Assessing and Mapping the Coastal Zone Changes in the Gaza Strip, Palestine, Using GIS and Remote Sensing Techniques

النسخ

1 The Islamic University of Gaza Deanship of Research and Postgraduate Faculty of Science Master of Environmental Sciences Environmental Management and Monitoring الجامعة اإلسالمية بغزة عمادة البحث العلمي والدراسات العليا كلية العلوم ماجستير العلوم البيئية اإلدارة والمراقبة البيئية Assessing and Mapping the Coastal Zone Changes in the Gaza Strip, Palestine, Using GIS and Remote Sensing Techniques تقييم ورسم خ ارئط تغي ارت المنطقة الساحلية في قطاع غزة فلسطين باستخدام تقنيات نظم المعلومات الجغ ارفية واالستشعار عن بعد By Mohammed A. Abd Rabou Supervised by Prof. Dr. Samir A. Afifi Professor of Environmental Sciences Environmental & Earth Science Dept. Faculty of Science Islamic University of Gaza Dr. Mazen T. Abualtayef Associate Prof. of Coastal Engineering Civil & Environmental Engineering Dept. Faculty of Engineering Islamic University of Gaza A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Environmental Sciences November, 2017

2 إق ارر أنا الموقع أدناه مقدم الرسالة التي تحمل العنوان: Assessing and Mapping the Coastal Zone Changes in the Gaza Strip, Palestine, Using GIS and Remote Sensing Techniques تقييم ورسم خ ارئط تغي ارت المنطقة الساحلية في تقنيات نظم المعلومات الجغ ارفية واالستشعار قطاع غزة فلسطين عن بعد باستخدام أقر بأن ما اشتملت عليه هذه الرسالة إنما هو نتاج جهدي الخاص باستثناء ما تمت اإلشارة إليه حيثما ورد وأن هذه الرسالة ككل أو أي جزء منها لم يقدم من قبل تعليمية أو بحثية أخرى. االخرين لنيل درجة أو لقب علمي أو بحثي لدى أي مؤسسة DECLARATION I understand the nature of plagiarism, and I am aware of the University s policy on this. The work provided in this thesis, unless otherwise referenced, is the researcher's own work, and has not been submitted by others elsewhere for any other degree or qualification. Student's name: Signature: Date: اسم الطالب: التوقيع: التاريخ: محمد عبد الفتاح نظمي عبد ربه Mohammed 29/11/2017 I

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4 ABSTRACT Worldwide, coastal zones have become seriously deteriorated because of urbanization and population increase. The Gaza coastal zone is seriously impacted since decades, and as a result, it needs more environmental protection and sustainable development. This study aims at assessing and mapping the changes of land use/land cover (LULC) and shoreline in the Gaza coastal zone using GIS and Remote Sensing techniques. Mapping the bathymetry of the near shore along the Gaza coast based on analysis of Landsat-8 imagery is targeted as well. The current study is based on three axes: the first axis included the detection of LULC changes from SPOT-5, Landsat and QuickBird for the years 2004, 2009 and Satellite images were categorized into 6 classes using the maximum likelihood supervised classification in order to detect the LULC changes along the Gaza coastal zone, using the ERDAS. The results showed that the Gaza coastal zone has changed significantly. The built-up area has increased by 3.62 km 2, the agricultural land increased by km 2 and the area of bare land/sand has also shrunk by km 2. In the second axis, the analysis was carried out using image processing technique and GIS to detect the shoreline changes of Gaza coastal zone. The variation in the shoreline along the Gaza coastal zone was determined by analyzing satellite images from 1972 to About 62.8% of the shoreline is estimated to be eroding over the period of 42 years. The northern zone was significantly eroded with an erosion rate of m 2 in period which represents the highest rate of erosion. There was a growing shoreline in the south of Gaza fishing harbor because of sediments transport, with an estimated m 2 have been added to the shoreline area in the period , which represents the highest rate of accretion. The third axis involved an attempt to determine the bathymetry mapping of the near-shore in Gaza coastal zone by applying the ratio transform algorithm on the newly acquired multispectral image. The results pointed out that the ratio transform algorithm can retrieve the depth up from -25m to -30m for Landsat-8 imagery. There was a good correlation coefficient between the estimated depth from algorithm and endorsed depth. Finally, the current study emphasized the importance of GIS and remote sensing techniques in providing time-series information regarding the coastal zone management issues of the Gaza Strip. This, in turn, can offer valuable data for decision-makers and planners to manage the Gaza coastal zone in a sustainable fashion. Keywords: Coastal zone; Gaza; GIS; remote sensing; land use; shoreline; bathymetric. III

5 ملخص البحث عالميا تدهورت المناطق الساحلية تدهو ار خطي ار بسبب التحضر وزيادة السكان. في قطاع غزة تأثرت المنطقة الساحلية تأث ار خطي ار منذ عقود ونتيجة لذلك فإنها تحتاج إلى المزيد من الحماية البيئية والتنمية المستدامة. تهدف هذه الد ارسة إلى تقييم ورسم خ ارئط التغي ارت في استخدام األ ارضي/الغطاء األرضي الساحلية لقطاع غزة باستخدام تقنيات نظم المعلومات الجغ ارفية واالستشعار عن بعد وتهدف األعماق على الشاطئ القريب على طول ساحل قطاع والخط الساحلي في المنطقة رسم إلى أيضا تستند الد ارسة الحالية إلى ثالثة محاور: المحور األول يشمل الكشف عن تغي ارت استخدامات األ ارضي من صور األقمار الصناعية األقمار الصناعية إلى سبوت- 5 الندسات و كويك بيرد للسنوات.4002 و 4002 و م 4 فئات باستخدام التصنيف الم ارقب تم تصنيف صور من أجل الكشف عن التغيي ارت استخدام األ ارضي/الغطاء األرضي على طول المنطقة الساحلية في قطاع غزة وذلك باستخدام إيرداس. وأظهرت النتائج أن المنطقة الساحلية في غزة قد تغيرت بشكل ملحوظ. وازدادت المساحة 2 00 الحضرية بمقدار 2.24 كم 4 و ازدت مساحة األ ارضي الز ارعية بمقدار كم 4 كما تقلصت مساحة األ ارضي الرملية بمقدار كم. 4 في المحور الثاني تم إج ارء التحليل باستخدام تقنية معالجة الصور ونظم المعلومات الجغ ارفية للكشف عن تغي ارت الشاطئ في المنطقة الساحلية لقطاع غزة. تم تحديد التباين في الخط الساحلي على طول المنطقة الساحلية في قطاع غزة من خالل تحليل صور األقمار الصناعية من 0294 إلى قدرت الد ارسة تآكل حوالي 24.8 من الشاطئ خالل فترة 24 عاما. تآكلت المنطقة الشمالية من قطاع غزة بشكل كبير بمعدل قدره في الفترة وهو ما يمثل أعلى معدل للتآكل. بالمقابل ازداد الترسيب على الخط ا سل احلي الواقع جنوب مرفأ الصيد في غزة بسبب نقل الرواسب حيث أضيف ما يقدر ب الشاطئ في الفترة وهو ما يمثل أعلى معدل ت اركم. إلى منطقة م 4 شمل المحور الثالث محاولة لتحديد لرسم خ ارئط قياس األعماق للشاطئ القريب في المنطقة الساحلية لقطاع غزة من خالل تطبيق خوارزمية تحويل النسبة على الصورة متعددة األطياف المكتسبة حديثا. وأشارت النتائج إلى أن غزة استنادا إلى تحليل صور الندسات -8. خوارزمية تحويل النسبة يمكن أن تسترد العمق من -45 متر إلى -20 معامل ارتباط جيد بين العمق المقدر من الخوارزمية والعمق المعتمد. ا متر لصور الندسات- 8. بينت النتائج أخي ار شددت الد ارسة الحالية على أهمية نظم المعلومات الجغ ارفية وتقنيات االستشعار عن بعد في توفير معلومات زمنية فيما يتعلق بقضايا إدارة المناطق الساحلية في قطاع غزة. وهذا بدوره يمكن أن يوفر بيانات قيمة لصانعي الق ارر والمخططين إلدارة المنطقة الساحلية في غزة بطريقة مستدامة. كلمات مفتاحية: المنطقة الساحلية غزة نظم المعلومات الجغ ارفية االستشعار عن بعد استعماالت األ ارضي/ الغطاء األرضي خط الشاطئ قياس األعماق البحرية. IV

6 EPIGRAPPAGE و ال ت ف س د وا ف ي األ ر ض ب ع د إ ص ال ح ه ا ذ ل ك م خ ي ر ل ك م إ ن ك نت م م ؤ م ن ي ن [األعراف : 58 [ أ ل م ت ر و ا أ ن الل ه س خ ر ل ك م م ا ف ي الس م او ات و م ا ف ي األ ر ض و أ س ب غ ع ل ي ك م ن ع م ه ظ اه ر ة و ب اط ن ة و م ن الن اس م ن ي ج اد ل ف ي الل ه ب غ ي ر ع ل م و ال ه د ى و ال ك ت اب م ن ي ر [لقمان: [ 02 V

7 DEDICATION I wish to dedicate this work to my father and mother who have supported me since the beginning of my life. Also, I dedicate the work to my lovely wife Kefah, my daughters Leen and Mariam, and my brothers and sisters who have been a capital source of motivation and inspiration. Finally, I dedicate this modest study to my family, friends, colleagues and all those who provided me with their support throughout the succeeding stages of this study. Mohammed VI

8 ACKNOWLEDGMENT First of all, all thanks and appreciations go to Allah for His unlimited blessings and for giving me the strength to complete this study. I would like to express my deepest gratitude to my supervisors Prof. Dr. Samir A. Afifi, Professor of Environmental Sciences and Dr. Mazen T. Abualtayef, Associate Professor of Coastal Engineering for their considerable guidance, advice, and assistance during this research work. I would like to express my sincere thanks to my father Dr. Abdel Fattah N. Abd Rabou Associate Professor of Environmental Sciences for his continuous guidance and fellow up throughout the course of my work. Furthermore, I would like to thank the Discussion and Judgment Committee members, Dr. Mohammed S. El-Eila Assistant Professor of Sustainable Development Planning and Dr. Ziad H. Abu Heen Assistant Professor of Geophysics, who accepted to examine and judge this thesis. I would like to extend my thanks to the Staff of Environmental and Rural Research Center at IUG for their kind assistance during my research work. Finally, I am very grateful to all people who helped me throughout the succeeding stages of my study. VII

9 TABLE OF CONTENTS DECLARATION... I ABSTRACT... III... IV ملخص البحث EPIGRAPPAGE... V DEDICATION... VI ACKNOWLEDGMENT... VII TABLE OF CONTENTS... VIII LIST OF TABLES... XI LIST OF FIGURES... XII LIST OF ABBREVIATIONS... XIV CHAPTER 1: INTRODUCTION Background Research Problem Research Aim and Objectives Significance Research Justifications Methodology Research Structure... 6 CHAPTER 2: LITERATURE REVIEW Scope Coastal Zone Changes Land Use and Land Cover (LULC) Definitions Mapping and Detection of LULC Changes Shoreline Definitions Mapping and Detection of Shoreline Changes Bathymetric Mapping Definition Bathymetric Mapping Techniques and their Importance Importance of Change Detection VIII

10 2.7 Geographic Information Systems Remote Sensing Image Classification Techniques in Remote Sensing Unsupervised Classification Supervised Classification Previous Studies: Global and Regional Previous Studies: Local CHAPTER 3: STUDY AREA DESCRIPTION Location and Geography General Description of Study Area Population Soil Topography and Geology Climate and Meteorological Conditions Rainfall Temperature Wind Speed Solar Radiation Physical Conditions of Gaza Coastal Zone Coast and Seabed Characteristics Bathymetry Coastal Geology Waves, Winds and Currents CHAPTER 4: MATERIALS AND METHODS Introduction Methodology Framework Tools Used Data Collection and Acquisition Image Pre-Processing Geometric Correction Radiometric Correction Image Enhancement and Visual Interpretation Image Classification Land use /Land cover Mapping IX

11 4.7.1 Development of Classification Scheme Supervised Classification Shoreline Mapping Shoreline Extraction Change Detection Analysis Bathymetric Mapping Water Separation Ratio Transform Algorithm Linear Regression Analysis Bathymetric Extraction Model Bathymetry Accuracy Assessment CHAPTER 5: RESULTS AND DISCUSSION Land use/land cover Classifications Discussion of the Result of LULC Classification Shoreline Changes Change Analysis of Shoreline Rate of Change of Shoreline in Gaza coastal zone Bathymetric Mapping Bathymetry Accuracy Assessment CHAPTER 6: CONCLUSION AND RECOMMENDATIONS Conclusion Recommendations REFERENCE APPENDIX X

12 LIST OF TABLES Table (4.1): Satellite images characteristics and source Table (4.2): Land use/land cover classification scheme Table (4.3): Part of list values of band blue and green to determine m1 and m Table (5.1): LULC for the Gaza coastal zone as extracted from the satellite data Table (5.2): Accretion and erosion rates for the study area Table (5.3): Rates of accretion and erosion for Zone (A) Table (5.4): Rates of accretion and erosion for Zone (B) Table (5.5): Rates of accretion and erosion for Zone (C) Table (5.6): Rates of accretion and erosion for Zone (D) Table (5.7): Rates of accretion and erosion for Zone (E) Table (5.8): Rates of accretion and erosion for Zone (F) Table (5.9): Rates of accretion and erosion for Zone (G) Table (5.10): Rates of accretion and erosion for Zone (H) XI

13 LIST OF FIGURES Figure (1.1): Methodology flowchart... 6 Figure (2.1): Concept of classification of remotely sensed data Figure (2.2): Schematic of the unsupervised classification procedure Figure (2.3): Scheme of supervised classification Figure (3.1): Gaza Strip location Figure (3.2): Map of the Gaza coastal zone, Palestine Figure (3.3): Population growth of Gaza Strip (PCBS, 2016) Figure (4.1): Research flowchart Figure (4.2): Radiometric correction (noise effects) of Landsat-5 satellite image using ERDAS Figure (4.3): Image enhancement "before and after" for SPOT-5 satellite image using ERDAS in year Figure (4.4): Steps for supervised classification Figure (4.5): Supervised classification of satellite images Figure (4.6): Supervised classification tool in ERDAS used signature file of spectral signatures Figure (4.7): Convert classified image from raster to vector Figure (4.8): Production maps phase by ArcGIS software Figure (4.9): LULC maps for years 2004, 2009 and Figure (4.10): Extraction of shorelines by supervised classification Figure (4.11): Convert polygon feature class to line feature class Figure (4.12): Line editing to extraction of shoreline Figure (4.13): Shoreline from 1972 to 2014 in Gaza Fishing harbor Figure (4.14): Convert two shoreline feature to polygon Figure (4.15): Change Detection in areas between years Figure (4.16): Landsat 8 satellite image of Gaza Strip dated 17 th June Figure (4.17): Calculation of NDWI in ArcGIS Figure (4.18): NDWI Map in study area Figure (4.19): Correlation between pixel values and hydrographic chart values Figure (4.20): Bathymetric extraction Model Figure (4.21): Result of analysis the bathymetric modelling Figure (5.1): Map of Land use Land Cover in Gaza Coastal Zone Figure (5.2): Map of Land use Land Cover in Gaza Coastal Zone Figure (5.3): Map of Land use Land Cover in Gaza Coastal Zone Figure (5.4): The LULC areas of the 6 classes derived from the classified maps, Figure (5.5): Examples of verification on classification process in map Figure (5.6): Changes in Built-up areas for years 2004, 2009 and Figure (5.7): Gaza shoreline change from 1972 to Figure (5.8): Rate of change area in North Governorate Figure (5.9): Groins which was built in in North Governorate Figure (5.10): Rate of change area in Northern Gaza fishing harbor Figure (5.11): Aerial photo showing the erosion areas in the northern Gaza fishing harbor zone Figure (5.12): Rate of change area in Southern Gaza fishing harbor Figure (5.13): Accretion rate in southern Gaza fishing harbor XII

14 Figure (5.14): Aerial photo showing the accretion sediments in the southern of Gaza fishing harbor zone Figure (5.15): Rate of change area in Sheikh Ajlin Area Figure (5.16): Rate of change area in Wadi Gaza Figure (5.17): Rate of change area in Middle Governorate Figure (5.18): Rate of change area in Khan Younis Governorate Figure (5.19): Rate of change area in Rafah Governorate Figure (5.20): Egyptian interventions to build groin in Sinai Figure (5.21): Rate of change in period Figure (5.22): Rate of change in period Figure (5.23): Rate of change in period Figure (5.24): Map of calculated bathymetry of the study area Figure (5.25): Map of measured bathymetry of Gaza Sea (Dutch project, 1994) Figure (5.26): Correlation between the endorsed and the calculated data XIII

15 LIST OF ABBREVIATIONS AOI Areas of Interest CCRS Canadian Centre for Remote Sensing CGIS Canada Geographic Information System DN Digital Number DSAS Digital Shoreline Analysis System EQA Environment Quality Authority ERRC Environmental and Rural Research Center ESRI Environmental Systems Research Institute ETM+ Enhanced Thematic Mapper Plus FAO Food and Agriculture Organization FCC False Color Composites GIS Geographic Information System GPS Global Positioning System ICZM Integrated Coastal Zone Management IUG Islamic University of Gaza LiDAR Light Detection and Ranging LULC Land use Land Cover MEnA Ministry of Environmental Affairs MOP Ministry of Planning MOPIC Ministry of Planning and International Cooperation MSL Mean Sea Level MSS Multispectral Scanner System NDVI Normalized Difference Vegetation Index NDWI Normalized Differential Water Index NIR Near Infrared NOAA The National Oceanic and Atmospheric Administration PCBS Palestinian Bureau Statistics Council R 2 R-squared RS Remote Sensing SWIR Shortwave Infrared TM Thematic Mapper TOA Top of Atmosphere USGS United States Geological Survey UTM Universal Transverse Mercator WGS84 World Geodetic System 1984 XIV

16 CHAPTER 1: INTRODUCTION Chapter (1) Introduction 1

17 CHAPTER 1 INTRODUCTION 1.1 Background Sustainable development of coastal zones is of local, regional and global concern nowadays. Anthropogenic activities including agriculture, construction, roads, metals and many others have important impacts in changing the landscape of earth. The changes occurring to coastal zones in particular in the different nations have a direct impact on planning, development operations and land management. Thus, the detection and mapping of these changes in the coastal zones using geographic information systems (GIS) and remote sensing techniques are important task in environmental monitoring. Accurate and updated information on land use and land cover (LULC) and shoreline changes can help planners, specialists and researchers in a large scale of coastal and marine environments e.g. the erosion-accretion patterns, distribution of LULC, design of coastal constructions, prediction of shoreline future positions, and coastal protection etc. (Louati, Saïdi, & Zargouni, 2015; Maiti & Bhattacharya, 2009). The development of GIS/remote sensing techniques is rapidly spreading in the recent years in order to monitor changes occurring to coastal and marine habitats. The coastal and marine environment of the Gaza Strip is subject to serious environmental problems and threats (Abd Rabou, 2013; Abd Rabou et al., 2007; MEnA, 2001). The coastal zone of the Gaza Strip is so important from different aspects including the marine ecology, fishery, land resources, transport, recreation and tourist. The current data concerning the coastal and marine environmental issues of the Gaza Strip are scattered throughout different local ministries, authorities, projects, and donor organizations. Thus, this sector is indeed needs for cooperation, support actions and coordination between the different agencies or organizations locally and internationally (MEnA, 2001). Today, the knowledge on the distribution of LULC, shoreline changes and bathymetric mapping in the Gaza Sea is still comparatively fragmented. In order to enable 2

18 sustainable coastal zone management, there is a need to monitor the changes on the spatiotemporal of marine and coastal environment in Gaza Strip. Accordingly, the mapping of LULC, shorelines and bathymetry are very important because it can provide basic data needed for local sustainable development and coastal management policies. Moreover, it can give good opportunities for the creation of suitable hydrodynamic models. Therefore, this study tries to produce maps to assess the changes related to LULC, shoreline and bathymetry of the coastal environment of the Gaza Strip for the periods enclosed between 1972 and 2016 using GIS/ remote sensing techniques. 1.2 Research Problem The coastal and marine environments of the Gaza Strip are facing many serious challenges that can be summarized as follows: mismanagement of coastal and marine ecosystems in a sustainable fashion, the increase demand on lands, pollution and degradation of coastal and marine habitats, overexploitation of fisheries resources using limited fishing areas and old fishing fleet, establishment of various developmental projects like ports, desalination plants, power stations, building constructions and many others (Abd Rabou, 2013; Abd Rabou et al., 2007; MEnA, 2001). The weakness of environmental and integrated planning measures and the fragility of the Gaza general ecosystem promoted the severity deterioration in both the structure and function of such coastal and marine ecosystems of the Gaza Strip. Thus, the need for accurate mapping and monitoring of Gaza coastal zone is increasing day by day in order to achieve careful sustainable planning and management. In the light of these facts, this study comes to support decision-makers policies with accurate spatial data and to avoid decisions relying upon wrong evaluations or arbitrary information. Really, there is a need to an up-to-date data to effectively manage the coastal zone of the Gaza Strip. This need comes as a reflection of the following points: There is a weakness or lack of the geospatial data regarding the coastal and marine environments in the Gaza Strip. There is a weakness or lack of effective and regular monitoring and assessment programs for Gaza coastal zone. 3

19 There are few published works investigating or dealing with the problems facing the coastal zone of the Gaza Strip. There are real changes targeting the shoreline and LULC in the Gaza coastal zone, which need continuous monitoring and follow-up. The cost for the conduction of bathymetric surveys is very high compared to free satellite images. There is a lack of remote sensing staffs and specialists working on coastal monitoring fields. 1.3 Research Aim and Objectives The main objective of the current study is to conduct an assessment and mapping of the changes occurred to the coastal zone of the Gaza Strip based on satellite imagery using GIS/remote sensing techniques. To achieve this goal, the following objectives are to be considered: To detect and monitor LULC changes by producing maps for the years of 2004, 2009 and To detect the shoreline changes in the Gaza coastal zone using satellite images and aerial photos from 1972 to To extract the bathymetric maps of the coastal habitats of the Gaza Strip depending on Landsat satellite images using multi-spectral imagery and GIS modeling. 1.4 Significance The importance of the current study comes from the fact that it will represent a baseline and a geo-database for the Gaza coastal zone that can benefit both planners and decision-makers for future coastal and marine developmental projects. The GIS/remote sensing techniques are good and effective tools having the ability to cover large scale areas in order to monitor temporal and spatial changes of the marine and coastal environments compared to the traditional tools that lack the required updating. 4

20 The use of satellite imagery is a cost-effective in the sense that it is relatively cheap and sometimes free of charge such as Landsat images. 1.5 Research Justifications The coastal zone of the Gaza Strip needs more attention and research, because it is an environmentally sensitive area. There are continuous changes occurring to the Gaza coastal zone in terms of shoreline and LULC because of the natural dynamic changes and the anthropogenic activities. The reasons standing behind the conduction of this study are as follows: Lack of Geodatabase up-to-date regarding the Gaza coastal zone including the information of the bathymetry of the local marine environment. Change detection for coastal zone requires a more powerful system like GIS/remote sensing techniques which can provide a better way to effective mapping and monitoring. GIS/remote sensing systems provide analytical tools and modeling processes that can be used in environmental monitoring actions, mapping and extraction of new information. This research will help for better planning of the coastal zone and future development projects. It is considered as a baseline for any development projects. 1.6 Methodology The methodology procedure in this research involves five phases (Figure (1.1)). Phases one and two consist of search concept and data acquisition. Phase three includes the practical part of data analysis, image classifications and GIS-Modeling for this study. Phase four includes the production of maps showing change detection. The findings of the current study and their analysis are completed in phase five. 5

21 Literature Review Data Collection & Preparation Data Analysis & Modeling Results & Recommendation Change Detection & Production Maps LULC Mapping Shoreline Mapping Bathymetric Mapping 1.7 Research Structure Figure (1.1): Methodology flowchart The current thesis is comprised of six chapters as follows: Chapter One (Introduction): It includes a general overview followed by research problem, aims and objectives, methodology tracked in order to achieve the objectives and the thesis outline. Chapter Two (Literature Review): It introduces a general literature review on coastal zone changes, LULC, shoreline changes, bathymetric mapping and change detection using GIS/remote sensing techniques. Chapter Three (Study Area Description): It gives an overview of the study area which is the Gaza coastal zone regarding its geography, population, topography, geology, climate and meteorological conditions and physical conditions. Chapter Four (Material and Methods): It highlights the processes of data collection and acquisition, image and change detection analysis of LULC, shoreline, bathymetry. Chapter Five (Results and Discussion): It presents and discusses the results gained throughout the course of the study. Chapter Six (Conclusion and Recommendations): It draws the main conclusions and recommendations of the study. 6

22 CHAPTER 2: LITERATURE REVIEW Chapter (2) Literature Review 7

23 CHAPTER 2 LITERATURE REVIEW 2.1 Scope Remotely sensed images are commonly used in analyzing the spatial and temporal patterns of environmental variables over past decades due to the dynamic changes occurring to the coastal areas worldwide (Jianya, Haigang, Guorui, & Qiming, 2008). This chapter reviews the literature concerning the use of GIS and remote sensing in monitoring and mapping the coastal areas such as the detection of shoreline, LULC and bathymetric mapping. Special concern was made on the literature regarding the coastal zone of the Gaza Strip. 2.2 Coastal Zone Changes The coastal zone is defined as the interface where the land meets the ocean, encompassing shoreline environments as well as adjacent coastal waters. Its components can include river deltas, coastal plains, wetlands, beaches and dunes, reefs, mangrove forests, lagoons, other coastal features (Post, Lundin, & Mundial, 1996). The importance of coastal zones comes from the fact that they are very productive and having a wealth of mixed species and genetically diversified habitats. They have the ability to regulate climate and global ecosystems (Nemani & Running, 1996). Moreover, coastal zones are among the most heavily exploited areas because of their rich resources (Post et al., 1996). As a result, these zones are most vulnerable for land use changes due to rapid industrialization and urbanization. The changes may include coastal erosion, sediment transport, environmental pollution and coastal development. Coastal changes may result in serious impacts such as loss of livelihood and property, alterations of the socio-economic status of coastal environments and decrease or degradation of coastal resources. Therefore, it is necessary to evaluate LULC and shoreline changes to develop efficient management and environmental protection strategies (Rasuly, Naghdifar, & Rasoli, 2010). It is well known that the status of coastal and marine ecosystems is reflected on the health and well-being of the coastal human populations and their survival. So, the good 8

24 management of this complexity implies good cooperation strategies between governmental and societal levels (Sharma, 2009). The concept of the integrated coastal zone management (ICZM) has emerged over the last 30 years due to the irreversible and synergistic nature of the impact of such development processes on coastal zones, where about 180 nations are located along coasts (Zviely & Klein, 2003). 2.3 Land Use and Land Cover (LULC) Definitions Land is a crucial natural resource, since life and developmental activities are based on it. Land use and land cover (LULC) stimulates the understanding of the interactions of human activities with the environment. So, it is very important to be able to simulate changes. According to Meyer (1995), every parcel of land on the Earth s surface is unique in the cover it possesses. Land could be used in grazing, agriculture, urban development, logging, and mining among many others. The term "land cover" is referred to the type and state of vegetation like croplands, forests, wetlands, pastures, roads, in addition to other things like human structures, soil type, biodiversity, surface, ground water etc. For example, "recreation area" is a land use term that may be applicable to different land cover types such as sandy surfaces, beaches, parks, woodlands etc. (di Greggio & Jansen, 2000) Mapping and Detection of LULC Changes Land use affects land cover and changes in land cover affect land use. The changes of LULC come as a result of the complex relations applied at different temporal and spatial scales (Reid et al., 2000). Change detection of LULC is necessary in order to update LULC maps and the management of natural resource. Many shifting land use patterns can result in land cover changes that affect biodiversity, water resources and other processes that come together to affect climate and ecosphere (Riebsame, Meyer, & Turner II, 1994). It is worth mentioning that land cover can be changed by human activities and natural changes that include weather, flooding, fire, climate fluctuations, ecosystem dynamics etc. 9

25 There are also incidental impacts on land cover coming from environmental pollution like acid rains and tropospheric ozone depletion (Meyer, 1995). To use land optimally, it is necessary to have the information on existing LULC and the capability to monitor the dynamics of land use resulting out of both human and natural actions that shape the landscape. According to Olorunfemi (1983), monitoring changes and time series analysis is quite difficult with traditional methods of surveying. In recent years, satellite remote sensing techniques have been developed, which have proved to be of immense value for preparing accurate LULC maps and monitoring changes at regular intervals of time. In case of inaccessible region, this technique is perhaps the only method of obtaining the required data on a cost and time effective basis. Satellite remote-sensing techniques have been widely used in detecting and monitoring land cover change at various scales with useful results. This is due to their potential of providing accurate and timely geospatial information describing changes in urban land cover (Xiao et al., 2006). The use of remote sensing technologies to develop LULC classification mapping is a useful and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region (Selçuk et al., 2003). In fact, the evolution in technology of remote sensing has caused it to become one of the most commonly used techniques in the world. Recently, remote sensing has been used in combination with geographical information systems (GIS) and global positioning systems (GPS) to assess land cover change more effectively than by remote-sensing data only (Muller & Zeller, 2002). 2.4 Shoreline Definitions The two terms "shoreline" and "coastline" are commonly defined as the physical interface of land and water (Dolan, Hayden, May, & May, 1980). They are one of the most important linear features on the earth s surface, which have a dynamic nature (Winarso, Judijanto, & Budhiman, 2001). The shoreline is a time-dependent phenomenon that may exhibit substantial variability (Morton, 1991), and this needs to be considered when acting on a single shoreline position (Boak & Turner, 2005). 10

26 2.4.2 Mapping and Detection of Shoreline Changes Shoreline change is an issue of concern in coastal management. The shoreline position changes in a continuous fashion through time (hours, days, months and years), because of the physical processes of sediment movement in the littoral zone and the dynamic nature of water levels at the coastal boundary because of waves, tides, groundwater, storm surge, setup, run-up etc. In addition to the physical processes, the coastal shorelines are changing rapidly as a result of human activities as well. These human activities are catalysts causing disequilibrium conditions that accelerate changes. Coastal maps are commonly needed to understand the driving factors and to provide relevant information for coastal resource management and environmental protection and for planning sustainable development of coastal areas. Finally, Coastline changes have resulted in over 70% of the world s beaches experiencing coastal erosion (Anthony, 2005) Accurate mapping of the instantaneous coastline positions are commonly associated with significant uncertainty (Crowell & Leatherman, 1999). The science of coastline mapping has changed over the past 70 years due to advances in technology and the need to reduce uncertainty (Chen, 1998). Remote sensing data may be effectively used to monitor the changes along the coastal zone including shoreline with reasonable accuracy. Remote sensing data helps and replaces the conventional survey by its repetitive and less cost-effectiveness (Thieler, Himmelstoss, Zichichi, & Ergul, 2009). Generally, two main techniques can be used to extract the coastline from satellite image: The first is the on screen digitizing method which is based on experience, expertise and specialist skills. The second is the automatic method which is more dependent on computer. It is based on different electromagnetic waves reflections in both of water and land based. A third method called semi-automatic can be added to the previous methods, where it tries to exploit benefits both of two ways. It is well known that image processing on satellite remote sensing data can provide a suitable tool for updating coastal maps over large areas at relatively low costs (Cracknell, 1999; Nayak, 2000). Additionally, space images can provide suitable temporal sampling for studying the highly dynamic coastline shapes. 11

27 2.5 Bathymetric Mapping Definition According to The National Oceanic and Atmospheric Administration (NOAA), the Bathymetry or submarine topography is a branch of the oceanography dealing with the study of the beds or floors of water bodies, including the ocean, rivers, streams, and lakes. It includes the shapes and depths of the underwater terrain. Bathymetric maps illustrate the land that lies underwater. In the same context, hydrography as a science includes the bathymetry and the study of the shape and features of the shoreline; the characteristics of tides, currents, and waves; and the physical and chemical properties of the water itself Bathymetric Mapping Techniques and their Importance With developments over the last 50 years, techniques for mapping bathymetry have changed to take advantage of acoustic and visual technology, as well as the improved processing rate of computers. However, no matter the technologic advancements, understanding of fluvial and glacial landforms is useful to create accurate bathymetric mapping (Wilke, 2007). Bathymetric data are so useful as follows: - Global bathymetry is a prerequisite for mapping the oceans and for understanding how the earth s global systems interact. - Nautical charts (navigation products) are based on data achieved by bathymetric surveys. These charts facilitate safe and efficient maritime transportation. - Bathymetric maps give good data concerning the effects of climate change on the environment. Additionally, the detailed knowledge of the shape of the ocean basins, ridges and mountains influences the flow of sea water carrying heat, salt, nutrients, and pollutants - Bathymetric surveys can explore maritime issues like beach erosion, sea-level rise, and subsidence (land sinking). - Bathymetric data are so crucial in the sense that they create hydrodynamic models (USGS, 2016). 12

28 - Bathymetry introduces important data on the depth and characteristics of the seabed that can define the ecological habitats of benthic (bottom-dwelling) organisms. - High-resolution bathymetry helps in determining the kind of food, microhabitats and breeding sites of marine fauna. - The accurate and affordable bathymetric mapping in coastal zones is so valuable for fishery managers and marine conservationists in developing nations. The advantages of bathymetric mapping can be summarized in its low cost, its wide availability of data: IKONOS, QuickBird, Landsat, etc., its large spatial coverage and its high spatial resolution. This mapping can derive water depth up to 25 m, depending on water turbidity and atmospheric conditions. On the other hand, the disadvantages of bathymetric mapping can be summarized its relatively low accuracy & reliability (Olsen & Olsen, 2007). 2.6 Importance of Change Detection Change detection is the process of identification of the differences in the state of an object or occurrence by observing it at different periods (Singh, 1989). According to Huang and Hsiao (2000), change detection can be expressed by the comparison of multi temporal images of the same geographical area. This is achieved by the use image-handling techniques to analyze the changed areas of the landscape over different times. Change detection is helpful in land use analysis, agriculture monitoring, deforestation estimation, and assessment of natural disasters or catastrophes (Inglis-Smith, 2006). Aspects of change detection include the detection of changes that have occurred, identification of the nature of change, and measurement of the size of the change occurred (Macleod & Congalton, 1998). 2.7 Geographic Information Systems The geographic information system (GIS) is a computer system capable of assembling, storing, manipulating, and displaying geographically referenced information. According to Dueker (1979), the GIS is a special case of information systems where 13

29 the database consists of observations on spatially distributed features, activities or events, which are definable in space as points, lines, or areas. The concept of the geographic information system emerged during the 1960 s and 1970 s as new trend to produce maps in order to be used for resource assessment, land evaluation, planning and environmental monitoring. Geographic information systems consist of three main items: computer hardware, sets of application of software modules, and a proper organization context (Burrough, 1986). GIS is beneficial for organizations of all sizes and in almost every industry as there is an escalating interest and awareness of the economic and strategic values of GIS. 2.8 Remote Sensing The remote sensing is the science or art of obtaining information about objects or areas from a distance, typically from aircraft or satellites depending on the energy reflected from Earth. In modern usage, the term generally refers to the use of aerial sensor technologies to detect and classify objects on Earth both on the surface, in the atmosphere and oceans by means of propagated signals e.g. electromagnetic radiation emitted from aircraft or satellites (Aggarwal, 2003). Remote sensing imagery has many applications in mapping LULC, agriculture, soils mapping, forestry, city planning, archaeological investigations, and bathymetric surveying, shoreline changes, deforestation, vegetation dynamics, water quality dynamics, urban growth, etc. 2.9 Image Classification Techniques in Remote Sensing Remotely sensed image are classified in order to specify corresponding levels which called "class" or "themes" with respect to groups with homogeneous characteristic, with the aim of discriminating multiple objects from each other within an image (Lillesand & Kiefer, 1994). Here, digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands (Figure (2.1)). Analysis by humans uses the elements of visual interpretation to identify homogeneous groups of pixels which represent various features or land cover classes of interest (Lillesand & Kiefer, 1994). 14

30 Figure (2.1): Concept of classification of remotely sensed data (Hashimoto, Takagi, & Kajiware, 1999) Two main image classification techniques in Remote Sensing are commonly known: Unsupervised and supervised image classification Unsupervised Classification In unsupervised classification, spectral classes are grouped first, based on the numerical information in the data, and are then matched by the analyst to information classes (if possible). Programs, called clustering algorithms, are used to determine the natural (statistical) groupings or structures in the data. Pixels are grouped based on the reflectance properties of pixels, which are called clusters. In this regard, the image classification software generates clusters. There are different image clustering algorithms such as K-means and ISODATA. The user merges clusters into a land cover type as shown in Figure (2.2). The unsupervised classification technique is commonly used when no sample sites exist. Unsupervised classification is not completely without human intervention. However, it does not start with a pre-determined set of classes as in a supervised classification (J. R. Eastman, Jin, Keym, & Toledano, 1995). 15

31 Figure (2.2): Schematic of the unsupervised classification procedure (Source: CCRS/CCT, 1998) Supervised Classification In supervised classification, the analyst identifies in the imagery homogeneous representative samples of the different surface cover types information classes (e.g. land cover type) of interest. The analyst selects representative samples for each land cover class in the digital image. These sample land cover classes are called training sites as shown in Figure (2.3). The selection of appropriate training areas is based on the analyst's familiarity with the geographical area and their knowledge of the actual surface cover types present in the image. The image classification software uses the training sites to identify the land cover classes in the entire image. Thus, in a supervised classification technique are first identifying the information classes which are then used to determine the spectral classes which represent them (Blaschke, 2010; J. R. Eastman et al., 1995; Weih Jr & Riggan Jr, 2010). 16

32 Figure (2.3): Scheme of supervised classification (Source: T. Lillesand et al., 1987) 2.10 Previous Studies: Global and Regional In Indonesia, Prasita (2015) used GIS to determine shoreline changes from 2002 to 2014 in the mangrove conservation areas of Pamurbaya. He found that the shoreline in the specified study area have changed during the period covered. Such results are so important for the government in adopting management strategies for coastal areas. In India, Jagalingam, Akshaya, and Hegde (2015) carried out a study aiming at determining the bathymetry mapping of the southwest coast of India using the ratio transform algorithm on the blue and green bands of Landsat 8 satellite imagery. The application of blue and green band in the southwest coast of India is that radiance in the blue band ( nm) decreases more rapidly with depth than radiance in the green band ( nm). In Bangladesh, Dewan and Yamaguchi (2009) used remote sensing/gis techniques to detect and monitor LULC change in Dhaka Metropolitan of Bangladesh during During this study, derived LULC maps were further validated using high resolution images such as SPOT, IRS, IKONOS and field data. The results indicated an urban expansion of Dhaka Metropolitan which led to considerable reduction of wetland areas, cultivated lands, vegetation and water bodies. The amount of urban land increased from 11% (in 1960) to 344% in

33 In the Kurdistan Region of Iraq, Mustafa, Ali, and Saleh (2012) used remote sensing and GIS techniques to monitor and evaluate land cover change in Duhok City. Three satellite datasets were used to detect and evaluate Duhok s urban expansion. The LULC maps of the years 1989, 2001 and 2012 were produced and the changes were determined with significant accuracies. The results showed that the Duhok city has changed significantly, where the urban area has increased more than 19 km 2 from 1989 to Moreover, 3.1% of wetland areas changed to agricultural land. Benny and Dawson (1983) using a satellite imagery as an aid to bathymetric charting in the Red Sea. This study describes a relatively simple method for obtaining depth contours. Data used in this research were Landsat MSS Band 4 and 5. The techniques, described by others, normally make use of local information, such as the attenuation coefficient of light in sea water, obtained at the region being examined. For regions which have not been extensively surveyed but for which a small amount of depth information has been obtained by conventional means, these contours can provide a useful supplement, providing information economically over a wide area. The technique is applied here to a region at the northern end of the Red Sea. In Egypt, Masria et al. (2015) used remote sensing analysis to detect shoreline and land cover changes around Rosetta Promontory; an area subject to continuous erosion crisis. The dramatic retreatment observed during the last century could be attributed to the construction of Aswan High Dam in 1964, which reduced the flow and sediment discharges. Four radio-metrically and geometrically corrected Landsat images covering the period from 1984 to 2014 were used. In another Egyptian study, Esmail, Masria, and Negm (2016) used remote sensing/gis techniques to monitor LULC changes along the north part of the Nile delta coastal zone specifically at Damietta promontory, Egypt in order to help decision makers to re-plan the use of natural resources efficiently. The results of the study showed that within the last 28 years, 3% of the coastal area at the promontory has been eroded. A considerable increase in urban settlements has taken place in the targeted area. Contraversly, agricultural areas have been decreased. 18

34 2.11 Previous Studies: Local AbuDagga (2015) used the remote sensing/gis techniques to assess and map LULC for the Gaza Strip in 2004 and He relied on the analysis of satellite images of SPOT 5 multispectral and spatial resolution up to 5 to 10 meters and four bands as follows: Red (R), Green (G), Blue (B) and shortwave infrared (SWIR). The study showed the ability of remote sensing in producing accurate maps dealing with land cover in the Gaza Strip> Moreover, the vital role of this technique in covering the study area is respected because of the difficulty of other techniques to conduct local field surveys. The study recommended the use of remote sensing in examining and updating the local LULC. Abualtayef et al. (2013) computed the spatial and temporal changes of Gaza shoreline between 1972 and They revealed that erosion was a crucial process in the area studied. The Gaza fishing harbor caused serious problems to the Beach or Al-Sha'ati Camp shoreline. The impact of harbor construction has extended to 2.6 km to the north and less than 2.4 km to the south of harbor. The study recommends the relocation of the harbor or the construction of an artificial wide-crested submerged breakwater as an effective structure in preventing sandy beach erosion and in improving the ecosystem of marine biota. Abualtayef et al. (2012) studied the impact of Gaza fishing harbour on the Mediterranean coast of Gaza. During the study, the changes of coastal area in Gaza city from 1972 to 2010 was detected in order to provide future database in coastal management research. By using ERDAS and GIS tools, the erosion and accretion patterns along the coast were identified. There was an advance in the shoreline south of the Gaza fishing harbor, and the annual beach growth rate was 15,900 m 2. On the northern side of the harbor, the shoreline retreated and beaches erode at an annual rate of 14,000 m 2. Alhin and Niemeyer (2009) use remote sensing/gis techniques to monitor and analyze the coastline dynamics during the last two decades in the Gaza Strip. using medium resolution satellite images. Different modern techniques such as the Tasselled Cap Transformation, Band Ratio and Normalized Difference Vegetation Index (NDVI) 19

35 were used to automatically extract the coastline. The results revealed that erosion has been the predominant process on the Gaza coast and sea level increase has affected the Gaza coastal zone. The southern side of Gaza fishing harbor obtains a positive rate and accretion in the beach area. The study concluded that the Gaza fishing harbor has prevented the free movement of sediments that lead to sedimentation in the south and erosion in the north. 20

36 CHAPTER 3: STUDY AREA DESCRIPTION Chapter (3) Study Area Description 21

37 CHAPTER 3 STUDY AREA DESCRIPTION 3.1 Location and Geography The Gaza Strip is a coastal area along the eastern Mediterranean Sea located on coordinates (31 31 N, E), and is a narrow zone of land bordered on the east and north by the Occupied Palestinian territories, west by the Mediterranean Sea, and south by Egypt (Figure (3.1)). The Gaza Strip has a total area of 365 km 2 which constitutes about 1.33% of the total area of Palestine, the length of territory is 45 km long and 6 12 km wide (UNDP, 2014). Nowadays, the seashore and the coastal sand dunes are the only natural and recreational areas in Gaza Strip. Figure (3.1): Gaza Strip location 22

38 3.2 General Description of Study Area The coastal zone of the Gaza Strip is a narrow piece of land lying on the eastern coast of the Mediterranean Sea. The study area extends from Beit Lahia in North Gaza Governorate to the Swedish-village in Rafah Governorate (Figure (3.2)). It covers an area of about 74 km 2, with 42 km long and 0.5 to 4 km width. The coastal zone includes the sand dunes in the south and north areas, the coastal cliffs in the middle to north and agricultural lands in El-Mawassi area in the southwest (MOPIC, 1996). This coastal zone is growing rapidly and suffering from several pressures due to urbanization, tourism and economic activities. The coastline of the Gaza strip forms only a small part of a larger system that extends from Alexandria at the west side of the Nile Delta to the Bay of Haifa in North. This part forms the south east corner of the Levantine Basin. Over the past 15,000 years, the coastline has been formed by sediments coming from the mountains of Africa, where the River Nile sand moves along the entire concave coastline in an anti-clockwise and north east direction (MEnA, 2001). Figure (3.2): Map of the Gaza coastal zone, Palestine 23

39 3.3 Population The Gaza Strip remains one of the most densely populated areas, based to Palestinian Central Bureau Statistics (PCBS, 2016). Nowadays, the population reached the threshold of 2.0 million inhabitants, of which 1.3 million (70%) are refugees. Figure (3.3) illustrates the population development in the last years (PCBS, 2016). 2,000,000 1,500,000 1,000, , Figure (3.3): Population growth of Gaza Strip (PCBS, 2016) 3.4 Soil According to Abu Samra (2014), the Gaza Strip has various soil types; sand, sandy loam, sandy clay loam, sandy clay, loam, clay loam, and clay. It consists of three basic types; clay, sand and loess shown in Figure (3.4). Figure (3.4): Soil map of Gaza (Abu Samra, 2014) 24

40 3.5 Topography and Geology The Gaza Strip is coastal plain and flat and sandy, with dunes pushing in from the beaches which run along the entire coast particularly in the south Gaza Strip. Based to Digital Elevation Model (DEM) from U.S Geological Survey the land elevations of Gaza Strip range from Mean Sea Level (MSL) to more than 100 meters above MSL particularly in the lands of east Gaza Strip as shows in Figure (3.5). Figure (3.5): Topography of the Gaza Strip from Digital Elevation Model (Source: USGS, 2016) 25

41 Speed Km/hour 3.6 Climate and Meteorological Conditions The Gaza Strip is a coastal plain having the climate of the Mediterranean basin, which is mild and dry in the winter and warm to hot-humid in the summer Rainfall The average annual rainfall varies from 440 mm/year in the North of Gaza Strip to 220 mm/year in the south of Gaza Strip. The rainy season starts from October to March, the rest of the rest of the year being completely dry (MoT, 2007) Temperature The average daily mean temperature ranges in the Gaza Strip from 15.1 C in winter January to 28.3 C in summer August with an average annual temperature of 21.7 C (Al-Banna, 2011). The daily relative humidity varies between 65% in the daytime and 85% at night in the summer, and between 60% and 80% respectively, in winter (Hidalgo, Slooten, Medina, & Carrera, 2005) Wind Speed Generally, the wind speed in the Gaza Strip is considered very low throughout the year. the data of 2005 indicates that the minimum annual mean of wind speed was 2.7 km/hour in February, while the maximum annual mean was 10.2 km/hour in October as shown in Figure (3.6) (PCBS, 2005) Months Figure (3.6): Mean wind speed in Gaza Strip by Month (km/hour),

42 No. of hours Solar Radiation The Solar radiation is relatively high in the Gaza Strip, where the mean annual solar radiation reaches 2200 J.cm 2 /day (D' Haeyer, 2000). The average solar radiation reaches approximately 8 hours in The highest rise was recorded in July and August at 10.5 hour/day, while the lowest level was 5.1 hour/day during December and January as shown in Figure (3.7) (PCBS, 2007) Month Figure (3.7): Mean solar radiation in Gaza Strip, 2007 (hour/day) 3.7 Physical Conditions of Gaza Coastal Zone Coast and Seabed Characteristics In fact, the Gaza coastal zone profile can be divided into several sections which are: beach, Kurkar cliffs, cliff plateau or dune and seabed. As shown in Figure (3.8), the seabed sediments consist mainly of soft sand which reaches a depth of -25m and Muddy exists nearby the outlet of Wadi Gaza in the Middle area at depth -100m due to Wadi Gaza fillings, and there are also some outcrops in the form of rocks and sand blocks parallel to and along of the coastline of Gaza Strip (MEnA, 2001). 27

43 Figure (3.8): Seabed Characteristics of Gaza coastal zone (Source: MEnA, 2001) Bathymetry Figure (3.9) shows the bathymetry of the sea in the Gaza Strip. The depth contour line -100m is located 14 km off Gaza in north areas and 28 km in the south areas. Therefore, the mean slope of seabed between the coast and the -100m depth contour line is approximately 1 in 200. In 1986 on a distance about 1 km south of the Gaza fishing harbor, bathymetric contour lines are found relatively straight and parallel to the shoreline, and the -20m bathymetric contour line is found about 1600 m off the shoreline (MEnA, 2001). 28

44 Figure (3.9): Bathymetric Map of Gaza coastal zone based to (MEnA, 2001) Coastal Geology The Holocene and Pleistocene epoch, deposits in the Gaza terrestrial area are approximately 160 m thick and cover the underlying Pliocene sediments includes marine Kurkar, quartz sands and sometimes calcareous sandstone. The thickness of the marine Kurkar varies between ( m). The continental Kurkar formation varies from friable to very hard, depending on the degree of cementation (MEnA, 2001). According to Grabowsky and Poort (1994), the Alluvial sediments can be distinguished to four types: 29

45 (1) Sand dunes which are abundant in the Khan Younis, Rafah and scattered in south area of 2 to 3 km from the coast. (2) Wadi fillings' consisting of gravel and sandy loess with its thickness reaches 10 to 20 m. (3) Alluvial and Aeolian deposits located in the northern areas from Wadi Gaza at a distance of 3-4 which includes heavy and loamy brown clay. (4) Beach formation consisting of a fairly thin layer of sand and shell fragments Waves, Winds and Currents In the Gaza Strip, the wave-induced currents in the breaker zone, under extremely severe wave conditions, might reach maximum velocities of 1.0 m/s. In winter the prevailing wind direction is south-west with an average speed of 4.2 m/s, and during summer the prevailing winds are from north-west directions (MEnA, 2001). 30

46 CHAPTER 4: MATERIALS AND METHODS Chapter (4) Materials & Methods 31

47 CHAPTER 4 MATERIALS AND METHODS 4.1 Introduction This research deals with the changes that have occurred in the coastal zone of Gaza Strip. These changes include: changes in land use land cover, changes in shoreline and extraction of bathymetry from Gaza Sea. Many of techniques are available in scientific literature for change detection of LULC, shoreline and bathymetric mapping. In this research, GIS and remote sensing is used as a technique of change detection, it is considered as effective techniques in studying the LULC change, shoreline changes and bathymetric mapping as it helps in modeling, trialing, analyzing, surveying lands, classification and calculating averages for studying categories. In addition, this technique is recognized with its ability to display analysis of spatial data and results. In this chapter, methodology framework is described in details. Then data collection criteria is explained and justified. After that, image pre-processing of satellites imageries are illustrated. Next, supervised and unsupervised classification process is discussed. Finally, change detection analysis is described. 4.2 Methodology Framework Figure (4.1) illustrates the methodology followed in this research which consist of many stages to get to the achievement of the goals, as the research deals with three main axes (i) Mapping the land use and land cover in the Gaza coastal zone from 2004 to 2016, (ii) Determination and extraction of shoreline changes from 1972 to 2014 in Gaza coastal zone, (iii) Retrieval the bathymetry mapping of near-shore from Landsat 8 images. 4.3 Tools Used In order to achieve this methodology, the following software and supporting tools are uses: ESRI ArcGIS Intergraph ERDAS Imagine 2015 Microsoft Excel

48 Figure (4.1): Research flowchart 33

49 4.4 Data Collection and Acquisition In this study the satellite images data was obtained at irregular periods between 1972 and 2016, covering a period time about 42 years. Where the seven satellite imagery of good-quality were obtained and covered the study area as shown in Table (4.1). This study is based on a variety of satellite imagery from U.S. Geological Survey (USGS) for the years 1972, 1998 and Also there are some images obtained from the Environmental and Rural Research Center (ERRC) at Islamic University - Gaza (IUG) for the years (SPOT, 2004) and (QuickBird, 2009), and it was obtained highresolution images from the Ministry of Planning (MOP) for the years 2007 and This study also relied on basic data to measurements of bathymetry of the Gaza Sea that have been created within the Dutch project in Table (4.1): Satellite images characteristics and source. Satellite Bands Acquisition Date Resolution [m m] Pixel Depth Image Source Landsat 5 TM 4 22-Oct Bit USGS Landsat 5 TM 7 22-Oct Bit USGS Aerial Image 3 01-Jun Bit MOP Quick Bird 4 21-Jan Bit ERRC - CLIMP Project Pleiades 3 07-Jun Bit MOP SPOT Nov Bit ERRC -CLIMP Project Landsat Jun Bit USGS Assessing and mapping of the shoreline and LULC changes requires developing a geographic database for the study area and extraction the shorelines and LULC patterns at different periods of time. In addition to create model for bathymetric mapping using ratio transform algorithm. To make change detection analysis, a geographic database for the study area was created based on maps from Ministry of Planning and Environmental Quality Authority (EQA) 4.5 Image Pre-Processing The image pre-processing stage is an important procedure to make the image more clearly and suitability in order to obtain the best results for image classification. This 34

50 requires many initial processing steps to improve the image e.g. atmospheric correction, geometric correction and image enhancement. (Akhter, 2006) Geometric Correction Geometric correction is important and required to avoid geometric distortions in satellite images, and is achieved by geo-referencing process based on Ground Control Points (GCPs) and calibration data of the satellite sensors. To resolve this problem, it was necessary to make geo-referencing correction all the imagery to the World Geodetic System (WGS 1984) datum and the Universal Transverse Mercator (UTM) projection system. The correction process was adopted on the aerial photo of Gaza Strip 2007 as the master image in georeferenced process. To verify accuracy, about 15 GCPs were examined and matched with all images. The assessment of the geometric quality of the images by overlaying maps e.g. road junctions, governorate boundaries and base maps layers and observe significant discrepancies in the images Radiometric Correction Radiometric correction is to avoid radiometric errors or distortions e.g. atmospheric contribution, reduction of illumination, sun angle and topography effects, and sensor calibration (Kiefer, 2004; Mather & Koch, 2011). Before the analysis and extraction of information from satellite image, noise and others radiometric errors must be corrected. Figure (4.2) showing the radiometric noise reduction using ERDAS 2015 to improvement quality of satellite image in this study. 35

51 Figure (4.2): Radiometric correction (noise effects) of Landsat-5 satellite image using ERDAS Image Enhancement and Visual Interpretation Image enhancement is usually used to enhance the quality for visual interpretation and understanding of imagery. Many satellite and aerial images suffer from poor and bad contrast and noise. It is necessary to enhance the contrast and remove the bad effects to increase image quality (Saichandana, Ramesh, Srinivas, & Kirankumar, 2014). The common types of image enhancement are: contrast stretch, composite generation and digital filtering or edge enhancement. the main goal of this types is (J. Eastman, 2001). Composite Generation/Color Composite was applied into images using False Color Composites (FCC) of bands (Green-Red-Near infrared) in SPOT-5 image in order to delineate land cover classes that could be easily interpreted such as agricultural areas and built-up areas as shows in Figure (4.3). 36

52 Figure (4.3): Image enhancement "before and after" for SPOT-5 satellite image using ERDAS in year Image Classification One of the most important applications of satellite remote sensing is to detect changes in land cover to discern those areas on digital images that change features of interest between two or more dates (Dewidar & Frihy, 2007). In this study, supervised classification is used in mapping of land use/ land cover patterns through using training sites based on Areas of Interest (AOI), so the spectral signatures of classes are developed and then the software assigns each pixel in the image to the type to which its signature is most similar. Figure (4.4) summarizes the steps performed for supervised classification. 37

53 Figure (4.4): Steps for supervised classification. 4.7 Land use /Land cover Mapping Development of Classification Scheme In order to mapping the LULC of the Gaza coastal zone, a list of categories to be classified should be prepared to make supervised classification using remote sensing. After reviewing the previous studies six categories for the land use/land cover classification were selected as shown in Table (4.2). Table (4.2): Land use/land cover classification scheme. LULC Class Description Agricultural Land Land covered with agricultural crops, grasses, orchards and etc. Urban/Built-up Area Includes human activities along the coastal area such as; constructions, streets and developments of commercial, services, utilities, as well as the towns and villages. Bare Land/Sand Lands with exposed soil, sand or rocks and dunes. Greenhouse Areas Land covered with green houses used in agriculture. Coastal Sand Beach area which is mainly covered with sand. Water Body Land covered with water, valleys and ponds Supervised Classification The main aim of the image classification procedure is to automatically categorize all pixels in an image into LULC classes. 38

54 In order to classify the images according on these signature files. Each pixel in the study area has a digital value/number in each of the Blue, Green, Red and other bands. The pixel is then assigned to the class type that has the most similar signature. In this study the Maximum Likelihood Classification (MLC) method is used for image classification through the use of training sites as recommended Jesús D. Chinea (2006). In The Maximum Likelihood Classifier, the distribution of reflectance values in a training site is described by a probability density function, developed on the basis of Bayesian statistics. This classifier evaluates the probability that a given pixel will belong to a category and classifies the pixel to the category with the highest probability of membership (Jesús D. Chinea, 2006). The classification process has been implemented using all spectral bands in the SPOT, QuickBird and Landsat images (2004, 2009 and 2016) Based on many trials, supervised classification using Maximum Likelihood Classification technique has been implemented to the three images. Six LULC classes have been selected as follows; agricultural land, Built-up area, bare land/sand, greenhouse areas, coastal sand and water body as shown in Table (4.2). Figure (4.5) and Figure (4.6) represent the image supervised classification process using ERDAS which begin with identifying Area of Interests (AOI). Figure (4.5): Supervised classification of satellite images 39

55 Figure (4.6): Supervised classification tool in ERDAS used signature file of spectral signatures After the completion of the analysis of satellite images, the results of analysis were converted from raster format to vector format in GIS environment which can be used as inputs for processing and calculations for change detection for areas. Figure (4.7) showing the analysis results of Landsat satellite image 2016 which were converted from raster format to vector format to create and produce a thematic map. Figure (4.7): Convert classified image from raster to vector At the end, land use maps are produced according to Vector shape file converted from Raster using ArcGIS software shown in Figure (4.8) and Figure (4.9). 40

56 Figure (4.8): Production maps phase by ArcGIS software. Figure (4.9): LULC maps for years 2004, 2009 and

57 4.8 Shoreline Mapping Recent advancements in GIS and remote sensing techniques have led to improvements in environmental and coastal studies, due to their cost-effectiveness. Shoreline delineation through remote sensing techniques relies on the varied spectral behavior or spectral response of water and other land surfaces at different wavelengths. In general, water bodies absorb most of the radiation in near-infrared and mid-infrared regions of the spectrum. Therefore, the reflectance of water is almost equal to zero in these wavelengths; meanwhile, the reflectance of various land covers in both regions is higher than water. Based on to this concept, coastline can be extracted from satellite images through the use of many methods such as: manual editing method "digitizing", estimating histogram threshold and supervised classification of imagery by selection of training sites as mentioned earlier, the shoreline is typically mapped from remote sensing data. In this study, after making correction of geometric and radiometric for satellite imagery, a technique has been applied supervised classification using Maximum Likelihood Classifier (MLC) and digitizing editing for coastlines, where used the training sites and spectral signatures of water and land in the coastal zone to identify each class from multispectral satellite imagery for years (1972, 1998, 2007 and 2014) in the classification process. Furthermore, the result of analysis data from supervised classification and digitized shoreline in 1972, 1998, 2007 and 2014 are displayed in a vector layers after convert classified image from raster to vector in a process called Vectorization. 42

58 (a) (b) (c) Figure (4.10): Extraction of shorelines by supervised classification. Figure (4.10) illustrates the process of image classification using supervised classification through selection of training sites as a spectral signatures as shown in Figure (4.10-a). Figure (4.10-b) shows the results of supervised classification for two classes (Water and Land). Figure (4.10-c) illustrates of the convert classified image from raster to vector, the blue color represents the water body while yellow color represents the land Shoreline Extraction After converting the results of classified images from raster to vector as "polygons", the polygons features will be converted to lines features using ArcGIS/Arc Toolbox and editing of edges and lines to remove of impurities and distortions that may occur as shown in Figure (4.11). At the end, the same steps are applied to all years to extract shorelines. The Figure (4.12) illustrates the editing of lines to extraction of shorelines. Figure (4.13) illustrates the all shoreline in Gaza Strip spatially in Gaza fishing harbor for example from 1972 to

59 Figure (4.11): Convert polygon feature class to line feature class. Figure (4.12): Line editing to extraction of shoreline. 44

60 Figure (4.13): Shoreline from 1972 to 2014 in Gaza Fishing harbor Change Detection Analysis In order to calculate and detect of the change in areas between periods time, the ERDAS and ArcGIS Toolbox was used. Both shorelines features are used to create the polygon feature class to be determined the total area. Thus, the areas as a result of changes in shoreline areas can be determined by ArcGIS Toolbox. Then by using Arc Toolbox to convert feature to polygon to detect the change in Gaza coastal zone as shown in Figure (4.14) illustrates the changes in coastal zone between years Figure (4.15) showing the result of analysis in the Gaza coastal zone, the erosion area in Red color and accretion Green color of shoreline. 45

61 Figure (4.14): Convert two shoreline feature to polygon Figure (4.15): Change Detection in areas between years

62 4.9 Bathymetric Mapping Bathymetric data is one of the most important information to explore the environment of marine habitats and ecosystem. Active remote sensing technologies, such as Light Detection and Ranging (LiDAR), SONAR (Sound Navigation and Ranging) and ALB (Airborne Laser Bathymetry), have been wildly used to survey the water-covered area. Although these technologies can provide covering, high accuracy and have better capability for bathymetric mapping, however this techniques is very expensive (Jagalingam et al., 2015). Almost all the above techniques are expensive. Therefore, with the advantage of larger coverage and much lower cost, water depth estimation with satellite image is still of value. The basic principle of this retrieval of bathymetric data from satellite imagery is that when light passes through water it becomes attenuated by interaction with the water column. Deep areas appear dark on the image since the water absorbs much of the reflected light. Shallow areas appear lighter on the image since less light reflected from the seabed is absorbed in the passage through the water column. Remote sensing can be regarded as one of the most promising alternative tool to map the bathymetry of the sea and ocean, because of its extensive coverage of the area, low cost and their repeated use. In last years, successful launches of remote sensing satellites such as SPOT, IKONOS, QuickBird, and Worldview-2 offer imageries with both high spatial and spectral resolution, but all these images need to be procured commercially. Since the procurement of commercially available images proves to be expensive for most of the developing countries. One of the satellites that can be used for mapping shallow water bathymetry is Landsat- 8. LANDSAT imagery has a spatial resolution of 30 meters are equipped with visible channel that required in the extraction of bathymetry information. Visible channel (blue, red and green) has the ability to penetrate the water to a certain depth, the blue channel has the ability to penetrate deeper into the water body (Setiawan, Adawiah, Marini, & Winarso, 2017). 47

63 Jupp (1988) concluded that Landsat imagery can be used in determining the water depth, band 2 (blue channel) has the ability to penetrate up to 25 meters of water depth, band 3 (green channel) up to 15 meters, band 4 (red channel) up to 5 meters, while band 5 (SWIR-1 channel) is only able to penetrate 0.5 meters of water depth. A number of empirical algorithms are available in previous researches such as Su et al. (2008); Stumpf et al. (2003) and analytical algorithms such as Lyzenga (1978, 1981); Lyzenga et al. (2006); Philpot (1989). In order to use the analytical method for mapping the bathymetry, a number of input parameters such as water column, properties of atmosphere and bottom material etc., are needed. Thus, it is very complex and difficult. By comparison empirical method requires only few parameters which are simple and easy for mapping the bathymetry. In this study, was utilized a ratio transform algorithm developed by Stumpf et al. (2003) on the Landsat-8 satellite image to extract the bathymetry map of the Gaza Sea, which can retrieve the depth between 20m to 30m in clear water, the procedures of modeling and analysis is adopted using ESRI ArcGIS software, where the Landsat satellite image was downloaded from U.S. Geological Survey (USGS) website. Figure (4.16) showing the Landsat-8 satellite image (path 175, row 38) which covers the Gaza Strip with coastal zone was captured on 17 th June 2016, at 08:61:43hours (UTC Time). 48

64 Figure (4.16): Landsat 8 satellite image of Gaza Strip dated 17 th June 2016 (Source: USGS, 2016) Water Separation It is important to separate land region from water in satellite image to estimate the bathymetry of water, because this research focused on the water object therefore land object not included in the processing. Where the water region of the image was separated using the Normalized Differential Water Index (NDWI). For this purpose, band NIR is used due to the appearance of water in dark and land in bright (Raj & Sabu, 2013; Setiawan et al., 2017). NDWI = Green NIR Green + NIR (1) Initially, the water region was separated using the NDWI. It was carried out using the equation1 based to band-2 "Green" and band-5 Near Infrared (NIR) of the Landsat-8 image. Raster Calculator Tool were used for the calculation of NDWI using in ArcGIS 49

65 as shown in Figure (4.17). The water region illustrated a value greater than one in the NDWI map as shown in a blue color at Figure (4.18). Figure (4.17): Calculation of NDWI in ArcGIS Figure (4.18): NDWI Map in study area The value of NDWI ranges from -1 to +1. The non-water regions have a value less than or equal to zero while the water regions have a value greater than zero. 50

66 4.9.2 Ratio Transform Algorithm A ratio transform algorithm was developed by Stumpf et al. (2003) to estimate the bathymetry of near-shore areas. The algorithm makes use of two bands (Blue and Green) in order to determine the bathymetry of the near-shore areas. The Stumpf algorithm is capable of retrieving depths from 20-30m in clear water of coastal zone efficiently. Typically in the coastal environment, radiance in the blue wavelength ( nm) attenuates faster with depth than light in the green wavelength ( nm) (Jerlov, 1976). Thus, the variation in ratio between the bands is affected further by the depth than by bottom reflectance. To infer the bathymetry, the algorithm establishes the linear relationship between the ratio of radiance in two bands (green and blue) and water depth (Stumpf, Holderied, & Sinclair, 2003). The algorithm relies on the circumstance that the absorptivity of water differs spectrally from band to band. As the depth of water increases, reflectance values of the band with higher absorption will decrease proportionately faster than reflectance value of the band with lower absorption. Therefore, when two bands are transformed, there exists a linear decrease between a ratio of high absorption band to a low absorption band and water depth. The following equation is used to determine the bathymetry of the nearshore coastal zone: (2) 51

67 4.9.3 Linear Regression Analysis The statistical analysis R 2 (the coefficient of determination) is adopted to estimate the extinction of depth. The parameters such as m1 and m0 are obtained from the analysis of coefficient of determination based to bathymetry data from hydrographic and pixel values from Landsat-8 image. In order to derive the bathymetry data, the hydrographic data of the same area is overlaid on the satellite image. It is important to note that the hydrographic data are collected using single beam echo sounders for Gaza seaport in Bathymetric points in the hydrographic data and the corresponding pixel values from the Landsat- 8 satellite image are obtained as shown in Table (4.3). The pixel value of the satellite derived bathymetry image is obtained with the reference of hydrographic chart point value to determine the m1 and m0 parameters. Table (4.3): Part of list values of band blue and green to determine m1 and m0. # X Coordinate Y Coordinate Depth Band 2 Band 3 Lobs (Blue) mm Lobs (Green) mm ln(blue) / ln(green) * Lobs : are observed radiance of bands 52

68 Hydrographic data value (meter) Correlation of Hydrographic and pixel Values Series1 Linear (Series1) Satellite values (pixel) y = x R² = Figure (4.19): Correlation between pixel values and hydrographic chart values. Finally, the calculated parameter such as observed radiance of band from the ratio of blue and green and the values of m1 and m0 are provided as input to the ratio transform algorithm to identified the water depths along the near-shore Gaza Sea. The procedure for estimating the bathymetry is processed using the ArcGIS modeling. Figure (4.19) illustrates R 2 of between the values of Landsat satellite image (pixel) versus hydrographic data value (in meter). While m0 is and m1 is , further the value of m0 and m1 is adopted to bathymetric mapping model Bathymetric Extraction Model In this stage, was applied equation 2 in modelling to extraction of bathymetric mapping in study areas, the modelling was carried out using the Arc Toolbox tools in ArcGIS Modelling environment as shown in Figure (4.20). The result of analysis is a raster map containing pixel values (depths of water = Z Calculated) in the study area. Figure (4.21) shows the range of depths in southern Gaza fishing harbor area, and the ratio-transform algorithm bathymetry values obtained are also found to be nearly m while the real value of depth is m. 53

69 Figure (4.20): Bathymetric extraction Model Figure (4.21): Result of analysis the bathymetric modelling Bathymetry Accuracy Assessment Finally, the results of the extraction of the calculated bathymetric measurements are compared with the actual measurements. 54

70 CHAPTER 5: RESULTS AND DISCUSSION Chapter (5) Results and Discussion 55

71 CHAPTER 5 RESULTS AND DISCUSSION This chapter presents results of analysis of GIS and RS data using ARCGIS and ERDAS software s, in addition to the discussion of the changes in LULC, shoreline and bathymetric mapping in the Gaza coastal zone. 5.1 Land use/land cover Classifications After carrying out of the supervised classification of the satellite images of year 2004, 2009 and 2016, six major land use classes were identified and mapped from both dates of satellite imageries to determine the changes and transformation (position and rate). These classes are: (1) water body, (2) agricultural lands, (3) built-up areas (4) green houses, (5) sandy land, and (6) coastal sand dune. The spatial distribution of these six different landforms for 2004, 2009 and 2016 are shown in Figures (5.1), (5.2) and (5.3) respectively. According to classification maps extracted for the years 2004, 2009 and 2016, the LULC classes and change statistics are summarized in Table (5.1). As well as, the classes and changed areas are graphically shown in Figure (5.4). Where the focus was paid to the built-up area and agriculture land classes. 56

72 Figure (5.1): Map of Land use Land Cover in Gaza Coastal Zone

73 Figure (5.2): Map of Land use Land Cover in Gaza Coastal Zone

74 Figure (5.3): Map of Land use Land Cover in Gaza Coastal Zone

75 Area (km 2 ) Table (5.1): LULC for the Gaza coastal zone as extracted from the satellite data. LULC Classification LULC 2004 LULC 2009 LULC 2016 Area Changed (Km 2 ) Area (km 2 ) % Area (km 2 ) % Area (km 2 ) % Agricultural Lands % % % Coastal Sand % % % Built-up Area % % % Greenhouses % % % Sand % % % Water % % % Total % % % Water Sand Greenhouses Class Built-up Area Coastal Sand Agricultural Lands Figure (5.4): The LULC areas of the 6 classes derived from the classified maps, Note that each hatching represents a class in the studied 3 years (2004, 2009 and 2016) 60

76 Figure (5.5): Examples of verification on classification process in map 2016 To verify the results of the analysis, Google Earth images 2016 were compared with the results of analysis classifications. Result of analysis classifications shows that for this region, built-up area (in gray), agriculture lands (in green), water body (in blue), sand (in brown) and the greenhouses class (in pink) as shown in Figure (5.5). The urban/built-up areas in Gaza coastal zone covered about 5.42 km 2 in 2004 and increased to 8.54 km 2 in 2009 with an average rate of 0.62 km 2 /year. The area was increased by a further 9.04 km 2 by 2016 with an average rate of 0.07 km 2 /year. The built-up areas was expanded by 3.62 km 2 over the entire study period from 2004 to 2016, with an average rate of 0.30 km 2 /year as shown in Figure (5.6). Significant change was between in urban coastal zone due to the Israeli withdrawal from Gaza lands in 2005 and return of people to their lands. 61

77 Figure (5.6): Changes in Built-up areas for years 2004, 2009 and 2016 On the other hand, the agricultural lands expanded from km 2 (in 2004) to km 2 (in 2009) with an average rate of 1.31 km 2 year 1 and to km 2 (in 2016) with an average rate of 1.12 km 2 year -1. The expansion rate is positively affected due to agricultural activities in the coastal zone since the occupation withdraw from Gaza in As for the Bare Land/sandy areas shrunk from km 2 (in 2004) to km 2 (in 2009) with an average rate of km 2 year 1 and to km 2 (in 2016) with an average rate of km 2 year -1. Given the trends of change in the built-up areas, we find that the change is moving towards the south, because of the availability of large areas of land that allow urban expansion in this areas after Israeli withdraw from Gaza in In addition to the government's implementation of housing projects such as the Hamad town project in Khan Younis area and Saudi 1 and 2 rehousing projects in Rafah area. As a result, it has led to the development of construction and urbanization particularly in the southwest direction of Rafah and Khan Younis. 62

78 This urban expansion came at the expense of the sand areas mainly and others, which are rainwater collection areas for recharge the groundwater aquifer. Where the area of sandy areas shrunk to about km 2 from 2004 to 2016 with an average rate of km 2 year Discussion of the Result of LULC Classification The result of the land use/land cover change was analyzed by supervised classification using maximum likelihood classification method. Statistical means presents that there was both positive and negative changes as shown below: Built-Up Areas: They were a great positive change in the built-up areas, from the statistical analysis of this research the built-up areas formerly occupied a proportion of 7.32% in 2004 and increased to 11.54% and 12.21% in 2009 and 2016, respectively. This is a clear indication of increase in population and infrastructure development in the Gaza coastal zone, regardless of use or pattern. Open/Bare Land/Sand: This class recorded negative change over the years under this study. Bare land proportions were 45.15% in 2004 but were decreased to 35.87% in 2009 and were decreased again to 25.54% in This can be attributed to human activities, which includes, construction of buildings and roads, land conversion and tourism activities. Agricultural lands: Agricultural lands also regardless of type of crops and their level of intensity; cultivated or uncultivated show a positive increase. In 2004, proportions were 34.60% and while in 2009 its 43.47%, and increased again in 2016 were inclined to 54.08%. This can be as a result of built-up areas above, which include construction of all capacity. This can be as a result of land reclamation and the 63

79 development of the cultivation of crops since the occupation withdraw from Gaza in Water Body: The coastal area doesn't contain water bodies except some wastewater ponds and path of Wadi Gaza. In 2004, proportions were 0.31% and while in 2016 its 0.18%. Greenhouses: This class recorded negative change over the years under this study. Greenhouses proportions were 9.02% in 2004 but were decreased to 5.41% in 2009 and were decreased again to 4.14% in This may be due land reclamation and other developmental activities along the Gaza coastal zone. Coastal Sand: This category has changed (2004 to 2016) to 0.18 km 2 and remained almost constant. 64

80 5.2 Shoreline Changes The shoreline change was estimated for the districts of Gaza coastal zone using remote sensing and GIS tools. The extents of the shoreline accretion or erosion for the three periods: , and are shown in Figure (5.7) for the entire length of 40 km, about 62.8% of the shoreline is observed to be eroding over the period of 42 years, according on the longitudinal shape of the Gaza Coastal Zone, the coastline was divided into eight zones. The computation results of erosion and accretion rates along Gaza coastal zone are shown in Table (5.2). Figure (5.7): Gaza shoreline change from 1972 to

81 Table (5.2): Accretion and erosion rates for the study area. Erosion Accretion Area 10 Period Zone Zone 3 Length Rate 10 3 Rate of change Area 10 3 Length Rate 10 3 Rate of change [m 2 ] [m] [m 2 /yr] [m/yr] [m 2 ] [m] [m 2 /yr] [m/yr] A North Governorate B Northern Gaza Harbor C Southern Gaza Harbor D Sheikh Ajlin E Wadi Gaza F Middle Governorate G Khan Younis Governorate H Rafah Governorate A North Governorate B Northern Gaza Harbor C Southern Gaza Harbor D Sheikh Ajlin E Wadi Gaza F Middle Governorate G Khan Younis Governorate H Rafah Governorate A North Governorate B Northern Gaza Harbor C Southern Gaza Harbor D Sheikh Ajlin E Wadi Gaza F Middle Governorate G Khan Younis Governorate H Rafah Governorate

82 Period Change Analysis of Shoreline After the extraction of shorelines from classified images, change areas were calculated according to zones using ArcGIS Toolbox as shown in Figure (5.7) Zone (A) North Governorate This zone is located from the Beach Camp to the northern border of Gaza strip. In general, the beach of the northern governorate are constantly eroded where it reached the peak of erosion in to m 2 with a rate of m 2 /year (Table (5.3)), while erosion continued until this period to m 2 in rate m 2 /year as shown in Figure (5.8). This is due to the cumulative human activities in the coastal zone since decades ago, which is still ongoing. Table (5.3): Rates of accretion and erosion for Zone (A). IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.8): Rate of change area in North Governorate 67

83 The study shows that the erosion is growing during this periods even after many groins were built in Al-Sudaniya area "Blue Beach Resort", however, erosion continues in this zone as shown in Figure (5.9). Figure (5.9): Groins which was built in in North Governorate Zone (B) Northern Gaza Fishing Harbor This zone extends from the north groin of Gaza fishing harbor to the end of the Beach camp. The analysis result showed that the total erosion between 1998 and 2007 was m 2 with a rate of m 2 /year as showing in Figure (5.10). The erosion rate during this period was the highest and this was due to the continuous wave actions and construction of Gaza fishing harbor. This caused the prevention of sediment movement and creation of sand trap in behind harbor (Southern harbor). In period the erosion is still exist, but became more aggressive in term of eroding, therefore, the results were recorded m 2 with a rate of m 2 /year as shown in Figure (5.11). 68

84 Period Table (5.4): Rates of accretion and erosion for Zone (B). IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.10): Rate of change area in Northern Gaza fishing harbor. Figure (5.11): Aerial photo showing the erosion areas in the northern Gaza fishing harbor zone. 69

85 Period Zone (C) Southern Gaza Fishing Harbor This zone is located from the South groin of Gaza fishing harbor to Sheikh Ajlin area in the south, the result analysis as shown in Figure (5.12) and Figure (5.13) estimated area of m 2 have been added to the beach area in period with a rate of m 2 /year, which represents the highest rate of accretion. Because sediments are deposited in this zone due to the blockage of Gaza fishing harbor during the passage of sea currents. This zone is considered to be one of the most coastal areas contain the sands due to accretion as shown in Figure (5.14). Table (5.5): Rates of accretion and erosion for Zone (C). IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.12): Rate of change area in Southern Gaza fishing harbor. 70

86 Figure (5.13): Accretion rate in southern Gaza fishing harbor Figure (5.14): Aerial photo showing the accretion sediments in the southern of Gaza fishing harbor zone 71

87 Period Zone (D) Sheikh Ajlin Area This zone is located from the North Sheikh Ajlin to Wadi Gaza, the result analysis shows negative rates in general, which means that erosion was the predominant process. Where the maximum erosion in was m 2 with a rate of m 2 /year as shown in Figure (5.15). Except for the period from 1972 to 1998 which showed a small accretion positive rate 0.62 m/year. Table (5.6): Rates of accretion and erosion for Zone (D). IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.15): Rate of change area in Sheikh Ajlin Area Zone (E) Wadi Gaza This zone is located to the south of Sheikh Ajlin to the Middle Area, the result analysis show negative rates in general, which means that erosion was the predominant process in all periods, Figure (5.16) showing the rates of erosion in Wadi Gaza zone, where the maximum rate was in the period was m 2 with a rate of m 2 /year. 72

88 Period Then gradually declining until period was m 2 with a rate of m 2 /year. Table (5.7): Rates of accretion and erosion for Zone (E). IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.16): Rate of change area in Wadi Gaza Zone (F) Middle Governorate This zone is located to the south of Wadi Gaza to Khan Younis city, the results showed a state of balance between accretion and erosion in period Where the maximum erosion has reached m 2 with a rate of m 2 /year in the period as shown in Figure (5.17). Table (5.8): Rates of accretion and erosion for Zone (F) IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL

89 Period Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.17): Rate of change area in Middle Governorate Zone (G) Khan Younis Governorate This zone is located to the border of Middle Governorate to border of Rafah Governorate in the south. Generally, this zone is constantly eroded by currents that effect on shoreline, where it reached maximum erosion in period was m 2 with a rate of m 2 /year as shown in Figure (5.18).While accretion and erosion was semi-constant in period Table (5.9): Rates of accretion and erosion for Zone (G) IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL

90 Period Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.18): Rate of change area in Khan Younis Governorate Zone (H) Rafah Governorate This zone is located from the border of Khan Younis city to border of Egypt in the south; it is evident that in this zone erosion processes are dominant during 1972 to Figure (5.19) shows the rates of erosion in Rafah, where the maximum rate in the period was m 2 with a rate of m 2 /year. This is due to Egyptian human activities in the Mediterranean Sea region Figure (5.20). Table (5.10): Rates of accretion and erosion for Zone (H) IMAGE PERIOD Area 10 3 [m 2 ] Length [m] EROSION Rate 10 3 [m 2 /yr] Rate of change [m/yr] Area 10 3 [m 2 ] ACCRETION Length [m] Rate 10 3 [m 2 /yr] Rate of change [m/yr] TOTAL

91 Period Rate 10 3 (m 2 /yr) Accretion Erosion Figure (5.19): Rate of change area in Rafah Governorate. Figure (5.20): Egyptian interventions to build groin in Sinai (Source: Google Earth, 2014) 76

92 m/year Rate of Change of Shoreline in Gaza coastal zone Shoreline change rates have been calculated according to area change and length, the rate results are listed according to study area as follow: Rate of change from 1972 to 1998 Before the construction of Gaza Fishing harbor, the average rate of change in erosion areas was 0.86 m/year, while the average accretion rate 0.56 m/year. In the Wadi Gaza area was the highest rate of erosion at 1.88 m/year as shown in Figure (5.21) due to the cut of sediments coming through Wadi Gaza to Mediterranean Sea North Gov. Northe rn Sea Harbor Southe rn Sea Harbor Sheikh Ajlin Wadi Gaza Middle Gov. Khan Younis Gov. Rafah Gov. Erosion Accretion Zone Figure (5.21): Rate of change in period Rate of change from 1998 to 2007 After the construction of Gaza sea harbor, the rate of erosion increased significantly, with the average rate being about 2.37 m/year to the north of Gaza fishing harbor, while the average accretion rate 0.93 m/year. The highest accretion rate in this period was 4.04 m/year in Southern Gaza fishing harbor area as shown in Figure (5.22). The reason for the increased rate of erosion is due to the construction of Gaza fishing harbor. 77

93 m/year m/year North Gov. Northe rn Sea Harbor Southe rn Sea Harbor Sheikh Ajlin Wadi Gaza Middle Gov. Khan Younis Gov. Rafah Gov. Erosion Accretion Zone Figure (5.22): Rate of change in period Rate of change from 2007 to 2014 With increasing the human activities in coastal areas, the average rate of erosion increased 1.25 m/year, where the all northern area of the Gaza fishing harbor remain heavily affected by erosion as shown in Figure (5.23). The southern Gaza fishing harbor area is still a sediment trap, with accretion rate 3.85 m/year North Gov. Northe rn Sea Harbor Southe rn Sea Harbor Sheikh Ajlin Wadi Gaza Middle Gov. Khan Younis Gov. Rafah Gov. Erosion Accretion Zone Figure (5.23): Rate of change in period

94 5.3 Bathymetric Mapping This study used two band of the Landsat 8 imagery. The two bands were Band-2 "Blue" (0.452 μm μm) and Band-3 Green (0.533 μm μm), which has a spatial resolution of 30 meters. The shallow water depth of the study area was obtained using the Ratio Transform Algorithm developed by Stumpf et al. (2003) for the Landsat 8 data. Figure (5.24) illustrates the estimate water depths in study area computed via model developed by Stumpf et al. (2003) using two respective visible bands, Band-2 (Blue) and Band-3 (Green) from the selected Landsat 8 satellite images. These results analysis were based on measured bathymetry data in 1994 as shown in Figure (5.25). Base on the satellite-derived bathymetric data, the depths of the seabed vary with locations. The result depicted that the water depth recorded around the study area were between datum levels to -30 meter. Therefore, the ratio transform algorithm can retrieve the depth up from -25m to -30m for Landsat-8 imagery in spatial resolution [30m 30m]. 79

95 Figure (5.24): Map of calculated bathymetry of the study area 80

96 Figure (5.25): Map of measured bathymetry of Gaza Sea (Dutch project, 1994) 81