۱۳۸۹/۱۰/۰۷

Study of Lake Urmia Level Fluctuations

Study of Lake Urmia Level Fluctuations and Predict Probable Changes Using
Multi-Temporal Satellite Images and Ground Truth Data Period (1976-2010)
New Challenge about Climate Change or Human Impact


















Mohsen Ahadnejad Reveshty
Assistance Professor, Dept. of Geography, Zanjan University, Iran  
ahadnejad@gmail.com
Yoshihisa Maruyama 
Associated Professor, Chiba University, Japan 
ymaruyam@tu.chiba-u.ac.jp
Abstract : 
Lake of  Urmia is the largest saline lake inside Iran and the second saline lake in the world after 
Dead  Sea,  that  average  area  about  5000  km2  is  located  in  northwestern  South Azerbaijan (Iranian Azerbaijan) .
Urmia or (Turkish Language: اورمو, Urmu, Orumiyeh, Urmiye, Urmiya) is a city in Northwestern South Azerbaijan (Iranian Azerbaijan) and the capital of West Azerbaijan Province. The city lies on an altitude of 1,330 m above sea level on the Shahar Chaye river (City River). Urmia is the 10th populated city in iran and 2nd of Azerbaijanian Turks provinces after Tabriz. Urmia is the trade center for a fertile agricultural region where fruit (Specially Apple and Grape) and Tobacco are grown. An important town by the 9th cent. Urmia was seized by the Oghuz Turks (11th cent.), sacked by the Seljuk Turks (1184), and later occupied a number of times by the Ottoman Turks.
The name Urmia or Urmu is thought to have come from Sumerian tongue, the earliest known civilization in the world located in southern Mesopotamia. Ur was a principle Sumerian city. Urmia, situated by a lake and surrounded by rivers, would be the cradle of water. The population of Urmia is predominantly Azerbaijanian Turks (over 90%), but with Kurdish,Assyrian and Armenian minorities.
In  recent  years  this  lake 
levels  affected  natural  and  human  factors,  including  successive  droughts  in  the  region  and  the 
construction  of  dams  and  the  indiscriminate  exploitation  of  water  resources  in  the  Basin .The  Lake 
surface changes that severe fluctuations from 5278 km2 in 1976 has been reached to about 3107.7 km2 in
2009 . 
In this paper, using multi-temporal satellite images, including MODIS, ETM+, TM, MSS images,
fluctuations  assessment in Urmia Lake in 1976-2009 with using the 18 image series in August and also 
using ground truth data methods from water level Lake of  Urmia has been studied. 
In  order  to  probable predict  changes  in  the  lake  in  the  coming  years  with  regard  to  continuous 
fluctuations  occurred  at  this  stage,  Markov  chain  and  cellular  automata  methods were  used .Based  on
results probable survival value for the lake in next ten years has been estimated about 64 percent .Also for
evaluation  of  role  each  of  the  natural  factors,  including  climate  change  and  human  impact  as  major 
challenges discussed in this paper was investigated. 
Key word: Urmia Lake, Fluctuations, GIS, Satellite imagery, Markov Chain



















Introduction
Features and phenomena in the Earth's surface were changed due to over time, the lakes as 
one  of  these  phenomena  and  due  to  having  a  closed  environment is  not  exception  and  due  to 
climatic  changes  such  as  reduced  rainfall  and  increased  temperature  and  uncontrolled  use  
of  surface  water  resources  in  watershed  areas  in  agriculture,  industrial  and  drinking  ever  level 
they  are  exposed  to  change .Supervision  and  monitoring  changes  in  these  lakes  should  be 
considered as  important  in  the  national  and  regional  development  and  natural  resource 
management .Currently  monitoring  the  coastal  areas  and  extraction  of  water  level  changes  at 
different intervals is an infrastructure research of interest because the coastal zone management 
and  dynamic  nature  of  such  sensitive  ecological  environments  need  to  accurate  information 
about  the  various  intervals(Rasoli,2007).  Among  the  remote  sensing  data  are  considered  as 
useful tools for the continuously monitoring and sequentially compared with traditional methods .
With  regard  to  temporal  resolution  from  half  days  to  one  month,  and  spatial  resolution  of  less 
than  one  meter  to  several  kilometers  and  multi-spectral  resolution  of  this  data  and  applying 
mathematical and statistical methods to detection of changes, the satellite image has become as a 
valuable  resource  for  earth  sciences  specialist for  studying  earth  surface  and  its  changing
(Ahadnejad, 2010)
In  the  field  of  application  satellite  images  to  monitoring  of  lakes  and  lagoon  surface
changes much research that is most important, they note : 
Ahadnejad  et  al(2010),  in  paper  entitled  “Detecting  and  Environmental  Assessment 
of Spatial Changes of Hamun-E-Saberi Lagoon Using Satellite Imagery and GIS studied these 
lagoon in the period of 1976-2008 using LANDSAT and MODIS satellite images and analyzes 
them with utilizing the Normalized Difference Water Index (NDWI) during the August months, 
to assess and evaluate its spatial variations .Al Sheikh et al (2007), in article entitled "coastline 
change  detection  using  remote  sensing "study  changes  in  coastline  Urmia  Lake  during  1989, 
1998  and  2001  and  paid  to  utilizing  Landsat  satellite  images  and  processing  them  Coastline 
change  detection  is  about  Urmia  Lake .Ma  and  Wan  (2007),"change  in  area  of  Ebinur  Lake 
during the 1998-2005", they used indicators such as NDWI for detection of water level changes 
in  this  lake .Rasoli  et  al (2007),  in  paper  “monitoring  of  Urmia  Lake  Water  level  fluctuations 
using  multi-temporal  satellite  images  processing .Qulin  TAN  et  al  (2004),  in  paper  entitled "
measuring  Lake  water  level  using  multi-source  remote  sensing  combined  with  hydrological 
statistical data for changing Poyang Lake in China and etc. 
In  this  paper  using  multi-temporal  satellite  images  such  as  MSS,  TM,  MODIS  data  and 
using Normalized Difference Water Index (NDWI), firstly occurred changes detected in Urmia 
Lake  and  then  using  data  such  as  water  level  measured  in  ground  stations  and  the  amount  of 
rainfall and water input to the lake to the trend of modeling with integrated remote sensing data 
and  ground  truth  data  and  ultimately  Urmia  Lake  drying  reasons  will  be  discussed  in  recent 
years. 
Study Area 
Urmia  Lake  as  the  largest  water  body  in  Iranian  plateau  is  located  between  two  major 
provinces of East Azerbaijan and west Azerbaijan .The lake is bounded between 37°5´ -38°16´
latitudes  and  45°01´ -46°  longitudes  at  1275  m  above  sea  level .Its  surface  area  ranges  from  
4750  to  6100  km2  and  the  average  and  greatest  depths  account  for  6  and  16  m,  respectively
(Azari Takami, 1993) .More than 20 permanent and seasonal rivers as well as a few submarine 
streams  and  springs  feed  the  lake .Average  salinity  of  the  lake  ranges  between  220-300 mg/lit
depending  upon  temporal  and  spatial  conditions, in  recently  years  it  arrived  more  than  380 
mg/lit. Due to the ecological heritage of Urmia Lake it is recorded as a protected habitat in the 
world by the United Nations. 
Material and methods 
-Material 
The  data  used  in  this  paper  refer  to  August  month  that  acquired  from  Landsat  and  Terra 
satellite sensors data. Table and figure 1 shows characteristic of data used in this paper.  
Table1 :The characteristic of data used in this paper 












Also  in  this  paper  ground  truth  data  such  as  daily  water  level  data  that  measured  in  during  
1976-2009 by east and west Azerbaijan water organizations in ground station at Sharaf khaneh 
and  Golmankhaneh  ports .Statistics  related  to  rainfall  and  water  volume  input  to  the  lake  are 
other data that used in this paper .Table 2 shows summarized data used in this article.  
Table 2 :The summarized ground truth data form Urmia Lake 












Fig1: Satellite image of Lake Urmia in during 1976 - 2009 



















Methods 
-Image processing : 
There  are  many  methods  for  detecting  of  changes  with  using  satellite  images  such  as 
subtraction  images,  and  ratio  and  difference  method,  supervised  classification,  vector  change 
analysis (VCA), indices and normalized difference ...mentioned. 
For  detecting  of  occurred  changes  in  this  study  satellite  images  of  the  area  and  available 
resources, including U.S .Geological Survey were collected .After the initial corrections such as 
geometric and radiometric correction changes detection of water level changes has been applied . 
Since  the  separation  of  water  bodies  on  satellite  imagery  is  done  carefully  and  high 
accuracy in compared with other phenomena in the earth surface .In this paper for separation and
detection of water from other phenomena, normalized difference water index were used .In this 
index  by  using  near  and  middle  infrared  bands  in  the  TM  and  ETM  sensors,  green  and  near 
infrared  bands  in   MSS  sensor  and  middle  infrared  and  short  wave  in  MODIS  sensor  and 
applying ratio and difference method water bodies has been separated from other phenomena's in
case study area .The equation number 1 to 3 show normalized difference water index for satellite
data are used in this paper. 








Based  on  normalized  difference  water  index  images  produced  by  this  index  value  for  water 
levels  towards  desire  to +1  value  and  for  other  surface  without  water  towards  desire -1  value.
Fig 2 shows images resulting from applying this index for Urmia Lake. 
- Trend Analaysis  
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.  Fig3  shows 
trend change map of Urmia Lake in during 1976-2009. 
(1) Markov chain 
The  Markov  chain  method  analyzes  a  pair  of  water  classification  images  and  outputs  a 
transition probability matrix, a transition area matrix, and a set of conditional probability images .
The transition probability matrix shows the probability that one 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 . 
The conditional probability images illustrate the probability that each class type would be
found after a specific time passes. These images are calculated as projections from the two input 
land  cover  images .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 . 
In this study, a series of image processing was performed to predict the trend of Urmia
Lake change in 2019 . 



















Fig2 :Images resulted from NDWI reclassify for separated land from water (1976-2009)  
(2) Combination of Cellular Automata 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 Urmia Lake
change estimation . 
In order  to  predict  the  trends of  Lake Changes, first  1976  and  2009 Lake  Map was 
analyzed with Markov Chain .Then, combined method of Cellular Automata and Markov Chain 
was  used  for  forecasting  land  use  change  in  2019 .According  to  the results Urmia  Lake areas
decrease  from 3107.78  Km2  in  2009  to 2095.44 km2  in  2019.  Fig4  shows  predicted  map 
of Urmia Lake in 2019 . 
The results of satellite images processing show that most changes occurred in the southern 
and eastern part of lake that indicates the water depth is low in these areas compared with other 
areas of the lake .The lowest lake retreat occurred in the north and northwest of lake. However
high the river water from flowing into the area to the lake but not much depth of water in these 
areas has caused a retreat in this section are vertically regions and less in these regions compared
with southern parts of East coverts to salty land . 
Notable  in  recent  years  especially  in  2008  and  2009  connecting  the  Aspire  and  Ashk 
islands in the middle part of Urmia Lake has caused this intensification and increasing areas of 
salt  in  this  area .The  resulting  map  method  based  on  Markov  chain  and  the  Cellular  Automata  
with the likely trend of the islands of the East Lake are connected to the land where the eastern
and  southern  areas  of  the  lake  completely  dry  and  this  can   be  associated  irreparable 
environmental effects.  




















Change trends analysis using ground truth data and image processing 
Based  on  existing  data  in  Table  2  can  be  realized  that Urmia  Lake long-term  average 
water level in the periods 1976-1998 about 1276.042 m above sea level, except in 1998 than the 
long-term  average  of  about  0.365  m  high  in  the  rest  of  the  years.  From  1999  to  2009  the  lake 
water level has fallen and garlic to the long-term average of about 4.898 meters has decreased. 
Based  on  the  predictions  done  based  on  time  series  method  if  this  trend  continues  to  be  in  the 
lake water level in 2019 decreased to 1267 meters and this will mean that the level of the lake 
with an average long-term reduction of about 9 m. Figure 5 shows graphs of Urmia Lake water 
level trend. 
Reducing  the  lake  water  level  will  be  reduced  lake  area.  Especially  in  the  southern  half  and 
eastern  parts  of  the  lake  that  available  evidence  shows  to  be  shallow  in  these  areas  than  the 
northern half of the lake. According to the results obtained from satellite image processing in the 
long term average lake area of about 5277 sq. km area is that from 1999 to 2009 had reduced the 
garlic  so  the  lake  area  in  2009  reached  approximately  3107  sq.  km  with  average  long-term
reduction of about 2119 sq. km. Based on the analysis carried out using the Markov chains and 
Cellular  Automata  analysis  and  time  series  until  2019  this  trend  with  regard  to  the  lake  area 
decreased by approximately 2000 square kilometers. Figure 4 shows predicted changes in 2019
and  also  figure 6  show  trend  graphs  area  of  Urmia  Lake  also  figure  7  shows  comparison 
between water level and area in Urmia Lake.   































Fig7: The comparison plot between Water Level and Area (KM2)     
The main Factors in reducing of Urmia Lake water level  
Data  of  rainfall  in  Table  2  shows  that  the  long-term  average  rainfall  in  the  Basin  of 
Urmia Lake is about 281 mm. During 1998 to 2001 for three consecutive years the amount of 
rainfall markedly decreased in the years 1998-1999 and reaches about 165 mm. this decreasing 
in  rainfall  is  starting  point  in Lake  water  level  reductions.  Because  of  concern  that  has  caused 
droughts in dams  after  this  year  will  be  built or existing  dams  will  be save  water. During  after 
2001  significantly  on  the  amount  of  rainfall  in  Urmia  Lake  basin  been  increased and  many 
long-term average of these years has been even higher. Then with consider to statistics such as 
rainfall,  climate  change  has  been  not  considered  only  factor  in  Urmia  lake  water  level 
reductions. But also uncontrolled use of water resources in the basin has led in recent years; the
lake  water  level  was  decline. Finally,  we  can  say  that  the  role  of  human  factors  and  impacts  
is more than natural factors in the destruction of lake. 
Conclusion  
The results of this paper shows that human effects and uncontrolled exploitation of water 
resources  has  caused  the  water  level  of  the  Urmia  Lake  suffered  a  sharp  drop  during  the  last 
decade  so  that  this  period   approximately  5  meters   reduced  lake  water  and  lake  area  of  5200 
square  kilometers  in  1998  reduced to  about  3107  square  kilometers  in  2009.  According  to 
analysis conducted in this paper include the use of  Markov Chains, Cellular Automata and time 
series if this trend continues, lake area in 2019  will be reduced to about 2000 sq. km. The issue 
that  caused  irreparable  environmental  effects  of  increased  salt  in  the  region,  the  loss  of 
agricultural  lands  adjacent  to  the  lake  of  salt  transport  by  the  winds  and  thus  cause  large 
economic losses will be happen in this region. on other hand reduce the water level increases the 
amount  of  saturated  salt  water  will  face  that  the  amount  currently  reached  380  mg/lit,  causing
destruction of the only existing live Artemia in the lake that as food for migratory birds. 
Also in this article the role and importance of remote sensing data and processing them 
for  purposes  such  as  monitoring  and  continuous  monitoring,  even  during  the  days,  weeks  or 
months can be considered the traditional methods no such ability and speed to act and sometimes
due to natural and human problem is not possible quickly data collecting. References
[1]Ahanejad,M and et al,2010.  Detecting and Environmental Assessment of Spatial Changes of Hamun-
E-Saberi Lagoon Using Satellite Imagery and GIS, ICEST2010,Bagkok, Thailand, April2010. 
[2]Ahanejad,M and et al, 2010. Evaluation and forecast of human impacts based on land use changes
using multi-temporal satellite imagery and GIS: A case study on Zanjan, Iran Journal of the Indian
Society of Remote Sensing , Pages -659-669 
[3]Alesheikh, A and et al, 2007. Coastline change detection using remote sensing, Int. J. Environ. Sci.
Tech., 4 (1), pp.61-66. 
[4]Azari Takami,G.,1993. Uraemia Lake as a valuable source of Artemia for feeding sturgeon fry.J.Vet,
Fac, Univ, Tehran,47.2-14. 
[5]Cosh, M., E. R. Hunt, Jr., T. J. Jackson, and T. M. Yilmaz, 2009. SMEX04 Landsat TM/ETM+ NDVI
and NDWI. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. 
[6]Gu, Y., Hunt, E., Wardlow, B., Basara, J.B., Brown, J.F., Verdin, J.P,2008. Evaluation of MODIS
NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data.
Geophysical Research Letters 35.
[7]http://www.azarwater.ir/MSathi_Rpt.asp
[8]http://www.agrw.ir/Farsi/Orumieh.asp?Id=11#P-1-1
[9]Liu  Cheng-lin,  Wu  Jian-jun,  2008. Crop  Drought  Monitoring  using  MODIS  NDDI  over  Mid-
Territory of China, International Geoscience & Remote Sensing Symposium. 
[10] Mcfeeters,S.k, 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of
open  water  features , International  Journal  of  Remote  Sensing,  Volume  17,  Issue  7 May  1996  , 
pp.1425-1432. 
[11] Ma,M  and  et  al,  2007.  Change  in  area  of  Ebinur  Lake  during  the  1998-2005  period  ,  International 
Journal of Remote Sensing, Vol.28,No.24, pp.5523–5533. 
[12] Maktav,  D  and  et  al.  Landsat  Thematic  Mapper  Monitoring  Lake  Salda  in  Turkey  ASPRS  ACSM,
1994. 
[13] Qulin  TAN,  Siwen  Bi,  Jiping  Hu,  Zhengjun  Liu,  2004.  Measuring  Lake  water  Level  Using  Multi-
source  Remote  Sensing  Combined  with  Hydrological  Statistical  Data,  Geosciences  and  Remote 
Sensing Symposium. 
[14] Rasoli, A and et al, 2007. Monitoring of Urmia Lake water level fluctuations using multi-temporal
satellite images processing.J, Modaress, No2, summer 2007. 
[15] Robert Gilmore Pontius Jr. and Hao Chen, 2008. "Land Change Modeling with GEOMOD", Clark
University.  
[16] Ronald Eastman J, 2008. "Idrisi Andes Tutorial ", Clark University.  
[17] Wang,  S  and  et  al,  2005.  Suspended  Substance  Content  Inversion  in  Lake  Taihu  Using  Remote 
Sensing Data, Geosciences and Remote Sensing Symposium.