Indian Journal of Geo-Marine Sciences Vol. 45(1), January 2016, pp. 29-37

Detection of Temporal Changes of Eastern Coast of for Better Natural Resources Management

Mohamed Elhag Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University. Jeddah, 21589. Kingdom of Saudi Arabia [E.Mail: [email protected]]

Received 19 February 2015 ; revised 7 March 2015

Three different data sets of images used to obtain the land cover changes in this study: Multi-Spectral Scanner (MSS) acquired in 1973, Landsat-5 Thematic Mapper (TM) acquired in 1990 and Landsat-8 Operational Land Imager (OLI) acquired 2013 consequently. For each data set, three Landsat scenes mosaicked to cover the whole study area. Supervised classification implemented to classify the area into six major land cover classes using two different classification algorithms. A total number of 400 points evenly distributed over the designated study area used in classification accuracy assessment. Kappa statistics obtained to specify the most appropriate classification algorithms in term of accuracy assessments. The results indicated that the rapid imbalance changes occurred among three land’s cover classes urban area, surrounding desert and sedimentation. Human impacts in the form of sedimentation process practiced constantly on the Eastern Coast of Saudi Arabia, besides the loss of vegetation cover over the last four decades.

[Keywords: Accuracy Assessment, Classification, Human Impacts, Remote Sensing Data, Sedimentation, Urbanization]

Introduction algorithms in term of classifications accuracy Monitoring of land cover changes is the assessment8, 13, 14, 15.Assessing the pattern and the keystone strategy for better natural resources magnitude of Land Use Land Cover changes is management. Land cover changes on a local scale critical to project the future of natural cannot satisfy the national demands to monitor the resourcesmanagement16, 17. global environmental changes1,2. Therefore, the Satellite images as a source of remote sensing use remote sensing data in the form of multi- data, combined with Geographic Information spectral images is becoming the fundamental tool Systems (GIS) has been extensively practiced and to detect environmental changes 3. acknowledged as an influential and operational Change detection techniques are specifically approach in detecting of Land Use Land Cover based on two or more temporal imageries changes 8, 14, 18. Coastal cities in Saudi Arabia have comparison 4, 5, 6. There are two principal methods experienced a major development process to detect anomalies in satellite images: a) pixel-to- including the alteration of Land Use Land Cover 4, pixel comparison and b) post-classification 19. Both of eastern and western coast of Saudi comparison6, 7, 8. The main improvement of the Arabia witnessed a temporal changes20, 21, 22, the post classification method over the pixel-by-pixel current study will focus on the eastern coast due it classification is that the former is based on the rapid development actions took place in Ras comparing two or more separately classified Tarout area. temporal images 9, 10, 11, 12. The aim of this study is to detect the changes There are several classification algorithms occurred in Ras Tarout land cover types over a that employs post classification method but period of four decades. Therefore, investigating Maximum Likelihood (ML) and Support Vector trends and rates of land cover alteration are Machine (SVM) are the most successful essential for the management planner in order to

30 INDIAN J. MAR. SCI., VOL. 45, NO. 1 JANUARY 2016 perform ratrationalional land and natural resources alalsoso buried with the development of the region, management plans. which are part of the history of the area of natural limits. Temporary sand dunes are flooded by Materials and Methods seawater during tide andand show during the islands StudyStudy area located in the eastern province of becomes dry sand as mostmost of the Tarout Bay the Kingdom of Saudi ArArabia,abia, within Longitudes regions are of shallow land. There are other small of 500 and 500 13'N and Latitudes 260 11' and 260 islands insignificant strewn in Tarout Bay and 27'E approximately (Figure 1). The area located in otheothersrs on the road from to an arid envenvironmentironment where is the precipitation is beach. very low (3.52 mm/year). However, the area may Image classification process receive a longer falls as heavy local showers, and some places receive significantly more First, all scenes in each temporal data set have precipitation than the general average 23. In the been geometrically corrected to a reference image case of longer rain, vegetation cover grows as of known geographic coordinate system (UTM N growing period is prolonged. Moreover, in sandy 36, WGS_84 datum). Second, an adequate areas, if the infiltration exceeds a depth of 10 cm, radiometric correction techtechniquenique based on image soil seals moisture in surface layers where rregressionegression was implemented to minimize or herbage grows for a longer period24. eliminate the effects of dissimilar dates of acquisition 25, 26. Third,Third, a simple atmospheric correction according to Yuan et al. 27to identify dark object subtraction was performed as: = ∗ + Eq. 1 Where Gresscale and Brescale are the channel specific rescaling gain and bias factors Figure 1. Location of the study area AccordingAccording to Song et al.28 equation, the Tarout Island is the center for all other islands calccalculationulation of the path radiance (Lp) based on located in the Tarout BayBay,, which was known as identify dark object subsubttractionraction is: the Gulf of Quepos. The Gulf is called the Gulf of = ∗ + − . [ ( ) ] Tarout related to the Tarout momotherther island where Eq. 2 the foot of history started. Tarout Island includes sseveraleveral villages in addition to the ten scattered Where neighborhoods small villages strewn about Tarout Θ0 is the solar zenith angle Bay. Tarout isis not a small island, maximum length E0 isis the solar spectral radiance reaches ((1010 km) and almost maximum width is (6 Tz is the atmospheric transmittance from the sun to km) with nearly fifty thousand inhainhabitantbitants. A very ground surface ssmallmall village located in the study area called, “Al- T vis the atmospheric transmittance from ground ZZour”our” used to have a very great importance in the surface to sensor Edown is the down-welling spectralspectral irradiance past. However, its importance has diminished or 28 has now ended completely. The village located on Song et al. assumed that Tz and Tv are equal to the island's tail from the north and ssurroundedurrounded by 1.0 and Edown is equal to zero, therefore, the water from the east and north. In West Al-Zour, a atatmosphericmospheric correction equation can be ssmallmall stream aqueous river located and the water reformulated as: present in it during the tidal, and this small a river = ∗ + − [ ( )] slot from the north, where the intervention of sea . Eq.3 water loaded with fish and heading south. The Where island is a trees breed growth in the region and ESUNλ is the exoatmospheric solar irradiance those trees are eevergreenvergreen and grows in the mud d is the sun-earth distance and have a pleasant smell while known locally as (Crimea) shall not exceed the height of this tree Supervised classification more than two meters, the island located west of Classification is the prpracticeactice of assigning Tarout baybay in the region known as (Umalshajar unkunknownnown pixels into individual categories of data, Island). There are some few important islands are ELHAG: DETECTION OF TEMPORAL CHANGES OF EASTERN COAST OF SAUDI ARABIA 31 based on uninformed/informed training data set. If mi = mean vector pixel fulfills certain criterion/criteria, then the Artificial Neural Networks: uses standard back pixel is assigned to a category that corresponds to propagation for supervised learning based on the those criteria29. Classification process based on following equation of Okwuashi et al.32: , unsupervised and/or supervised classification = (, ) = ∑ , (, ) Eq. 7 through a distinguish patterns in the data. Training Where sets are the process of identifying the S’i (x, t) denotes the site attributes given by criterion/criteria by which these patterns are variable (neuron) i 30 distinguished . In the current study, six different Wi,j is the weight of the input from neuron i to supervised classification algorithms are used. neuron j Supervised signature extractions with six netj (x,t) is the signal received for neuron j of cell different classification algorithms were performed x at time t to select the optimum classification algorithm. Parallelepiped: uses parallelepiped dimensions The different classification algorithms according and the standard deviation threshold from the 31 to Richards are: mean of each selected category in two bands Mahalanobis Distance: a deterministic distance based on the following equation: classifier that uses statistics for each assigned () () + = 1 Eq. 8 categories based on the following equation: () () Where ‖ − ‖ ∑ −1 = ( − ) ∑ ( − ) Eq.4 σA and σB are standard deviation of selected pixels ∑-1 considered as a stretching factor on the space in band A and band B Minimum Distance: uses the mean vectors of each µA and µB are average of selected pixels in band A end member and calculates the Euclidean distance and B from each unknown pixel to the mean vector for K is adjustable for altering the size of each class based on the following equation: parallelepiped Support vector machine (SVM): distinguishes the ∑ ( ) = − µ categories upon margin maximization of a Eq. 5 decision surface between the categories based on Where sigmoidal function equation: , = ℎ + Eq. 9 Li is the label found in the dataset µ is the mean Where: x is the distance between each unlabeled sample g is the gamma term in the kernel function for all vector need to be classified kernel types except linear Maximum Likelihood: uses the statistics for each r is the bias term in the kernel function for the class in each band as a normally distributed polynomial and sigmoid kernels. function and computes the likelihood of a given Based on the calculation of the Optimum pixel belongs to a specific category based on the Index Factor (OIF), six classes were assigned to following equation: describe the LULC in each data set, the LULC () = 1( ) − 1 1|∑ | − 1 ( − classes are Sea, Sediment, Vegetation, Bare land, 2 2 Desert and Urban areas. −1−µ Eq. 6 A total number of 400 training sites were Where selected in each data set and a total number of 300 i = class points of ground truth data for validation were x = n-dimensional data (where n is the number of collected during March 2014, the points were for bands) the major LULC demonstration in the study area. p(ωi) = probability that class ωi occurs in the The 400 training sites were divided based on image and is assumed the same for all classes class’s separability and proportionally to the total |Σi| = determinant of the covariance matrix of the area of each class per data set according to the data in class ωi following table: Σi-1 = its inverse matrix

32 INDIAN J. MAR. SCI., VOL. 45, NO. 1 JANUARY 2016

Table 1: Total number of the training and validation points per class

Class name Total number of the training Total number of the validation Training points percentage per points points class Sea 84 74 20 % Sediment 65 45 15 % Vegetation 37 24 8 % Bare land 81 57 19 % Desert 53 36 12 % Urban 80 64 19% Classification accuracy assessment Unsupervised Classification The final step in the digital image analysis is OIF the assessment of the accuracy of the computer Potential Land Cover Classes

MSS data Classified MSS Changes from derived classification results. Producers, users and (1973) data (1973) 1973 to 1990 Overall accuracy were derived from a Matrix.

Training TM -5 data Classified TM -5 Changes from These results often articulated in plane form, sites (1990) data (1990) 1990 to 2013 known as a confusion matrix. The Kappa alidation Decision Rolealidation Classification Comparison lassification lassification DecisionRole V C Post examination is the distinct multivariate method OLI -8 data Classified OLI -8 Changes From used in accuracy assessment to specify statically (2013) data (2013) 1973 to 2013 whether one error matrix is significantly different Figure 2. Thematic Change Detection Workflow to another 33. The accuracy assessment was 34, 35 carried out following to estimate Khat for Results and Discussion different the employed classification algorithms: Supervised classification using different ∑ ∑ × = Eq. 10 classification algorithms were performed. ∑ × Where Graphical and statistical analyzes of feature r is a number of rows in the error matrix; selection were piloted taken into consideration all of the visible and near infrared bands. Table xii is a number of observations in row i and column i (the diagonal cells); 2shows the summary classification results in term xi+ is total observations of row i; of accuracy and Kappa statistics of each x+1 is total observations of column i; classification algorithm. N is total of observations in the matrix Support Vector Machine showed better Post classification Comparison classification results than the rest of classification algorithms in principle. The result of the Post Classification techniques are used classification algorithm, both of overall accuracies principally to classify images decision rules, to and kappa statistics were increased gradually from compute category statistics and to estimate MMS 1973 toward Landsat OLI- 8 acquired in majority or minority analysis to the three different 2013. This could be explained due to the fact that 16 temporal classification images. Mas developed, the date acquisition of the third data set (Landsat 9 then Coppin et al. improved techniques well OLI-8 acquired in 2013) is relatively closer to the know now as the Post-Classification Comparison date of the training and validation points’ (PCC), PCC used in a current research study to collection and also prove the adequacy of the spatially detect changes which occurred after SVM classifier over the rest classifier in complex classifying the temporal images separately from areas 4, 19. the three time periods 1973, 1990 and 2013 as it Post Classification Comparison is illustrated in Figure 2. Each temporal date set of demonstrated in Figures 3 – 8 and explains the images was adequately classified. The temporal thematic changes occurred in different LULC classified images were compared and analyzed in classes that exist in the study area. order to detect spatially the temporal LULC changes10, 36, 37.

ELHAG: DETECTION OF TEMPORAL CHANGES OF EASTERN COAST OF SAUDI ARABIA 33

Table 2. Overall accuracies and Kappa statistics of each classification algorithm Year of acquisition 1973 1990 2013 Classification algorithm Overall Kappa Overall Kappa Overall Kappa Spectral Angle Mapper 82.4% 0.73 91.8% 0.89 92.3% 0.91 Support Vector Machine 87.7% 0.84 93.2% 0.91 95.2% 0.93 Changes in LULC in term of percentages The expansion took place toward the sea and between the year 1973 and 1990 are demonstrated the surrounding bare land. This fact is related to in Figure 3. A strong loss in bare land and sea the extensive human impacts 38. Continues process total area were encountered by the expansion of of shallow land sedimentation has its negative both the urban area and sedimentation process. effects on natural vegetation cover and coastal According to Figure 4, the expansion of the urban habitat existing in area 39.The drying of the areas was toward the bare land, while the wetlands and shallow lands for industrial purposes expansion of sedimentation was toward the sea. represents a new natural resource hazard added to LULC changes that occurred between the year other environmental hazards that may threaten the 1990 and 2013 (Figure 5), revealed by an Tarout Bay during the coming years 40. The result additional loss in the total area of the bare land in contextually reports the lack of rational natural addition to a decrease in the sea came along with resources management in the designated study the increase of sedimented areas (Figure 6). The area. final stage of change detection demonstrated in 1990/1973

Figure 7, the total area of urbanization was 8.5% Urban approximately increased by only 18.3 % over two four decades which is rather similar to the -7.5% Bare land increase of the sedimented areas (12 %), the expansion of urban areas was observed to be -0.9% Desert. against the bare land and vegetation cover. A slight decrease in desert and vegetation total area -0.5% Vegetation was observed (Figure 8). 7.7% Sediment Figures 9 – 11 shows the contribution of each

LULC classes of the three different data sets to -7.3% Sea the total cover of the study area. Dataset acquired in 1973 considered as a reference point to the Figure 3. Post-classification changes from the year 1973 current changes in LULC of the Tarout Bay. till the year 1990 Figure 9 demonstrates the ratios among the six different land cover classes, where the sea computed to be more than a half of the total area (52 %) and bare land and desert are almost the other half (40 %). In the year 1990, the noticeable expansion of both urban areas and sedimented areas were directed against the total area of the sea and bare land. Slight decrease state in the total area of desert and vegetation were mapped (Figure 10). The final changes in the year 2013 demonstrate a further loss in the total area of the bare land and sea in accordance with vegetation cover land decreases, a remarkable increase in urban areas as well as sedimented areas. The total Figure 4.Thematic change detection map temporal area of the desert state remains the same (Figure resolution of 1973-1990 11).

34 INDIAN J. MAR. SCI., VOL. 45, NO. 1 JANUARY 2016

2013/1990

9.8% Urban

-8.9%

Bare land

0.0%

Desert.

-0.9% Vegetation

4.3% Sediment

-4.2% Sea Figure 8.Thematic change detection map temporal resolution of 1973-2013 Figure 5. Post-classification changes from the year 1990 till the year 2013 1973 Urban 1%

Bare land Sea 29% 52%

Desert. 11%

Vegetation Sediment 4% 3% Figure 6.Thematic change detection map temporal Figure 9. Land Use Land Cover classes’ percentage in resolution of 1990-2013 1973

2013/1973 1990 Urban 10% 18.3%Urban

Sea -12.5% Bare land Bare land 45% 21%

-0.9% Desert.

-1.4% Vegetation

12.0% Sediment Desert. 10%

-11.6% Sea Vegetation Sediment 3% 11% Figure 7. Post-classification changes from the year 1973 Figure 10. Land Use Land Cover classes’ percentage in till the year 2013 1990 ELHAG: DETECTION OF TEMPORAL CHANGES OF EASTERN COAST OF SAUDI ARABIA 35

2013 have also increased by almost the same percentage (12%), regardless the proportional area Urban 19% of each class. The desert class has decreased due to human intervention. Sedimentation deposits are

Sea remarkably noticed along the shoreline of the 41% study area.

Bare land The use of remote sensing data in term multi- 13% spectral and multi-temporal imageries provides a cost-effective tool to obtain meaningful information for better understanding and Desert. 10% monitoring land change patterns and Vegetation Sediment 2% developments. Knowledge of GIS delivers an 15% interactive environment of loading, examining, Figure 11.Land Use Land Cover classes’ percentage in and envisaging digital data compulsory for change 2013 detection database developments. Conclusion Consequently, the findings of the study Post Classification Comparison method was strongly propose adoption of new policies to be performed in the current study using six different taken into consideration for adjacent areas that supervised classification algorithms. Six Land Use may directly or indirectly influence by the land Land Cover classes were produced and only two cover changes of the study area. For instance, the main classes (urban area, sediments) have urban expansion should be strongly prohibited increased significantly in the study area. The over the vegetation cover and the deposits of the urban class has been increased almost 19% during sediment towards the shoreline should be wisely the period 1973–2013. Meanwhile, sediments selected to peruse an effective management plan. Acknowledgment 5. Dewan, A. M. and Yamaguchi, Y., Using Remote Sensing and GIS to Detect and Monitor Land Use This work was funded by the Deanship of and Land Cover Change in Dhaka Metropolitan of Scientific Research (DSR), King Abdulaziz Bangladesh during 1960–2005. Environmental Monitoring and Assessment, 150 (2009): 237 – University, Jeddah, under grant No. (155-597- 249. D1435). The authors, therefore, acknowledge with 6. Elhag, M., Psilovikos, A. and Sakellariou, M., thanks, DSR technical and financial support. Detection of land cover changes for water recourses management using remote sensing data over the References Nile Delta region. Environment, Development and Sustainability, 15(5) (2013): 1189-1204. 1. Adamo, S. B. and Crews-Meyer, K. A., Aridity and 7. Pilon, P. G., Howarth, P. J. and Bullock, R. A., An Desertification: Exploring Environmental Hazards enhanced classification approach to change in Jáchal, Argentina. Applied , 26(1) detection in semi-arid environments. (2006): 61-85. Photogrammetric Engineering and Remote 2. Gao, J. and Liu, Y., Determination of Land Sensing, 54 (1988): 1709-1716. Degradation Causes in Tongyu County, Northeast 8. Lu, D., Mausel, P., Brondizio, E. and Moran, E., China via Land Cover Change Detection. Change Detection Techniques. International International Journal of Applied Earth Observation Journal of Remote Sensing, 25(12) (2004): 2365- and Geoinformation, 12 (2010): 9–16. 2407. 3. Shalaby, A. and Tateishi, R., Remote Sensing and 9. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. GIS for Mapping and Monitoring Land Cover and and Lambin, E., Digital Change Detection Methods Land-Use Changes in The Northwestern Coastal in Ecosystem Monitoring: A Review. International Zone Of Egypt. Applied Geography, 27(1) (2007): Journal of Remote Sensing, 25(9) (2004): 1565- 28-41. 1596. 4. Batisani, N. and Yarnal, B., Urban Expansion in 10. Rivera, V. O., Hyperspectral Change Detection Centre County, Pennsylvania: Spatial Dynamics Using Temporal Principal Component Analysis. and Landscape Transformations. Applied Spain (2005): University Of Puerto Rico. Geography, 29(2) (2009): 235-249.

36 INDIAN J. MAR. SCI., VOL. 45, NO. 1 JANUARY 2016

11. Warner, T. A. and Campagna, D. J., Remote 25. Jensen, J., Introductory Digital Image Processing: sensing with IDRISI Taiga: A beginner’s guide. A Remote Sensing Perspective, Second Edition, Hong Kong (2009): Geocarto International Centre. Prentice Hall, Upper Saddle River, N.J., (1996): 12. Zhou, W., Troy, A. and Grove, M., Object-Based 305 P. Land Cover Classification and Change Analysis in 26. Yuan, D., Elvidge, C. D. and Lunetta, R. S., Survey the Baltimore Metropolitan Area Using of Multispectral Methods for Land Cover Change Multitemporal High Resolution Remote Sensing Analysis. In R. S. Lunetta, And C. D. Elvidge Data. Sensors, 8(3) (2008): 1613-1636. (Eds.), Remote Sensing Change Detection: 13. Chen, X., Vierling, L. and Deering, D., A Simple Environmental Monitoring Methods and And Effective Radiometric Correction Method to Applications, (1998): 21-39 Pp. Chelsea, MI: Ann Improve Landscape Change Detection Across Arbor Press. Sensors and Across Time. Remote Sensing of 27. Song, C., Woodcock, C. K., Seto, C., Lenney,M. P. Environment, 98(1) (2005): 63-79. and Macomber, S. A., Classification and Change 14. Geymen, A. and Baz, I., The Potential of Remote Detection Using Landsat TM Data: When and How Sensing for Monitoring Land Cover Changes and to Correct Atmospheric Effects? Remote Sensing of Effects on Physical Geography in the Area of Environment, 75(2) (2001): 230-244. Kayisdagi Mountain and Its Surroundings 28. Kumar, A., Asif Khan, M. and Abdul Muqtadir., (Istanbul). Environmental Monitoring and Distribution of Mangroves along the Coast Assessment, 140 (2008): 33 - 42. of the : Part-I: the Northern 15. Abd El-Kawy, O. R., Rod, J. K., Ismail, H. A. and Coast of Western Saudi Arabia. Earth Science Suliman, A. S., Land Use and Land Cover Change India, 3(I) (2010): 28-42. Detection In The Western Nile Delta Of Egypt 29. Singh, A., Digital Change Detection Techniques Using Remote Sensing Data. Applied Geography, Using Remotely-Sensed Data. International 31 (2011): 483-494. Journal of Remote Sensing, 10(6) (1989): 989- 16. Mas, J. F., Monitoring Land-Cover Changes: A 1003. Comparison of Change Detection Techniques. 30. Kloer, B., Hybrid Parametric/Non-Parametric International Journal of Remote Sensing, 20(1) Image Classification. Technical Papers, ACSM- (1999): 139-152. ASPRS Annual Convention, (1994): 307-316. 17. Yuan, F., Sawaya, K. E., Loeffelholz, B. and 31. Richards J., Remote Sensing Digital Image Bauer, M. E., Land Cover Classification and Analysis, Springer-Verlag, Berlin, (1999): P. 240- Change Analysis of the Twin Cities (Minnesota) 280. Metropolitan Area by Multitemporal Landsat 32. Okwuashi O., Isong M., Eyo, E., Eyoh. A., Remote Sensing. Remote Sensing of Environment, Nwanekezie, O., Olayinka, D. N., Udoudo, D. O. 98 (2-3) (2005): 317-328. and Ofem, B., GIS Cellular Automata Using 18. Wang, F. and Xu, Y. J., Comparison of Remote Artificial Neural Network for Land-use Change Sensing Change Detection Techniques for Simulation of Lagos, Nigeria. Geography and Assessing Hurricane Damage to Forests. Geology, 4(2) (2012): 94-101. Environmental Monitoring and Assessment, 162 33. Cohen, J., A Coefficient of Agreement for Nominal (2010): 311 – 326. Scales. Educational and Psychological 19. Hsu, C. W., Chang, C. C. and Lin, C. J., A Measurement, 20 (1960): 37-46. Practical Guide to Support Vector Classification 34. Congalton , R. and Mead, R., A Quantitative (2007): National Taiwan University. Method to Test for Consistency and Correctness in 20. Elhag, M. and Bahtawi, J., Potential Rainwater Photointerpretation. Photogrammetric Engineering Harvesting Improvement Using Advanced Remote and Remote Sensing, 49(1) (1983): 69-74. Sensing Applications. The Scientific World 35. Congalton, R., A Review of Assessing the Journal, 2014 (2014c): 1-8. Accuracy of Classification of Remote Sensed Data. 21. Elhag, M. and Bahrawi, J., Conservational use of Remote Sensing of Environment, 37 (1991): 35-46. remote sensing techniques for a novel rainwater 36. Frazier, P. and Page, K., Water Body Detection and harvesting in arid environment. Environmental Delineation with Landsat TM Data. Earth Sciences, 72(12) (2014b): 4995-5005. Photogrammetric Engineering and Remote 22. Vesey-Fitzagerald, D. F., The vegetation of Central Sensing, 66(12) (2000): 1461-1467. and Eastern Saudi Arabia. Journal of Ecology, 45 37. Huang, W., Liu, H., Luan, Q., Jiang, Q., Liu, J. and (1957): 779-789. Liu, H., Detection And Prediction of Land Use 23. Al-Nafie, A.H., Phytogeography of Saudi Arabia. Change in Beijing Based on Remote Sensing and Saudi Journal of Biological Sciences, 15(1) (2008): GIS. The International Archives of The 159–176. Photogrammetry, Remote Sensing and Spatial 24. Jensen, J., Rutchey, K., Koch, M. and Narumalani, Information Sciences. Vol. XXXVII (2008): Part S., Inland Wetland Change Detection in the B6b, Beijing. Everglades Water Conservation Area 2A Using a 38. McGranahan, G., Balk, D. and Anderson, B., The Time Series of Normalized Remotely Sensed Data. rising tide: assessing the risks of climate change Photogrammetric Engineering and Remote and human settlements in low elevation coastal Sensing, 51 (1995): 199–209. zones: Environment and Urbanization, 19(1) (2007): 17-37.

ELHAG: DETECTION OF TEMPORAL CHANGES OF EASTERN COAST OF SAUDI ARABIA 37

39. Al-Zahrani, K. H., Water demand management in the Kingdom of Saudi Arabia. Conference of International Journal of Art and Science, 2 (2010): 68 – 76. 40. Al-Zahrani, K. H. and Baig, M. B., Water in the Kingdom of Saudi Arabia: Sustainable Management Options. The Journal of Animal and Plant Sciences, 21 (2011): 601– 604.