Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Barren Land Index to Assessment ...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Longitude 44° 34Latitude1.3 Clim...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Figure 2. Geological map2. Metho...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Table 1. Landsat image character...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Figure 4.2.5.2 ThresholdingThe t...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013MinimumMaximumFigure 6. BLI imag...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013MinimumMaximumFigure 8. BLI imag...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013MinimumMaximum3. Result of Chang...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013the study area the most sandy ar...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013-800-600-400-2000200400600800197...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Figure 16. Some locations of bar...
Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Escadafal R. and Bacha S., 1996....
This academic article was published by The International Institute for Science,Technology and Education (IISTE). The IISTE...
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Barren land index to assessment land use land cover changes in himreen lake and surrounding area east of iraq

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

  1. 1. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Barren Land Index to Assessment Land Usein Himreen Lake and Surrounding Area East of IrMousa Abdulateef AhmedDepartment of Earth Sciences,*E-mail of the corresponding author:AbstractThe study area lies in the eastern part of IraqGovernorates. The eastern boundary of the map represents Iraqi(7010) Km².The present study depends on two scenes ousing area of interest (AOI) file and made use of a nearest neighbor polynomial correction within the ERDAS9.2 software. The images carried out with WGS84 datum and UTM N38 projection usingresampling.Barren Land Index (BLI) was adopted as a practical tool forand surrounding area.The obtained result showed the distributions of1976-1992-2010 and the change which occurred in the periods (1976soil and salt flat classes, mixed barren land class,Keywords: Land use - Land cover (LULC)detection.1. IntroductionChange detection refers to the process of identifying differences in the state of land features by observing them atdifferent times. (Lu D.et.al 2003), (Singh 1989)aid of remote sensing software. Manual interpretation ofanalyst defining areas of interest and comparing them betweenchange detection applies comparison of a set of multitemporal images covering the time period of interest usingspecific change detection algorithms. There are many changes detection approaches. They can be classifiedaccording to different criteria. Among the most common methods of change detection using remote sensing datais image differencing, principal component analysis (PCA), postanalysis, change vector analysis and integrating GIS iof 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, damageassessment, crop stress detection, disaster monitoring, day/night analysis of thermal characteristics as well asother environmental changes (Singh 1989). Change detectioneconomic growth.The information derived from remote sensing change detection may provide a better understanding of thebiophysical relationships in an ecosystem, than is possible with field data alone. With thmanagers 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 theenvironment, it included a description of physical conditions of the classes. The base of this study depended onLandsat data for the years (1976-19921.1 Objective of the studyThe 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 areaThe study area lies in the eastern part of IraqGovernorates. 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 Science3216 (Paper) ISSN 2225-0948 (Online)15to Assessment Land Use-Land Coverin Himreen Lake and Surrounding Area East of IrMousa Abdulateef Ahmed*, Walid A. Ahmad**Department of Earth Sciences, College of Sciences, University of Baghdad, Baghdad, Iraqmail of the corresponding author: Mousageo@yahoo.comeastern part of Iraq, within Diyala and small parts of Salah AlThe eastern boundary of the map represents Iraqi-Iranian International borders;two scenes of Thematic Mapper (TM5) data of Landsat, these data are subset byusing area of interest (AOI) file and made use of a nearest neighbor polynomial correction within the ERDASThe images carried out with WGS84 datum and UTM N38 projection usingwas adopted as a practical tool for study and assessment the changesdistributions of Land use - Land cover classes in the study areand 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), Changerefers to the process of identifying differences in the state of land features by observing them atdifferent times. (Lu D.et.al 2003), (Singh 1989). This process can be accomplished either manually or with theaid of remote sensing software. Manual interpretation of change from satellite images involves an observer oranalyst defining areas of interest and comparing them between images from two dates. Remote sensing basedchange detection applies comparison of a set of multitemporal images covering the time period of interest usingspecific change detection algorithms. There are many changes detection approaches. They can be classifiedfferent criteria. Among the most common methods of change detection using remote sensing datais image differencing, principal component analysis (PCA), post-classification comparison, spectral mixtureanalysis, change vector analysis and integrating GIS into the analysis (Lu D.et.al 2003), as well as a combinationof 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, damageassessment, crop stress detection, disaster monitoring, day/night analysis of thermal characteristics as well asother environmental changes (Singh 1989). Change detection may also be important for tracking urban andThe information derived from remote sensing change detection may provide a better understanding of thebiophysical relationships in an ecosystem, than is possible with field data alone. With thmanagers 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 thedescription of physical conditions of the classes. The base of this study depended on1992-2010).are study and assessment the changes in Himreen lake and surrounding areaeastern part of Iraq, within Diyala and small parts of Salah AlThe eastern boundary of the map represents Iraqi-Iranian International borders,and it is determined by the following coordinates (Fig.1).www.iiste.orgLand Cover Changesin Himreen Lake and Surrounding Area East of IraqUniversity of Baghdad, Baghdad, Iraq, within Diyala and small parts of Salah Al-Din and SulamanyahIranian International borders; it covers about, these data are subset byusing area of interest (AOI) file and made use of a nearest neighbor polynomial correction within the ERDASThe images carried out with WGS84 datum and UTM N38 projection using nearest neighborthe changes in Himreen lakeLand cover classes in the study area for the years2010) represented by bareThresholding, Barren Land Index (BLI), Changerefers 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 theimages involves an observer or. Remote sensing basedchange detection applies comparison of a set of multitemporal images covering the time period of interest usingspecific change detection algorithms. There are many changes detection approaches. They can be classifiedfferent criteria. Among the most common methods of change detection using remote sensing dataclassification comparison, spectral mixturento the analysis (Lu D.et.al 2003), as well as a combinationChange 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, damageassessment, crop stress detection, disaster monitoring, day/night analysis of thermal characteristics as well asmay also be important for tracking urban andThe information derived from remote sensing change detection may provide a better understanding of thebiophysical 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 thedescription of physical conditions of the classes. The base of this study depended onare study and assessment the changes in Himreen lake and surrounding area for the periods, within Diyala and small parts of Salah Al-Din and Sulamanyahnternational borders, it covers about
  2. 2. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Longitude 44° 34Latitude1.3 Climate of the study areaThe climate is one of the important environmental components of environman important role effect in the other environmental components such as desertification, transportation,weathering, erosion, precipitation and water quality.The climatic data of Khanaqin stationMeteorological Organization (I.M.O.humidity, evaporation, wind speed and direction, for the period 1976climate above mentioned the study area has an arid climate and characterized by hot summer and cold winterwith seasonal rainfall, the major portion of rainfall is received during the months of December to May.1.4 Geological SettingThe 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 arecovering part of the study area representedDeposits, Valley Fill Deposits, SheetDepression 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 Science3216 (Paper) ISSN 2225-0948 (Online)16Longitude 44° 34 48˝ 45° 39 01˝Latitude 33° 44 51˝ 34° 33 20˝Figure 1. Location map of the study areaThe climate is one of the important environmental components of environmental change studies because it playsan 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 onMeteorological Organization (I.M.O. 2008) .The elements of climate included ; temperature, rain full, relativehumidity, evaporation, wind speed and direction, for the period 1976-2008. Depend on the elementsthe study area has an arid climate and characterized by hot summer and cold winterwith 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 arerepresented by River Terraces, Polygenetic Deposits, Slope Deposits,Sheet-runoff Deposits, Aeolian Deposits, Gypcrete, Anthropogen Deposits,Deposits, Inland Sabkha Deposits, Alluvial Fan Deposits. Pre-quaternaryPliocene) are represented by Fatah, Injana, Mukdadiyah , and Bai Hassan FormationsBarwary and Slewa 1991, 1993, Sissakian and Ibrahim 2005, Fouad 2010)www.iiste.orgental change studies because it playsan important role effect in the other environmental components such as desertification, transportation,he climate in the study area depending on Iraqincluded ; temperature, rain full, relative2008. Depend on the elements of thethe study area has an arid climate and characterized by hot summer and cold winterwith 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 areRiver Terraces, Polygenetic Deposits, Slope Deposits, FloodAnthropogen Deposits,quaternary Deposits, belong toPliocene) are represented by Fatah, Injana, Mukdadiyah , and Bai Hassan FormationsBarwary and Slewa 1991, 1993, Sissakian and Ibrahim 2005, Fouad 2010)
  3. 3. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Figure 2. Geological map2. Methodology2.1 Data CollectionThe present study depends on the following available data:Two scenes of Thematic Mapper (TM5) data of Landsat2/7/2010 in spatial resolution 30m and six spectral bands (b1, b2, b3, b4, b5 and b7) downloaded from theWebsite of USGS (http://glovis.usgs.gov/(GEOSURV) besides the field data.FigureJournal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)17Figure 2. Geological map of the study areaThe 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, andal resolution 30m and six spectral bands (b1, b2, b3, b4, b5 and b7) downloaded from thehttp://glovis.usgs.gov/ ), (Fig.3), (Table1) and ancillary data from the Iraq Geological SurveyAFigure 3. A-TM scene-1992, B-TM scene-2010www.iiste.org) Acquisition in 9/8/1992, andal resolution 30m and six spectral bands (b1, b2, b3, b4, b5 and b7) downloaded from theTable1) and ancillary data from the Iraq Geological SurveyB
  4. 4. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Table 1. Landsat image characteristics (Lillesand and Keifer 2000)2.2 SoftwareERDAS Imagine V. 9.2 and Arc GIS V.9.3 softwares have been used. ERDAS Imagine 9.2 software was used forimage processing and change detection. Arc GIS 9.3 software was used for data analysis and map compositIn order to obtain accurate location point data for each LULC class in field survey, the Global PositioningSystem (GPS) was used.2.3 Pre-processingTwo scenes of TM images are subset by using Area of Interest (AOI) filedatum and UTM N38 projection using nearest neighbor resampling. The nearest neighborhoods resamplingprocedure were preferred to others resembling such as bilinear or cubic and bicubic convolution, because it issuperior in retaining the spectral information of the image (Lillisand and Kiefer 20002.4 Field ObservationsField observations carried out in 2012 and spent approximately ten days touring including taking field notes,taking photos and collecting GPS coordinates representing final outputimage analysts to have a better understanding of the relationships between the satellite images and actual groundconditions.2.5 Image processing2.5.1 Implementing Change DetectionThe basic principle of change detection from remote sensing images is based on the difference in reflectance orintensity values between the images taken at two different times due to changes on the Earth’s surface. Anecessary requirement for change detecimages. That mean; the images must be aligned with each other such that corresponding locations in the imagesare present at identical pixel positions. A registration accuracy of lessrecommended to achieve a change detection error within 10 percent (Dai and Khorram 1998). Any registrationerrors (or misregistration) present in the images may lead to incorrect change detection. Due to its paramountimportance as a pre-processing step for applications like change detection and image fusion, image registrationhas been an active area of research for many years and a number of new image registration algorithms have beendeveloped (Brown 1992, Zitova and Fearlier been studied by (Townshend et al. 1992) and (Dai and Khorram 1998).Also, no relative comparison of theperformance of different change detection algorithms in the presence of ras the studies are based on a particular algorithm devised only by them.In general, implementing change detectionsome steps) depending on availableInstrumentSensorAcquisition datePath / RowSpectral bands (µµµµm)Ground resolutionDynamic range (bit)Journal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)18Table 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 forimage processing and change detection. Arc GIS 9.3 software was used for data analysis and map compositIn order to obtain accurate location point data for each LULC class in field survey, the Global PositioningTwo scenes of TM images are subset by using Area of Interest (AOI) file. The images carried out withdatum and UTM N38 projection using nearest neighbor resampling. The nearest neighborhoods resamplingprocedure were preferred to others resembling such as bilinear or cubic and bicubic convolution, because it isformation 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 forimage analysts to have a better understanding of the relationships between the satellite images and actual groundDetection uses remote sensing technologyThe basic principle of change detection from remote sensing images is based on the difference in reflectance orintensity values between the images taken at two different times due to changes on the Earth’s surface. Anecessary requirement for change detection from remote sensing images is the accurate registration of temporalimages. That mean; the images must be aligned with each other such that corresponding locations in the imagesare present at identical pixel positions. A registration accuracy of less than one-fifth of a pixel has beenrecommended to achieve a change detection error within 10 percent (Dai and Khorram 1998). Any registrationerrors (or misregistration) present in the images may lead to incorrect change detection. Due to its paramountprocessing step for applications like change detection and image fusion, image registrationhas been an active area of research for many years and a number of new image registration algorithms have beendeveloped (Brown 1992, Zitova and Flusser 2003). The effect of registration errors on change detection hasearlier been studied by (Townshend et al. 1992) and (Dai and Khorram 1998).Also, no relative comparison of theperformance 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 orsome steps) depending on available data, (Fig.4).Landsat 5TM9/8/1992 2/7/2010168/36 168/367 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 bands8 bitwww.iiste.orgTable 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 forimage 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 PositioningThe images carried out with WGS84datum and UTM N38 projection using nearest neighbor resampling. The nearest neighborhoods resamplingprocedure were preferred to others resembling such as bilinear or cubic and bicubic convolution, because it isField observations carried out in 2012 and spent approximately ten days touring including taking field notes,categories. The field data was useful forimage analysts to have a better understanding of the relationships between the satellite images and actual groundThe basic principle of change detection from remote sensing images is based on the difference in reflectance orintensity values between the images taken at two different times due to changes on the Earth’s surface. Ation from remote sensing images is the accurate registration of temporalimages. That mean; the images must be aligned with each other such that corresponding locations in the imagesfifth of a pixel has beenrecommended to achieve a change detection error within 10 percent (Dai and Khorram 1998). Any registrationerrors (or misregistration) present in the images may lead to incorrect change detection. Due to its paramountprocessing step for applications like change detection and image fusion, image registrationhas been an active area of research for many years and a number of new image registration algorithms have beenlusser 2003). The effect of registration errors on change detection hasearlier been studied by (Townshend et al. 1992) and (Dai and Khorram 1998).Also, no relative comparison of theegistration errors has been performed,processing software requires a number of steps (all or30m*30m for multispectral bands, 120*120m for thermal bands
  5. 5. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Figure 4.2.5.2 ThresholdingThe threshold selection is commonly based on a normal distribution characterized by its mean and its standarddeviation threshold values are scenecontent. However, the thresholds can be determined by three approaches: (1) interactive, (2) statistical and (3)supervised. In the first approach, thresholds are interactively determibased on statistical measures from the histogram of techniques for selecting appropriate thresholds are based onthe modelling of the signal and noise (Radke et al. 2005 , Rogerson 2002, Rosin 2002), which is carrthis study. Third, the supervised approach derives thresholds based on a training set of change and nopixels.2.5.3 Change Detection using Barren Land Index (BLI)The Barren Land Index is used for change detection of multivegetation, land use - land cover and geology. Barren Land Index (BLI) can be obtained by linear combination ofmeasurements in the spectral domains of the bands green (G), red (R) and near infrared (NIR). It can usedevaluate bare soil and desertification processes .In addition, bright soils have been shown to reduce the values invegetation indices such as NDVI (Heute et al. 1985), and water areas such as WI. Barren Land Index (BLI) iswell suited to arid and semi arid areas where dominant vegetation types such as shrubs and other smallvegetation are often photosynthetically inactive (Escadafal and Bacha 1996). A decrease in brightness can be dueeither to the wetting of the soil surface, soil roughness, or in degThe equation used for this index has been created by the researcher after so many attempts which uses the MSSand TM images as followsBLI = 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 1992for the year 2010.Journal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)19Figure 4. General steps of implementing change detectionThe threshold selection is commonly based on a normal distribution characterized by its mean and its standardene-dependent, they should be calculated dynamically based on the imagecontent. 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 isbased on statistical measures from the histogram of techniques for selecting appropriate thresholds are based onthe modelling of the signal and noise (Radke et al. 2005 , Rogerson 2002, Rosin 2002), which is carrthis study. Third, the supervised approach derives thresholds based on a training set of change and noBarren Land Index (BLI)The Barren Land Index is used for change detection of multi-spectral images and successfully used for studies ofland cover and geology. Barren Land Index (BLI) can be obtained by linear combination ofmeasurements in the spectral domains of the bands green (G), red (R) and near infrared (NIR). It can usedevaluate bare soil and desertification processes .In addition, bright soils have been shown to reduce the values invegetation indices such as NDVI (Heute et al. 1985), and water areas such as WI. Barren Land Index (BLI) isarid areas where dominant vegetation types such as shrubs and other smallvegetation are often photosynthetically inactive (Escadafal and Bacha 1996). A decrease in brightness can be dueeither 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 MSSBLI = 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 Histogramwww.iiste.orgThe threshold selection is commonly based on a normal distribution characterized by its mean and its standarddependent, they should be calculated dynamically based on the imagecontent. However, the thresholds can be determined by three approaches: (1) interactive, (2) statistical and (3)ned visually tests. The second approach isbased on statistical measures from the histogram of techniques for selecting appropriate thresholds are based onthe modelling of the signal and noise (Radke et al. 2005 , Rogerson 2002, Rosin 2002), which is carried out inthis study. Third, the supervised approach derives thresholds based on a training set of change and no-changes and successfully used for studies ofland cover and geology. Barren Land Index (BLI) can be obtained by linear combination ofmeasurements in the spectral domains of the bands green (G), red (R) and near infrared (NIR). It can used toevaluate bare soil and desertification processes .In addition, bright soils have been shown to reduce the values invegetation indices such as NDVI (Heute et al. 1985), and water areas such as WI. Barren Land Index (BLI) isarid areas where dominant vegetation types such as shrubs and other smallvegetation are often photosynthetically inactive (Escadafal and Bacha 1996). A decrease in brightness can be dueraded soil (Escadafal and Bacha 1996).The equation used for this index has been created by the researcher after so many attempts which uses the MSSBLI = G² + R² + NIR² / 60show 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. 6. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013MinimumMaximumFigure 6. BLI image in 1976Journal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)20Figure 5. Model of Barren Land Index (BLI)Table 2. Statistics of BLI image in 1976Area-km²Histogram153153Count6406.611971872Total41.8712888Mean00Minimum17855292Maximum53.8716579Std.dev.Figure 7. Histogram of76www.iiste.orgHistogram of BLI image in 1976
  7. 7. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013MinimumMaximumFigure 8. BLI image in 1992Figure 10. BLI image in 2010Journal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)21Table 3. Statistics of BLI image in 1992Area-km²Histogram103103Count55986219648Total543560385Mean2.192439Minimum122135783Maximum31.6334318Std.dev.in 1992Figure 9. Histogram of BLIFigure 11. Histogram of BLI2010www.iiste.orgHistogram of BLI image in 1992Histogram of BLI image in 2010
  8. 8. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013MinimumMaximum3. Result of Change Detection for Barren Land IndexBarren land is a land use-land cover category used to classify lands with limited capacity to support lifewhich less than one-third of the area has vegetation or other cover. In general, it is an area of thin soil, sand, orrocks. Vegetation, if present, is more widely spaced and scrubbyRangeland. Categories of Barren Land are: Dry Salt Flats, Beaches, Sandy Areas other than Beaches; BareExposed Rock; Strip Mines, Quarries, and Gravel(Anderson et al. 1976).In the study area barren Land is represented by:Exposed rocks and sandy areas classesshow the classes of BLI index of 1976, 1992 and 2010 respectively. (Table 6) showsthe three dates. (Table 7) shows the LULC change (BLI) for the periods (1976shows a plot diagram of LULC change (BLI) for two periods, and (Fig.16) shows some locations of barren landsin the study area.The detailed descriptions of these classes are described hereinafter:3.1 Bare soil and Salt flats classes3.1.1 Bare soil classThe Bare soil class covers a huge area distribution in different location of the study area. The results of imageprocessing 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 classSalt flats represent accumulation place of water in rainy seasons and be dry in the summer that led display thesalt 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 beenevaporated, resulting in a higher albedo than other adjacent desert features.from 1976 to 1992 then increased in 203.2 Mixed Barren Land classThe Mixed Barren Land category is used when a mixture of barren land features occurs such as a desert regionwhere combinations of salt flats, sandy areas, bare rock and surface extraction, in the study area it covers widearea. It is decreased from 1976-1992due 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 classes3.3.1 Exposed Rocks classThe Exposed Rock category includes areas of bedrock exposure, scarps, talus, slides, and otrocks represented by sedimentary rocks that are exposed in the study area, which belong to different geologicalformations. It is decreased from 19761992-2010 due to retraction the water level in Himrren Lake.3.3.2 Sandy Areas classSandy Areas composed primarily of dunesmost commonly are found in deserts although they also occur on coastal plains, rJournal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)22Table 4. Statistics of BLI image in 2010Area-km²Histogram8989Count59976663501Total67.3874871Mean26.5329475Minimum10371151829Maximum106117724Std.dev.Barren Land Indexland cover category used to classify lands with limited capacity to support lifethird of the area has vegetation or other cover. In general, it is an area of thin soil, sand, orVegetation, if present, is more widely spaced and scrubby than that in the Shrub and Brush category ofCategories of Barren Land are: Dry Salt Flats, Beaches, Sandy Areas other than Beaches; BareExposed Rock; Strip Mines, Quarries, and Gravel Pits; Mixed Barren Land, besidesIn the study area barren Land is represented by: Bare soil and Salt flats classes, Mixed barren Landclasses. (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 areathe LULC change (BLI) for the periods (1976-1992) and (1992shows a plot diagram of LULC change (BLI) for two periods, and (Fig.16) shows some locations of barren landsThe 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 imageprocessing 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 thesalt 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 beenresulting in a higher albedo than other adjacent desert features. The area offrom 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 regionwhere combinations of salt flats, sandy areas, bare rock and surface extraction, in the study area it covers wide1992 as a result of growth in agricultural lands and expansion in the area of waterdue to the construction of the dam and development of Himrren Lake, also Mixed Barren Land is decreased fromher classes in the study area such as (bare soil, salt flats)Exposed Rocks and Sandy Areas classesThe Exposed Rock category includes areas of bedrock exposure, scarps, talus, slides, and otrepresented by sedimentary rocks that are exposed in the study area, which belong to different geologicalformations. It is decreased from 1976-1992 due to expansion in the area of Himrren Lake, and increased fromo retraction the water level in Himrren Lake.Sandy Areas composed primarily of dunes-accumulations of sand transported by the wind. Sand accumulationsmost commonly are found in deserts although they also occur on coastal plains, river floodwww.iiste.orgland cover category used to classify lands with limited capacity to support life and inthird of the area has vegetation or other cover. In general, it is an area of thin soil, sand, orthan that in the Shrub and Brush category ofCategories of Barren Land are: Dry Salt Flats, Beaches, Sandy Areas other than Beaches; Bareriver wash; mud flats.Mixed barren Land class, andesholding of the three dates, (Fig.12, 13 and 14)distributions of BLI area of1992) and (1992-2010). (Fig.15)shows a plot diagram of LULC change (BLI) for two periods, and (Fig.16) shows some locations of barren landsThe Bare soil class covers a huge area distribution in different location of the study area. The results of imageprocessing 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 thesalt flat on the surface. The salt flats have a different spectral reflection depending on water content, dry salt flatsppear white or light toned because of the high concentrations of salts at the surface as water has beenThe area of salt flats is decreasedThe Mixed Barren Land category is used when a mixture of barren land features occurs such as a desert regionwhere combinations of salt flats, sandy areas, bare rock and surface extraction, in the study area it covers wideas a result of growth in agricultural lands and expansion in the area of wateralso Mixed Barren Land is decreased fromsalt flats) and (exposed rocks,The Exposed Rock category includes areas of bedrock exposure, scarps, talus, slides, and other accumulations ofrepresented by sedimentary rocks that are exposed in the study area, which belong to different geological1992 due to expansion in the area of Himrren Lake, and increased fromaccumulations of sand transported by the wind. Sand accumulationsiver flood plains, and deltas. In
  9. 9. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013the study area the most sandy areas are represented by sand dune and sand sheet occur in the study areaparticularly in the Southwestern part.expansion in the area of Himrren Lake, and increased from 1992Himrren Lake and degradation of agricultural lands.Land use - Land cover( BLIBare soil, Salt flatMixed barren landExposed Rocks, Sandy areaFigure 12. Classes of BLI in 19Journal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)23the study area the most sandy areas are represented by sand dune and sand sheet occur in the study areapart. It is decreased from 1976-1992 due to growth in agricultural lands andn in the area of Himrren Lake, and increased from 1992-2010 due to retraction the water level inHimrren Lake and degradation of agricultural lands.Land coverBLI )Thresholding1976 1992 2010Bare soil, Salt flat 254-159 255-232 255xed barren land 158-123 231-185 254-216s, Sandy area 122-102 184-153 215-167BLI in 1976 Figure 13. ClassesTable 5. BLI ThresholdingFigure 14. Classes of BLI in 2010www.iiste.orgthe study area the most sandy areas are represented by sand dune and sand sheet occur in the study area1992 due to growth in agricultural lands and2010 due to retraction the water level in2010255216167Classes of BLI in 1992
  10. 10. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013-800-600-400-20002004006008001976-19921992-2010Bare soil, Salt flatkm²Land use - Land cover( BLI )Bare soil, Salt flatMixed barren landExposed Rocks, Sandy areaTable 7. LULC change (BLI) for two periodsLand use(Bare soil, Salt flatMixed barren landExposed RockFigure 15. Plot diagram of the LULC change (BLI) for two periodsJournal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)24LULC Change (BLI)-73.5 -545.1 -190.3639.3 -649.9 410.1Bare soil, Salt flat Mixed barren landExposed Rocks ,Sandy areaTable 6. Distributions of BLI areaLand cover Surface area in km²1976 p.% 1992 p.% 2010l, Salt flat 470.9 7 397.4 7 1036.7Mixed barren land 4077 64 3531.9 63 2882s, Sandy area 1858.7 29 1668.4 30 2078.5Table 7. LULC change (BLI) for two periodsLand use - Land cover( BLI )Surface area in km²1976-1992 1992-2010Bare soil, Salt flat -73.5 639.3Mixed barren land -545.1 -649.9Exposed Rocks, Sandy area -190.3 410.1Figure 15. Plot diagram of the LULC change (BLI) for two periodswww.iiste.orgExposed Rocks ,p.%1036.7 17482078.5 35Figure 15. Plot diagram of the LULC change (BLI) for two periods
  11. 11. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Figure 16. Some locations of barre4. ConclusionMany approaches have been applied for the monitoring land useadvantages and disadvantages. Many factors such as selection of suitable change detection approach, suitableband and optimal threshold, may affect the success of the classification. This research aimed to examine thebenefit of the Barren Land Index. This method proves its ability for detecting land usethe study area. Presented study allooccurred in the study area during the periods (1976and Salt flat classes were negativechanges of LULC for Mixed Barren Landperiod (1992-2010), and the changes of LULC forperiod (1976-1992) and positive of the periodother techniques such as image differencing, image rationing, image regression and advanced classificationperform and provide better change detection detBesides this research demonstrated theland cover processes. The authors recommend using a spectrometer device to measuredifferent features of land use - land cover anduse-land cover processes. Also updating land usestudies in the study area because it is undergoing continues changes especially in agricultural lands and waterlevel of Himreen Lake.ReferencesAnderson, J., Hardy, E., Roach, J. and Witmer, R., 1976. A Land Use and Land Cover Classification System foruse with Remote Sensor Data, United States Government Printing Office, Washington. 18PP.Ahmed M.A.and Al_Saady Y.I., 2010.Quadrangle, sheet. Ni-38-10 (LULCM 20),Barwary, A.M. and Slewa, 1991. The geological map of Sammara quadrangle, sheet NiBaghdad, Iraq.Brown, L.G., 1992. A survey of image registration techniques,Barwary, A.M. and Slewa, 1993. The geological map of KhanaqinBaghdad, Iraq.Dai, X., and S. Khorram, 1998. The effects of image misregistration on the accuracy of remotely sensed changedetection, IEEE Transactions on Geoscience and Remote sensingJournal of Environment and Earth Science3216 (Paper) ISSN 2225-0948 (Online)25Figure 16. Some locations of barren lands in the study areaMany approaches have been applied for the monitoring land use-land cover change; each method has someadvantages and disadvantages. Many factors such as selection of suitable change detection approach, suitableand and optimal threshold, may affect the success of the classification. This research aimed to examine theThis method proves its ability for detecting land use -the study area. Presented study allows estimating the amount of significant land useoccurred in the study area during the periods (1976-1992) and (1992-2010). The changes of LULC inof the period (1976-1992) and positive of the periodMixed Barren Land class were negative of the period (1976-1992) and also negative of theand the changes of LULC for Exposed rocks and sandy areas classes wereof the period (1992-2010). However the combination of barren Land Indexother techniques such as image differencing, image rationing, image regression and advanced classificationperform and provide better change detection details results of the land use-land cover than simple method.this research demonstrated the efficiency of RS and GIS as tools for detecting and monitoringThe authors recommend using a spectrometer device to measure the spectral signature forland cover and will be more effective for the assessment andprocesses. Also updating land use - land cover maps are highly recommended for the futurestudy area because it is undergoing continues changes especially in agricultural lands and waterAnderson, J., Hardy, E., Roach, J. and Witmer, R., 1976. A Land Use and Land Cover Classification System fore Sensor Data, United States Government Printing Office, Washington. 18PP.Ahmed M.A.and Al_Saady Y.I., 2010. Series land use - land cover maps of Iraq scale 1:250 000, Baghdad10 (LULCM 20), geosurv, Baghdad, Iraq., 1991. The geological map of Sammara quadrangle, sheet NiBrown, L.G., 1992. A survey of image registration techniques, ACM Computing Surveys, 24:325, 1993. The geological map of Khanaqin quadrangle, sheet NiDai, X., and S. Khorram, 1998. The effects of image misregistration on the accuracy of remotely sensed changeIEEE Transactions on Geoscience and Remote sensing, 36:1566–1577.Salt flats Mixed barren landSandy areaswww.iiste.orgland cover change; each method has someadvantages and disadvantages. Many factors such as selection of suitable change detection approach, suitableand and optimal threshold, may affect the success of the classification. This research aimed to examine the- land cover changes inws estimating the amount of significant land use - land cover changesThe changes of LULC in Bare soilthe period (1992-2010) while theand also negative of theExposed rocks and sandy areas classes were negative of thebarren Land Index withother techniques such as image differencing, image rationing, image regression and advanced classificationland cover than simple method.efficiency of RS and GIS as tools for detecting and monitoring land use -the spectral signature forfor the assessment and monitoring landland cover maps are highly recommended for the futurestudy area because it is undergoing continues changes especially in agricultural lands and waterAnderson, J., Hardy, E., Roach, J. and Witmer, R., 1976. A Land Use and Land Cover Classification System fore Sensor Data, United States Government Printing Office, Washington. 18PP.land cover maps of Iraq scale 1:250 000, Baghdad, 1991. The geological map of Sammara quadrangle, sheet Ni-38-6, geosurv,, 24:325–376.quadrangle, sheet Ni-38-7, geosurv,Dai, X., and S. Khorram, 1998. The effects of image misregistration on the accuracy of remotely sensed changeMixed barren landBare soil
  12. 12. Journal of Environment and Earth ScienceISSN 2224-3216 (Paper) ISSN 2225Vol. 3, No.4, 2013Escadafal R. and Bacha S., 1996. Strategy for the dynamic study of desertication. proceedings of the ISSSInternational Symposium Ouagadougou, Burkino Faso, 6Fouad, S.F.A., 2010.Tectonic and structural evolution of the MMin. Vol. 6. No.2, P 41-53.Huete, A.R., Jackson, R.D. and Post, D.F., 1985. Spectral response of a plant canopy with different soilbackgrounds. Remote Sensing of EnvironmentI.M.O. (Iraqi Meteorological Organization), 2008. “Climatological section”, Baghdad, Iraq.Jano A., Jefferies R.L. and Rockwell R.F., 1998. The detection of vegetational change by multitemporal analysisof Landsat data: the effects of goose foraging. J. Ecology. 86:93Jensen, J.R., 2000. Remote sensing of environment: an earth resource prospective. Prentice Hall, upper saddleRiver. N. J, UAS.Jensen J.R., 2005 Introductory Digital Image Processing: a remote sensing perspective, 3rd Ed., Ch. 12: DigitalChange Detection, p. 467 – 494.Lillesand, T.M. and Kiefer, R.W., 2000. Remote Sensing and image interpretation, 4Sons, New York.Lu, D., Mausel, P., Brondizio, E. and Moran, E., 2003. Change detection techniques.Remote Sensing, 25, 2365-2407.Mas, J.F., 1999. Monitoring Land-Cover Changes: a Comparison of Change Detection Techniques.Journal of Remote Sensing, 20 (1), 139Radke, R., Andra, S., Al Kofahi, O. and Roysam, B., 2005. Image change detectionsurvey. Ieee Transactions on Image Processing, 14 (3): 294Rogerson, P., 2002. Change detection thresholds for remotely sensed images. Journal of Geographical Systems, 4(1): 85–97.Rosin, P., 2002. Thresholding for change deSingh, A., 1989. Digital Change detection Techniques Using Remotely Sensed Data.Remote Sensing, 10, 989-1003.Sissakian, V. K. and Ibrahim, F.A., 2005. 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  13. 13. This academic article was published by The International Institute for Science,Technology and Education (IISTE). The IISTE is a pioneer in the Open AccessPublishing service based in the U.S. and Europe. The aim of the institute isAccelerating Global Knowledge Sharing.More information about the publisher can be found in the IISTE’s homepage:http://www.iiste.orgCALL FOR PAPERSThe IISTE is currently hosting more than 30 peer-reviewed academic journals andcollaborating with academic institutions around the world. There’s no deadline forsubmission. Prospective authors of IISTE journals can find the submissioninstruction on the following page: http://www.iiste.org/Journals/The IISTE editorial team promises to the review and publish all the qualifiedsubmissions in a fast manner. All the journals articles are available online to thereaders all over the world without financial, legal, or technical barriers other thanthose inseparable from gaining access to the internet itself. Printed version of thejournals is also available upon request of readers and authors.IISTE Knowledge Sharing PartnersEBSCO, Index Copernicus, Ulrichs Periodicals Directory, JournalTOCS, PKP OpenArchives Harvester, Bielefeld Academic Search Engine, ElektronischeZeitschriftenbibliothek EZB, Open J-Gate, OCLC WorldCat, Universe DigtialLibrary , NewJour, Google Scholar

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