Utilizing of Remote Sensing and GIS for Desertification Monitoring in the Agricultural Areas, Part of Iraq

Transcription

1 Ministry of Higher Education and Scientific Research College of Science Department of Physics Utilizing of Remote Sensing and GIS for Desertification Monitoring in the Agricultural Areas, Part of Iraq A thesis Submitted to the College of Sciences University of Baghdad in Partial Fulfillments for the Requirements of the Degree of Doctorate of Philosophy in Physics ( Remote Sensing and Image Processing) By: Fadia Wadah Al-Azawi Supervised By: Prof. Dr. Saleh M. Ali Ass. Prof. Dr. Alaa S. Mahdi And Ass. Prof. Dr. Qutaybah M. Hassan 2013 A.D 1434 H.

2 جمهورية العراق وزارة التعليم العالي و البحث العلمي جامعة بغداد / كلية العلوم استخدام تقنيات الا ستشعار عن بعد ونظم المعلومات الجغرافية لرصد التصحر في المناطق الزراعية جزء من العراق اطروحة مقدمة الى كلية العلوم في جامعة بغداد و هي جزء من متطلبات نيل درجة دكتوراه علوم في الفيزياء (التحسس الناي ي ومعالجة الصور) تقدمت بها فادية وضاح العزاوي با شراف ا.د. صالح مهدي علي ا.م.د علاء سعود مهدي ا.م.د قتيبة محمد حسن ۲۰۱۳ م ۱٤۳٤ ه

3 بسم الله الرحمن الرحيم لا ي ك ل ف الل ه ن ف سا إ لا و س ع ه ا ل ه ا م ا ك س ب ت و ع ل ي ه ا م ا اك ت س ب ت ر بن ا لا ت ؤ اخ ذ ن ا إ ن نس ين ا أ و أ خ ط ا ن ا ر بن ا و لا ت ح م ل ع ل ي ن ا إ ص ار ك م ا ح م ل ت ه ع ل ى ال ذ ين م ن ق ب ل ن ا ر بن ا و لا ت ح مل ن ا م ا لا ط اق ة ل ن ا ب ه و اع ف ع نا و اغ ف ر ل ن ا و ار ح م ن ا أ نت م و لا ن ا ف انص ر ن ا ع ل ى ال ق و م ا ل ك ا ف ر ي ن صدق االله العظيم

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5 Supervisor Certification The preparation of this thesis was made under our supervision at the College of Science, University of Baghdad in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Physics (Remote Sensing and Image Processing Application). Signature: Title: Professor Name: Dr. Saleh M. Ali Signature: Title: Assistant Professor Name: Dr. Alaa. S. Mahdi (Supervisor) (Supervisor) Date: 5/ 3/2013 Date: 5 / 3/2013 Signature: Title: Assistant Professor Name: Dr. Qutaybah M. Hussan (Supervisor) Date: 5/ 3/2013 Approved by the Head of department of Physics Signature: Title: Professor Name: Dr. Raad M. S. Al-haddad Date: 5/ 3/2013

6 Abstract ABSTRACT The increasing rate of desertification on a wide global scale is one of the most important problems suffered by the world these days. One of the most important factors affecting the widening of the phenomenon of desertification is climate changes; therefore, some of these changes have been selected and investigated in this study (e.g. temperature, evaporation, relative humidity, and rainfall). The selected climate data have been collected from many of climate monitoring stations scattered over an area of Iraq (e.g. Mosul, Kirkuk, Ramadi, Khanaqin, Baghdad, Karbala, Najaf, Nasyria, and Basra). Several statistical methods have been applied to demonstrate the impact of these factors on the problem of desertification and to determine the relationship between them. Evaporation and humidity coefficients have also calculated and used for each monitoring station. The south west of Iraq is the dry to very dry region, for this reason these regions have been chosen in this study. The Inverse Distance Weighted (IDW) kriging-based techniques have been frequently used for local analysis and surface mapping. Field studies have been conducted in November and December 2012 to collect soil samples from different locations of the study area, the samples have been analyzed to measure the acidic amount contained in these samples. The performance of conventional statistics showed that soil ph has a low variation in this study. The locations of the collected samples have been dropped on the map of the area, using Geographic Information System (GIS) techniques. The geographic coordinates of the collected samples have been measured by utilizing a handled Global Positioning System (GPS) device, for the purpose of diagnosing the differences between the actual location of the land and the location in the satellite images. IIII

7 ABSTRACT Satellite imagery (Landsat) for the south west of Iraq has been used, for the time period from 1990 to 2010, to monitor the ocurred changes in land cover of the study area in these periods. Landsat images of the sensors (TM-1990, TM-2000, and ETM ) have been adopted to achieve the study, to study the expansion of desertification in the region of study and to compare among the factors leading to it. Three types of classification methods have been used to classify areas of these images; these were: Supervised Classification method, Unsupervised Classification method, and the classification based on Scatterplot Scheme. Five adopted categories have been adopted to perform the classification process; i.e. Water area, Wet Vegetation area, Dry Vegetation area, Moist Soil area, and Dry Soil area. The study relied in its analyses on a series of indicators related to the plants spectral (i.e. Vegetation Indices), which represents transformations of the spectral bands that illustrate the difference between the vegetation and soil. The output of this project is; images, maps, and charts viewing the expansion in the phenomenon of desertification in the study area. The results from this study lead to that there is a direct relationship between desertification and the climatic factors and the results from field observation show that there are revealed different signs of desertification and land degradation in the study area as judged by change in patterns of land use and land cover types, this changes indicated that there is a decreasing of farming land, vegetation cover, and increasing in sandy soil and sand dunes around and inside the study area. IIIII

8 List of Contents Series Subject Page No. Abstract List of contents List of figures List of tables List of abbreviations Chapter One : Desertification Phenomena and its Effects 1-1 Preface Justification / Problem Specification Causes, Effects and Consequences of 5 Desertification Climatic Variability Human Activities The Effects of Desertification The Consequences of Desertification Policy Factors Contribution to Desertification Relation Between Landuse and Desertification Desertification Indicators Remote Sensing for Monitoring the Extend of 11 Land Degradation 1-10 Canopy Response Thematic Mapper Bands Literatures Review Research Objective Thesis Organization 20 Chapter Two : Materials & Methodologies 2-1 Steps of Methodologies Work, Data and Programs 22 Used in This Study 2-2 The Link among Desertification and Biodiversity 23 Loss 2-3 Statistical analysis General Linear Model The Least Squares (LS) The Partial Correlation Predicted and Residual Scores0T Residual Variance and R-square Interpreting the Correlation Coefficient Potential Evapotranspiration 32 III I III V IX XI

9 2-6 Geographical Information System (GIS) with 33 Desertification 2-7 Interpolation Techniques The Topography Maps Test Sites and Samples Collection Satellite Imagery Image Preprocessing Digital Image Classification Methods Supervised Classification Unsupervised Classification Evaluating the Accuracy of a Classification Scatter Plot Vegetation Indices Ratio Vegetation Index (RVI) Normalized Difference Vegetation Index (NDVI) 53 Chapter Three : Results & Discussion 3-1 Climatic Data Processing, Calculation and 55 Analysis 3-2 Potential Evapotranspiration (PE) Calculation 63 Results 3-3 Geographical Information System (GIS) maps for 75 climatic data 3-4 Test Sites and Samples Collection Layer stack of the Landsat Images The land covers classification for the selected 92 images 3-7 Vegetation Indices Ratio Vegetation Index (RVI) Normalized Difference Vegetation Index NDVI Change Detection 111 Chapter four: Conclusions & Recommendations 4-1 Conclusions Recommendations 116 References 117 Appendix 130 IV

10 List of Figures Series Subject Page No. 1-1 Relief Map of Iraq Shows the Inland and Border 3 Information 1-2 Water cycle of the Earth's surface, showing the 14 individual components [33] 2-1 Flow chart of the methodology used in this study Linkages and Feedback Loops among 24 Desertification, Global Climate Change, and Biodiversity Loss [29] 2-3 Scatterplot visualizing correlation between two 30 variables X and Y, with residual points [54] 2-4 The structure of out GIS [56] GIS different layers [60] Inverse distance weighthing (IDW) interpolation 38 technique 2-7 Relief Map of Iraq shows the inland and border 39 information 2-8 Shows four scenes for the region of interest Image of landsat TM 1990 dimensions 5116 x (Byte) 2-10 Image of landsat TM 2000 dimensions x 5070 (Byte) 2-11 Image of landsat ETM dimensions 5203 x (Byte) 2-12 Spectral reflectance curve of 3 land covers [66] Image Classification Supervised Classification Methods Brightness of Soil Line GIS maps for the annual mean temperature in 0 C GIS maps for the annual mean relative humidity 76 (%) 3-3 GIS maps for the annual mean evaporation (mm) GIS maps for the annual mean rainfull (mm) ph map for the soil samples by using IDW 82 technique 3-6 Layer Stack for the Area of Study Layer Stack for the Area of Study ETM+ Image 2010 Bands (1,2,3,4,5, and 7) and its 90 Correct Image by Matlab for the Same Bands 3-9 Layer Stack for the Area of Study V

11 3-10 Classification of the Scene 1990 Using Isodata Classifier Technique The Percentage Areas for the ROI Scene 1990 Using Isodata Classifier Technique Classification of the Scene 2000 Using Isodata Classifier Technique The Percentage Areas for the ROI Scene 2000 Using Isodata Classifier Technique Classification of the Scene 2010 Using Isodata Classifier Technique The Percentage Areas for the ROI Scene 2010 Using Isodata Classifier Technique Classification of the Scene 1990 Using K-Mean Classifier Technique The Percentage Areas for the ROI Scene 1990 Using K-Mean Classifier Technique Classification of the Scene 2000 Using K-Mean Classifier Technique The Percentage Areas for the ROI Scene 2000 Using K-Mean Classifier Technique Classification of the Scene 2010 Using K-Mean Classifier Technique The Percentage Areas for the ROI Acene 1990 Using Minimum Distance Classifier Technique Classification of the Scene 1990 Using Minimum Distance classifier Technique The Percentage Areas for the ROI Scene 1990 Using Minimum Distance Classifier Technique Classification of the Scene 2000 Using Minimum Distance Classifier Technique The Percentage Areas for the ROI Scene 2000 Using Minimum Distance Classifier Technique Classification of the Scene 2010 Using Minimum Distance Classifier Technique The Percentage Areas for the ROI Scene 2010 Using Minimum Distance Classifier Technique Classification of the Scene 1990 Using Maximum Likehood Classifier Technique The Percentage Areas for the ROI Scene 1990 Using Maximum Likehood Classifier Technique Classification of the Scene 2000 Using Maximum Likehood Classifier Technique The Percentage Areas for the ROI Scene 2000 Using Maximum Likehood Classifier Technique. VI

12 3-32 Classification of the Scene 2010 Using Maximum Likehood Classifier Technique The Percentage Areas for the ROI Scene 2010 Using Maximum Likehood Classifier Technique Classification of the Scene 1990 Using Scatter Plot Classifier Technique by using matlab The Percentage Areas for the ROI Scene 1990 Using Scatter Plot Classifier Technique Classification of the Scene 2000 Using Scatterplot Classifier Technique by using matlab The Percentage Areas for the ROI Scene 2000 Using Scatter Plot Classifier Technique Classification of the Scene 2010 Using Scatter Plot Classifier Technique by using matlab The Percentage Areas for the ROI Scene 2010 Using Scatter Plot Classifier Technique The percentage areas of dry soil for the ROI in the period of study The percentage areas of wet soil for the ROI in the period of study 3-42 The percentage areas of dry vegetation for the ROI in the period of study The percentage areas of wet vegetation for the ROI in the period of study 3-44 The percentage areas of water for the ROI in the period of study Shows the differences in the RVI for the scenes (1990, 2000 and 2010) respectively 3-46 Shows the differences in the NDVI for the scenes (1990, 2000 and 2010) respectively 3-47 Shows the change detection by taken the differences in maps for the images among band 3 ( , , and ) respectively 3-48 Shows the change detection by taken the differences in maps for the images among band 4 ( , , and ) respectively VII

13 List of Tables Series Subject Page No Shows the Different Types of Desertification 10 Indicators [26] 1-2 Characteristics of TM Characteristics of ETM The Indices Formulas Utilized in this Research ANOVA Table Analysis of Variance for Temperature (C 0 ) as the Dependent Variable 3-2 ANOVA Table Analysis of Variance for Relativ Humidity (%) as the Dependent Variable ANOVA Table Analysis of Variance 57 Evaporation (mm) as the Dependent Variable 3-4 ANOVA Table Analysis of Variance for Rainfall 57 (mm) as the Dependent Variable 3-5 The Statistical Calculations for Mosul Station The Statistical Calculations for Kirkuk Station The Statistical Calculations for Ramadi Station The Statistical Calculations for Khanaqen Station The Statistical Calculations for Baghdad Station The Statistical Calculations for Kerbala Station The Statistical Calculations for Najaf Station The Statistical Calculations for Nasyria Station he Statistical Calculations for Basra Station Correlation coefficient between climatic factors in this study Monthly Mean of Climatic Factor for Mosul 64 Station 3-16 Monthly Mean of Climatic Factor Kirkuk Station Monthly Mean of Climatic Factor for Ramadi 66 Station 3-18 Monthly Mean of Climatic Factor for Khanaqen 67 Station 3-19 Monthly Mean of Climatic Factor for Baghdad 68 Station 3-20 Monthly Mean of Climatic Factor for Kerbala 69 Station 3-21 Monthly Mean of Climatic Factor for Najaf 70 Station 3-22 Monthly Mean of Climatic Factor for Naysria Station 71 VIII

14 3-23 Monthly Mean of Climatic Factor for Basra 72 Station 3-24 The Climatic Classification was According to 73 [82] 3-25 Classification of the Study Region Climatic Classification According to [84] Classification of the Study Region 74 IX

15 List of Abbreviations Abbreviation Meaning CV Coefficient of Variation DF Degree of Freedom ENVI Environment for Visualizing Images EOSAT's Earth Observation Satellite Company ETM+ Enhanced Thematic Mapper FAO Food and Agriculture Organization of the United Nations GIS Geographic Information System GLM General Linear Model HI Humidity Index IDS Inverse Distance Squared IDW Inverse Distance Weighted IM Index of Moisture ISD Inverse Square Distance LMR Linear Multiple Regression LS Least Squares LST Land Surface Temperature MLE Maximum Likelihood Estimates MRM Multiple Regression Model MS Mean Square MSE Mean Square Error MSS Multi Spectral Scanner µ Mean NDVI Normalized Difference Vegetation Index ph Pachic Humbriorthox PE Potential Evapotranspiration RH Relative Humidity R MSE Root Mean Square Error ROI Region Of Interest RS Remote Sensing SE Standard Error Ϭ Standard Deviation RVI Ratio Vegetation Index SPSS Statistical Package of Social Sciences SS Sum of Squares X

16 T TM UN UNCCD USD WRS VIs Temperature Thematic Mapper United Nation United Nations Conventions to Combat Desertification United States Dollar Worldwide Reference System Vegetation Indices XI

17 CHAPTER 1 THEORETICAL PART 1.1 Preface Chapter One Desertification Phenomena and its Effects Desertification phenomena started spreading worldwide at high speed since the second half of the last century. United Nation (UN) reports indicate that about 35 million square kilometers of land are subjected to desertification effects, [1]. Aubreville (1949) defined desertification as the changing of productive land into a desert as the result of ruination of land by man-induced soil erosion, [2]. It is also defined by Dregne (1976) as a process of impoverishment of arid, semiarid and sub-humid ecosystems by the combined impacts of man's activities and drought, [3]. It is the process of deterioration in these ecosystems that can be measured by reduced productivity of desirable plants, undesirable alterations, accelerated soil deterioration, and increased hazards for human occupancy. Hellde'n (1988) quoted some of the above definitions together with the one adopted by Rapp (1974), [2] that states that desertification is the spread of desertlike conditions in arid or semi-arid areas due to man's influence or to climatic change. In the Summit of (1992) held in Rio de Janeiro-Brazil, the term desertification was defined as land degradation in arid, semi-arid and dry subhumid areas resulting from various factors including climatic variations and human activities, [4]. Eden-Foundation (1994), [2] defined desertification as a man-induced process that leads to soil nutrient depletion and reduction of biological productivity. The most general definition of desertification defined by UN Convention as a process of "land degradation in arid, semi-arid and dry sub-humid 11

18 CHAPTER 1 THEORETICAL PART areas resulting from various factors including; climatic variations and human activities. Patches of degraded land may develop hundreds of kilometers from the nearest desert. But these patches can expand and join together, creating desert-like conditions. Desertification contributes to other environmental crises, such as the loss of biodiversity and global warming, [5]. Peoples are directly affected by desertification and one billion people in over 100 countries are at risk. Fighting desertification is essential to ensuring the longterm productivity of inhabited drylands. It can only be reversed through profound changes in local and international behavior. Step by step, these changes can ultimately lead to sustainable land use and food security for a growing world population. Fighting desertification, then, is really just part of a much broader objective; the sustainable development of countries affected by drought and desertification; e.g. Iraq, according to The United Nations Convention to Combat Desertification (UNCCD) addressing desertification as a global problem and some of its effects as follows [6]. One quarter of the Earth's surface is threatened by desertification an area of over 3.6 billion hectares. Since 1990, 6 million hectares of productive land are lost every year due to land degradation. The world's drinking water supplies have fallen by almost two thirds since Every year, 12 million people die as a result of water shortages or contaminated drinking water. Desertification threatens the livelihoods of one billion people and has already made 135 million people homeless. 2 2

19 CHAPTER 1 THEORETICAL PART Every year, desertification generates income losses totaling 42 billion USD. One of the basic premises of the United Nations Conventions to Combat Desertification (UNCCD) is that land degradation is both a cause and a consequence of rural poverty. Therefore, desertification makes for poverty, and poverty makes for further desertification, [7]. 1.2 Justification/ Problem Specification Desertification is becoming a serious problem in Iraq. According to FAO: 1984, Iraq with a total area of km² including 924 km² of inland waters. It is surrounded by Iran-East, Turkey-North, Syria-Jordan-west, Saudi Arabia-Kuwait- South, and the Arabian Gulf-South East, as illustrated in figure (1-1), [8]. Figure (1-1): Relief Map of Iraq shows the inland and border information. [8] 3 3

20 CHAPTER 1 THEORETICAL PART Topographically, Iraq is shaped like a basin, consisting of the Great literally, the land between two rivers. This plain is surrounded by mountains in the north and the east, which can reach altitudes of 3550 m above sea level, and by desert areas in the south and west, which account for over 40% of the land area [9]. It is estimated that 26% of the total area used for agriculture 8 million hectare, which is almost 93% of the cultivable area. To remedy many of the weaknesses, remote sensing (RS) can be combined with ancillary information and spatial models in a geographic information system (GIS) environment. Modern development in GIS aims at integrating data from different sources with remotely sensed data to improve the quality of the resulting thematic maps. Besides, to assess the vulnerability of an ecosystem to desertification, the information on causes and factors are critical, [10]. The critical research priority in this study, therefore, is to examine and analyze the causes of desertification and the extent of its severity in Iraq by means of statistical study, using remote sensing (RS) and geographic information systems (GIS) techniques. The climatic data source in this research was the Iraqi metrological department which operates and control wide climatic stations distributed over country [11]. Landsat images (Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+)) with seven spectral bands have been used; the choice is based on the fact that they have improved spectral and spatial characteristics and consequently affords a better detection of changes as well as improved elaboration of surface phenomenon, [12]. 4 4

21 CHAPTER 1 THEORETICAL PART 1.3 Causes, Effects and Consequences of Desertification There are different factors lead to desertification that change over time and vary by location, [13]. These include: Climatic Variability There are many different climatic factors lead to desertification; some of these were chosen to achieve this study these are: rainfall, temperature, evaporation and relative humidity, [14]. Desertification is marked by the decline of grasses and the replacement of continuous grasslands by scattered shrubs and thorny vegetation, leaving much bare soil. One result is that soil resources become more concentrated around the large plants, and conditions grow increasingly difficult for most organisms in the bare areas. These exposed surfaces are then vulnerable to further degradation through erosion, evaporation, and high temperatures. Desertification can also result when cultivated areas are abandoned and soil conditions have been so altered as to impede recovery of natural ecosystems. Such alterations include erosion, increased salt from irrigation, and loss of soil organisms. Minor changes in average climate may have potentially large effects on semi-arid vegetation; hence "global warming" could exacerbate desertification. Because air temperature, carbon dioxide (COR2R) concentrations, and relative humidity affect plant growth and water use in complex, interacting ways, it is difficult to predict the net effect of atmospheric and climatic changes on dryland vegetation. Even if warming climate were to result in greater moisture and hence more precipitation in some areas, some areas, such as continental interiors, would likely experience warming without significant additions of precipitation; hence concerns about desertification may be well founded, [13,14]. 55

22 CHAPTER 1 THEORETICAL PART Human activities The human activities that lead to desertification are as follows, [15]: A. Overgrazing: It is one of the major causes of desertification worldwide. Plants of semi-arid areas are adapted to being eaten by sparsely scattered, large, grazing mammals which move in response to the patchy rainfall common to these regions. The human moved their small groups of domestic animals in response to food and water availability. B. Overexploiting land: It can happen due to expand in human population and hence the need for more crops; international economic forces that can lead to short-term exploitation of local resources for export. C. Deforestation: destruction of vegetation in arid regions; mainly for fuelwood. D. Poor irrigation practices: It causes a buildup of salt in the soil. Farmers are using canal irrigation and other poor techniques because of the lack of water. E. Natural and man-made disasters: Natural disasters such as floods and droughts; and manmade disasters such as wars and national emergencies can destroy productive land by causing heavy concentration of migrants to overburden an area. F. Destruction of plants in dry regions: It is causing desertification to occur. People are cutting down trees to use them as a source of fuel. Once all these trees are cut down there is nothing to protect the soil, [16]. 6 6

23 CHAPTER 1 THEORETICAL PART 1.4 The Effects of Desertification The ability of land to support life, affecting wild species, domestic animals, agricultural crops and people reduces by desertification. The reduction in plant cover that accompanies desertification leads to accelerated soil erosion by wind. Water is lost off the land instead of soaking into the soil to provide moisture for plants. A reduction in plant cover also results in a reduction in the quantity of humus and plant nutrients in the soil, and plant production drops further. Desertification is self-reinforcing, i.e. once the process has started, and conditions are set for continual deterioration, [17]. 1.5 The Consequences of Desertification Desertification may possibly have caused more human suffering than any other environmental issue facing our planet today. It impairs the ability of land to support life. It is particularly destroying because of its self reinforcing nature; once desertification begins it progresses by feeding on itself. As well, it aggravates many other environmental problems. And despite control measures that have been taken to fight desertification in some countries, it continues to spread at an alarming and accelerating pace, [18]. Some direct effects of desertification are as in the followings:- The drylands affected by desertification are rich in genetic diversity, even more so than rain forests. It is from the species found in these drylands that valuable pharmaceuticals are derived and they are also a great source of genetic material for strengthening crop resistance to drought and disease due to their ability to adapt particularly well to their surroundings, [19]. The disappearance of protective plant cover makes the occurrence of floods more often and more severe. 77

24 CHAPTER 1 THEORETICAL PART The absence of plant cover makes the land more susceptible to soil erosion from wind, [20]. 1.6 Policy Factors Contribution to Desertification Policies leading to an unsustainable use of resources and the lack of adequate infrastructures are major contributors to land degradation. Agriculture can play either a positive or a negative role, depending on how it is managed. Local institutions can contribute to preventing desertification by allowing land users to manage and use ecosystem services more effectively through enhanced access to productive land, capital, labor, and technology, [21]. Policies favoring sedentary farming over nomadic herding can contribute to desertification. The majority of dryland areas (65%) are rangelands that are more suited to sustainable grazing than crop production. Sedentarization nomads and limiting their movements leads to desertification because it reduces their ability to adjust their economic activities in the face of stresses such as droughts, [22]. When farmers and herders lose control or long-term security over the land they use, the incentives for maintaining environmentally sustainable practices are lost, and this can greatly contribute to desertification. Control and security does not necessarily require private property rights; and collective and community-based management practices have proven quite effective. Inappropriate decision making can cause problems of water scarcity, Groundwater depletion, soil erosion, and salinization, [23]. 8 8

25 CHAPTER 1 THEORETICAL PART 1.7 Relation between Landuse and Desertification Historically, dryland livelihoods have been based on a mixture of hunting, gathering, farming, and herding. This mixture varied with time, place, and culture, since the harsh conditions forced people to be flexible in their use of the land. Population pressure, however, has led to the extension of cultivated lands in dryland areas. In some cases this has caused desertification and conflicts between herders and farmers who claimed access to the same land. In other cases, when policy and market conditions were favorable, it led to positive interactions between the two groups, [24]. In some areas, the increase in cultivation has been driven by increased irrigation which has often been unsustainable. Irrigation can cause environmental problems such as water logging, salinization, water pollution, eutrophication, and excessive groundwater use. On the whole, irrigation has had a strong impact on inland waters, causing a decline in biodiversity and further desertification. Frequent and intensive fires can contribute to desertification when they affect natural vegetation. However, limited controlled fires can play an important role in the management of dryland herding and farming systems, promoting nutrient cycling and forage quality, [25]. 9 9

26 CHAPTER 1 THEORETICAL PART 1.8 Desertification Indicators Various indicators of desertification are listed in table (1-1), [26]. Table (1-1): Shows the different types of desertification indicators [26]. Decrease in soil depth Decrease in soil organic matter Physical indicators Decrease in soil fertility Soil crust formation Appearance/increase in frequency/severity of dust sandstorms/dune formation and movement Salinization/alkalinization Decline in quality and quantity of ground and surface water Decrease in cover Biological indicators Vegetation Animal Decrease in above-ground biomass Decrease in yield Failure of species successfully to reproduce Change in population of domestic animals Change in herd composition Decline in livestock (production and yield) Change in land use/water use indicators Social/economic Change in settlement pattern (e.g. abandonment of villages) Change in population (biological) parameters (public health information) Change in social process indicators - increased conflict between groups/tribes, marginalization, migration, decrease in incomes, change in relative dependence on cash crops

27 CHAPTER 1 THEORETICAL PART 1.9 Remote Sensing for Monitoring the Extend of Land Degradation It is the more simple to study the desertification and like these phenomena using remote sensing techniques. Continuity of observations is required to account for the high year-to-year variability of dryland ecosystem conditions. Combining the use of satellite-based remote sensing or aerial photographs with ground-based observations can provide consistent, repeatable, cost-effective data on vegetation cover. Access to affordable satellite imagery is thus critical, particularly in developing countries, [27]. Both human actions and climate variability influence vegetation and its growth. Long-term monitoring is needed to distinguish between these two influences. Quantifying such impacts requires an established baseline against which changes can be assessed. But this comparison is complicated by year-to-year and even decade-to-decade fluctuations, [28] Canopy Response The solar energy that transits through the atmosphere to the vegetation canopy is made available to the atmosphere by reflectance and transformation of radiant energy absorbed by plants and soil into fluxes of sensible and latent heat and thermal radiation through a complicated series of bio-physiological, chemical and physical processes, [29]. The interaction cross-section (extinction coefficient) that appears in the following equation is treated as wavelength independent considering the size of the scattering elements (leaves, branches, twigs, etc.) relative to the wavelength of solar radiation. Although the scattering and absorption processes are different at different wavelengths [30]. 111

28 CHAPTER 1 THEORETICAL PART Probabilities for photons in vegetation media are determined by the structure of the canopy rather than photon frequency or the optics of the canopy. This feature results in canopy spectral invariant behavior for a vegetation canopy bounded from below by a non-reflecting surface; that is, the difference between numbers of photons incident on phyto elements within the vegetation canopy at two arbitrary wavelengths is proportional to the difference between numbers of photons scattered by phyto elements at the same wavelengths and is purely a function of canopy structural arrangement. A wavelength-independent coefficient of proportionality is the probability that a photon scattered from a phyto element will interact within the canopy again the recollection probability. The canopy spectral absorbance is an explicit function of the wavelength-independent recollection probability and canopy intercept, and wavelength-dependent absorbance of an average leaf. These three variables, re-collision probability, canopy intercept and leaf absorbance, therefore, determine the partitioning of the top of canopy radiation into its absorbed and canopy leaving portions. The canopy leaving radiation can further be broken down into its reflected and transmitted portions as in the following figure (1-2). A small set of independent variables that appear in the spectral invariant relationships suffice to fully describe the law of energy conservation in vegetation canopies at any wavelength in the solar spectrum. Such a result is essential to both modeling and remote sensing communities as it allows the measured and modeled canopy signal to be decomposed into structurally varying and spectrally invariant components. The former are a function of canopy age, density and arrangement while the latter are a function of canopy biochemical behavior. Consequently, the canopy spectral invariants offer a simple and accurate parameterization for the shortwave 12 12

29 CHAPTER 1 THEORETICAL PART radiation block in many global models of climate, hydrology, biogeochemistry, and ecology, [31]. In remote sensing applications, the information content of hyperspectral data can be fully exploited if the wavelength independent variables can be retrieved, for they can be more directly related to structural characteristics of the vegetation canopy. There are the three basic components of the law of shortwave energy conservation which describe canopy spectral response to incident solar radiation at the canopy scale. If reflectance of the ground below the vegetation is zero, the portion of radiation absorbed, a(λ), transmitted, t(λ), or reflected, r(λ), by the canopy is unity, i.e., [32] t ( λ) + r( λ) + a( λ) = 1 (1-1) [32] The leaf transmittance (reflectance) is the portion of radiation flux density incident on the leaf surface that the leaf transmits (reflects) as shown in the following figure (1-2). These variables characterize the canopy spectral behavior at the leaf scale, are determined by the leaf biochemical constituents, and can vary with tree species, growth conditions, leaf age and their location in the canopy space. The leaf albedo, ω(λ), is the sum of the leaf reflectance, ρ L (λ), and transmittance, τ L (λ), than is, [32] ω( λ) = ρ ( λ) τ ( λ) L + L (1-2) [32] 13 13

30 CHAPTER 1 THEORETICAL PART Figure (1-2): Water cycle of the Earth's surface, showing the individual components [33] 1.11 Thematic Mapper Bands The Landsat Multispectral Scanner (MSS) (Landsat 1) launched in 1972 imaged the Earth s surface in 4 spectral bands (3 in the visible and 1 in the NIR regions) at a spatial resolution of m. The Thematic Mapper has a better spatial, spectral, and radiometric resolution and extends spectral coverage into the blue, the short-wavelength infrared, and the thermal infrared regions, and is therefore a more sophisticated multispectral sensor

31 CHAPTER 1 THEORETICAL PART The Landsat 6 (launched in October 1993) also carried this sensor but failed to attain orbit during launch. It is similar in operational modes to the MSS (which was also part of the Landsat 4 and 5 payloads to maintain continuity) but TM consists of 7 bands that have the following characteristics as in table (1-2). Table (1-2):- Characteristics of TM Characteristics of TM Band No. Wavelength (µm) Resolution (m) The Thematic Mapper (TM) designed to achieve higher image resolution, sharper spectral separation, improved geometric fidelity, and greater radiometric accuracy and resolution than that of the MSS sensor. The Landsat TM sensor represents the most sophisticated satellite sensor to provide Earth observation data. The seven spectral bands offer the most comprehensive set of multispectral measurements for land and water surface mapping, monitoring, and analysis

32 CHAPTER 1 THEORETICAL PART EOSAT's construction of a Landsat 6 satellite was intended to continue acquisition of TM data with a so-called Enhanced Thematic Mapper (ETM). Images consist of eight spectral bands with a spatial resolution of 30 meters for Bands 1 to 7. The resolution for band 8 (panchromatic) is 15 meters as shown in table (1-3). All bands can collect one of two gain settings (high or low) for increased radiometric sensitivity and dynamic range, while Band 6 collects both high and low gain for all scenes. Table (1-3): Characteristics of ETM + Characteristics of ETM + Band No. Wavelength (µm) Resolution (m)

33 CHAPTER 1 THEORETICAL PART 1.12 Literatures Review There are many studies investigated desertification in arid and semi-arid region in the literature. Mostly, they have covered the use of the familiar classification methods, vegetation indices and change detection to monitor vegetation health and other landcover. Some of them will be mentioned below: Al-Quraish (2003) [34]: Predicted the soil erosion risk usus RS and GIS for the northwestern of Hebei province-china. He used landsat TM 1996 to produce land use and land cover maps of the study area depending upon the maximum likelihood classification method using ERDAS program 32 was used to achieve these results and it appears that there is simple clear erosion in the soil and it shows that the remote sensing and GIS were active in this field. Kawamura et. al., (2003) [35]: Analyzed the slope failures due to the 2000 Tokai heavy rainfall by using high resolution satellite images IKONS and a forest database. GIS using to estimation the rainfall annual and it is active to achieve like these results. Yokabi and Laneve (2004) [36]: Used ground-based spectral data and Landsat Enhanced Thematic Mapper (ETM + ) images to validate the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) obtained from Landsat ETM with spectral reflectance measured from the field. From this result it was found that there is a high variance in the in the spectral characteristics in the different types of vegetation and soils. The data from ETM+ were processed by covering all the dry seasons on one of the ecosystem dry and spectral measurement using in this field to the same season and region. Kate'a et. al., (2005) [28]: Used remote sensing techniques to determine the agricultural ecology for western region of Al-Razzaza lake, Karbala Province-Iraq, 17 17

34 CHAPTER 1 THEORETICAL PART using MSS and TM Landsat sensors. It is found that there is a spectral clustering relation with natural resources like soil, water and the interference between them. Sun, and et. al., (2005) [37]: Used assess desertification processes and changes in Minqin County, using Landsat-5 for period 1988 to Zhou1 and Yang (2005) [38]: Analyzed the landscape changes for desertification process and human activity impacts on desertification of Yanchi County in Northern China. Lu, and et. al., (2007) [39]: Studied the vulnerability assessment of rainfallinduced debris flows in Taiwan in the three parts; i.e. triggering rainfall, debris flow susceptibility factors, and vulnerability assessment. Farajzadeh et. al., (2007) [40]: Used ten environmental data items including climatic and physical variables for colza cultivation collected from 24 samples farmlands within the period of agricultural year The better suitable statistical model depending upon the temperatures and dry and the spatial maps were drawn by using GIS. Ziboon (2008) [41]: Determined the desertification in Karbala by analyzing two satellite images. The first was SPOT panchromatic image, 10 m resolution taken in 1999 and the second was Landsat (TM) visible image, 30 m resolution taken in Depending upon the field observation it could not be considered us Karbala has constant value and it is different from region to another the years has a maximum desertification in Karbala. Ololade et al., (2008) [42]: Determined change detection in the Rustenburg mining region using Landsat (Multi Spectral Scanner) MSS, Landsat (Thematic Mapper) 18 18

35 CHAPTER 1 THEORETICAL PART TM, and Landsat (Enhanced Thematic Mapper) ETM + images, acquired for the periods 1972 to Kadimet. al., (2009) [43]: Studied the nature, origin, movement and extension of sand dunes by using sediment logical aspects and remote sensing imagery (Landsat TM images and topographic maps) in Baiji area, North Iraq. Zhao et. al., (2009) [44]: Extracted and analyzed the vegetation information in desert areas by using multi-spectral remote sensing data. Kamel and Mohammed (2010) [45]: Examined the effects of different variables on the water harvesting in Iraqi western desert; e.g. catchments area, bed slope geometric and topographic properties by using GIS system. Qadir and Benni (2010) [46]: Monitored and evaluated soil salinity in term of spectral response, using Landsat images and GIS in Mesopotamian plain-iraq. Al-Rekabi et. al., (2011) [47]: Determined the active generating area of dust storm on Iraq land, using MODIS remote sensing data. Fan (2011) [48]: Used remote sensing technology for monitoring the desertification phenomenon in Mu Us desert. Tan, and et. al., (2012) [49]: Made a comparison of radiometric correction techniques in the evaluation of the relationship between Land Surface Temperature (LST) and NDVI in Landsat imagery. Jin, and et. al., (2012) [50]: Studied the causes of early Holocene desertification in arid central Asia

36 CHAPTER 1 THEORETICAL PART 1.13 Research Objective This study aims to quantify the causes and results of desertification as follows:- To analyze the climate variability and climate changes, particularly; rainfall, temperature, evaporation and relative humidity in Iraq during the period Study the interaction between desertification and climatic variability through the analysis of the vegetative cover and soil degradation. In this study long-term satellite measurements have been compared with the field observation data to determine if desertification is extended or not and the amount of extension. Regions Of Interest (ROI) in this study did not consider desertified areas unless desertification where permanent. For this reason, and to track the change in the study areas for long periods Landsat images have been used to cover 20 years from 1990 to 2010, for the sensors (TM taken in 1990, TM taken in 2000, and ETM + taken in 2010). Scatter plot was used to show the classification of the ground features (wet vegetation, wet soil, water, dry vegetation, and dry soil) Thesis Organization This thesis has been organized into four chapters, with Appendix. Chapter One: Demonstrate the desertification problem, seeking many main topics: the cause, effect and consequences of desertification, the policy factors contribute to desertification, the relation between landuse and desertification, the link between desertification and biodiversity loss, desertification indicators, remote sensing monitoring to desertification, canopy response, thematic Mapper bands, and lastly; several published studies have been reviewed

37 CHAPTER 1 THEORETICAL PART Chapter Two: Gives details about the materials and methodologies used for in this research, such as data/maps/images, software used seeking many main topics: general linear model, GIS for estimating and monitoring desertification, interpolation, Satellite Imagery, maps, digital image classification method, and vegetation indices. Chapter Three: Provide details of the computed results and discussion involved with many main topics: Climatic Data Processing; Calculation and analysis; potential evapotranspiration; GIS maps for climatic data; Test Sites and Samples Collection. The land covers classification for the selected images and vegetation indices. Chapter Four: Presents the list of conclusions derived for the gained results and suggestions for future works. Appendix: Illustrates the details of the sample of all test scenes, by reviewing the main topography maps from which these samples were extracted

38 CHAPTER 2 EXPERIMENTAL WORK Chapter Two Materials and Methodologies 2.1 Steps of Methodologies Work, Data and Programs Used in This Study This flowchart shows the procedure of the thesis methodologies and the programs used to achieve the results. Figure (2-1): Flow chart of the methodology used in this study 222

39 CHAPTER 2 EXPERIMENTAL WORK 2.2 The Link among Desertification and Biodiversity Loss Vegetation and its diversity are instrumental in soil conservation and in the regulation of surface water and local climate. The disruption of the interlinked services that are provided by dryland plant biodiversity is a key trigger for desertification and its various consequences, including the loss of habitats for other species. Desertification affects global climate change through soil and vegetation losses. Indeed, dryland soils contain a lot of carbon which could be released into the atmosphere as a result of desertification, with significant consequences for the global climate system, [14]. The effect of global climate change on desertification is complex and not yet sufficiently understood. On the one hand, higher temperatures can have a negative impact through increased loss of water from soil and reduced rainfall in drylands. On the other hand, an increase in carbon dioxide in the atmosphere can boost plant growth for certain species. Although climate change may increase aridity and desertification risk in many areas, the consequent effects of biodiversity loss on desertification are difficult to predict. Environmental management approaches for combating desertification, conserving biodiversity, and mitigating climate change are linked in many ways, thus a joint implementation of the UN, [26]. Conventions that target desertification, biological diversity, and climate change can yield multiple benefits. The major components of biodiversity loss (in blue) directly affect major dryland services (in bold). The inner loops connect desertification to biodiversity loss and climate change through soil erosion. The outer loop interrelates biodiversity loss and climate change. On the top section of the outer loop, reduced primary 23 23

40 CHAPTER 2 EXPERIMENTAL WORK production and microbial activity reduce carbon sequestration and contribute to global warming, [27]. On the bottom section of the outer loop, global warming increases evapotranspiration, thus adversely affecting biodiversity; changes in community structure and diversity are also expected because different species will react differently to the elevated CO 2 concentrations, [28]. Feedback loops on desertification as shown in the figure (2-2). Figure (2-2): Linkages and Feedback Loops among Desertification, Global Climate Change, and Biodiversity Loss [29] 24 24

41 CHAPTER 2 EXPERIMENTAL WORK 2.3 Statistical analysis Expansion in the phenomenon of desertification over Iraqi territory is mainly caused by the dry climatic cycles experienced by the country, according to the geographical location of Iraq, which is required by this site to be a climate desert or semi-desert. Climatic data for the main Meteorological stations in the region of study have been obtained from the Iraqi Department of Meteorology for nine metrological monitoring stations (Mosul, Kirkuk, Ramadi, Khanaqen, Baghdad, Kerbala, Najaf, Nasyria, and Basra). The data represent rainfall amounts, temperature, evaporation, and relative humidity for an extended period of twenty years (1990 to 2010), for all the nine monitoring stations mentioned above. These information, suffered from a loss in some measurements. The General Linear Model (GLM) is a flexible model that includes statistical dependent variables and normally continuously distributed independents. This model applied using the statistical package of Social Sciences (SPSS). 2.4 General Linear Model The theory of general linear models posits that many statistical tests can be solved as a regression analysis, including t-tests and ANOVA s, the ANOVA table give us the following information, [51]. Degrees of Freedom The Sum of the Squares The Mean Square The F ratio The p-value 25 25

42 CHAPTER 2 EXPERIMENTAL WORK The GLM enables us to specify any degree of interaction (crossed effects) and nested effects. It also provides for polynomial, continuous-by-class, and continuous-nesting-class effects. Through the concept of estimability, the GLM procedure can provide tests of hypotheses for the effects of a linear model regardless of the number of missing cells or the extent of confounding. GLM displays the Sum of Squares (SS) associated with each hypothesis tested and, upon request, the form of the estimable functions employed in the test. PROC GLM can produce the general form of all estimable functions, [51]. In this study there are number of observations N and number of predictor variables, therefore there are degrees of freedom column. The reason for split the ANOVA table into rows for model, error and total is to examine how much error was haven when using the predictive equation, and to determine how much error has disappeared because predictive equation was been used, [52]. The values of mean square (MS) error are determined by: SSSSSS oooo SSSSSSSSSSSS EEEEEEEEEE dddd EEEEEEEEEE SS = df (2 1) [5555] To determine the overall significance of the model and not just of one individual coefficient. The F-Ratio is determined by:- FF = MMMM mmmmmmmmmm MMMM EEEEEEEEEE (2 2) [5555] The estimator of Mean Square Error (MSE) for a multiple regression model with independent variables is determined by:- MMMMMM = SSSSSS nn (kk + 1) (2 3) [5555] 26 26

43 CHAPTER 2 EXPERIMENTAL WORK The GLM can be seen as an extension of Linear Multiple Regression (LMR) for a single dependent variable which quantifies the relationship between several independent or predictor variables, [51]. The general computation problem that needs to be solved in LMR is: Least squares. Unique prediction and partial correlation. Predicted and residual scores. Residual variance and R-square. Interpreting the correlation coefficient The least Squares (LS):- It is aimed to minimize the sum of squared deviations of observed values for the dependent variable from those predicted values. It is calculated according to the following equation:- nn Q = [YY ii ff ii (xx)] 2 ii=1 (2 4) [5555] Where: YY ii = ff ii (xx) + εε ii (2 5) [5555] f i (x) is a known function of x. For the regression equation, suppose that a line in a two dimensional or twovariable space is defined by the equation: YY = bb + cccc (2 6) [52] Where: x: -Constant referred to as the intercept, and the slope

44 CHAPTER 2 EXPERIMENTAL WORK Y: - It is a variable expressed in terms of two constants (b and c). In the case of Multiple Regression Model (MRM), consider a random sample of n observations (x i1, x i2 x ip, y i ), i=1, 2 n. The p+1 random variables are assumed to satisfy the following linear model: yy ii = ββ 0 + ββ 1 xx ii1 + ββ 1 xx ii2 + + ββ pp xx iiii + uu ii (ffffff ii = 1,2,., nn) (2 7) [5555] Where: u i :- the values of an unobserved error term u. 0, β1, β2,... β p β, : - constants values. Because the observations y 1, y 2 y n are random samples, they are mutually independent and hence the error terms are also mutually independent. Therefore, the parameters 0, β1, β2,... β p β, can be estimated using the least squares procedures, which minimize the sum of squares of errors; i.e. nn SS = (yy ii ββ 0 ββ 1 xx 1 ββ 1 xx 2 ββ pp xx pp ) 2 (2 8) [5555] ii=0 Minimizing the sum of squares leads to the following equations, from which the values of β can be computed: nn yy ii = nnββ 00 + ββ 11 xx iiii + ββ 22 xx iiii + + ββ pp xx iiii ii=11 nn nn ii=11 nn ii=11 xx iiii yy ii = ββ 00 xx iiii + ββ 11 xx iiii ii=11 nn nn ii=11 nn ii= xx iiii yy ii = ββ 00 xx iiii + ββ 11 xx iiii xx iiii + ii=11 nn ii=11 nn ii=11 nn nn ii=11 ββ 22 xx iiii xx iiii + + ββ pp xx iiii xx iiii ii=11 nn ii ββ 22 xx iiii ii=11 nn ii=11 nn + + ββ pp xx iiii xx iiii ii=11 nn xx iiii yy ii = ββ 00 xx iiii + ββ 11 xx iiii xx iiii + ββ 22 xx iiii xx iiii ii=11 nn ii=11 nn ii=11 nn ii=11 nn ββ pp xx iiii ii=11 (22 99)[5555] 28 28

45 CHAPTER 2 EXPERIMENTAL WORK The Partial Correlation The partial correlation is a useful approach to study the relationship between two variables x and y in the presence of a third variable z is to determine the correlation between x and y after controlling the effect of z. It is the correlation of two variables while controlling for a third or more other variables. The Partial correlation coefficient is a measure of the linear association between two variables after adjusting for the linear effect of a group of other variables. If the number of other variables is equal to 1, the partial correlation coefficient is called the first order coefficient. If the number of other variables is equal to 2, the partial correlation coefficient is called the second order coefficient, and so on, [52]. The first order partial correlation between x i and x j holding constant x l is computed by the following formula; rr iiii.llll = rr iiii rr ii1 rr jj rr ii1 1 rrjj 1 [52] Where r ij, r i1 and r j1 are zero order. (2 10) From the first order partial correlations the second (or higher) order partial correlations can be computed by using basically the same formula, rr iiii.llll = rr iiii rr iiii.kk rr jjjj.kk (1 rr ii1 2 )(1 rr jj 1 2 ) (2 11) [5555] where "k" stands for other variables to be partialled. The third order partial correlations can be getten from the second order partial correlation and so forth

46 CHAPTER 2 EXPERIMENTAL WORK Since it is possible to compute multiple regressions from partial correlations, so it should be able to compute partial correlations from multiple regressions, and as follows: rr jjjj.kk = RR 2 2 jj.iiii RR jj.kk 2 (2 12) [5555] 1 RR jj.kk Predicted and Residual Scores: The regression line expresses the best prediction of the dependent variable (Y), given the independent variables (X). However, nature is rarely perfectly predictable, and usually there is substantial variation of the observed points around the fitted regression line (as in the scatterplot shown in the following figure (2-3). The deviation of a particular point from the regression line is called the residual value, [53]. Figure (2-3): Scatterplot visualizing correlation between two variables X and Y, with residual points [54] 30 30

47 CHAPTER 2 EXPERIMENTAL WORK Residual Variance and R-square: The smaller the variability of the residual values around the regression line relative to the overall variability, the better is our prediction. For example, if there is no relationship between the X and Y variables, then the ratio of the residual variability of the Y variable to the original variance is equal to 1.0. If X and Y are perfectly related then there is no residual variance and the ratio of variance would be 0.0. In most cases, the ratio would fall somewhere between these extremes (i.e. between 0.0 and 1.0), 1.0 minus this ratio is referred to as R-square. This value is immediately interpretable in the following manner. If we have an R-square of 0.4 then we know that the variability of the Y values around the regression line is times the original variance, [55] Interpreting the Correlation Coefficient: The correlation coefficient is a measure of the strength of the straight-line or linear relationship between two variables. The correlation coefficient takes on values ranging between -1 and +1. The following points are the accepted guidelines for interpreting the correlation coefficient;0 indicates no linear relationship, +1 indicates a perfect positive linear relationship: as one variable increases in its values, the other variable also increases in its values via an exact linear rule, while -1 indicates a perfect negative linear relationship: as one variable increases in its values, the other variable decreases in its values via an exact linear rule. Otherwise the correlation is either strong / moderate / or weak positive/ or negative. In terms of the covariance of two variables X and Y [Cov(X, Y)], the correlation coefficient "ρ" is presented by, [52]. ρρ XXXX = CCCCCC (XX, YY) σσ XX σσ YY = EE{(XX μμ XX)(YY μμ YY )} σσ XX σσ YY (2 13) [5555] 31 31

48 CHAPTER 2 EXPERIMENTAL WORK 2.5 Potential Evapotranspiration Evapotranspiration is a specific process occurred specifically in the hot season; the values recorded in the cold season are insignificant, it is calculated by using the following equations, [81]. 10t a PE 16[ ] J = (2-14) [81] j = ( t ) 5 (2-15) [81] J = 12 j= 1 j (2-16) [81] a = 0.016J (2-17) [81] P HI = PE (2-18) [81] IM P = [( ) 1] x100 PE (2-19) [81] Where: PE: Monthly potential evapotranspiration in (mm) P: is the yearly sum for rainfall. J: Yearly temperature coefficient. j: Monthly temperature coefficient. t: Monthly average temperature. a: Constant HI: Humidity Index IM: Climate index 32 32

49 CHAPTER 2 EXPERIMENTAL WORK 2.6 Geographical Information System (GIS) with Desertification Geographical information system (GIS) is a computer system capable of creating, storing, managing, analyzing, and displaying geographically referenced information. It allows users to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts, [54]. The master plan for GIS in Union County is to have a system where all county departments will have access to GIS software and data. Rather than maintaining incompatible hardcopy maps, each department will be maintaining information in the GIS. Once this has been implemented the county offices will be able to store and manipulate information in a very efficient manner. The tools of remote sensing and geographic information system are important to monitoring land cover, temperature, dryness indices. The climatic data and geographic information system (GIS) facilities were used to study and produce maps for Iraq country. The data, for the period ( ) after prepared to supply the database with layers. Scanning utility was used to enter data in form of maps. To be able geocoding these data with others and manipulating all layers of information, GIS techniques will be used to analyze and create data. The components of environmental GIS database, which from the structure of out GIS are described as follows: The data input are converted from their existing form (digital or hardcopy) into standard digital data sets form. Data storage and management. This component reflects the structure of database, the position, linkages (topology) and attributes of geographical elements (points, lines and area) 333

50 CHAPTER 2 EXPERIMENTAL WORK Data analysis and transformation. This means the achievement of transformation needed to remove errors from data or to update the data, on the other hand the data manipulation and analysis functions determine the information could be generated and extracted by GIS layers (dryness, erosion, desertification, etc). Data output and presentation. The results will be in form of maps, tables of attribute values and test describing the final results. These results will be copied on CD-ROM for data storage and distribution, [55]. All these steps are shown in the following figure (2-4). Figure (2-4): The structure of out GIS [56] Due to the serious cost of desertification, it became an important priority to fight desertification. The first step is to monitor desertification by measuring land degradation and desertification processes. The GIS serves as a means of integrating and manipulating in situ surface observations, satellite-based environmental indicators, aerial photography and other available spatial information in support of effective dry land planning, decision making and researches, [57]

51 CHAPTER 2 EXPERIMENTAL WORK GIS is a more efficient way to perform complete tasks with short time, using management and analysis of digital spatial data. The remote sensing provides opportunities in desertification monitoring because it provides a large-scale, repetitive and accurate coverage, while GIS technique could be very useful in the assessment of desertification and in identifying the high-risk areas, [58]. The past two decades has witnessed several projects which applied the RS and GIS techniques for assessing and monitoring desertification at the national and regional levels in the Arab region, [59]. A GIS map can combine many layers of information as in the following figure (2-5). Figure (2-5):- GIS different layers [60]

52 CHAPTER 2 EXPERIMENTAL WORK 2.7 Interpolation Techniques Spatial continuous data plays a significant role in planning, risk assessment and decision making in environmental management. They are, however, usually not readily available and often difficult and expensive to acquire, especially for mountainous and deep marine regions. As geographic information systems (GIS) and modeling techniques are becoming powerful tools in natural resource management. Environmental managers often require spatial continuous data over a region of interest to make effective and informed decisions and scientists need accurate data which are well-distributed across a region to make justified interpretations. However, spatial distribution data of natural phenomena are often collected from point sources. The spatial interpolation methods have been developed and applied to various disciplines. They are data-specific or even variable-specific. Many factors including sample size, sampling design and data properties affect the estimations of the methods, [61]. Once data has been collected it can be implemented into a mapping environment such as GIS (Geographic Information Systems) for management and control to make the necessary changes. There are no consistent findings about how these factors affect the performance of the spatial interpolators. Therefore, it is difficult to select an appropriate spatial interpolation method for a given input dataset. This review aims to provide some guidelines and suggestions in relation to the application of the spatial interpolation methods to environmental data by comparing the features of the commonly applied spatial interpolators. The best type of interpolation techniques for such these data (climatic data) is the Inverse Distance Weighted (IDW) as in the following, [62]

53 CHAPTER 2 EXPERIMENTAL WORK Inverse Distance Weighting The Inverse Distance Weighted (IDW) method estimates the values of an attribute at unsampled points using a linear combination of values at sampled points weighted by an inverse function of the distance from the point of interest to the sampled points. The assumption is that sampled points closer to the unsampled point are more similar to it than those further away in their values,[63]. The weights can be expressed as: λλ ii = 1 dd ii pp nn ii=0 1 dd pp ii (2 20) [6666] Where: di: is the distance between x 0 and xi. P: is a power parameter. Which is the main factor affecting the accuracy of IDW. n: represents the number of sampled points used for the estimation. The choice of power parameter and neighborhood size is arbitrary. The most popular choice of p is 4 and the resulting method is often called inverse square distance (ISD) or inverse distance squared (IDS). The power parameter can also be chosen on the basis of error measurement (e.g., minimum mean absolute error, resulting the optimal IDW). The smoothness of the estimated surface increases as the power parameter increases, and it was found that the estimated results become less satisfactory when p is 1 and 2 compared with p is 4. IDW is referred to as moving average when p is zero, linear interpolation and when p is 1 and weighted moving average when p is not equal to 1, [64]

54 CHAPTER 2 EXPERIMENTAL WORK Figure (2-6): Inverse distance weighthing (IDW) interpolation technique 2.8 The Topography Maps Seven regions of interest from the relief map for Iraq as shown in the figure (2-6) were chosen to achieve this study, these are: a) Geological map of Al-Juthum at a scale of 1: 1, (1988). A copy of this map has been obtained from University of Baghdad/Remote sensing unit. b) Geological map of Al-Malutiyat at a scale of 1: 1, (1999). A copy of this map has been obtained from University of Baghdad/Remote sensing unit. c) Geological map of Najaf Al-Ashraf at a scale of 1: 1, (1987). A copy of this map has been obtained from University of Baghdad/Remote sensing unit. d) Geological map of Umm Al-Adhum at a scale of 1: 1, (1987). A copy of this map has been obtained from University of Baghdad/Remote sensing unit. e) Geological map of Khan Al-Abid at a scale of 1: 1, (1988). A copy of this map has been obtained from University of Baghdad/Remote sensing unit

55 CHAPTER 2 EXPERIMENTAL WORK f) Geological map of Ruhaimwi at a scale of 1: 1, (1986). A copy of this map has been obtained from University of Baghdad/Remote sensing unit. g) Geological map of Qasr Al-Akhaidher at a scale of 1:1, (1986). A copy of this map has been obtained from University of Baghdad/Remote sensing unit. Figure (2-7): Relief Map of Iraq shows the inland and border information 39 39

56 CHAPTER 2 EXPERIMENTAL WORK 2.9 Test Sites and Samples Collection Field measurements were carried out in November and December 2011 at seven study areas in Iraq including Al-Juthum, Al-Malutiyat, Najaf Al-Ashraf, Umm Al-Adhum, Khan-Al-Abid, Ruhaimwi and Qasr-Al-Akhaidher. The data were collected from different sites depending on the different soil type in the study area. Each site was registered by using GPS device first to check the differences in position of the features in the ground with the landsat images and the second purpose is to integration the results with the climatic data and the landsat images data. Some of the study regions as shown in the following figure (2-8). Figure (2-8): Shows four scenes for the region of interest 40 40

57 CHAPTER 2 EXPERIMENTAL WORK The Pachichumbriorthox (ph) which is a measure of the acidity or alkalinity in the soil were measured for the collected samples for the region of interest and the output results appear as GIS maps. The ph of a soil will change over time influenced by factors including parent material, weathering and current agricultural practices. It will also fluctuate through the year; soil ph will affect how plants grow, [64] Satellite Imagery The image dataset used in this study consists of Landsat-2 TM and 3 landsat- 7 ETM+ images (Worldwide Reference System (WRS-2) path 169 row 38) in the south of Iraq for the period from 1990 to Only images acquired in summer months were considered. These were downloaded from Glovis website (The USGS Global Visualization Viewer (GloVis) is an online search and order tool for selected satellite data). and then processing of 2010 done by special algorithm by using matlab program. The landsat images with bands (4-3-2) for the tested scenes and for different times (1990, 2000 and 2010) as in the following figures (2-9 to 2-11). Figure (2-9): Image of landsat TM 1990 dimensions 5116 x 5070 (Byte) 41 41

58 CHAPTER 2 EXPERIMENTAL WORK Figure (2-10): Image of landsat TM 2000 dimensions 5116 x 5070 (Byte) Figure (2-11): Image of landsat ETM dimensions 5203 x 5159 (Byte) 42 42

59 CHAPTER 2 EXPERIMENTAL WORK 2.11 Image Preprocessing Remote sensing system gathered data in many forms and techniques. There are numerous errors associated with gathered data. Therefore, it is usually necessary to preprocess the remotely sensed data. A technique in which the data from an image are digitized and various mathematical operations are applied to the data, generally with a digital computer, in order to create an enhanced image that is more useful or pleasing to a human observer, or to perform some of the interpretation and recognition tasks usually performed by humans. Before the classification of images, first the landsat ETM+ image (2010) must be corrected using Matlab program Digital Image Classification Methods The intent of the classification process is to categorize all pixels in a digital image into one of several land cover classes, or themes. Normally, multispectral data are used to perform the classification and, indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object or type of land cover these features actually represent on the ground, [65]. The reflectance curve for the three bands (Red, Green and Blue) as in the following figure (2-12)

60 CHAPTER 2 EXPERIMENTAL WORK Figure (2-12): Spectral reflectance curve of 3 land covers [66] In this work two main classification methods (Supervised and Unsupervised) which shown in figure (2-13) were used and new scatterplot classification method was also used. Their results will be compared to show their worth especially on remote sensed multispectral bands. Figure (2-13): Image classification 444

61 CHAPTER 2 EXPERIMENTAL WORK Supervised classification The principle of this classification technique is the utilizing of training areas (region of interest "ROI") the image. The selection of appropriate training areas is based using the knowledge for the actual surface cover types present. The image processing software system is then used to develop a statistical characterization of the reflectance for each information class. This stage is often called signature analysis and may involve developing a characterization as simple as the mean or the range of reflectance on each bands, or as complex as detailed analyses of the mean, variances and covariance over all bands, [67]. Once a statistical characterization has been achieved for each information class, the image is then classified by examining the reflectance for each pixel and making a decision about which of the signatures it resembles most. Spectral classes are groups of pixels that are uniform (or near-similar) with respect to their brightness values in the different spectral channels of the data, [68]. The objective is to match the spectral classes in the data to the information classes of interest which are shown in figure (2-14). In fact, selecting the interest regions is not, always, an easy task, it required to perform field surveys and utilizing available information can be obtained from existed maps and from actual fieldwork, [69]. There are many different types of supervised classification, in this research two of these methods were used which are described as follows: 45 45

62 CHAPTER 2 EXPERIMENTAL WORK Figure (2-14): Supervised classification methods Minimum Distance Supervised Classifier Minimum distance classifiers image data on a database file using a set of 256 possible class signature segments as specified by signature parameter. Only the mean vector in each class signature segment is used. Other data, such as standard deviations and covariance matrices, are ignored (though the maximum likelihood classifier uses this). The result of the classification is a theme map directed to a specified database image channel. A theme map encodes each class with a unique gray level. The gray-level value used to encode a class is specified when the class signature is created. If the theme map is later transferred to the display, then a pseudo-color table should be loaded so that each class is represented by a different color, [70]. The minimum distance classifier is used to classify unknown image data to classes which minimize the distance between the image data and the class in multifeature space. The distance is defined as an index of similarity so that the minimum distance is identical to the maximum similarity. The following distances are often used in this procedure, [71]

63 CHAPTER 2 EXPERIMENTAL WORK dd 2 kk = (XX μμ) tt (XX μμ) (2 21) [71] It is used in cases where the variances of the population classes are different to each other. The Euclidian distance is theoretically identical to the similarity index. Maximum likelihood Supervised Classifier Maximum likelihood classifier method is a statistical decision criterion to assist in the classification of overlapping signatures; pixels are assigned to the class of highest probability. The maximum likelihood classifier is considered to give more accurate results than the other classification methods however it is much slower due to extra computations. Display the three-band overlay composite image. The visible channel, nearinfrared channel and the nm channel are associated with red, green and blue, respectively so that the clouds look white, vegetation looks green, water looks dark and lands without vegetation looks different shades of brown. Take a careful look at the available features and determine the set of classes into which the image is to be segmented. The training samples were used to estimate the mean vectors and covariance matrices for maximum likelihood classifier. These two parameters determine the properties of the multivariate normal models. Using the trained classifier to classify every pixel in the image into one of the desired classes. Since we have no useful information about the priori probability for each class, in which case a situation of equal prior probabilities is assumed, [70]. The final discriminate function g (x) is taken as: gg(xx) = ln ii (xx mmmm)tt ii (2 22) [7777] Where mi and are tee mea eecoor an caaaraacce matrix of the data in class ii

64 CHAPTER 2 EXPERIMENTAL WORK In order to reduce poor classification due to small probabilities, threshold values i are determined for each class based on that 95% of the pixels would be caassffee.. Accrr ii n χ tabee,, tee trr esool aalee ca ee obtained by: ii = 12.6 ln ii (2 23) [7777] Finally, we can get the decision rule for maximum likelihood supervised algorithm: xx ωωωω, iiii gggg(xx) > gggg(xx)aaaaaa gggg(xx) > TTTT ffffff aaaaaa jj ii (2 24) [7777] Classes don t meet the above decision rule will be classified as unknown class Unsupervised classification Unsupervised classification also called clustering is a method which examines a large number of unknown pixels and divides them into a number of classes based on natural groupings present in the image values. Unsupervised classification does not require analyst-specified training data, [70]. The classes that result from unsupervised classification are spectral classes which based on natural groupings of the image values, the identity of the spectral class will not be initially known, we must compare classified data to some form of reference data (such as larger scale imagery, maps, or site visits) to determine the identity and informational values of the spectral classes. In the unsupervised approach the computer determines spectrally separable class, and then defines their information value, [70]. Today several different unsupervised classification algorithms are commonly used in remote sensing. The two most frequently used algorithms are the Isodata and K-Mean clustering algorithm. Both of these algorithms are iterative procedures. In general, both of them assign first an arbitrary initial cluster vector

65 CHAPTER 2 EXPERIMENTAL WORK The second step classifies each pixel to the closest cluster. In the third step the new cluster mean vectors are calculated based on all the pixels in one cluster. The second and third steps are repeated until the "change" between the successive iteration becomes is small. The "change" can be defined in several different ways; either by measuring the distances the mean cluster vector has changed from one iteration to another or by the percentage of pixels that have changed between iterations. The Isodata algorithm has some further refinements by splitting and merging of clusters. Clusters are merged if either the number of members (pixel) in a cluster is less than a certain threshold or if the centers of two clusters are closer than a certain threshold. Clusters are split into two different clusters if the cluster standard deviation exceeds a predefined value and the number of members (pixels) is twice the threshold for the minimum number of members, [72]. The difference between the two methods is that the Isodata algorithm allows for different number of clusters while the k-means assumes that the number of clusters is known a priori. The objective of the k-means algorithm is to minimize the within cluster variability. The objective function (which is to be minimized) is the sums of squares distances (errors) between each pixel and its assigned cluster center, [72]. SSSS dddddddddddddddd = [xx CC(xx)] 2 xx (2 25) [7777] Where: - C(x) is the mean of the cluster that pixel x is assigned to. Minimizing the SS distances is equivalent to minimizing the Mean Squared Error (MSE). The MSE is a measure of the within cluster variability

66 CHAPTER 2 EXPERIMENTAL WORK MMMMMM = xx [xx CC(xx)]2 (NN cc)bb = SSSS dddddddddddddddd (NN cc)bb (2 26) [7777] Where: - N is the number of pixels, c indicates the number of clusters, and b is the number of spectral bands. From a statistical viewpoint, the clusters obtained by k-mean can be interpreted as the Maximum Likelihood Estimates (MLE) for the cluster means if we assume that each cluster comes from a spherical Normal distribution with different means but identical variance (and zero covariance).these touches upon a general disadvantage of the k-means algorithm (and similarly the Isodata algorithm): k-means works best for images with clusters that are spherical and that have the same variance. This is often not true for remote sensing images. For example, a cluster with "desert" pixels is compact/circular. A "forest" cluster, however, is usually more or less elongated/oval with a much larger variability compared to the "desert" cluster. While the "desert" cluster is usually very well detected by the k-means algorithm as one distinct cluster, the "forest" cluster is often split up into several smaller clusters. The way the "forest" cluster is split up can vary quite a bit for different starting values and is thus arbitrary Evaluating the Accuracy of a Classification The basic idea is to compare the predicted classification (supervised or unsupervised) of each pixel with the actual classification as discovered by ground truth. A good review of methods is given by (Congalton, 1991).There is four kinds of accuracy information: 1. Nature of the errors. 2. Frequency of the errors

67 CHAPTER 2 EXPERIMENTAL WORK 3. Magnitude of errors. 4. Source of errors Scatter plot The basic of these methods is separation of soil from vegetation response. This roughly triangular distribution of point's results from the reflectance characteristics of vegetation and soil, and the mixing of these characteristics within image pixels. Image pixels containing only bare soil lie along the diagonal of the triangle. The soil line is a linear relationship between the red (R) and near-infrared (NIR) reflectance of bare soils as characterized by its slope and offset. The length of the linearity is affected by the soil's contents. This information has intensively been used for characterization of the earth s surface for retrievals of vegetation biophysical parameters and soil surface status from remotely sensed reflectance spectra. If the "R" and "NIR" responses of many different bare soil surfaces are plotted on a scatter diagram, soil response will form a more or less straight line because, as response becomes brightness in the visible "R" it and in the "NIR", [73]. The soil line represents a robust relationship between the red and NIR reflectances from a single soil type, which is modeled by RR SSSS = aa RR SSSS + bb (2 27) [7777] Where a and b are soil line parameters, and RsNand RsRare the red and NIR reflectance's of a target soil, respectively. The brightness of each feature is shown in figure (2-13), wet soils appear at the bottom of the line and they appear more darken compare with dry soil which is at the top part of the line. Spectral response of vegetation always has a consistent 51 51

68 CHAPTER 2 EXPERIMENTAL WORK relationship to the line. Vegetation has low values in the "R" and high values in the "NIR", [73]. NIR- Reflectance 2.13 Vegetation indices R- Reflectance Figure (2-15): Brightness of soil line Vegetation Indices (VIs) are combinations of surface reflectance at two or more wavelengths designed to highlight a particular property of vegetation. They are derived using the reflectance properties of vegetation. Each of the VIs is designed to accentuate a particular vegetation property, [74]. The fact that many VIs have been proposed for the most suitable vegetation index to use in remote sensing application. The use of VIs as an information obtain from remote sensing not just frequent subject of remote sensing studies but also used for monitoring and evaluating Earth s vegetation cover and environmental resources management. The following detection methods and vegetation indices were used in this study as follows:

69 CHAPTER 2 EXPERIMENTAL WORK Ratio Vegetation Index (RVI) Band rationing means dividing the pixels in one band by the corresponding pixels in a second band. The reasons for this are that differences between the spectral reflectance curves of surface types can be brought out and the second is that illumination, and consequently radiance, may vary, the ratio between an illuminated and a not illuminated area of the same surface type will be the same. Thus, this will aid image interpretation, particularly the near-infrared/ red (NIR/R) band ratio. From the general spectral reflectance the following observation can be made, [74]. Vegetation NNNNNN RR >>> 1 WWWWWWWWWW NNNNNN RR < 1 SSSSSSSS NNNNNN RR > 1 (2 28) [7777] NIR/R images can serve as a crude classifier of images, and indicate vegetated areas in particular. Therefore this ratio has been developed into a range of different vegetation indices. NIR/R (TM4/TM3), with values ranging from 0 to 8, soil to vigorous vegetation, vegetated areas shown in white. NIR/R, stretched, with values ranging from 16 to 254, giving a smoother transition between surface types, [75,76] Normalized Difference Vegetation Index (NDVI) Normalized Difference Vegetation Index (NDVI) is a variant of the simple ratio calculation corresponding pixel values in two bands are first subtracted, and this difference is then normalized by dividing by the sum of two brightness 53 53

70 CHAPTER 2 EXPERIMENTAL WORK values. The normalization tends to reduce artifacts related to sensor noise, and most illumination effects still are removed. The most widely used example is the Normalized Difference Vegetation Index (NDVI), which is, [77]. NDVI = (NNNNNN RRRRRR) (NNNNNN+RRRRRR) (2 29) [7777] Raw index values range from -1 to +1, and the data range is symmetrical around 0 (NIR = RED), making interpretation and scaling easy. Normalized difference images can be combined to form color composite images for visual interpretation. The color image to the left incorporates three ratio images with R = TM3 / TM1, G = TM4 / TM3, and B = TM7 / TM5. Vegetated areas appear bright blue-green, iron-stained areas appear in shades of pink to orange, and other rock and soil materials are shown in a variety of hues that portray subtle variations in their spectral characteristics. Normalized difference vegetation index calculations are based on the principle that actively growing green plants strongly absorb radiation n the visible region of the spectrum. Table (2-1):- The indices formulas utilized in this research Index Name Index Formula Index Application RVI NDVI RVI = NIR / R (NIR-R)/(NIR+R) Indices showing vegetation covers, and related to change in amount of green biomass, and pigment content

71 CHAPTER 3 RESULTS & DISCUSSION Chapter Three Results and Discussion 3.1 Climatic Data Processing, Calculation and Analysis Desertification is land degradation in typically dry areas resulting from various factors, including climatic variations and human activities. In this study some of the main climatic factors (Temperature, Relative Humidity, Evaporation, and Rainfall) that lead to desertification were chosen here. Permanent changes in climate, particularly rainfall, are responsible for natural desertification, [78]. The climatic data which were taken from the Iraqi metrological department were analyzed using SPSS program within the framework of General Linear Model (GLM). From this model the missing data were estimated, and some statistical analyses were calculated to show the effect of each factor on desertification and to show the relation among these data as listed in tables (3-1 to 3-4). In this study long-term satellite measurements have been compared with the field observation data to determine if desertification is extended or not and the amount of extension. Regions Of Interest (ROI) in this study did not consider desertified areas unless desertification where permanent. For this reason, and to track the change in the study areas for long periods Landsat images have been used to cover 20 years from 1990 to 2010, for the sensors (TM taken in 1990, TM taken in 2000, and ETM + taken in 2010). 555

72 CHAPTER 3 RESULTS & DISCUSSION Table (3-1): ANOVA table analysis of variance for temperature ( 0 C) as the dependent variable Source DF SS MS F Value P Value Model Error Corrected Total DF R-Square C.V. Root MSE Temperature Yield Mean Table (3-2): ANOVA table analysis of variance for Relative Humidity (%) as the dependent variable Source DF SS MS F Value P Value Model Error Corrected Total DF R-Square C.V. Root MSE RH Yield Mean

73 CHAPTER 3 RESULTS & DISCUSSION Table (3-3): ANOVA table analysis of variance for Evaporation (mm) as the dependent variable Source DF SS MS F Value P Value Model Error Corrected Total DF R-Square C.V. Root MSE Evaporation Yield Mean Table (3-4): ANOVA table analysis of variance for Rainfall (mm) as the dependent variable Source DF SS MS F Value P Value Model Error Corrected Total DF R-Square C.V. R MSE Rainfall Yield Mean DF represents the degrees of freedom which were used to ensure correctness of the data and the model. The Model's degrees of freedom for a randomized complete block are (b-1) and (t-1). where b is the number of block levels and t is the number of treatment levels. The variables which also calculated were:-the Sum of Squares (SS), Mean Square (MS), F-Value, P-Value, and the Root Mean Square Error (RMSE) which 57 57

74 CHAPTER 3 RESULTS & DISCUSSION is an estimate of the Standard Deviation (SD) of the dependent variable. The Coefficient of Variation (C.V.) is a unitless measure of variability; it is used here to describe the amount of variation in the population. Normally, the C.V. is hundred times larger than the standard deviation of the dependent variable divided by the Mean. From the estimation of losing values, the least squares line can be calculated, and the final calculation was presented in the last column of the above table gives the p-value which is , indicating that the model as a whole accounts for a significant portion (the overall F test is significant) of the variability in the dependent variable. There are differences among the climatic factors (Temperature, Relative Humidity, Evaporation, and Rainfall); they do not reveal any information about the nature of the differences, as agrees with, [79]. Standard error (SE) refers to the deviation of any estimate from the intended values and the Standard deviation (ϭ) shows how much variation exists from the average (mean, or expected value). A low standard deviation indicates that the data points tend to be very close to the mean, while high standard deviation indicates that the data points are spread out over a large range of values. In GLM, there are another statistical analysis that has been computed like; mean (µ), standard error (SE) and standard deviation (ϭ) for each of the chosen station, as listed in tables (3-5 to 3-13)

75 CHAPTER 3 RESULTS & DISCUSSION Table (3-5): Shows the statistical calculations for Mosul station Variable N µ SE ϭ 0 Temperature (P PC) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm) Table (3-6): Shows the statistical calculations for Kirkuk station Variable N µ SE ϭ 0 Temperature (P PC) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm) Table (3-7): Shows the statistical calculations for Ramadi station Variable N µ SE ϭ 0 Temperature (P PC) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm)

76 CHAPTER 3 RESULTS & DISCUSSION Table (3-8): Shows the statistical calculations for Khanaqen station Variable N µ SE ϭ Temperature ( 0 C) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm) Table (3-9): Shows the statistical calculations for Baghdad station Variable N µ SE ϭ Temperature ( 0 C) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm) Table (3-10): Shows the statistical calculations for Kerbala station Variable N µ SE ϭ Temperature ( 0 C) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm)

77 CHAPTER 3 RESULTS & DISCUSSION Table (3-11): Shows the statistical calculations for Najaf station Variable N µ SE ϭ Temperature ( 0 C) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm) Table (3-12): Shows the statistical calculation for Nasyria station Variable N µ SE ϭ Temperature ( 0 C) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm) Table (3-13): Shows the statistical calculations for Basra station Variable N µ SE ϭ Temperature ( 0 C) Relative Humidity RH (%) Evaporation (mm) Rainfall (mm)

78 CHAPTER 3 RESULTS & DISCUSSION From the above results shown in tables, there is a noticeable correlation among the climatic factors (Temperature, Relative humidity, Evaporation, and Rainfall), as listed in table (3-14). There is a maximum relation between the mean temperatures and the percentage evaporation which is equal to Always when the temperature increases it leads to evaporation increase, and as consequence leads to drought and desertification. Another relation between rainfall in (mm) and the relative humidity (%) which is equal to 0.25 and gradually until the lowest relation between relative humidity and the evaporation which is equal to (- 0.80), these results agreed with, [80]. Table (3-14): Correlation coefficient between climatic factors in this study Parameters Max. Temp. Min.Temp. RH % Evaporation Rain full Max. Temperature ** ** 0.80 ** ** Min. Temperature ** 0.71 ** ** RH % ** 0.25 ** Evaporation ** Rainfall ** (P<0.01) 62 62

79 CHAPTER 3 RESULTS & DISCUSSION 3.2 Potential Evapotranspiration (PE) Calculation Results The potential evapotranspiration (PE) values were calculated according to Thornthwaite method as mentioned in (2-5). The variation in PE values through the months for each year referring to the variation in temperature values which is a function for PE. The results shown in tables (3-15 to 3-23) point out that variations in PE values ranging from the high value for the all selected stations in July and the maximum value for Basra station and the low value for the all selected stations in January except Kirkuk station the minimum value in December, the minimum value for Mosul station

80 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-15): Monthly mean of climatic factor for Mosul Station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) j PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Humidity Index (HI) = 0.6 Index of Moisture (IM) =

81 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-16): Monthly mean of climatic factor for Kirkuk Station Month Rainfall (mm) Evaporation (mm) Relative Humidity (%) Temperature 0 PC) j PE Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI= 0.23 IM = *

82 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-17): Monthly mean of climatic factor for Ramadi Station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) j PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI = IM =

83 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-18): Monthly mean of climatic factor for Khanaqen Station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) j PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI = 0.46 IM =

84 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-19): Monthly mean of climatic factor for Baghdad Station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) j PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI = 0.09 IM =

85 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-20): Monthly mean of climatic factor for Kerbala station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) J PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI = 0.16 IM =

86 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-21): Monthly mean of climatic factor for Najaf station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) j PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI = 0.05 IM =

87 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-22): Monthly mean of climatic factor for Nasyria station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) j PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI = 0.08 IM =

88 (P CHAPTER 3 RESULTS & DISCUSSION Table (3-23): Monthly mean of climatic factor for Basra station Month Rainfall (mm) Evaporation (mm) Relative Humidity Temperature 0 PC) j PE (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HI = 0.08 IM =

89 CHAPTER 3 RESULTS & DISCUSSION Table (3-24): The climatic classification was according to [82]. Climate type Range H I Humid 1 HI Moist 0.5 <HI 1 Moist to dry Dry V-dry Not Humid 0.25<HI < HI 0.25 HI 0.1 Table (3-25): Classification of the study region Climate type Range H I Moist Moist to dry Dry Not Humid 0.5 <HI <HI < HI 0.25 V-dry HI 0.1 As shown in table (3-25) and according to,[82], the nature of the study area is moist for Mosul (0.61), for Khanaqen (0.46) which is moist to dry, for Kirkuk and Karbala (0.23, 0.16) consequently which is dry and for (Ramadi, Baghdad, Najaf, Nasyriya and Basra) are ( 0.065, 0.09, 0.05, 0.08 and 0.08) respectively 73 73

90 CHAPTER 3 RESULTS & DISCUSSION which are very dry, for this reason the more dry region have been chosen in this study. These results agreed with, [83]. There is another climatic classification according to, [84] depending on the climate Index of Moisture (IM). When the value of IM is negative, it indicates a java environment, and when it is positive, represents a humid atmosphere and the divisions of the climate according to, [84] as shown in the following tables (3-26 and 3-27). Table (3-26):- Climatic classification according to, [84] Climate type Dry sub humid Semi Arid Arid IM Range 0.0 to to to -100 Table (3-27): Classification of the study region Climate type Dry sub humid Semi Arid Arid IM Range 0.0 to to to -100 According to this classification method; Kirkuk is classified as to be dry-sub humid region (- 7.7*10P -3 P), Mosul and khanaqen are semi-arid region, and the 74 74

91 CHAPTER 3 RESULTS & DISCUSSION residual regions (i.e. Ramadi, Baghdad, Kerbala, Najaf, Nasyria, and Basra) are arid regions. 3.3 Geographical Information System (GIS) maps for climatic data The output of the climatic data by using GIS maps as in the following figures (3-1) to (3-4). Figure (3-1): GIS maps for the annual mean temperature in 0 C 75 75

92 CHAPTER 3 RESULTS & DISCUSSION Figure (3-2): GIS maps for the annual mean relative humidity (%) 76 76

93 CHAPTER 3 RESULTS & DISCUSSION Figure (3-3): GIS maps for the annual mean evaporation (mm) 777

94 CHAPTER 3 RESULTS & DISCUSSION Figure (3-4): GIS maps for the annual mean rainfull (mm) 78 78

95 CHAPTER 3 RESULTS & DISCUSSION The Mean for these data was presented output as GIS maps which is shown in figures (3-1 to 3-4) in order to show the spatial analysis by using (IDW) kriging method which is the optimal method for these data according to [84, 85, 86]. From the temperature data calculation it appears that there is a clear variation in the means of temperature with years and the most degree appear in 2006 with temperature value (32) 0 C to be close to 2010 which equal to (26) 0 C the minimum temperature which is equal to (22.5) 0 C in In the other hand for the relation between the temperature with month; most mean temperature was recorded (36 and 35.75) 0 C for (July and August) respectively. The last parameter which was studied with temperature is station the most temperature for Basra station which is equal to (28.3) 0 C and the minimum temperature for Mosul which is equal to (20.74) 0 C. From the relative humidity data calculation; it appears that there is a clear variation in the means of relative humidity with years and the most value appears in 1997 which is equal to (49) % with the minimum value which is equal to (42) mm in In the other hand for the relation between the relative humidity with month the mean value was recorded as (71) % and the minimum mean value was recorded as (26) % for January and July respectively. The last parameter which was studied with relative humidity is station; the most value was for Ramadi which is equal to (52.6) % and the minimum value for Mosul which is equal to (39.3) %. From the evaporation data calculation, it appears that there is a clear variation in the means of evaporation with years and the most value appeared in 1999 with evaporation value (282 mm) and the min. which equal to (212 mm). In the other hand for the relation between the evaporation with month the most mean value was recorded (479) mm for July and (65) mm for January. The last parameter which was studied with evaporation is the station the most value for Basra station 79 79

96 CHAPTER 3 RESULTS & DISCUSSION which is equal to (303) mm and the minimum evaporation value for Mosul which is equal to (167) mm. From the Rainfall data calculation; it appears that there is a clear variation in the means of rainfall with years and the most value appear in 1993 with rainfall value (24) mm and the minimum value which is equal to (9) mm for the years 2008 and In the other hand for the relation between the rainfall with the months, the most mean value was recorded as (59) mm for January and the lowest mean value was recorded as (0) mm for August. The last parameter which was studied with rainfall is station the most rainfall for Mosul station which is equal to (27) mm and the minimum rainfall for Najaf station which is equal to (7) mm. 3.4 Test Sites and Samples Collection Field measurements were carried out in November and December 2012 at seven study areas in Iraq including Al-Juthum, Al-Malutiyat, Najaf Al-Ashraf, Umm Al-Adhum, Khan-Al-Abid, Ruhaimwi and Qasr-Al-Akhaidher. The data were collected from different sites depending on the different soil type in the study area. Each site was registered by using GPS device first to check the differences in position of the features in the ground with the landsat images and the second purpose is to integration the results with the climatic data and the landsat images data. Soil ph has a major effect on plant nutrient availability by controlling the chemical structure of the nutrient. Adjusting soil acidity or alkalinity improves soil nutrition without adding extra fertilizers. Soil nutrients needed by plants in the largest amount are referred to as macronutrients. In addition to macronutrients, plants also need trace nutrients and both macro and trace nutrient availability is 80 80

97 CHAPTER 3 RESULTS & DISCUSSION controlled by soil ph. Understanding of spatial variability of soil properties is important in site-specific management, [87, and 88]. Analysis of spatial variation of soil properties is fundamental to sustainable agricultural and rural development. The special variability of soil property is often measured using various interpolation methods resulting in map generation. Selecting a proper spatial interpolation method is crucial in surface analysis, since different methods of interpolation can lead to different surface results. Among statistical methods, geo-statistical kriging-based techniques have been frequently used for spatial analysis and surface mapping. In this work, one of the most important interpolation methods is used to study the spatial distributions of soil ph in a vineyard. Interpolation technique was used to estimate the ph measurement in unsampled points and create a continuous dataset that could be represented over a map of the entire study area. The method investigated includes; Inverse Distance Weighting (IDW). The performance of conventional statistics showed that soil ph had a law variation in this study, [89] as shown in the following figure (3-5)

98 CHAPTER 3 RESULTS & DISCUSSION Figure (3-5): ph map for the soil samples by using IDW technique 82 82

99 CHAPTER 3 RESULTS & DISCUSSION 3.5 Layer stack of the Landsat Images Layer stacking is used to bulid a new multiband file from georeferenced images of various pixel sizes, extents, and projections as shown in figures (3-6 to 3-9). TM 1990 Figure (3-6): Layer stack for the area of study

100 CHAPTER 3 RESULTS & DISCUSSION TM 2000 Figure (3-7): Layer stack for the area of study

101 CHAPTER 3 RESULTS & DISCUSSION The ETM+ image taken in 2010 was processing by using matlab 2010 program, first every band was corrected (band1- band 7) and the georefrence was done then the layer stack as in the following figure (3-8). The Linear Local Histogram Matching (LLHM) was used here. a b 85 85

102 CHAPTER 3 RESULTS & DISCUSSION c d 86 86

103 CHAPTER 3 RESULTS & DISCUSSION e f 87 87

104 CHAPTER 3 RESULTS & DISCUSSION g h 888

105 CHAPTER 3 RESULTS & DISCUSSION i j 89 89

106 CHAPTER 3 RESULTS & DISCUSSION k l Figure (3-8): ETM+ image 2010 bands (1,2,3,4,5, and 7) and its correct image by matlab for the same bands 90 90

107 CHAPTER 3 RESULTS & DISCUSSION ETM Figure (3-9): Layer stack for the area of study

108 CHAPTER 3 RESULTS & DISCUSSION 3.6 The land covers classification for the selected images As mentioned in (2-10), the classification methods which applied in the current study are (unsupervised, supervised and scatter plot) classification techniques. The supervised classification techniques which were used are (Minimum distance classifier and Maximum likelihood Supervised Classifier). Unsupervised classification techniques which were used in this study are Isodata and K-Means classification techniques. The study area could be classified into five classes. These classes represent five major features in the study area (wet vegetation, dry vegetation, wet soil, dry soil, and water) as illustrated in the following figures (3-10 to 3-39). There are no differences in the percentage of area when using the two types of unsupervised classification technique but there are some differences when using the two types of supervised technique and the scatter plot technique. All these methods appear that there is an expansion in the desertification according to increasing in the area of soil and decreases in the area of vegetation. Figure (3-10):- Classification of the scene 1990 using Isodata classifier technique

109 CHAPTER 3 RESULTS & DISCUSSION Figure (3-11): The percentage areas for the ROI scene 1990 using Isodata classifier technique. Figure (3-12): Classification of the scene 2000 using Isodata classifier technique

110 CHAPTER 3 RESULTS & DISCUSSION Figure (3-13): The percentage areas for the ROI scene 2000 using Isodata classifier technique. Figure (3-14): Classification of the scene 2010 using Isodata classifier technique

111 CHAPTER 3 RESULTS & DISCUSSION Figure (3-15): The percentage areas for the ROI scene 2010 using Isodata classifier technique. Figure (3-16): Classification of the scene 1990 using K-Mean classifier technique

112 CHAPTER 3 RESULTS & DISCUSSION Figure (3-17): The percentage areas for the ROI scene 1990 using K-Mean classifier technique. Figure (3-18): Classification of the scene 2000 using K-Mean classifier technique

113 CHAPTER 3 RESULTS & DISCUSSION Figure (3-19): The percentage areas for the ROI scene 2000 using K-Mean classifier technique. Figure (3-20): Classification of the scene 2010 using K-Mean classifier technique

114 CHAPTER 3 RESULTS & DISCUSSION Figure (3-21): The percentage areas for the ROI scene 2010 using K-Mean classifier technique. Figure (3-22): Classification of the scene 1990 using Minimum Distance classifier technique

115 CHAPTER 3 RESULTS & DISCUSSION Figure (3-23): The percentage areas for the ROI scene 1990 using Minimum Distance classifier technique. Figure (3-24): Classification of the scene 2000 using Minimum Distance classifier technique. 999

116 CHAPTER 3 RESULTS & DISCUSSION Figure (3-25): The percentage areas for the ROI scene 2000 using Minimum Distance classifier technique. Figure (3-26): Classification of the scene 2010 using Minimum Distance classifier technique. 100

117 CHAPTER 3 RESULTS & DISCUSSION Figure (3-27): The percentage areas for the ROI scene 2010 using Minimum Distance classifier technique. Figure (3-28): Classification of the scene 1990 using Maximum Likehood classifier technique. 101

118 CHAPTER 3 RESULTS & DISCUSSION Figure (3-29): The percentage areas for the ROI scene 1990 using Maximum Likehood classifier technique. Figure (3-30): Classification of the scene 2000 using Maximum Likehood classifier technique. 102

119 CHAPTER 3 RESULTS & DISCUSSION Figure (3-31): The percentage areas for the ROI scene 2000 using Maximum Likehood classifier technique. Figure (3-32): Classification of the scene 2010 using Maximum Likehood classifier technique. 103

120 CHAPTER 3 RESULTS & DISCUSSION Figure (3-33): The percentage areas for the ROI scene 2010 using Maximum Likehood classifier technique. Figure (3-34): Classification of the scene 1990 using scatterplot classifier technique. 104

121 CHAPTER 3 RESULTS & DISCUSSION Figure (3-35): The percentage areas for the ROI scene 1990 using scatter plot classifier technique. Figure (3-36): Classification of the scene 2000 using scatterplot classifier technique. 105

122 CHAPTER 3 RESULTS & DISCUSSION Figure (3-37): The percentage areas for the ROI scene 2000 using scatter plot classifier technique. Figure (3-38): Classification of the scene 2010 by using scatterplot classifier technique. 106

123 CHAPTER 3 RESULTS & DISCUSSION Figure (3-39): The percentage areas for the ROI scene 2010 using scatter plot classifier technique. Dry Soil Percentage Dry Soil Year Figure (3-40): The percentage areas of dry soil for the ROI in the period of study. 107

124 CHAPTER 3 RESULTS & DISCUSSION Wet soil Percentage Year Wet soil Figure (3-41): The percentage areas of wet soil for the ROI in the period of study. Dry vegetation Percentage Year Dry vegetation Figure (3-42): The percentage areas of dry vegetation for the ROI in the period of study. 108

125 CHAPTER 3 RESULTS & DISCUSSION Wet vegetation 20 Percentage Wet vegetation Year Figure (3-43): The percentage areas of wet vegetation for the ROI in the period of study. Water Percentage Year Water Figure (3-44): The percentage areas of water for the ROI in the period of study. 109

126 CHAPTER 3 RESULTS & DISCUSSION 3.7 Vegetation indices Ratio Vegetation Index (RVI) Ratio of green leaf scattering in near-infrared, red absorption in chlorophyll. The differences in the RVI clearly appears among there of study as shown in the following figure (3-45). This technique Produces vegetation index separately, then subtracts the second-date vegetation index from the first-date vegetation index. It emphasizes differences in the spectral response of different features, [90]. Figure (3-45): The differences in the RVI for the scenes (1990, 2000 and 2010) respectively Normalized Difference Vegetation Index NDVI Normalized difference of green leaf scattering in near-infrared, chlorophyll absorption in red. The data were applied for monitoring vegetation changes in the study area through the period of the study. The results produced from these NDVI images gave good indicators of vegetation degradation through the period The final result as shown in the following figure (3-46) was an image of assessment of desertification, which gives good indication of areas descertified and those under risk of desertification. The experimental data which agreed with the theoretical data range for this ratio is -1 to +1. Negative values generally represent non-vegetated surfaces (clouds, snow, water, soil, and so forth). As values 110

127 CHAPTER 3 RESULTS & DISCUSSION approach +1, photosynthetic activity is strong, with highest values for irrigated farm lands, golf courses, and riparian areas, [91]. Figure (3-46): The differences in the NDVI for the scenes (1990, 2000 and 2010) respectively Change detection The change detection difference map used to produce an ENVI classification image characterizing the differences between any pair of initial state and final state images. The input images which used here was a single-band images of any data type. The difference was computed by subtracting the initial state image from the final state image (that is, final - initial), and the classes are defined by change thresholds. A positive change identifies pixels that became brighter (final state brightness was greater than the initial state brightness), while a negative change identifies pixels that became dimmer (final state brightness was less than initial state brightness). Its results are strictly dependent on pixel-for-pixel comparisons. There are five number of classes here (water, wet vegetation, dry vegetation, wet soil, and dry soil). Each class is defined by a difference threshold that represents a varying amount of change between the two images. The resulting Difference Map classification image is color coded to indicate the magnitude of the change between the two images. Positive changes display in shades of red, grading 111

128 CHAPTER 3 RESULTS & DISCUSSION from gray for no change to bright red for largest positive change. Negative changes display in shades of blue, grading from gray for no change to bright blue for the largest negative change, [92]. The default classification thresholds here are spaced between (-2) and (+2) for differences. The changes in the vegetation areas and the expansion of the desertification have been computed by subtract the first data image from the second data image; pixel by pixel, (Image ( )), Image ( ) and ( ) as in the following figures (3-47 and 3-48). Figure (3-47): The change detection by taken the differences in maps for the images among band 3 ( , , and ) respectively. 112

129 CHAPTER 3 RESULTS & DISCUSSION Figure (3-48): Shows the change detection by taken the differences in maps for the images among band 4 ( , , and ) respectively. 113

130 CHAPTER 4 CONCLUSION & RECOMMENDATION Chapter Four Conclusions & Recommendations 4.1 Conclusions Results obtained in this research led us to several important conclusions, including: (1) Climatic factors such as; temperature, relative humidity, evaporation, and rainfall, that have been selected to achieve the goal of this research, have a direct impact on the desertification phenomenon and it is found that there is a strong existed correlation between these factors. (2) General rise in temperatures contribute effectively in raising the rate of evaporation, which leads to a decrease in soil moisture and increase in the phenomenon of drought, and thus contribute to the deterioration of soil surface, particularly in the areas of poor rain, thus directly affect the expansion of desertification. (3) From the results of the nature of the studied area, there was a thought that the climate is ranged between wet and very dry, while according to the latest classification it is changed from dry - dry sub-humid. (4) Remote sensing data is a powerful tool for monitoring desertification. Repeated coverage of satellite imagery provides an effective tool for monitoring this harmful phenomenon in the region, such as Iraq. Therefore, it is very useful to employ remote sensing technology to monitor desertification through various means including: direct approach by examining the area affected by desertification periodically, and using 114

131 CHAPTER 4 CONCLUSION & RECOMMENDATION indirect means through the study of desertification indicators that can be measured in the satellite images. (5) The classification techniques offer effective techniques to monitor the land cover changes and to show the impact of desertification in the study area. (6) The results of the vegetation indices (NDVI and RVI) show that the desertification is remarkably increased in the region of study. NDVI followed by RVI yield higher recognition for the desertification expansion. The results were interpreted due to the utilized mean for each of them. (7) Based on the statistical study for the climatic data, ancillary data, in addition to information obtained during the field observations, analyzed landsat images and relevant literature, its appear that significant land cover changes has taken place during the period of study. 115

132 CHAPTER 4 CONCLUSION & RECOMMENDATION 4.2 Recommendations (1) Use other natural factors such as wind's speed, and human effecting factors; such as population density (human and animal), utilization others unwise process; such as excessive use of wood as firewood, and unsustainable practices; such as growing food without soil depletion, and how they interact in the further expansion of desertification. (2) The daily change in the climatic factors can by utilized to determine the effects on this phenomenon. (3) In addition to ph measure for the soil samples, other physical and chemical parameters can be determined; such salinity and the soil silt percentage. (4) Another statistical models can be build which lead to the prediction of desertification hazard in Iraq. (5) More effort should be made to improve the classification of the landcover in order to distinguish among these features and thus to be able to monitor long-term vegetation degradation. Hyper spectral imagery, spectrometry, and long term field monitoring experiments. (6) Recommendation of the Ministry of Agriculture and the Ministry of Environment to pursue the phenomenon of desertification and its expansion in Iraq and find solutions to this problem. 116

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146 Appendix APPENDIX Figure (1): Topography map for Al-Juthum 130

147 APPENDIX Figure (2): Topography map for Al-Malutiyat 131

148 APPENDIX Figure (3): Topography map for Najaf-Alashraf 132

149 APPENDIX Figure (4): Topography map for Karbala 133

150 APPENDIX Figure (5): Topography map for Khan Al-Abid 134

151 APPENDIX Figure (6): Topography map for Ruhaimwi 135

152 APPENDIX Figure (7): Topography map for Qus Al-Aqedr 136