Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

1. Journal of Environment and Earth Science
ISSN 2224-3216 (Paper) ISSN 2225
Vol. 3, No.4, 2013
Barren Land Index to Assessment Land Use
in Himreen Lake and Surrounding Area East of Ir
Mousa Abdulateef Ahmed
Department of Earth Sciences,
*
E-mail of the corresponding author:
Abstract
The study area lies in the eastern part of Iraq
Governorates. The eastern boundary of the map represents Iraqi
(7010) Km².
The present study depends on two scenes o
using area of interest (AOI) file and made use of a nearest neighbor polynomial correction within the ERDAS
9.2 software. The images carried out with WGS84 datum and UTM N38 projection using
resampling.
Barren Land Index (BLI) was adopted as a practical tool for
and surrounding area.
The obtained result showed the distributions of
1976-1992-2010 and the change which occurred in the periods (1976
soil and salt flat classes, mixed barren land class,
Keywords: Land use - Land cover (LULC)
detection.

1. Introduction
Change detection refers to the process of identifying differences in the state of land features by observing them at
different times. (Lu D.et.al 2003), (Singh 1989)
aid of remote sensing software. Manual interpretation of
analyst defining areas of interest and comparing them between
change detection applies comparison of a set of multitemporal images covering the time period of interest using
specific change detection algorithms. There are many changes detection approaches. They can be classified
according to different criteria. Among the most common methods of change detection using remote sensing data
is image differencing, principal component analysis (PCA), post
analysis, change vector analysis and integrating GIS i
of different methods of change detection (Jensen 2005).
Change detection is useful in such diverse applications as land use analysis, monitoring shifting cultivation,
assessment of deforestation, the study of changes in vegetation, seasonal changes in pasture production, damage
assessment, crop stress detection, disaster monitoring, day/night analysis of thermal characteristics as well as
other environmental changes (Singh 1989). Change detection
economic growth.
The information derived from remote sensing change detection may provide a better understanding of the
biophysical relationships in an ecosystem, than is possible with field data alone. With th
managers can use remote sensing as a tool for sustainable environmental management (Jensen 2000, Mas 1999).
In this study Barren Land Index (BLI) was used to analyze and output data related to the change in the
environment, it included a description of physical conditions of the classes. The base of this study depended on
Landsat data for the years (1976-1992
1.1 Objective of the study
The main objectives are study and assessment the changes in Himreen lake and surrounding area
(1976-1992) and (1992-2010).
1.2 Location of the study area
The study area lies in the eastern part of Iraq
Governorates. The eastern boundary of the map represents Iraqi
(7010) Km² and it is determined by the following coordinates (Fig.1).
Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
15
to Assessment Land Use-Land Cover
in Himreen Lake and Surrounding Area East of Ir

Mousa Abdulateef Ahmed
*
, Walid A. Ahmad
**

Department of Earth Sciences, College of Sciences, University of Baghdad, Baghdad, Iraq
mail of the corresponding author: [email protected]

eastern part of Iraq, within Diyala and small parts of Salah Al
The eastern boundary of the map represents Iraqi-Iranian International borders;
two scenes of Thematic Mapper (TM5) data of Landsat, these data are subset by
using area of interest (AOI) file and made use of a nearest neighbor polynomial correction within the ERDAS
The images carried out with WGS84 datum and UTM N38 projection using
was adopted as a practical tool for study and assessment the changes
distributions of Land use - Land cover classes in the study are
and the change which occurred in the periods (1976-1992) and (1992-2010) represented by bare
, mixed barren land class, exposed rocks and sandy area classes.
(LULC), Image processing, Thresholding, Barren Land Index (BLI), Change
refers to the process of identifying differences in the state of land features by observing them at
different times. (Lu D.et.al 2003), (Singh 1989). This process can be accomplished either manually or with the
aid of remote sensing software. Manual interpretation of change from satellite images involves an observer or
analyst defining areas of interest and comparing them between images from two dates. Remote sensing based
change detection applies comparison of a set of multitemporal images covering the time period of interest using
specific change detection algorithms. There are many changes detection approaches. They can be classified
fferent criteria. Among the most common methods of change detection using remote sensing data
is image differencing, principal component analysis (PCA), post-classification comparison, spectral mixture
analysis, change vector analysis and integrating GIS into the analysis (Lu D.et.al 2003), as well as a combination
of different methods of change detection (Jensen 2005).
Change detection is useful in such diverse applications as land use analysis, monitoring shifting cultivation,
, the study of changes in vegetation, seasonal changes in pasture production, damage
assessment, crop stress detection, disaster monitoring, day/night analysis of thermal characteristics as well as
other environmental changes (Singh 1989). Change detection may also be important for tracking urban and
The information derived from remote sensing change detection may provide a better understanding of the
biophysical relationships in an ecosystem, than is possible with field data alone. With th
managers can use remote sensing as a tool for sustainable environmental management (Jensen 2000, Mas 1999).
In this study Barren Land Index (BLI) was used to analyze and output data related to the change in the
description of physical conditions of the classes. The base of this study depended on
1992-2010).
are study and assessment the changes in Himreen lake and surrounding area
eastern part of Iraq, within Diyala and small parts of Salah Al
The eastern boundary of the map represents Iraqi-Iranian International borders,
and it is determined by the following coordinates (Fig.1).
www.iiste.org
Land Cover Changes
in Himreen Lake and Surrounding Area East of Iraq
University of Baghdad, Baghdad, Iraq
, within Diyala and small parts of Salah Al-Din and Sulamanyah
Iranian International borders; it covers about
, these data are subset by
using area of interest (AOI) file and made use of a nearest neighbor polynomial correction within the ERDAS
The images carried out with WGS84 datum and UTM N38 projection using nearest neighbor
the changes in Himreen lake
Land cover classes in the study area for the years
2010) represented by bare
Thresholding, Barren Land Index (BLI), Change
refers to the process of identifying differences in the state of land features by observing them at
. This process can be accomplished either manually or with the
images involves an observer or
. Remote sensing based
change detection applies comparison of a set of multitemporal images covering the time period of interest using
specific change detection algorithms. There are many changes detection approaches. They can be classified
fferent criteria. Among the most common methods of change detection using remote sensing data
classification comparison, spectral mixture
nto the analysis (Lu D.et.al 2003), as well as a combination
Change detection is useful in such diverse applications as land use analysis, monitoring shifting cultivation,
, the study of changes in vegetation, seasonal changes in pasture production, damage
assessment, crop stress detection, disaster monitoring, day/night analysis of thermal characteristics as well as
may also be important for tracking urban and
The information derived from remote sensing change detection may provide a better understanding of the
biophysical relationships in an ecosystem, than is possible with field data alone. With this understanding,
managers can use remote sensing as a tool for sustainable environmental management (Jensen 2000, Mas 1999).
In this study Barren Land Index (BLI) was used to analyze and output data related to the change in the
description of physical conditions of the classes. The base of this study depended on
are study and assessment the changes in Himreen lake and surrounding area for the periods
, within Diyala and small parts of Salah Al-Din and Sulamanyah
nternational borders, it covers about

2. Journal of Environment and Earth Science
ISSN 2224-3216 (Paper) ISSN 2225
Vol. 3, No.4, 2013
Longitude 44° 34
Latitude























1.3 Climate of the study area
The climate is one of the important environmental components of environm
an important role effect in the other environmental components such as desertification, transportation,
weathering, erosion, precipitation and water quality.
The climatic data of Khanaqin station
Meteorological Organization (I.M.O.
humidity, evaporation, wind speed and direction, for the period 1976
climate above mentioned the study area has an arid climate and characterized by hot summer and cold winter
with seasonal rainfall, the major portion of rainfall is received during the months of December to May.
1.4 Geological Setting
The study area is located within the foothill zone and Mesopotamian zone. It is characterized by undulated plains,
hilly and mountainous areas that increase in elevation toward the East and Northeast. Quaternary sediments are
covering part of the study area represented
Deposits, Valley Fill Deposits, Sheet
Depression Fill Deposits, Inland Sabkha Deposits,
Tertiary (Middle Miocene -Pliocene) are represented by Fatah, Injana, Mukdadiyah , and Bai Hassan Formations
(Ahmed and Al-Saady 2010, Barwary and Slewa 1991, 1993, Sissakian and Ibrahim 2005, Fouad 2010)
(Fig.2).
Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
16
Longitude 44° 34' 48˝ 45° 39' 01˝
Latitude 33° 44' 51˝ 34° 33' 20˝
Figure 1. Location map of the study area
The climate is one of the important environmental components of environmental change studies because it plays
an important role effect in the other environmental components such as desertification, transportation,
weathering, erosion, precipitation and water quality.
station is used to evaluate the climate in the study area depending on
Meteorological Organization (I.M.O. 2008) .The elements of climate included ; temperature, rain full, relative
humidity, evaporation, wind speed and direction, for the period 1976-2008. Depend on the elements
the study area has an arid climate and characterized by hot summer and cold winter
with seasonal rainfall, the major portion of rainfall is received during the months of December to May.
is located within the foothill zone and Mesopotamian zone. It is characterized by undulated plains,
hilly and mountainous areas that increase in elevation toward the East and Northeast. Quaternary sediments are
represented by River Terraces, Polygenetic Deposits, Slope Deposits,
Sheet-runoff Deposits, Aeolian Deposits, Gypcrete, Anthropogen Deposits,
Deposits, Inland Sabkha Deposits, Alluvial Fan Deposits. Pre-quaternary
Pliocene) are represented by Fatah, Injana, Mukdadiyah , and Bai Hassan Formations
Barwary and Slewa 1991, 1993, Sissakian and Ibrahim 2005, Fouad 2010)
www.iiste.org
ental change studies because it plays
an important role effect in the other environmental components such as desertification, transportation,
he climate in the study area depending on Iraq
included ; temperature, rain full, relative
2008. Depend on the elements of the
the study area has an arid climate and characterized by hot summer and cold winter
with seasonal rainfall, the major portion of rainfall is received during the months of December to May.
is located within the foothill zone and Mesopotamian zone. It is characterized by undulated plains,
hilly and mountainous areas that increase in elevation toward the East and Northeast. Quaternary sediments are
River Terraces, Polygenetic Deposits, Slope Deposits, Flood
Anthropogen Deposits,
quaternary Deposits, belong to
Pliocene) are represented by Fatah, Injana, Mukdadiyah , and Bai Hassan Formations
Barwary and Slewa 1991, 1993, Sissakian and Ibrahim 2005, Fouad 2010)

3. Journal of Environment and Earth Science
ISSN 2224-3216 (Paper) ISSN 2225
Vol. 3, No.4, 2013





















Figure 2. Geological map
2. Methodology
2.1 Data Collection
The present study depends on the following available data:
Two scenes of Thematic Mapper (TM5) data of Landsat
2/7/2010 in spatial resolution 30m and six spectral bands (b1, b2, b3, b4, b5 and b7) downloaded from the
Website of USGS (http://glovis.usgs.gov/
(GEOSURV) besides the field data.













Figure
Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
17
Figure 2. Geological map of the study area
The present study depends on the following available data:
s of Thematic Mapper (TM5) data of Landsat (Path/Row 168/36) Acquisition in 9/8/1992, and
al resolution 30m and six spectral bands (b1, b2, b3, b4, b5 and b7) downloaded from the
http://glovis.usgs.gov/ ), (Fig.3), (Table1) and ancillary data from the Iraq Geological Survey


A
Figure 3. A-TM scene-1992, B-TM scene-2010
www.iiste.org
) Acquisition in 9/8/1992, and
al resolution 30m and six spectral bands (b1, b2, b3, b4, b5 and b7) downloaded from the
Table1) and ancillary data from the Iraq Geological Survey
B

4. Journal of Environment and Earth Science
ISSN 2224-3216 (Paper) ISSN 2225
Vol. 3, No.4, 2013
Table 1. Landsat image characteristics (Lillesand and Keifer 2000)















2.2 Software
ERDAS Imagine V. 9.2 and Arc GIS V.9.3 softwares have been used. ERDAS Imagine 9.2 software was used for
image processing and change detection. Arc GIS 9.3 software was used for data analysis and map composit
In order to obtain accurate location point data for each LULC class in field survey, the Global Positioning
System (GPS) was used.
2.3 Pre-processing
Two scenes of TM images are subset by using Area of Interest (AOI) file
datum and UTM N38 projection using nearest neighbor resampling. The nearest neighborhoods resampling
procedure were preferred to others resembling such as bilinear or cubic and bicubic convolution, because it is
superior in retaining the spectral information of the image (Lillisand and Kiefer 2000
2.4 Field Observations
Field observations carried out in 2012 and spent approximately ten days touring including taking field notes,
taking photos and collecting GPS coordinates representing final output
image analysts to have a better understanding of the relationships between the satellite images and actual ground
conditions.
2.5 Image processing
2.5.1 Implementing Change Detection
The basic principle of change detection from remote sensing images is based on the difference in reflectance or
intensity values between the images taken at two different times due to changes on the Earth’s surface. A
necessary requirement for change detec
images. That mean; the images must be aligned with each other such that corresponding locations in the images
are present at identical pixel positions. A registration accuracy of less
recommended to achieve a change detection error within 10 percent (Dai and Khorram 1998). Any registration
errors (or misregistration) present in the images may lead to incorrect change detection. Due to its paramount
importance as a pre-processing step for applications like change detection and image fusion, image registration
has been an active area of research for many years and a number of new image registration algorithms have been
developed (Brown 1992, Zitova and F
earlier been studied by (Townshend et al. 1992) and (Dai and Khorram 1998).Also, no relative comparison of the
performance of different change detection algorithms in the presence of r
as the studies are based on a particular algorithm devised only by them.
In general, implementing change detection
some steps) depending on available
Instrument
Sensor
Acquisition date
Path / Row
Spectral bands (µµµµm)

Ground resolution
Dynamic range (bit)
Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
18
Table 1. Landsat image characteristics (Lillesand and Keifer 2000)
9.2 and Arc GIS V.9.3 softwares have been used. ERDAS Imagine 9.2 software was used for
image processing and change detection. Arc GIS 9.3 software was used for data analysis and map composit
In order to obtain accurate location point data for each LULC class in field survey, the Global Positioning
Two scenes of TM images are subset by using Area of Interest (AOI) file. The images carried out with
datum and UTM N38 projection using nearest neighbor resampling. The nearest neighborhoods resampling
procedure were preferred to others resembling such as bilinear or cubic and bicubic convolution, because it is
formation of the image (Lillisand and Kiefer 2000).
Field observations carried out in 2012 and spent approximately ten days touring including taking field notes,
taking photos and collecting GPS coordinates representing final output categories. The field data was useful for
image analysts to have a better understanding of the relationships between the satellite images and actual ground
Detection uses remote sensing technology
The basic principle of change detection from remote sensing images is based on the difference in reflectance or
intensity values between the images taken at two different times due to changes on the Earth’s surface. A
necessary requirement for change detection from remote sensing images is the accurate registration of temporal
images. That mean; the images must be aligned with each other such that corresponding locations in the images
are present at identical pixel positions. A registration accuracy of less than one-fifth of a pixel has been
recommended to achieve a change detection error within 10 percent (Dai and Khorram 1998). Any registration
errors (or misregistration) present in the images may lead to incorrect change detection. Due to its paramount
processing step for applications like change detection and image fusion, image registration
has been an active area of research for many years and a number of new image registration algorithms have been
developed (Brown 1992, Zitova and Flusser 2003). The effect of registration errors on change detection has
earlier been studied by (Townshend et al. 1992) and (Dai and Khorram 1998).Also, no relative comparison of the
performance of different change detection algorithms in the presence of registration errors has been performed,
as the studies are based on a particular algorithm devised only by them.
detection using image-processing software requires a number of steps (all or
some steps) depending on available data, (Fig.4).
Landsat 5
TM
9/8/1992 2/7/2010
168/36 168/36
7 bands
(10)- 0.45-0.52 (blue)
(20)- 0.52-0.60 (green)
(30)- 0.63-0.69 (red)
(40)- 0.76-0.90 (near-infrared)
(50)- 1.55-1.75 (mid-infrared)
(60)-10.4-12.5 (thermal bands)
(70)- 2.08-2.35 (mid-infrared)
30m*30m for multispectral bands, 120*120m for thermal bands
8 bit
www.iiste.org
Table 1. Landsat image characteristics (Lillesand and Keifer 2000)
9.2 and Arc GIS V.9.3 softwares have been used. ERDAS Imagine 9.2 software was used for
image processing and change detection. Arc GIS 9.3 software was used for data analysis and map composition.
In order to obtain accurate location point data for each LULC class in field survey, the Global Positioning
The images carried out with WGS84
datum and UTM N38 projection using nearest neighbor resampling. The nearest neighborhoods resampling
procedure were preferred to others resembling such as bilinear or cubic and bicubic convolution, because it is
Field observations carried out in 2012 and spent approximately ten days touring including taking field notes,
categories. The field data was useful for
image analysts to have a better understanding of the relationships between the satellite images and actual ground
The basic principle of change detection from remote sensing images is based on the difference in reflectance or
intensity values between the images taken at two different times due to changes on the Earth’s surface. A
tion from remote sensing images is the accurate registration of temporal
images. That mean; the images must be aligned with each other such that corresponding locations in the images
fifth of a pixel has been
recommended to achieve a change detection error within 10 percent (Dai and Khorram 1998). Any registration
errors (or misregistration) present in the images may lead to incorrect change detection. Due to its paramount
processing step for applications like change detection and image fusion, image registration
has been an active area of research for many years and a number of new image registration algorithms have been
lusser 2003). The effect of registration errors on change detection has
earlier been studied by (Townshend et al. 1992) and (Dai and Khorram 1998).Also, no relative comparison of the
egistration errors has been performed,
processing software requires a number of steps (all or
30m*30m for multispectral bands, 120*120m for thermal bands

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Vol. 3, No.4, 2013





















Figure 4.
2.5.2 Thresholding
The threshold selection is commonly based on a normal distribution characterized by its mean and its standard
deviation threshold values are scene
content. However, the thresholds can be determined by three approaches: (1) interactive, (2) statistical and (3)
supervised. In the first approach, thresholds are interactively determi
based on statistical measures from the histogram of techniques for selecting appropriate thresholds are based on
the modelling of the signal and noise (Radke et al. 2005 , Rogerson 2002, Rosin 2002), which is carr
this study. Third, the supervised approach derives thresholds based on a training set of change and no
pixels.
2.5.3 Change Detection using Barren Land Index (BLI)
The Barren Land Index is used for change detection of multi
vegetation, land use - land cover and geology. Barren Land Index (BLI) can be obtained by linear combination of
measurements in the spectral domains of the bands green (G), red (R) and near infrared (NIR). It can used
evaluate bare soil and desertification processes .In addition, bright soils have been shown to reduce the values in
vegetation indices such as NDVI (Heute et al. 1985), and water areas such as WI. Barren Land Index (BLI) is
well suited to arid and semi arid areas where dominant vegetation types such as shrubs and other small
vegetation are often photosynthetically inactive (Escadafal and Bacha 1996). A decrease in brightness can be due
either to the wetting of the soil surface, soil roughness, or in deg
The equation used for this index has been created by the researcher after so many attempts which uses the MSS
and TM images as follows
BLI = G² + R² + NIR² / 60
(Fig.5) shows Model of Barren Land Index. (Table 2, 3 and 4)
1992 and 2010 respectively, (Fig. 6 and 7) show the BLI image and Histogram for the year 1976, (Fig. 8 and 9)
show the BLI image and Histogram for the year 1992
for the year 2010.

Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
19
Figure 4. General steps of implementing change detection
The threshold selection is commonly based on a normal distribution characterized by its mean and its standard
ene-dependent, they should be calculated dynamically based on the image
content. However, the thresholds can be determined by three approaches: (1) interactive, (2) statistical and (3)
supervised. In the first approach, thresholds are interactively determined visually tests. The second approach is
based on statistical measures from the histogram of techniques for selecting appropriate thresholds are based on
the modelling of the signal and noise (Radke et al. 2005 , Rogerson 2002, Rosin 2002), which is carr
this study. Third, the supervised approach derives thresholds based on a training set of change and no
Barren Land Index (BLI)
The Barren Land Index is used for change detection of multi-spectral images and successfully used for studies of
land cover and geology. Barren Land Index (BLI) can be obtained by linear combination of
measurements in the spectral domains of the bands green (G), red (R) and near infrared (NIR). It can used
evaluate bare soil and desertification processes .In addition, bright soils have been shown to reduce the values in
vegetation indices such as NDVI (Heute et al. 1985), and water areas such as WI. Barren Land Index (BLI) is
arid areas where dominant vegetation types such as shrubs and other small
vegetation are often photosynthetically inactive (Escadafal and Bacha 1996). A decrease in brightness can be due
either to the wetting of the soil surface, soil roughness, or in degraded soil (Escadafal and Bacha 1996).
The equation used for this index has been created by the researcher after so many attempts which uses the MSS
BLI = G² + R² + NIR² / 60
(Fig.5) shows Model of Barren Land Index. (Table 2, 3 and 4) show Statistics of BLI images for the years 1976,
1992 and 2010 respectively, (Fig. 6 and 7) show the BLI image and Histogram for the year 1976, (Fig. 8 and 9)
istogram for the year 1992 and, (Fig.10 and 11) show the BLI image and Histogram

www.iiste.org
The threshold selection is commonly based on a normal distribution characterized by its mean and its standard
dependent, they should be calculated dynamically based on the image
content. However, the thresholds can be determined by three approaches: (1) interactive, (2) statistical and (3)
ned visually tests. The second approach is
based on statistical measures from the histogram of techniques for selecting appropriate thresholds are based on
the modelling of the signal and noise (Radke et al. 2005 , Rogerson 2002, Rosin 2002), which is carried out in
this study. Third, the supervised approach derives thresholds based on a training set of change and no-change
s and successfully used for studies of
land cover and geology. Barren Land Index (BLI) can be obtained by linear combination of
measurements in the spectral domains of the bands green (G), red (R) and near infrared (NIR). It can used to
evaluate bare soil and desertification processes .In addition, bright soils have been shown to reduce the values in
vegetation indices such as NDVI (Heute et al. 1985), and water areas such as WI. Barren Land Index (BLI) is
arid areas where dominant vegetation types such as shrubs and other small
vegetation are often photosynthetically inactive (Escadafal and Bacha 1996). A decrease in brightness can be due
raded soil (Escadafal and Bacha 1996).
The equation used for this index has been created by the researcher after so many attempts which uses the MSS
BLI = G² + R² + NIR² / 60
show Statistics of BLI images for the years 1976,
1992 and 2010 respectively, (Fig. 6 and 7) show the BLI image and Histogram for the year 1976, (Fig. 8 and 9)
, (Fig.10 and 11) show the BLI image and Histogram

6. Journal of Environment and Earth Science
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Minimum
Maximum
Figure 6. BLI image in 1976
Journal of Environment and Earth Science
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Figure 5. Model of Barren Land Index (BLI)








Table 2. Statistics of BLI image in 1976
Area-km² Histogram
153 153 Count
6406.61 1971872 Total
41.87 12888 Mean
0 0 Minimum
178 55292 Maximum
53.87 16579 Std.dev.


Figure 7. Histogram of
76
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Histogram of BLI image in 1976

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Minimum
Maximum


Figure 8. BLI image in 1992
Figure 10. BLI image in 2010
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Table 3. Statistics of BLI image in 1992
Area-km² Histogram
103 103 Count
5598 6219648 Total
5435 60385 Mean
2.19 2439 Minimum
122 135783 Maximum
31.63 34318 Std.dev.







in 1992
Figure 9. Histogram of BLI
Figure 11. Histogram of BLI
2010
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Histogram of BLI image in 1992
Histogram of BLI image in 2010

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Minimum
Maximum

3. Result of Change Detection for Barren Land Index
Barren land is a land use-land cover category used to classify lands with limited capacity to support life
which less than one-third of the area has vegetation or other cover. In general, it is an area of thin soil, sand, or
rocks. Vegetation, if present, is more widely spaced and scrubby
Rangeland. Categories of Barren Land are: Dry Salt Flats, Beaches, Sandy Areas other than Beaches; Bare
Exposed Rock; Strip Mines, Quarries, and Gravel
(Anderson et al. 1976).
In the study area barren Land is represented by:
Exposed rocks and sandy areas classes
show the classes of BLI index of 1976, 1992 and 2010 respectively. (Table 6) shows
the three dates. (Table 7) shows the LULC change (BLI) for the periods (1976
shows a plot diagram of LULC change (BLI) for two periods, and (Fig.16) shows some locations of barren lands
in the study area.
The detailed descriptions of these classes are described hereinafter:
3.1 Bare soil and Salt flats classes
3.1.1 Bare soil class
The Bare soil class covers a huge area distribution in different location of the study area. The results of image
processing are showing change in the area of this class. It is decreased from 1976 to 1992 then increased in 2010.
3.1.2 Salt flats class
Salt flats represent accumulation place of water in rainy seasons and be dry in the summer that led display the
salt flat on the surface. The salt flats have a different spectral reflection depending on water content,
tend to appear white or light toned because of the high concentrations of salts at the surface as water has been
evaporated, resulting in a higher albedo than other adjacent desert features.
from 1976 to 1992 then increased in 20
3.2 Mixed Barren Land class
The Mixed Barren Land category is used when a mixture of barren land features occurs such as a desert region
where combinations of salt flats, sandy areas, bare rock and surface extraction, in the study area it covers wide
area. It is decreased from 1976-1992
due to the construction of the dam and development of Himrren Lake,
1992-2010 due to increase the other classes in the study area such as (
sandy areas).
3.3 Exposed Rocks and Sandy Areas classes
3.3.1 Exposed Rocks class
The Exposed Rock category includes areas of bedrock exposure, scarps, talus, slides, and ot
rocks represented by sedimentary rocks that are exposed in the study area, which belong to different geological
formations. It is decreased from 1976
1992-2010 due to retraction the water level in Himrren Lake.
3.3.2 Sandy Areas class
Sandy Areas composed primarily of dunes
most commonly are found in deserts although they also occur on coastal plains, r
Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
22
Table 4. Statistics of BLI image in 2010
Area-km² Histogram
89 89 Count
5997 6663501 Total
67.38 74871 Mean
26.53 29475 Minimum
1037 1151829 Maximum
106 117724 Std.dev.
Barren Land Index
land cover category used to classify lands with limited capacity to support life
third of the area has vegetation or other cover. In general, it is an area of thin soil, sand, or
Vegetation, if present, is more widely spaced and scrubby than that in the Shrub and Brush category of
Categories of Barren Land are: Dry Salt Flats, Beaches, Sandy Areas other than Beaches; Bare
Exposed Rock; Strip Mines, Quarries, and Gravel Pits; Mixed Barren Land, besides
In the study area barren Land is represented by: Bare soil and Salt flats classes, Mixed barren Land
classes. (Table 5) shows BLI thresholding of the three dates, (Fig.12, 13 and 14)
BLI index of 1976, 1992 and 2010 respectively. (Table 6) shows distributions of BLI area
the LULC change (BLI) for the periods (1976-1992) and (1992
shows a plot diagram of LULC change (BLI) for two periods, and (Fig.16) shows some locations of barren lands
The detailed descriptions of these classes are described hereinafter:

The Bare soil class covers a huge area distribution in different location of the study area. The results of image
processing are showing change in the area of this class. It is decreased from 1976 to 1992 then increased in 2010.
represent accumulation place of water in rainy seasons and be dry in the summer that led display the
salt flat on the surface. The salt flats have a different spectral reflection depending on water content,
ppear white or light toned because of the high concentrations of salts at the surface as water has been
resulting in a higher albedo than other adjacent desert features. The area of
from 1976 to 1992 then increased in 2010.
The Mixed Barren Land category is used when a mixture of barren land features occurs such as a desert region
where combinations of salt flats, sandy areas, bare rock and surface extraction, in the study area it covers wide
1992 as a result of growth in agricultural lands and expansion in the area of water
due to the construction of the dam and development of Himrren Lake, also Mixed Barren Land is decreased from
her classes in the study area such as (bare soil, salt flats)
Exposed Rocks and Sandy Areas classes
The Exposed Rock category includes areas of bedrock exposure, scarps, talus, slides, and ot
represented by sedimentary rocks that are exposed in the study area, which belong to different geological
formations. It is decreased from 1976-1992 due to expansion in the area of Himrren Lake, and increased from
o retraction the water level in Himrren Lake.
Sandy Areas composed primarily of dunes-accumulations of sand transported by the wind. Sand accumulations
most commonly are found in deserts although they also occur on coastal plains, river flood
www.iiste.org
land cover category used to classify lands with limited capacity to support life and in
third of the area has vegetation or other cover. In general, it is an area of thin soil, sand, or
than that in the Shrub and Brush category of
Categories of Barren Land are: Dry Salt Flats, Beaches, Sandy Areas other than Beaches; Bare
river wash; mud flats.
Mixed barren Land class, and
esholding of the three dates, (Fig.12, 13 and 14)
distributions of BLI area of
1992) and (1992-2010). (Fig.15)
shows a plot diagram of LULC change (BLI) for two periods, and (Fig.16) shows some locations of barren lands
The Bare soil class covers a huge area distribution in different location of the study area. The results of image
processing are showing change in the area of this class. It is decreased from 1976 to 1992 then increased in 2010.
represent accumulation place of water in rainy seasons and be dry in the summer that led display the
salt flat on the surface. The salt flats have a different spectral reflection depending on water content, dry salt flats
ppear white or light toned because of the high concentrations of salts at the surface as water has been
The area of salt flats is decreased
The Mixed Barren Land category is used when a mixture of barren land features occurs such as a desert region
where combinations of salt flats, sandy areas, bare rock and surface extraction, in the study area it covers wide
as a result of growth in agricultural lands and expansion in the area of water
also Mixed Barren Land is decreased from
salt flats) and (exposed rocks,
The Exposed Rock category includes areas of bedrock exposure, scarps, talus, slides, and other accumulations of
represented by sedimentary rocks that are exposed in the study area, which belong to different geological
1992 due to expansion in the area of Himrren Lake, and increased from
accumulations of sand transported by the wind. Sand accumulations
iver flood plains, and deltas. In

9. Journal of Environment and Earth Science
ISSN 2224-3216 (Paper) ISSN 2225
Vol. 3, No.4, 2013
the study area the most sandy areas are represented by sand dune and sand sheet occur in the study area
particularly in the Southwestern part.
expansion in the area of Himrren Lake, and increased from 1992
Himrren Lake and degradation of agricultural lands.
Land use - Land cover
( BLI
Bare soil, Salt flat
Mixed barren land
Exposed Rocks, Sandy area

































Figure 12. Classes of BLI in 19
Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
23
the study area the most sandy areas are represented by sand dune and sand sheet occur in the study area
part. It is decreased from 1976-1992 due to growth in agricultural lands and
n in the area of Himrren Lake, and increased from 1992-2010 due to retraction the water level in
Himrren Lake and degradation of agricultural lands.

Land cover
BLI )
Thresholding
1976 1992 2010
Bare soil, Salt flat 254-159 255-232 255
xed barren land 158-123 231-185 254- 216
s, Sandy area 122-102 184-153 215- 167

BLI in 1976 Figure 13. Classes
Table 5. BLI Thresholding
Figure 14. Classes of BLI in 2010
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the study area the most sandy areas are represented by sand dune and sand sheet occur in the study area
1992 due to growth in agricultural lands and
2010 due to retraction the water level in
2010
255
216
167
Classes of BLI in 1992

10. Journal of Environment and Earth Science
ISSN 2224-3216 (Paper) ISSN 2225
Vol. 3, No.4, 2013
-800
-600
-400
-200
0
200
400
600
800
1976-1992
1992-2010
Bare soil, Salt flat
km²
Land use - Land cover
( BLI )
Bare soil, Salt flat
Mixed barren land
Exposed Rocks, Sandy area
Table 7. LULC change (BLI) for two periods
Land use
(
Bare soil, Salt flat
Mixed barren land
Exposed Rock












Figure 15. Plot diagram of the LULC change (BLI) for two periods

Journal of Environment and Earth Science
3216 (Paper) ISSN 2225-0948 (Online)
24
LULC Change (BLI)
-73.5 -545.1 -190.3
639.3 -649.9 410.1
Bare soil, Salt flatMixed barren land
Exposed Rocks ,
Sandy area
Table 6. Distributions of BLI area
Land cover Surface area in km²
1976 p.% 1992 p.% 2010
l, Salt flat 470.9 7 397.4 7 1036.7
Mixed barren land 4077 64 3531.9 63 2882
s, Sandy area 1858.7 29 1668.4 30 2078.5

Table 7. LULC change (BLI) for two periods

Land use - Land cover
( BLI )
Surface area in km²
1976-1992 1992-2010
Bare soil, Salt flat -73.5 639.3
Mixed barren land -545.1 -649.9
Exposed Rocks, Sandy area -190.3 410.1


Figure 15. Plot diagram of the LULC change (BLI) for two periods

www.iiste.org
Exposed Rocks ,
p.%
1036.7 17
48
2078.5 35
Figure 15. Plot diagram of the LULC change (BLI) for two periods

11. Journal of Environment and Earth Science
ISSN 2224-3216 (Paper) ISSN 2225
Vol. 3, No.4, 2013
















Figure 16. Some locations of barre
4. Conclusion
Many approaches have been applied for the monitoring land use
advantages and disadvantages. Many factors such as selection of suitable change detection approach, suitable
band and optimal threshold, may affect the success of the classification. This research aimed to examine the
benefit of the Barren Land Index. This method proves its ability for detecting land use
the study area. Presented study allo
occurred in the study area during the periods (1976
and Salt flat classes were negative
changes of LULC for Mixed Barren Land
period (1992-2010), and the changes of LULC for
period (1976-1992) and positive of the period
other techniques such as image differencing, image rationing, image regression and advanced classification
perform and provide better change detection det
Besides this research demonstrated the
land cover processes. The authors recommend using a spectrometer device to measure
different features of land use - land cover and
use-land cover processes. Also updating land use
studies in the study area because it is undergoing continues changes especially in agricultural lands and water
level of Himreen Lake.

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Figure 16. Some locations of barren lands in the study area
Many approaches have been applied for the monitoring land use-land cover change; each method has some
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This method proves its ability for detecting land use -
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