Master Thesis
Submitted within the UNIGIS MSc programme At the Centre for GeoInformatics (Z_GIS)
Salzburg University
Land Cover Map Delineation, for Agriculture Development, Case Study in North Sinai, Egypt Using
SPOT4 Data and Geographic Information System By
Nasser Hussien Salem Saleh 459507
A thesis submitted in partial fulfillment of the requirement of the degree of
Master of Science (Geographical Information Science&System)-M.Sc (GISc)
Advisor:
Dr. Mohamed A. Aboelghar Salzburg, February, 2013
I
Abstract
Land Cover Map Delineation, for Agriculture Development, Case Study in North Sinai, Egypt Using SPOT4 Data and Geographic Information System.
By Nasser Saleh
Sinai Peninsula is a strategic place for national developmental in Egypt. It has various natural resources such as soil, water, exotic places and mineral stocks. Till now, most of Sinai natural resources have not yet used in the proper way. This situation calls for an effective decision support system (DSS) for land use planning in the whole of Sinai Peninsula. This system will help all levels of decision taking process to establish the most effective development management policies. The inputs of this system will be composed of different geospatial layers and one of these layers is the land cover maps for the current area of study.
The study of north Sinai area was covered by Eleven SPOT4 (HRVIR) images with 20 meter spatial resolution, 26 days temporal resolution and four spectral bands; band 1, green (0.50 - 0.59 µ), band 2, red (0.61 - 0.68 µ), band 3, near infrared (0.78 - 0.89 µ) and band 4, shortwave infrared (1.58 - 1.75 µ) acquired in the period from 27/02/2011 to 8/07/2011. As a first step, images were geometrically and atmospherically corrected. Land cover classification was carried out using all reflective bands with normalized difference vegetation index (NDVI) and principal component analysis (PCA). Analysis of the images was assessed using ground check points collected through intensive field observation. Land cover classification followed FAO land cover classification system (LCCS).
The identified land cover classes of the study area include vegetated areas, either by artificial irrigation or by rainfed, non vegetated area, and water bodies, irrigated crops include irrigated terrestrial tree crops, terrestrial herbaceous crops and aquatic herbaceous crops. Rainfed crops include rainfed tree crops, and herbaceous crops. The naturally vegetated areas include terrestrial vegetation of sparse herbaceous, terrestrial very open herbaceous with shrubs, aquatic vegetation of closed herbaceous and aquatic vegetation of sparse herbaceous. Non-vegetated areas include either bare areas or artificial surfaces. Bare areas include stony soils, very stony soils, shifting sand, dunes, salt marches and bare rock. Artificial surfaces are of linear features as railways and roads or as nonlinear features as urbanized areas. Water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing).
II
Acknowledgment
Many people contributed to this thesis directly or indirectly. It would be impossible to thank them all here because the listing would double the size of my thesis, so I just mention the closest friends and colleagues who have been with me during this unforgettable time of my life.
I would like to express my deepest appreciation, gratitude and thanks to Dr. Mohamed Amin Aboelghar of the National Authority for Remote Sensing and Space Sciences (NARSS) for his guidance, endless patience and kindness, thoughtful suggestions and continual encouragement during the course of this study. I am also thankful to Professor Sayed Madany Arafat for providing the necessary research facilities and the perfect atmosphere for research.
I would like to express my deep gratitude to my instructor Prof. Khalid Eldarandaly, and Prof. Abd El Nasser Zeyad for their generous support, advice, scientific vision and their enthusiasm for accessing new fields of research and new scientific methods.
My sincere thanks go to Dr. Sayed Ali Hermas of the National Authority for Remote Sensing and Space Sciences (NARSS) for guiding me, giving me very useful information and providing me with the necessary data for this work.
I wish to thank all my colleagues in Agricultural Applications Department of the National Authority for Remote Sensing and Space Sciences (NARSS) for their immeasurable cooperation, their participation in the discussion of the work and constructive comments.
I am deeply grateful to my family and friends for their incredible support, love and friendship and for just being there. Please know how much I appreciate all that you have given. I would like to express my gratitude to the participants and the instructors of UNIGIS program who took time to share their knowledge with me in interviews.
Finally I would like to extend my warmest thanks to all people without their help and support this work would not have been possible.
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Table of contents
Subject Page
Number
Abstract I
Acknowledgment II
Table of contents III
List of figures VI
List of tables VIII
List of abbreviations VIII
Table of transliterate IX
1-Chapter 1 Introduction
1.1 Motivation 2
1.2 Problem description and task 4
1.3 Objectives 5
1.4 Data source 6
1.5 Approach 6
1.6 Expected result 8
2-Chapter 2 Location and general physical characteristics
2.1 Study area 10
2.2 Geomorphology 11
2.2.1 North Sinai sand sea 11
2.2.2 Coastal forms 13
2.2.3 El-Tina deltaic plain 13
2.3 Regional geology 14
2.3.1 Triassic 15
2.3.2 Jurassic 16
2.3.3 Cretaceous 18
2.3.4 Quaternary 18
2.3.4.1 Coastal facies 18
2.3.4.2 Land facies 19
2.4 Climate 20
2.4.1 Rainfall 20
2.4.2 Wind 21
2.4.3 Temperature 22
2.4.4 Seasonal climatic conditions 22
2.4.4.1 Winter 22
2.4.4.2 Spring 22
2.4.4.3 Summer 23
2.4.4.4 Autumn 23
3-Chapter 3 Concepts and Methodology
3.1 General concept of remote sensing 25
3.2 Land cover classification system (LCCS) 25
3.3 Methodology 26
3.3.1 Processing of digital image data 26
IV
3.3.1.1 Data pre-processing 26
3.3.1.2 Data processing 27
3.3.2 Image classification 28
3.4 Field observation 32
3.5.1 Accuracy assessment 33
3.6 GIS definition 36
3.6.1 GIS applications 36
3.6.1.1 Input of data 36
3.6.1.2 Map making 36
3.6.1.3 Manipulation of data 37
3.6.1.4 File management 37
3.6.1.5 Query and analysis 38
3.6.1.6 Visualization of the results 39
4-Chapter 4 Land Cover Classification
4.1 Definitions of land cover, land use and classification 41
4.2 Land cover classification 45
4.3 Primary vegetated areas 48
4.3.1 Terrestrial 48
4.3.1.1 Cultivated and managed terrestrial areas 48
4.3.1.2 Natural and semi-natural aquatic or regularly flooded vegetation 49
4.3.2 Aquatic or regularly flooded 50
4.3.2.1 Cultivated aquatic or regularly flooded areas 50 4.3.2.2 Natural and semi-natural aquatic or regularly flooded vegetation 50
4.4 Primarily non-vegetated area 51
4.4.1 Terrestrial 51
4.4.1.1 Artificial surfaces and associated areas 51
4.4.1.2 Bare areas 51
4.4.2 Aquatic or regularly flooded 54
4.4.2.1 Natural water bodies, snow, and ice 54
4.5 Land Cover Maps 57
4.5.1Bour Saeid (NH36.N2) 57
4.5.2Rommanah (NH36.N3) 59
4.5.3 El Bardaweel (NH36.O1) 60
4.5.4 El Arish (NH36.O2) 62
4.5.5 Rafah (NH36.O3) 63
4.5.6 Ismailia (NH36.J5) 65
4.5.7 Qatebah (NH36.J6) 66
4.5.8 Gabal Maghara (NH36.K4) 68
4.5.9 Gabal Lebny (NH36.K5) 69
4.5.10 Al Qassima (NH36.K6) 71
4.5.11 Monkhafad Ramen (NH36.L4) 73
4.5.12 Land cover map of whole study area 74
5-Chapter 5 Results and Conclusions
5. Land Cover classification and development opportunities 77
5.1 The El-Tina Plain and the South El-Kantara Shark 77
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5.2 El-Bardaweel, Bir El-Abd and Rabaa 78
5.2.1 Lake Bardaweel 79
5.2.2 Bir Al-Abd 80
5.2.3 Individual sabkhas, marshes and swamps 80
5.2.4 Sand sheet and sand dunes (Active and Passive) 80
5.2.5 Sand Sea of North Sinai 81
5.3 El Salam Canal (Irrigation canal) 84
5.4 Wadi El-Arish (Very open herbaceous) 86
5.5 Gebel El Maghara (Bare Rock/Very Stony Soil) 89
5.6 El Arish, Rafah and AL Sheikh Zuweid 89
5.6.1 El Arish 89
5.6.2 Rafah and AL Sheikh Zuweid 90
5.7 Conclusions 92
References
VI
List of figures
Figure 1: SPOT 4 images that were used in LCCS for North Sinai 7
Figure 2: Location map of the study area 11
Figure 3: Geomorphological map of the study area 12
Figure 4: Geological map of study area 15
Figure 5: NDVI image showing irrigated herbaceous crops El Arish region 29
Figure 6: Calculation of PCA 30
Figure 7: Layer stacking process between multi spectral image, PCA and NDVI 30 Figure 8: The index of topographic maps (cods) that were used to produce land
cover maps
31 Figure 9: The index of topographic maps (names) that were used to produce land
cover maps
31 Figure 10: The routes and the check points of the two field trips in North Sinai 32
Figure 11: The GIS-based cartographic database 37
Figure 12: Composition of geographic data base 38
Figure 13: Visualization of GIS data 39
Figure 14: Abstract presentation of a classification consisting of a continuum with two gradients
42 Figure 15: Concrete situation in the field in a particular area from Kuechler and
Zonneveld (1988)
42 Figure 16: Legend as application of a classification in a particular area 43 Figure 17: Example of a priori (above) and a posteriori (below) classification
related to a concrete situation in the field adapted from Kuechler and Zonneveld (1988)
44 Figure 18: An example of cultivated and managed terrestrial area in the study
area
48 Figure 19: An example of natural and semi-natural vegetation in the study area 49
Figure 20: Bare rock 52
Figure 21: Bare rock with a layer of sand 52
Figure 22: Bare very stony soils 53
Figure 23: Bare stony soils 53
Figure 24: Sand dunes 54
Figure 25: Natural water bodies in the study area 55
Figure 26: An example of classified image of SPOT4 data 56
Figure 27: Attribute file with complete information 56
Figure 28: Land cover map of BOUR SAEID region 58
Figure 29: Land cover map of ROMMANAH region 59
Figure 30: Land cover map of EL BARDAWEEL lake region 61
Figure 31: Land cover map of EL ARISH region 62
Figure 32: Land cover map of RAFAH region 64
Figure 33: Land cover map of ISMAILIA region 65
Figure 34: Land cover map of QATEBAH region 67
Figure 35: Land cover map of GABAL MAGHARA region 68
VII
Figure 36: Land cover map of GABAL LOBNY region 70
Figure 37: Land cover map of EL QASSIMA region 71
Figure 38: Land cover map of MONKAFAD RAMEN region 73
Figure 39: Land cover map of the STUDY AREA 74
Figure 40: Land cover map of El Tina Plain and El Kantara Shark region 78 Figure 41: Land cover map of El-Bardaweel, Bir El-Abd and Rabaa region 79 Figure 42: Marshes (A), sabkhas (B) east of El-Bardaweel Lake 80 Figure 43: Sand dunes (A) and sand sheet (B) south of El-Bardaweel Lake 81 Figure 44: Land cover map of sand sheet, sand dunes and sand sea of North Sinai 82
Figure 45: Sand dunes class area 84
Figure 46: Reclamation Area on El Salam Canal 86
Figure 47: Irrigated Tree Crops, Olive (A) Citrus and peach (B) at Rafah Region 88 Figure 48: Irrigated herbaceous crops Corchorus olitorius (A), Medicinal herbs (B), Medicinal herbs and date palm (C), Maize (D) at El Arish Region
88
Figure 49: Gabal El Maghara 89
Figure 50: Urban Area of North Sinai region 91
VIII
List of tables
Table 1: Index of (K, J) of SPOT 4 imagery that were used in the present study 7 Table 2: Correlation and areal distribution of the mapped rock units in Sinai
Peninsula
17
Table 3: Statistics of the Accuracy Assessment 34
Table 4: Dichotomous approach for setting up the primary land covers classes in FAO / LCCS
46 Table 5: The four level of classification according to the FAO LCCS 47 Table 6: The area of the different land covers in the BOUR SAEID 58 Table 7: The area of different land cover in the ROMMANAH 60 Table 8: The area of the different land covers in EL BARDAWEEL LAKE 61 Table 9: The area of the different land covers in EL ARISH 63 Table 10: The area of the different land covers in RAFAH 64 Table 11: The area of the different land covers in ISMAILIA 66 Table 12: The area of the different land covers in QATEBAH 67 Table 13: The area of the different land covers in GABAL MAGHARA 69 Table 14: The area of the different land covers GABAL LOBNY 70 Table 15: The area of the different land covers in EL QASSIMA 72 Table 16: The area of the different land covers in MONKAFAD RAMEN 73 Table 17: The area of the different land covers in the study area 75
List of Abbreviations
DSS Decision support system
UNEP United Nations Environment Programme FAO Food and Agriculture Organization LCCS Land covers classification system LULCC Land use/ cover changes
UNCED United Nations Conference on Environment and Development GDP Gross domestic product
TDS Total dissolved salt
IX
Table of Transliterate
Word Equivalent
El Arish AL Arish
Damietta Demiatt
Al-Daqahlia El Dakahlia
Al-Sharqia El Sharkia
Ismailia Ismailiyyah
El Tina plain El-Tineh plain
El-Bardaweel Bardaweil
Bour Saeid Port Said
Raba Rabaa
Gabal Lobna Gabal Lebny
Gabal Gebel-Jabal
1
Chapter One
Introduction
2
Chapter 1: Introduction
1.1 Motivation
Decision makers, must have adequate information on many complex interrelated aspects of activities in order to make decision. Land cover is not only one such aspect, but knowledge about land cover has become increasingly important as the nation plans to overcome the problems of inadequate information, uncontrolled development, deteriorating environmental quality, loss of prime agricultural lands, destruction of important wetlands, and loss of fish and wildlife habitat. Land cover data are needed in the analysis of environmental processes and problems that must be understood if living conditions and standards are to be improved or maintained at current levels. Knowledge of the present distribution and area of such agricultural, recreational, and urban lands, as well as information on their changing proportions, is needed by legislators, planners, and state and local governmental officials to determine better land use policy, to project transportation and utility demand, to identify future development pressure points and areas, and implement effective plans for regional development.
Changes in land cover and land use are among the most important human alterations affecting the surface of the earth (Lambin and Ehrlich, 1997). Land use/cover changes (LULCC) directly impact biological diversity and contribute to local and regional climate change as well as to global climate warming (Chase et al, 1999 Houghton, et al.1999), and may cause land degradation by altering ecosystem services and livelihood support system, thereby disrupting the socio-cultural practices and institution associated with managing those biophysical systems (Vitousek, et al; 1997). (LULCC) also affect the vulnerability of people’s places to climate, economic or sociopolitical perturbations (Kasperson, et al; 1995).
The framework of a national land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of decision makers for an up-to-date overview of land cover throughout the country on a basis that is uniform in categorization at the more generalized third level and that will be receptive to data from SPOT4. The proposed system uses the FAO land cover classification system (LCCS). It is an international land cover classification system that has flexibility in developing more detailed land cover classification at third level to meet the particular needs and in the same time remain compatible with FAO system.
In Egypt, there is a high and urgent need for a recent land cover map for Sinai to support national developing and land use planning. Also, the dynamic situation of land covers in Sinai calls for annual land cover mapping process
3
to support developing plans. The produced land cover map should be compatible with the international standard and definition. A standardized and up-to-date land cover dataset is required to assess the condition of the natural resource base, modeling water quality, soil erosion, soil health and the sustainable production of food and fiber.
Development of Sinai Peninsula is the core of the national developing strategy of Egypt. Sinai has various natural resources and economic potentials such as soil that is suitable for many cropping compositions, natural vegetation with rich and various floristic compositions, surface and ground water and relatively high annual precipitation, exotic places and mineral stocks. Till now, most of Sinai natural resources have not yet used in the proper way which calls for an effective decision support system (DSS) for land use planning in the whole of Sinai Peninsula. This system will help all levels of decision taking process to establish the most effective development management policies. The inputs of this system will include different geospatial layers and one of these layers is the current land cover map in Sinai that is the focus of the present work.
Land cover map for a part of North Sinai will be produced using the FAO (2004) Land Cover Classification System (LCCS). The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products with the regional and global land cover datasets. The total proposed study area is 14,812 km² (3,526,714 feddans).
The land cover classification was performed on SPOT4 data acquired in 2011 using combined multispectral bands of 20 meter spatial resolution.
Geographic Information System (GIS) software (ESRI ARC GIS 9.2) was used to edit the classification result in order to reach the maximum possible accuracy and to include all necessary information. It was found that the study area includes the following land covers: the vegetated land cover includes irrigated herbaceous crops, irrigated tree crops and rain fed tree crops and non-vegetated land covers as well as water bodies and artificial surface. The non-vegetated land covers in the study area are: bare rock, bare soil, bare stony soil, bare very stony soil, bare salt crusts soil, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigation canals) and natural perennial water bodies as lakes (standing) and rivers (flowing). Artificial surfaces in the study area include linear and non-linear. The produced maps were attached with statistics and full description of the different land covers.
4
Many research studies of land cover mapping were carried out in many parts of Egypt including Sinai; however, these studies did not follow an international standardization. Land use and land cover change detection in the western Nile delta was carried out by Abd El-Kawy et al. (2011). Monitoring land cover changes using both hybrid classification and NDVI at west delta was done by Bakr et al. (2010). Monitoring land cover in the Northwestern coastal zone of Egypt using supervised classification was achieved by Shalaby and Tateishi (2007).The quantitative structure of main land cover patterns across the Israel-Egypt border was determined by Qin et al. (2006). Because of the high need for standardization and compatibility between the different land cover data sets and for the possibility to map, evaluate and monitor wide areas, in 1993, UNEP and FAO organized a meeting to perform actions towards harmonization of data collection and management and to take a first step towards an internationally agreed reference base for land cover and land use UNEP/FAO (1994). The main objective of the initiative for definition of a reference classification is to respond to the need for standardization and to develop a common integrated approach to all aspects of land cover. This implies a methodology that is applicable at any scale, and which is comprehensive in the sense that any land cover identified anywhere in the world can be readily accommodated. Data generation must be conducted to satisfy the logical approaches of standard land cover classification systems to be compared with multi-temporal inter-state and international data. Here, the priori Land Cover Classification System (LCCS) adopted by the FAO can be used as the standard to build a local land cover classification system for Egypt starting from Sinai Peninsula. The FAO LCCS system is applicable in any region of the world regardless of the economic conditions and data source.
FAO classification is considered as a concept based on visual classification, which uses the directly visible and knowledge based components on the ground. The FAO document defines the land according to its contribution to productivity. The main resource controlling primary productivity for terrestrial ecosystems can be defined in terms of land: the area of land available, land quality and the soil moisture characteristics (Di Gregorio and Jansen. 1996).
1.2 Problem description and task
Historically, the socio-economic development of Egypt has been exclusively devoted to the Nile Valley and Delta, which lead to high intensity of population of the Delta governorates and the Nile Valley (Upper Egypt) governorates. In the Delta governorates, agricultural land is subjected to
5
strenuous human pressure involving exploitation intensity far beyond its natural carrying capacity. For a better urban development and infrastructure planning, municipal authorities need to know urban sprawl phenomenon and in what way it is likely to move in the following years.
People have reshaped the earth continually, but the present magnitude and rate are unprecedented. Nowadays, it is realized that it is very important to know how the land cover has changed over time in order to predict the possible future changes and the impact of these changes on peoples' lives. Due to the lack of appropriate land cover data, many assessments have used models to delimit potential land cover (Alexandratos, 1995). Although the use of potential land cover is important in modeling simulated future scenarios, there are major limitations. Information describing the current land cover is an important input for planning and modeling, but the quality of such data defines the reliability of the simulation outputs (Townshend, 1992; Belward, 1996).
1.3 Objectives
The main objective of the initiative for definition of a reference classification is to respond to the need for standardization (or harmonized collection of data, as mentioned in UNCED's Agenda 21 Chapter 10, for which FAO is Task Manager within the UN system) and to develop a common integrated approach in all aspects of land cover. This implies a methodology that is applicable to any scale, and, which is comprehensive in the sense that any land cover identified anywhere in the world can be readily accommodated. The objectives of the Present work are to:
Respond to the need for land cover data of a variety of end-users,
Apply the methodology in mapping exercises, independent of the means used, which may range from high resolution satellite imagery to aerial photography,
Link with existing classifications and legends, allowing comparison and correlation,
Support to the extent possible, international ongoing initiatives on classification and definition of land cover, and
Prepare and up to date land cover map for north Sinai to support dissection makers for better planning.
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1.4 Data Source
The data used in this research was collected from the following sources:
SPOT4 (HRVIR) images with 20 meters spatial resolution, 26 days temporal resolution and four spectral bands. Band 1, green (0.50 - 0.59 µ), band 2, red (0.61 - 0.68 µ), band 3, near infrared (0.78 - 0.89 µ) and band 4, shortwave infrared (1.58 - 1.75 µ) acquired in the period from 27/02/2011 to 8/07/2011.
Topographic map with scale of 1:100 000 (Egypt Survey Authority).
Geomorphology map of Sinai Peninsula 1:250 000 (National Authority National Authority for Remote Sensing and Space Sciences).
Geology Map of Sinai Peninsula 1:250 000 (The Egyptian Geological Survey and Mining Authority, 1981).
Landsat ETM Images for the study area (August, 2003), source:
http://www.land cover.org/data/landsat/
1.5 Approach
North Sinai area is covered by eleven SPOT4 (HRVIR) images as presented in table (1) and figure (1).
The FAO LCCS system is an approach that could be applied to any region of the world. Initially, the FAO method is a “priori” classification system, which defines all the classes before the classification is conducted. The advantage of this approach is the possibility to maintain standardization of classes. For this purpose, LCCS developed pre-defined classification criteria, or classifier to identify each class, instead of identifying the class itself. This concept is based on the idea that a land cover class can be defined without considering its location or its type, using a set of pre-selected classifiers. Therefore, when the user requires a large number of classes, a large number of classifiers are required. To organize the classification more easily, FAO system used a dichotomous (divide into subcategories), approach in hierarchical levels and used eight classifiers to group all land cover types at the third level. In other words, any location on the earth surface can be categorized into one of the eight classes without having a conflict. Up to this third level, FAO used the presence of vegetation, edaphic (plant conditions generated by soil and not by climate), and artificiality of land cover for classification.
7
Table 1: Index of (K, J) of SPOT 4 imagery that were used in the present study.
Figure 1: SPOT 4 images that were used in LCCS for North Sinai.
8
1.6 Expected results
The main product of the current study is an updated land cover map for North Sinai that follows the international standardization of (LCCS). The produced map includes all existing land cover classes such as the following classes:
Irrigated crops including irrigated terrestrial tree crops, terrestrial herbaceous crops and aquatic herbaceous crops.
Rainfed crops including rainfed tree crops, and herbaceous crops.
The naturally vegetated areas including terrestrial vegetation of sparse herbaceous, terrestrial very open herbaceous with shrubs, aquatic vegetation of closed herbaceous and aquatic vegetation of sparse herbaceous.
Non-vegetated areas including either bare areas or artificial surfaces.
Bare areas include stony soils, very stony soils, shifting sand, dunes, salt marches and bare rock.
Artificial surfaces, which are of linear features as railways and roads and nonlinear features as urbanized areas.
Water bodies which were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing).
9
Chapter Two
Location and general physical
characteristics
10
2. Location and general physical characteristics
2.1 Study Area
The Sinai Peninsula covers an area of about 61,000 km² in the northeastern part of Egypt. The study area is located in the northern part of Sinai Peninsula, bordered from the north by the Mediterranean Sea and by the Suez Canal, from the west and political boundary from east with a total area of 14,812 km². Figure (2) shows the study area that located between latitudes 31° 19`
44.249`` and 30° 30` 8.572`` North and between longitudes 34° 29` 44.149``
and 34° 29` 53.181`` East. The study area covers the following locations; El Kantara, Baloza, Rommanah, Raba, El Bardaweel Lake, Bir El Abd, El Arish, Rafah and AL Sheikh Zuweid and El Tina plain and the zone of El-Sheikh Gaber Al-Sabah canal that is the back stone of El-Salam canal project (one of the national major developing projects for the past two Decades). The area includes largest drainage basin in Sinai, El Arish Basin that extents from the middle of Sinai to the north and diverts its waters in the Mediterranean.
11
Figure 2: Location map of the study area.
2.2 Geomorphology
The study area is characterized by landforms that were developed by erosional process rather than structural agents. The, landforms of this region constitute the north Sinai sand sea, the costal forms and the El-Tina deltaic plain.
Description of these land forms is illustrated in the following subsection.
2.2.1 North Sinai Sand Sea
Sand dunes represent the most widespread form in northern Sinai, covering an area of about 12.200 km². This is why this area is called “North Sinai Sand Sea” (Embabi, 2000). The area covered by dunes extends from the international borders in the east to the Suez Canal in the west. Dunes of this sand sea were the subject of several studies (Tsoar, 1974; Misak and Attia,
12
1983; Hereher, 2000). Linear barchan dunes predominate in this sand sea are shown in figure (3). Their height varies between 10 and 20 m, and length between 100 and 1000 m. The general orientation of dunes is ENE-WSW.
Topographic and geologic maps, aerial photographs and space images show that there is at least two generation of dunes in this sand sea. The older generation is composed of a fixed dune with some vegetation covers. Its age was estimated to be about 20,000 years (Pye and Tsoar, 1990). The recent generation developed on the older one. The main source of sand was found to be the Nile sediments, which have been transported by the costal current to the beaches of northern Sinai. From these beaches, wind transported the sand islands, forming the dunes of these sand seas. A secondary source of sand is wadi deposits, which were laid down in northern Sinai.
Figure 3: Geomorphology map of study area.
Several wadis cross this sand sea, of which Wadi El-Arish is the most significant from the geomorphologic point of view. Due to the gentle slope of this part of Sinai, Wadi El-Arish is wide, shallow and braided in various parts.
The cutting of this main wadi across the sand sea may indicate that this wadi
13
developed either at a later stage than that of the dunes or at the same time.
This may also indicate that Wadi El-Arish was formerly draining internally after crossing the dome of Gabal Halal. In favor of this possibility is the absence of a delta for such large wadi (Wadi El-Arish) and the presence of a fluvial plain between Gabal Halal and Gabal El-Maghara, which have possibly developed by the deposits of Wadi El-Arish. In favor of this postulation is what was found recently that several palaeolakes developed behind natural dam formed by folds of Syrian Arc of Gabal Halal (Kusky and El-Baz, 2000). However, the relationship between Wadi-El-Arish and the North Sinai Sand Sea represent a geomorphologic problem that needs further research.
2.2.2 Coastal Forms
Except for sand beaches, the Bardaweel Lagoon is the largest costal form along Sinai Mediterranean cost. This lagoon is connected with the Mediterranean by narrow openings. Sand dunes cover the bars, which separate the lagoon from the sea. The highest dune in Sinai (54m), which is formed at the eastern tip of the western bar, is called locally El-Qals. This lagoon is composed of two connected basins, with several small islands and peninsulas with forms indicating that they represent submerged dunes such as the Island of El-Artah.
2.2.3 El-Tina Deltaic Plain
To the west of El-Bardaweel lagoon, extends a broad low-lying plain known locally as El-Tina Plain. Its level approaches the sea level or a little lower in some places. This plain is composed of Nile silt, which is deposited by the ancient Pelusium Nile branch (Sneh and Weissbrod, 1973). The presence of several parallel ancient costal bars along the northern fringes indicates the gradual development of the plain.
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2.3 Regional Geology
Sinai, the triangular-shaped peninsula of Egypt, is situated between Asia and Africa. The separation of the two continents caused the form and geographical shape of Sinai the way it looks today. Sinai is approx. 380 km long (north - south) and 210 km wide (west - east). The surface area has an extension of 61,000 km². The coasts are stretching about 600 km on the west and on the east. On the western part there is the Gulf of Suez (with the Suez Canal) and the eastern part of Sinai brings up the much deeper Gulf of Aqaba. The depth of the sea in the Gulf of Suez measures approx. 80 meters only, while the profile of the Gulf of Aqaba goes down to approx. 1,830 meters. The latter is a part of the big land rift that extends until Kenya in Africa.
Seismic activity and the tremendous eruptive phenomena have given Sinai its characteristic looks. The highest mountains are the Gebel Musa (Moses’
mountain) with 2,285 meter, and the Sinai's highest mountain Mount St.
Catherine (Gebel Katrina) with 2,642 meter. Many of the Pharaohs got their precious stones from southern Sinai.
The west coast of the Gulf of Aqaba, reaching from Ras Mohammed to Taba is filled with rich coral reefs sections, one after another. This under water paradise is giving ideal conditions for flora and marine fauna, and finally nowadays for divers. The northern part of Sinai mainly consists of sandstone plains and hills. The Tih Plateau forms the boundary between the northern flat area and the southern mountainous area with towering peaks.
Sinai geological structure falls within two groups, a Precambrian basement that is largely exposed in the south and a triangular area of sedimentary layers in the north that becomes thicker and more pronounced near the Mediterranean coast. Boundary between the two lies on an east-west plane from Gebel Hammam Faraun on the Gulf of Suez to Nuweiba on the Gulf of Aqaba; this coincides with the Tih and Egma escarpments in the high centre of the peninsula. Based on age, the surface geology can be broken into seven groups. From oldest to youngest they are: Precambrian intrusives and metamorphics, Jurassic sedimentary strata, Cenomanian-Turonian limestone and dolomites, Senonian chalk, Eocene chalk and limestones, Oligocene and Miocene sediments and Miocene dikes, and Quaternary alluvium.
The exposed rocks in Northern Sinai belong to the Phanerozoic Era and range in age from Triassic to Quaternary. Table (2) shows the correlation and areal distribution of the exposed rock units in Sinai. Figure (4) is a compiled
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geological map of Northern Sinai that comprises the five map sheets published by the Egypt Geological Survey in 1994 at a scale of 1: 250,000.
The five map sheets are BOUR SAID, EL ARISH, AL-ISMA’ILIA, AL QUSAIMAH and MA’AN). Stratigraphy and description of the exposed rock units are dealt with in thefollowing section.
Figure 4: Geological map of study area.
2.3.1 Triassic
Marine Triassic outcrops in Sinai are only known at the core of the Arif An- Naga dome, at the northeastern limit of northern Sinai, near the eastern borders. The identification of this rock unit was made and confirmed in 1947 by Eicher. The lithostratigraphy was worked by Said (1971). In (1988) Allam and Khalil recorded another marine unit occurs above the 'Urayf An Naqah Formation and was described as Abu Nusrah Formation. Several paleontological and biostratigraphical studies of this exposure were carried out by various authors such as Druckman (1974), Parnes (1964) and Hirsch (1976). Druckman et al. (1975) and Bartov et al. (1980) have studied different
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aspects of its sedimentology and tectonics. Non-marine Triassic sediments are reported in west central and southwestern Sinai under the name of Qusayb Formation (Abdallah et al. 1963).
2.3.2 Jurassic
Marine Jurassic rocks are exposed in northern Sinai as inliers appearing in the core of the major anticlinal structures of Jabal Al Magharah and Jabal Al Minsherah. The lithostratigraphy of this sequence was studied by Al Far (1966) and various authors. Various marine Jurassic exposures are also recorded around the Gulf of Suez (Sadek, 1926; Nakkady, 1955; Abdallah et al. 1963).
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Table 2: Correlation and areal distribution of the mapped rock units in Sinai Peninsula.
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2.3.3 Cretaceous
The stratigraphic subdivision of the Cretaceous in Sinai emerged after a long series of investigations which started from the beginning of the century (Barron, 1907; Ball, 1916; Hume et al. 1920; Moon and Sadek, 1925;
Beadnell, 1927; Youssef and Shinnawi, 1953; Shata, 1956; Omara, 1956;
Youssef, 1957; Ghorab, 1961; Ansary and Emara, 1962; Ansary and Twefik, 1969; El Dakkak, 1973; El Shinnawy and Sultan, 1973; Lewy, 1975; Lewy and Raab, 1977; Bartov and Steinitz, 1977; Issawi et al. 1981). In 1992, 1993 and 1994 the Geological Survey of Egypt published five sheets representing the Geological Map of Sinai at a scale of 1: 250 000. In the present work the classification of the Cretaceous and other Phanerozoic rock units established by the Egyptian Geological Survey were adapted.
2.3.4 Quaternary
The Quaternary deposits in Sinai as shown on the geologic map exhibit two main facies; land and coastal facies, each is distinguished into several rock units. The coastal facies are those sediments, which were laid down near the coasts of the Gulfs of Suez and Aqaba and the Mediterranean as well. In these facies marine impact is more prevalent in the formation of its rocks. The land facies are deposited far from the coasts and are formed, by alluvial or aeolian or both processes. A short description of each facies is given below:
2.3.4.1 Coastal Facies
These facies comprise the following units disregarding the chronological relationships.
1. Mediterranean Coastal Facies: Includes the following units:
A) Sabkha Deposits: Occupy most of the depression near the seashore and are made up of salts mixed with sand and silt.
B) Coastal Dunes: These are seif and barchan dunes consisting of carbonate sands and shell fragments.
C) Coquina Deposits: Represent an older Mediterranean shore line and are made up of a mixture of gastropod and pelecypod shells embedded into calcareous matrix.
E) Sahl El Tina Formation: Mixture of black and white sands and silt covering El Tina Plain.
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2. Gulfs of Suez and Aqaba facies: Includes mainly two units, which are:
A) Sabkha Deposits: Mostly near the shore of the Gulf of Suez, best observed at Belaim Bay, consists of sands and silt mixed with salts.
B) Old Coral Reefs: Form ridges running parallel to the shore line, some of them occupy higher levels while others are emerging from the gulf's water.
2.3.4.2 Land Facies
These facies comprise the following units:
Wadi Deposits: Composed of sands and gravels filling and delineating the wadi courses.
Sand dunes and sheets: These deposits are made up of aeolian sands arranged into compound and barchan dunes, some of them are stable while others are mobile. They cover the plain south of the Mediterranean coast in northern Sinai. Some of the sand sheet areas are cultivated, particularly in the area between Bir El-Abd and Rafah.
Al Kantarah Formation: Sands and grits extending parallel to the Suez Canal. They most probably represent the deposits of the Pelusium branch of the Nile.
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2.4 Climate
The climate of the Sinai peninsula is characterized by a hot dry summer with a temperature average of 32.5°C in August to 10°C in January (in winter).
Rainfall varies in the Sinai, from scarce rainfall at Port Said of about 75 mm, and more than 130 mm in El-Arish and about 244mm in Rafah. The temperature regimes have been defined as torric and thermic (El-Shazly and Abdel-Gaphour, 1990).
2.4.1 Rainfall
The northern Sinai coast is located within the rainy belt of Egypt; while the aridity increases generally to the south (El-Ghazawi, 1989). Rainfall is scarce and varies from place to place and increases in the north direction, ranging from about 30 mm/year in the southwest at Ismailia to about 300 mm/year in the northeast at Rafah. The annual rainfall in northwest Sinai varies between 36 to 54.8 mm (El-Sheikh, 2008). The rainfall has a direct contribution to groundwater recharge in the sand dune aquifer north Sinai, particularly in El Arish−Rafah area (El-Ghazawi, 1989). The northern Sinai is occupied by sand dunes that are mostly acting as water bearing formation, where the groundwater exists as a thin layer above the main saline water (El-Shazly et al., 1974). The salinity values of Quaternary aquifer between Rommanah and Bir El-Abd region range from 1876 to 7937 mg/l. This variation is due to the variation of the annual rainfall (Eweida et al., 1992). The sources of high salinity in the area between Baloza and Rommanah can be attributed to evaporation, the dissolution of evaporates, salt water intrusion and the influence of brines (Groschke, 2010). The total dissolved salts (TDS) of groundwater in El-Tina plain and its vicinities increase in the direction of groundwater flow and range from 2450 mg/l to 16940 mg/l (Deiab, 1998).
The salinity of surface and subsoil water in northwest Sinai is a mixture of meteoric and marine water due to salt water intrusion from the Mediterranean Sea and leaching processes of the lagoon deposits (Deiab, 1998; Mohamed, 2007) found that the danger of sea water intrusion on the Quaternary aquifer may not permit exploiting north Sinai as an industrial district for Bir El-Abd and its surroundings as were planned before.
The annual rainwater amounts falling over the peninsula, according to the meteorological data obtained from the weather forecasting stations in Sinai, are generally less than 200 mm in far northern zone at Rafah and El Arish, and are less than 20 mm in the south at the area of Ras Mohamed. Excluding from
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these data is the high mid-southern areas (the mountain region), where the annual rainfall amounts thereon range from 50 to 150 mm. Central Sinai, known as the Hills region (Hidhab zone), is regarded as the most dry zone in the peninsula, where maximum annual rainfall amounts to about 30 mm.
Accordingly, rainfall cannot be solely depended upon in agricultural development purposes in theses middle areas of Sinai, in comparison to other zones extending along the northern coast which can accommodate permanent cultivated fields/villages. The minimum annual rainwater falls on the coastal zone, and diminishes quickly as we go inland. The average rainfall amounts range annually from 80 to 100 mm only, while this amount reaches 150 mm on the western coast. In addition, the annual rainfall amounts increase on the cost as we move eastward by about 80 mm and 100 mm in Rafah and Gaza respectively. The amount of rainfall decreases wherever we move inland as it reached 50 mm at latitude 30' 30' N and to about 25 mm in Nakhl and 20 mm in Suez.
In winter, rainwater falls in the form of sprinkles reaching its maximum amounts in the months of January and December. The amounts of rainwater falling daily may reach up to 30 mm or more. In spring, the rainfall obviously decreases in comparison to winter, but may be accompanied with thunders and flows abundantly sometimes causing torrents in the areas exposed to rainwater sliding. No rainfall is depicted in summer, while in autumn; late October and November are both characterized by strong sprinkling rainfall that may result in torrents in water sliding areas.
2.4.2 Wind
In winter, the wind is generally changeable, but is characterized by the blowing southern winds that range from moderate to light wind, that may reach a speed of 50 km/hr once or twice every month. In spring, the wind is also changeable and blows from the north and northeastern directions, plus blowing also from the southwestern direction mostly in the morning. The hot southern wind may be heavy on the advent of air depressions, causing sandstorms once or twice per month. In summer, the dominant wind direction is mainly north and northwestern.
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2.4.3 Temperature
During winter, temperature degrees decrease, where the average high degree at noon reaches 20 ºC, while the low degree may average to about seven degrees in the early morning, but may drop to below zero degree in high inlands. In spring, the temperature degrees are changeable, where the average high degree may reach about 26 ºC, and a low of about 13 ºC, although the hot Khamasein wind waves may increase the temperature to above 40 ºC.
However, in summer, temperature degrees become moderate near the coast and increases as we move inward. The average high temperature degree may reach about 33 ºC, while the low temperature degree may reach about 18 ºC.
Temperature degrees are almost equivalent in autumn and spring with a tendency to increase, with an average high degree of about 30 ºC and an average low degree of about 15 ºC. During times of hot waves of weather, temperature degrees rarely exceed 40 ºC.
2.4.4 Seasonal Climatic Conditions
Generally, the prevailing climatic conditions in north Sinai are characterized by low rainfall, high temperatures, moderate wind, and high relative humidity.
The seasonal climate conditions are explained in the following sub section.
2.4.4.1 Winter
During winter average maximum temperature is 19.8 ºC where the day temperature typically varies from 8.6 ºC to 36.72 ºC and average minimum temperature is 8.3 ºC where the night temperature typically decreases to be between 0 ºC and 19.1 ºC. Precipitation in this season is 62.2 mm; it represents 60% of the total precipitation in the year. Average wind speed in this season is 8.8 m/sec.
2.4.4.2 Spring
Spring is characterized by unsettled weather. Average maximum temperature is 23.57 ºC where the day temperature typically varies from 12.4 ºC to 42 ºC and average minimum temperature is 11.13 ºC where the night temperature typically decreases to be between 1 ºC and 24.6 ºC. Precipitation in this season is 25.2 mm, it represents about 24% of the total precipitation in the year. Average wind speed in this season is 9.6 m/sec.
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2.4.4.3 Summer
It is a humid and hot with no precipitation during most of the season from June to September. Average maximum temperature is 30.83 ºC where the day temperature typically varies from 21°C to 45.4 ºC and average minimum temperature is 19.1 ºC where the night temperature typically decreases to be between 8 ºC and 27.8 ºC at night. Precipitation in this season is 5.0 mm, it represent about 4% of the total precipitation in the year. Relative humidity can fall to 65% during the day, and increase to 85% at night. Summer is the agricultural cropping season with maize vegetables, and medicine plants and the planting starts in May. Average wind speed in this season is 7 m/sec.
2.4.4.4 Autumn
It is a humid season, with precipitation during the season. Average maximum temperature is 26.8 ºC where the day temperature typically varies from 15.4 ºC to 41.6 ºC and average minimum temperature is 14.6 ºC where the night temperature typically decreases to be between 0 ºC and 24.2 ºC.
Precipitation in this season is 12.7 mm, it represents about 12% from total precipitation in the year. Average wind speed in this season is 7 m/sec.
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Chapter Three
Concepts and Methodology
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Chapter 3 Concepts and Methodology
3.1 General concept of remote sensing
In recent literature, remote sensing is defined as “a mean to gather information about an object, area or phenomenon by which the analysis of data is obtained by a device, which is not in physical contact with the studied matter”.
Although no touch contact exists between remote sensor and target, some physical emanation from the target must be found to investigate its properties and behavior. Barrett and Curtis (1992) indicated that the most important physical links between objects and remote sensing devices involve electromagnetic energy, acoustic waves and force fields associated with gravity and magnetism. There are two basic processes, mentioned by Lillesand and Kiefer (1994) in remote sensing. The first is data acquisition which comprises: energy sources, propagation of energy through the atmosphere, energy interaction with earth surface features, airborne and/or space-borne sensors, and resulting in the generation of sensor data in pictorial and/or numerical form. The second includes data analysis and interpretation processes with different techniques. Data presented in the form of maps, tables and reports or as computer files that can be merged with other "layers"
of information in a Geographic Information System (GIS). The final results can be then applied for the decision-making process.
3.2 Land cover classification system (LCCS)
The FAO LCCS system is considered as a good classification available today, which can be applied to any region of the world regardless of the economic conditions and data source. FAO classification can be considered as a concept based on visual classification, which uses the directly visible and knowledge based components on the ground. The FAO document defines the land according to its contribution to productivity. The main resource controlling primary productivity for terrestrial ecosystems can be defined in terms of land: the area of land available, land quality and the soil moisture characteristics (Di Gregorio and Jansen, 2000). This main resource or the land further explained by its physical appearance as land cover.
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Several studies on land cover mapping and land cover change detection in Egypt and other arid or semi-arid and agricultural productive lands. Lambin and Ehrlich (1997) used ten years of NOAA-AVHRR data to assess and analyze land cover changes in the African continent between 1982 to 1991.
The study showed that continuous unidirectional change process affected less than 4% of Sub-Saharan regions during the study period. Rembold et al (2000) studied land cover changes in lake regions of central/south Ethiopia using aerial photographs dated 1972 and 1994 Landsat TM image. Mendoza and Etter (2002) combined black and white aerial photographs with fieldwork and GIS to monitor land cover changes covering 56 years (1940-1996) in parts of Bogota, Colombia. Palmer and Rooyen (1998) used Landsat TM data to explore the impacts of land management policies on vegetation structure in two study areas in southern Kalahari desert in South Africa in the period from 1989 to 1994. Ram and Kolarkar (1993) studied land use changes in arid areas in India by visual comparison of satellite imagery, maps and aerial photographs. In Egypt, Sadek (1993) used satellite imagery to highlight agricultural boundaries and monitor reclamation process. Lenney et al. (1996) used field calibrated, multi-temporal NDVI features derived from ten Landsat TM images dating from 1984 to 1993 to assess land cover changes in Egypt.
The study showed a high-rate of reclamation in the period from 1986 to 1993.
The objective of the current study is producing recent land cover map for North Sinai following international standardization and definitions of FAO land cover classification system. The land cover database will fit and reflect the need for reliable information that are essential for sustainable management of agricultural land, natural vegetation as biodiversity, fresh water resources, as well as for environmental protection and land use planning and land degradation.
3.3 Methodology
3.3.1 Processing of digital image data
3.3.1.1 Data pre-processing
There are two techniques that can be used to correct the various types of geometric distortion present in digital image data. The first is to model the nature and magnitude of the sources of distortion and use these models to
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establish correction formulae. This technique is effective when the types of distortion are well characterized, such as that caused by earth rotation. The second approach depends upon establishing mathematical relationships between the addresses of pixels in an image and the corresponding coordinates of those points on the ground (via a map or corrected image).
These relationships can be used to correct the image geometry irrespective of the analyst's knowledge of the source and type of distortion. This procedure is the most commonly used and, as a technique, is independent of the platform used for data acquisition and each band of image data has to be corrected (Richards, 1994). In order to analyze the satellite imagery it has to be transformed to a common coordinate System. This process is known as geocooding.
In many cases the image must also be oriented so that the north direction corresponds to the top of the image. In the rectification process the grid of the raw data has to be projected onto a new grid. Resambling is the process of extrapolating data values for the pixels on the new grid from the values of' the source pixels (Lillesand and Kiefer, 1997). In the present study, the digital SPOT 4 data were corrected geometrically for the four complete scenes covering the northern part of Sinai. Geometric correction (GC) is applied to raw images data to transform them to map projection. The rectified images were projected according to the Universal Transverse Mercator System (UTM).This GC used image-to-image registration and Ground Control Points (GCP) system for the four raw scenes.
3.3.1.2 Data processing
Normalized difference vegetation index (NDVI) and principal component analysis (PCA) were applied on all SPOT4 data as a pre-classification step in order to reach the maximum accuracy from classification process. PCA allows redundant data to be compacted into fewer bands. The bands of PCA data are none correlated and independent and are often more interpretable than the source data (Jensen, 1986). According, to Canas and Barnet (1985) in terms of formal mathematical operation, PCA can be characterized by the following stages:
Calculate the variance-covariance matrix for the image data set.
Compute the Eigen values and Eigenvectors of variance covariance matrix (The Eigen vectors define the PC direction and the Eigen values
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measure the variances of the feature space distribution along these new PC axes).
Implement the PCA by forming a weighted sum of the raw images using the eigenvector components as the weighting factors. NDVI is a numerical indicator that uses the visible and near-infrared bands of the electromagnetic spectrum, and is adopted to analyze remote sensing measurements and assess whether the target being observed contains live green vegetation or not.
The NDVI is calculated from reflectance measurements in the red and near infrared (NIR) portion of the spectrum:
NDVI= (NIR-Red) / (NIR+Red) (1) Where NIR is the reflectance of NIR radiation and Red is the reflectance of visible red radiation. The NDVI has been correlated to many variables such as crop nutrient deficiency, final yield in small grains, and long-term water stress. However, rather than exclusively reflecting the effect of one parameter, NDVI has to be considered as a measurement of amalgamated plant growth that reflects various plant growth factors.
3.3.2 Image classification
Multispectral classification is the process of sorting pixels into a finite number of individual classes or categories of data, based on their data file values. If a pixel satisfies a certain set of criteria, the pixel is assigned to the class that corresponds to those criteria (ERDAS, 1997). The computer system must be trained to recognize spatial patterns in the data. Defining, the criteria by which these patterns are recognized are called training (Hord, 1982). Training can be performed with either a supervised or an unsupervised classification. The training process uses the computer to calculate a specific spectral signature on which the classification process will be based. Each signature is supposed to correspond to a class. Using a specific equation (classification algorithm) tests every pixel on the image and assigns it to a specific class.
In unsupervised training, the analyst’s inputs to the computer are some parameters that will be used to uncover statistical patterns inherent in the data.
These patterns do not necessarily correspond to real classes on the ground or any other features in the area represented by the image. They are simply determined mathematically. Some of the produced classes may need to be merged together, while others may need to be deleted. The detailed technical
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steps of the used approach are explained as follows: pre-classification steps including layer stacking. Layer stacking (4 bands multi-spectral + NDVI) in case of large area of vegetation. NDVI when combined with multi-spectral bands shows high capability to classify vegetative and non-vegetative areas while combining multi-spectral bands with principal components 1 and 2 improves the capability to differentiate the different types of bare lands.
Figures (5) and (6) show an example of NDVI image and calculation of principal component analyses (PCA). Layer stacking (4 bands multi-spectral + NDVI + PCA 1, 2) has been used in case of sparse areas of vegetation as shown in figure (7). Using ArcGIS software all land cover classes were re- labeled according to FAO land cover classification system. All land cover maps were produced to be adapted with 1:100,000 digital topographic maps.
The index of the topographic maps covering the study area is shown in figures (8) and (9).
Figure 5: NDVI image showing irrigated herbaceous crops El Arish region.
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Figure 6: Calculation of PCA.
Figure 7: Layer stacking process between multi spectral image, PCA and NDVI.
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Figure 8: The index of topographic maps (cods) covering the study areas in the background are the eleven SPOT images covering the study area.
Figure 9: The index of topographic maps (names) covering the study areas in the Backgrounds are the eleven SPOT images covering the study area.
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3.4 Field observation
Two field trips were carried out for ground check and collect control points, part of these points were used for accuracy assessment process while others were used to increase the accuracy of the classification. A compete database for the ground check points showing full description of each point with a photo is available to assess any future work related to land cover classification in North Sinai. Figure (10) shows the routes of the two field trips and the points that were used for classification and accuracy assessment.
Figure 10: The routes and the check points of the two field trips in North Sinai.
