Journal of Geographic Information System, 2011, 3, 298-305
doi:10.4236/jgis.2011.34026 Published Online October 2011 (
Copyright © 2011 SciRes. JGIS
The Assessment and Predicting of Land Use Changes to
Urban Area Using Multi-Temporal Satellite Imagery and
GIS: A Case Study on Zanjan, IRAN (1984-2011)
Mohsen Ahadnejad Reveshty
Department of Geography, Faculty of Human Science, Zanjan University, Zanjan, Iran
Received August 10, 2011; revised September 15, 2011; accepted September 25, 2011
Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, es-
pecially cropland, have become a great challenge for sustainable urban development [1]. Detection of such
changes may help decision makers and planners to understand the factors in land use and land cover changes
in order to take effective and useful measures. Remote sensing and GIS techniques may be used as efficient
tools to detect and assess land use changes.In recent years, a considerable land use changes have occurred in
the greater Zanjan area. In order to understand the type and rate of changes in this area, Landsat TM images
captured in 1984 and 2011 have been selected for comparison.First, geometric correction and contrast stretch
are applied. In order to detect and evaluate land use changes, image differencing, principal component
analyses and Fuzzy ARTMAP classification method are applied. Finally, the results of land cover classifica-
tion for three different times are compared to reveal land use changes.Then, combined Cellular Automata
with Markov Chain analysis is employed to forecast of human impacts on land use change until 2020 in
Zanjan area. The results of the present study disclose that about 44 percents of the total area changed their
land use, e.g., changing agricultural land, orchard and bare land to settlements, construction of industrial
areas and highways. The crop pattern also changes, such as orchard land to agricultural land and vice versa.
The mentioned changes have occurred within last 27 years in Zanjan city and its surrounding area.
Keywords: Cellular Automata, Change Detection, Forecast, Fuzzy ARTMAP, Land-Use, Urban Area
1. Introduction
Land cover/land use changes are very dynamic in nature
and have to be monitored at regular intervals for sus-
tainable environment development. Remote Sensing data
is very useful because of its synoptic view, repetitive
coverage and real time data acquisition. The digital data in
form of satellite imageries, therefore, enable to accurately
compute various land cover/land use categories and helps
in maintaining the spatial data infrastructure (SDI) which
is very essential for monitoring urban expansion and
change detections studies [2]. In other words, the remote
sensing satellite data in multi-resolution and multispectral
means to provide spatial information for land cover/ land
use at different levels for various aspects as built-up land,
agricultural land, forests, wastelands and water bodies etc.
So, the land cover/land use maps prepared using multi-
date and multispectral data provides different levels of
spatial information which are used in change detection
studies [3].
In the present research, supervised classification based
on Fuzzy Artmap is employed to detect land use changes
occurred in the Zanjan area, Iran. For forecasting of
land -use change until 2020, both Cellular Automata and
Markov Chain were employed.
The study area is located between 36˚3856" to 36˚42'
22"N and 48˚2542" to 48˚3305"E. The area covers
Zanjan city and its surrounding area with 10,193 hectares.
The study area comprises two topographic units’ foothill
and plain. Zanjan population in 1986 was about 215,458
people and its population has been reached to 349,713
people in 2006, the population growth rate in this period
was about 3.93 percent. The main reason to select this
area is that considerable land -use changes have occurred
due to urban developments, rural developments, and in-
dustrial developments in the east, west and south areas,
and that major changes in the crop pattern are ongoing.
Many researchers have employed satellite imagery for
land use mapping as well as change detection. Sunar
(1996) has compared the results of five different tech-
niques: band combination, subtraction, band division,
principal component analysis and classification, in Eki-
tally, Turkey [4]. This study revealed that the principal
component analysis (PCA) shows better results compar-
ing with classification results. Gupta and Parakash (1998)
used a combined method of color composite, band sub-
traction, band division and supervised classification to
prepare a land-use map for change detection in a coal-
mining district in India [5]. They concluded that the su-
pervised classification gives better results for detecting
changes. Ahanejad (2002) used PCA, image differencing
and classification methods for change detection in
Maragheh region, Iran. He concluded that a crosstab
method and a comparison image classification method are
very suitable for land use change assessment [6]. Neshat
(2002) employed Markov Chain to detect the change of
forest areas to urban use in Golestan province, Iran [7].
2. Material and Methods
In this paper, Landsat TM images captured in 1984, 1991,
2006 and 2011 are employed for digital image processing.
Figure 1 shows Landsat TM image were used in this
study. Also Figure 2 shows the flowchart of this study.
Various methods have been employed for classification
of satellite imagery. Recently, artificial fuzzy methods are
used widely because they show very high accuracy in
comparison with the conventional ones like Maximum
Likelihood Classification (MLC), Minimum Distance
Zanjan Area-Landsat5-1984-02-26 Zanjan Area-Landsat5-1991-06-16
Zanjan Area-Landsat5-2006-09-27 Zanjan Area-Landsat5-2010-06-05
Figure 1 . 741 Landsat TM colo ur composite i mages.
Classification, and Parallelepiped Classification [8,9].
In this paper, the fuzzy adaptive resonance theory
(Fuzzy Artmap) is employed for image classification.
First, 741 RGB color composites of Landsat images were
prepared. Then, training areas were selected for 6 land
use and Land cover classes, which are built-up area,
orchards, irrigated agriculture land, dry farming, water,
regolith and waste land. These training areas were de-
termined, referring to aerial photographs and GIS the-
matic maps. To assess the accuracy of classification,
topographic maps and aerial photos were employed.
Overall accuracy was estimated to be around 96.%
Figures 3 to 6 shows the results of land use classification
and Table 1 shows the summary of the classification.
The classification results for the four different times
revealed that the land use of the target area has changed
about 44% during the period of 1984-2011. Table 2
shows the estimated land use transitions based on the
comparison of the classification results for the 1984 and
2011 images. More than 68% of the area that belongs to
built-up changes to dry farming and waste areas. Dry
land farming attains the least changes 25% in this period
(Table 2).
The results also show that built -up area changed from
1418 hectare in 1984 to 4662 hectare in 2011. The in-
crease is mainly due to the needs of settlements in Zanjan
City because its population has increased from 215,458 in
1986 to 349,713 in 2006. New suburban areas, such as
Sayan, Elahieh, Amir Kabir, GolShahr and Kazemieh,
have also developed in the period. Figure 7 shows built-
up area growth in case study area.
The Results of Land use changes analysis show that in
case study area dry farming and regolith and waste land
have most change to built -up area that respectively 2187
and 885 hectares. Also water body and orchards have
minimum changes to built-up area that respectively 4 and
58 hectares. Table 3 shows land use transition to built-up
area in case study area during 1984-1991, 1991-2006 and
In totally in Land use and Land cover changes in
1984-2011, Built -up area have maximum changes with
47.04 percent and minimum changes related to water
body with 0.01 percent changes. Figures 8-11 shows the
areas that have changed to built -up ones in the period of
One of other analysis in this paper related to land use
persistence in the period of 1984-2011 in case study area.
It means that how much of land use and land cover and
what areas have persistence in during of study periods and
has not changes. According to analysis in this case study
area about 4329 hectares of land use and land cover have
not any changes and 5864 hectare of land use and land
cover has been changed in the study period 1984-2011.
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Figure 2. Flow chart showing the major steps resea rch.
Figure 3. Result of land use classification for Zanjan, Iran
using Landsat TM image captured in 1984.
Table 4 and Figure 12 shows that spatial distribution
map of land use and land cover persistence in the period of
In between land use Dry Farming with 2167 hectare has
most persistence in comparing with another land use and
Figure 4. Result of land use classification using Landsat
ETM + image captured in 1991.
Irrigated Agriculture Land with 58 hectare has lowest per-
sistence in case study area. Also dry farming with 2375
hectare has maximum changes and orchards with 198
hectare have minimum changes in Zanjan area between
1984 and 2011.
Copyright © 2011 SciRes. JGIS
Figure 5. Result of land use classification using Landsat
ETM + image captured in 2006.
Figure 6. Result of land use classification using Landsat
ETM + image captured in 2011.
Table 1. Summary of land use change to urban area in
study area ( Hectares).
2011 2006 1991 1984 Landuse Type Class
4662 3669 2068 1418 Built-Up Area 1
2547 3016 4453 4542 Dry Farming 2
907 877 973 651 Orchards
3 3 4 5 Water 4
191 174 210 449
Irrigated Agri-
1883 2453 2485 3128
Regolith and
10,19310,193 10,193 10,193Tot al-Hectare
3. Discussion
The other object of this paper is to predict the trend of land
use changes in the future. Many methods can be applied
to predict the trend. In this paper, two methods are used.
Built-up Area 1984-02-26 Built-up Area 1991-06-16
Built-up Area 2006-09-27 Built-up Area 2011-06-05
Figure 7. Built-up area growth at during 1984-2011 in case
study area.
The first method is Markov chain method analyses a
pair of land cover images and outputs a transition prob-
ability matrix, a transition area matrix, and a set of con-
ditional probability images. The transition probability
matrix shows the probability that one land -use class will
change to the others. The transition area matrix tells the
number of pixels that are expected to change from one
class to the others over the specified period [10].
The conditional probability images illustrate the prob-
ability that each land cover type would be found after a
specific time passes These images are calculated as pro-
jections from the two input land cover images [11,12].
The output conditional probability images can be used as
direct input for specification of the prior probabilities in
Maximum Likelihood Classification of remotely sensed
imagery )such as with the MAXLIKE and BAYCLASS
modules.(A raster group file is also created listing all the
conditional probability images [12,13]).
The second method is Combination of Cellular Auto-
mata and Markov Chain. To know the changes that have
occurred in the past may help to predict future changes.
Combination of Cellular Automata and Markov Chain is
often employed to predict land cover change estimation.
In this study, a series of image processing was per-
formed to predict the trend of land use change in 2020
(Table 5). The result shows that the probability to change
to Built-up area is highest. Figure 13 shows the prob-
ability that the area will be converted to Build -up area in
In order to predict the trends of land use changes, first
1984 and 2011 land use map were analyzed with Markov
Chain. Then, combined method of Cellular Automata and
Markov Chain was used for forecasting land use change in
2020. According to the results (Figure 14 and Table 6 ),
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Figure 8. The areas that have changed to built-up ones in the period of 1984-1991.
Figure 9. The areas that have changed to built-up in the period of 1991-2006.
Table 2. Estimated land use transitions in Zanjan area between 1984 and 2011 (Hectare).
Class 1 2 3 4 5 6 Total Change
1 1418 0 0 0 0 0 1418 14
2 2187 2167 97 0 53 39 4542 45
3 58 41 454 0 53 45 651 6
4 4 0 0 0 0 1 5 0
5 111 30 220 0 58 30 449 4
6 885 310 136 2 28 1768 3128 31
Total 4662 2547 907 3 191 1883 10,193
Change 46 25 9 0 2 18 100
(Row related to 1984 land use and Column related to 2011 land use)
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Table 3. Summary of image cl assification performed in st udy area (Hectares).
2006-20 111991-20 061984 -1991 Land use Type Class
2187 621 1161 405 Dry Farming 2
58 21 20 17 Orchards 3
4 1 2 1 Water 4
111 5 68 38 Irrigated Agriculture5
885 345 350 189 Regolith and Waste 6
3244 993 1601 650 Total(H)
Table 4. Estimated land use persistence and changes in Zanjan Area during 1984 and 2011.
Changes Persistence Type ID
0.00 1418 Built-Up Area 1
2375 2167 Dry Farming 2
198 454 Orchards 3
390 58 Irrigated Agriculture Land 5
1361 1768 Regolith and Waste Land 6
4329 5864 Total
Table 5. The probability of land use changes based on Markov Chain in the period of 2011-2020.
Class 1 2 3 4 5 6
1 1.000 0.000 0.000 0.000 0.000 0.000
2 0.258 0.713 0.010 0.000 0.014 0.005
3 0.002 0.035 0.834 0.000 0.089 0.041
4 0.762 0.000 0.000 0.000 0.000 0.238
5 0.153 0.051 0.540 0.000 0.208 0.048
6 0.105 0.079 0.023 0.001 0.006 0.785
(Row related to 2011 and Column related to 2020)
Figure 1 0. The areas that have changed to built-up in the period of 2006-2011.
built -up areas increase from 4662 hectare in 2011 to 5550
hectares in 2020 and the probability change dry farming to
built-up area is highest in comparing with other land use
and land cover types.
Figure 1 1. The areas that have changed to built-up in the period of 1984-2011.
Figure 12. The areas have that Land use persistence be-
tween 1984-2011.
4. Conclusions
In this paper, using Landsat Satellite images in 1984 and
2011, land use changes in Zanjan city area, Iran were
evaluated. For classification of the images, Fuzzy
Artmap classification method was applied, which has
very high confidence comparing with other classification
methods. In addition, combined Cellular Automata with
Markov Chain method was employed to forecast human
impacts on land use change until 2020 for the study area.
The results revealed that the land use change has oc-
curred for the area of about 4329 hectares in the period
Figure 13. The probability to remain/change to built -up
areas by 2020 obtained by Markov Chain.
Table 6. The result of prediction of land use in 2020 by the
combination of Cellular Automata and Markov Chain.
Land use1 2 3 4 5 6 Total
1 46620 0 0 0 0 4662
2 6591869 0 0 19 0 2546
3 0 2 8970 8 0 907
4 2 0 0 1 0 0 3
5 290.3 250 137 0 191
6 198137 6 1 1.9 1538 1882
Total 5550 2008 929 2 166 1538 10193
(Row related to 2011 land use and Column related to 2020 land use)
1984-2011. These changes due to developments of set-
tlements on orchards and agriculture lands, which oc-
curred mostly in the urban fringe of Zanjan city, are rec-
ognized as highly impacted areas from the environmental
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Figure 14. Predicted result of land-use change in 2020 by
the combination of Cellular Automata and Markov Chain.
point of view.
According to Cellular Automata and Markov Chain
Forecasting model, built -up areas will increase from 4662
hectares in 2011 to 5550.4 hectares in 2020. The con-
tinuation of such a trend may endanger the surrounding
land as well as the agricultural lands and orchards in the
area. Hence, it is recommended to protect these critical
The results of this study also revealed that dry farming
land around major towns and settlements are recognized
as critical regions in terms of land use changes, and spe-
cial protection measures are needed to be taken. In case of
improper planning, these regions will be changed to set-
tlements in a very short time, which is totally in contra-
diction to sustainable development.
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