 ha per year, coming from a total transformation of 241 and
701 ha of vegetated and bare lands to residential and industrial areas and 88 ha in the other
direction, from urban to vegetated or bare. In the 18 years following 1972, urban areas expanded
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by 92%. The expansion then continued at a higher rate and urban areas increased 1.7-fold
between 1990 and 2006.
In this study, change trends over five decades were also investigated. Results show that
drastic changes occurred in the land use/cover of Arak and its surrounding area over 50 years.
The urban area increased by 3893 ha at a rate of about 78 ha per year. Bare lands and vegetation
cover including natural vegetation and farmlands around the 1956 extent of Arak were converted
to houses and a variety of factories and companies during 50 years. This is due to
industrialization and rapid urbanization in Arak. Rural to urban immigration and migration from
other cities of Iran to Arak due to job opportunities caused population growth; the population of
Arak increased about 8-fold and grew from 58,998 people in 1956 to 446,760 people in 2006.
Urban expansion and subsequent degradation of farmlands and natural vegetation has been
occurring and continues to occur in many places in Iran and all over the world such as Bankok in
Thailand and the Greater Toronto Area (GTA) in Canada as the change-detection analysis in
Bangkok Metropolitan area indicated that that 2% of agricultural land was lost and there was a
14% increase in the commercial areas between 1988 and 1994. Moreover, the analysis of
changes in the built and non-built environment for the Greater Toronto Area (GTA) revealed that
of the 56,081 ha of non-built land lost to settlement in the GTA between 1986 and 2001,
approximately 45486 ha were classified as suitable for agriculture of one form or another
(Madhavan et al., 2001; Tole, 2008).
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Table 1. Land use/cover change matrix from 1956 to 1972 (ha)
1956
Urban
Vegetation cover
1972
Barren land
Mountain
Total
Urban
Vegetation
cover
Barren land
Mountain
Total
411.3
241.2
701.3
0
1353.8
47.5
2557.1
2086.6
0
4691.2
41.3
1178.2
4174.8
0
5394.4
0
0
0
4371.3
4371.3
500.1
3976.5
6962.7
4371.3
15810.7
Table 2. Land use/cover change matrix from 1972 to 1990 (ha)
1972
Urban
Vegetation cover
1990
Barren land
Mountain
Total
Urban
Vegetation
cover
Barren land
Mountain
Total
1028.6
532.5
1041.9
0
2602.9
178.4
1884.6
548.3
0
2611.4
146.8
2274.1
3804.1
0
6225
0
0
0
4371.3
4371.3
1353.8
4691.2
5394.4
4371.3
15810.7
Table 3. Land use/cover change matrix from 1990 to 2006 (ha)
1990
Urban
Vegetation cover
2006
Barren land
Mountain
Total
Urban
Vegetation
cover
Barren land
Mountain
Total
2398.3
488.5
1506.5
0
4393.3
73.8
1427.9
941.8
0
2443.5
130.8
695
3776.8
0
4602.6
0
0
0
4371.3
4371.3
2602.9
2611.4
6225.1
4371.3
15810.7
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Table 4. Land use/cover change matrix from 1956 to 2006 (ha)
1956
Urban
Vegetation cover
2006
Barren land
Mountain
Total
Urban
Vegetation
cover
Barren land
Mountain
Total
453.5
1028.2
2911.5
0
4393.3
14.1
1475
954.5
0
2443.5
32.5
1473.3
3096.8
0
4602.6
0
0
0
4371.3
4371.3
500.1
3976.5
6962.7
4371.3
15810.7
4. Conclusion
In the present study, post-classification comparison was successfully employed for detecting
and monitoring land use/cover changes between 1956 and 2006.
The outcomes of this research indicate that Landsat TM and IRS-P6 LISS-III images can be
effectively used for generating accurate land use/cover maps as the overall accuracies of all
generated land use/cover maps were found to be over 95%.
The urban area of city of Arak has grown without considering transformation of other land
use/cover types during five decades due to rapid industrialization and population growth. A lot of
people immigrated to Arak because of increased job opportunities during recent years. The
demographic change and industrialization not only caused drastic changes in urban area, but also
caused changes in other land use/cover types in the study area such as vegetation cover. The
results indicate that the area of vegetation cover has degraded considerably and the urban area of
Arak has enlarged approximately 9 times during five decades.
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Acknowledgments
The authors would like to acknowledge all the help of their colleagues at Young Researchers and
Elite Club, Majlesi Branch, Islamic Azad University, Isfahan, Iran.
Conflicts of interest statement
The authors declare that they are no conflicts of interests.
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