barren land index to assessment land use land cover changes in himreen lake and surrounding area...

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Journal of Environment and Earth Sc ISSN 2224-3216 (Paper) ISSN 2225 Vol. 3, No.4, 2013 Barren Land Index to in Himreen La Mo Department of Earth Scie * E-mail of Abstract The study area lies in the eastern p Governorates. The eastern boundar (7010) Km². The present study depends on two s using area of interest (AOI) file and 9.2 software. The images carried o resampling. Barren Land Index (BLI) was adopt and surrounding area. The obtained result showed the dis 1976-1992-2010 and the change whi soil and salt flat classes, mixed barre Keywords: Land use - Land cover ( detection. 1. Introduction Change detection refers to the proce different times. (Lu D.et.al 2003), ( aid of remote sensing software. Ma analyst defining areas of interest an change detection applies comparison specific change detection algorithm according to different criteria. Amon is image differencing, principal co analysis, change vector analysis and of different methods of change detec Change detection is useful in such assessment of deforestation, the stud assessment, crop stress detection, d other environmental changes (Sing economic growth. The information derived from rem biophysical relationships in an eco managers can use remote sensing as In this study Barren Land Index ( environment, it included a descriptio Landsat data for the years (1976-199 1.1 Objective of the study The main objectives are study and a (1976-1992) and (1992-2010). 1.2 Location of the study area The study area lies in the eastern p Governorates. The eastern boundar (7010) Km² and it is determined by t cience 5-0948 (Online) 15 o Assessment Land Use-Land Co ake and Surrounding Area East o ousa Abdulateef Ahmed * , Walid A. Ahmad ** ences, College of Sciences, University of Baghdad, Ba f the corresponding author: [email protected] part of Iraq, within Diyala and small parts of Salah A y of the map represents Iraqi-Iranian International b scenes of Thematic Mapper (TM5) data of Landsat, th d made use of a nearest neighbor polynomial correct out with WGS84 datum and UTM N38 projection ted as a practical tool for study and assessment the ch stributions of Land use - Land cover classes in the s ich occurred in the periods (1976-1992) and (1992-201 en land class, exposed rocks and sandy area classes. (LULC), Image processing, Thresholding, Barren Lan ess of identifying differences in the state of land feature (Singh 1989). This process can be accomplished eithe anual interpretation of change from satellite images in nd comparing them between images from two dates. n of a set of multitemporal images covering the time ms. There are many changes detection approaches. T ng the most common methods of change detection usi omponent analysis (PCA), post-classification compa d integrating GIS into the analysis (Lu D.et.al 2003), a ction (Jensen 2005). h diverse applications as land use analysis, monitori dy of changes in vegetation, seasonal changes in pastu disaster monitoring, day/night analysis of thermal ch gh 1989). Change detection may also be important f mote sensing change detection may provide a better osystem, than is possible with field data alone. W a tool for sustainable environmental management (Jen (BLI) was used to analyze and output data related on of physical conditions of the classes. The base of 92-2010). assessment the changes in Himreen lake and surround part of Iraq, within Diyala and small parts of Salah A y of the map represents Iraqi-Iranian International b the following coordinates (Fig.1). www.iiste.org over Changes of Iraq aghdad, Iraq Al-Din and Sulamanyah borders; it covers about hese data are subset by tion within the ERDAS using nearest neighbor hanges in Himreen lake study area for the years 010) represented by bare nd Index (BLI), Change es by observing them at er manually or with the nvolves an observer or Remote sensing based period of interest using They can be classified ing remote sensing data arison, spectral mixture s well as a combination ing shifting cultivation, ure production, damage haracteristics as well as for tracking urban and r understanding of the With this understanding, nsen 2000, Mas 1999). d to the change in the this study depended on ing area for the periods Al-Din and Sulamanyah borders, it covers about

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Page 1: Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

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

Page 2: Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

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)

Page 3: Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

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

Page 4: Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

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

Page 5: Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

Journal of Environment and Earth Science

ISSN 2224-3216 (Paper) ISSN 2225

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

Page 6: Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

Journal of Environment and Earth Science

ISSN 2224-3216 (Paper) ISSN 2225

Vol. 3, No.4, 2013

Minimum

Maximum

Figure 6. BLI image in 1976

Journal of Environment and Earth Science

3216 (Paper) ISSN 2225-0948 (Online)

20

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

www.iiste.org

Histogram of BLI image in 1976

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Journal of Environment and Earth Science

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Vol. 3, No.4, 2013

Minimum

Maximum

Figure 8. BLI image in 1992

Figure 10. BLI image in 2010

Journal of Environment and Earth Science

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21

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

www.iiste.org

Histogram of BLI image in 1992

Histogram of BLI image in 2010

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Journal of Environment and Earth Science

ISSN 2224-3216 (Paper) ISSN 2225

Vol. 3, No.4, 2013

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

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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

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Journal of Environment and Earth Science

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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

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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

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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 flat Mixed barren landExposed 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

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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.

References

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use with Remote Sensor Data, United States Government Printing Office, Washington. 18PP.

Ahmed M.A.and Al_Saady Y.I., 2010.

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Brown, L.G., 1992. A survey of image registration techniques,

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Dai, X., and S. Khorram, 1998. The effects of image misregistration on the accuracy of remotely sensed change

detection, IEEE Transactions on Geoscience and Remote sensing

Journal of Environment and Earth Science

3216 (Paper) ISSN 2225-0948 (Online)

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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

advantages and disadvantages. Many factors such as selection of suitable change detection approach, suitable

and and optimal threshold, may affect the success of the classification. This research aimed to examine the

This method proves its ability for detecting land use -

the study area. Presented study allows estimating the amount of significant land use

occurred in the study area during the periods (1976-1992) and (1992-2010). The changes of LULC in

of the period (1976-1992) and positive of the period

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e Sensor Data, United States Government Printing Office, Washington. 18PP.

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