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