Shoreline change assessment using remote sensing ...

Shoreline change assessment using remote sensing and GIS techniques: a case study of the Medjerda delta coast, Tunisia Mourad Louati, Hanen Saïdi & Fouad Zargouni

Arabian Journal of Geosciences ISSN 1866-7511 Arab J Geosci DOI 10.1007/s12517-014-1472-1

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Author's personal copy Arab J Geosci DOI 10.1007/s12517-014-1472-1

ORIGINAL PAPER

Shoreline change assessment using remote sensing and GIS techniques: a case study of the Medjerda delta coast, Tunisia Mourad Louati & Hanen Saïdi & Fouad Zargouni

Received: 5 August 2013 / Accepted: 16 May 2014 # Saudi Society for Geosciences 2014

Abstract Eight scenes of Landsat Multispectral Scanner, Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager sensors, covering the period between 1972 and 2013, were used to demarcate shoreline positions and estimate shoreline change rates of the Medjerda delta coast, northeastern Tunisia. The method relies on image processing techniques using the IDRISI software, and the Digital Shoreline Analysis System, a free extension for ArcGIS software, which provides a set of tools permitting transects-based calculation of shoreline displacement. First, the Landsat images were radiometrically and geometrically corrected. Second, band ratioing, reclassification, raster to vector conversion, and smoothing techniques are applied successively to detect and extract the multi-temporal shoreline data. Third, these data are overlaid and the changes are calculated using the end points and linear regression methods. The results indicate significant shoreline changes ranging from 8.6 to −42.6 m/ year, while some parts remained unchanged. The estimated shoreline change rates are comparable with those obtained through in situ measurements and from the analysis of multidate aerial photos and toposheets. The main causes of erosion in particular are related to the natural shifting of the Medjerda River course and mouth, damming of Medjerda and its tributaries, construction of Ghar El Melh port, and the destruction of the small bordering dunes in addition to the wave-induced M. Louati (*) : H. Saïdi : F. Zargouni Laboratory of Geomatics and Structural and Applied Geology, Department of Geology, Faculty of Sciences of Tunis, University of Tunis El Manar, 2092 Tunis, Tunisia e-mail: [email protected] H. Saïdi e-mail: [email protected] F. Zargouni e-mail: [email protected]

longshore currents, relative sea level rise due mainly to accelerated coastal subsidence, and sand mining. Keywords Shoreline change . Landsat imagery . Band ratioing . Digital shoreline analysis system . Medjerda delta . Tunisia

Introduction Rapidly changing shorelines are a serious problem in most deltas on earth, such as sections of the Medjerda delta coast. In addition to rapid shoreline changes (Oueslati 2004), this coast faces many other issues such as the progressive deterioration of the Ghar El Melh lagoon ecosystem (Moussa et al. 2005), degradation of the natural environment of sebkhet Ariana (Turki et al. 2009), and sedimentation in the navigation channel of the Ghar El Melh port (STUDI 2007a). Accurate and up-to-date information on shoreline change magnitudes can help researchers in a large range of coastal studies such as the erosion–accretion aspects, coastal defense designing, prediction of shoreline future positions, hazard zoning, environment protection, safe navigation, and sustainable coastal resources management (Szmytkiewicz et al. 2000; Dellepiane et al. 2004; Maïti and Bhattacharya 2009; Davidson et al. 2010). Shoreline or coastline is defined as the line of contact between land and a body of water (Pajak and Leatherman 2002). Shoreline is one of the rapidly changing coastal landforms (Mills et al. 2005; Marfai et al. 2008; Mujabar and Chandrasekar 2013). There are a number of approaches to mapping and detection of shoreline change. The conventional field surveys achieve high-accuracy measurements but are labor intensive, costly, and time consuming (Liu et al. 2007; Natesan et al. 2013). High-resolution aerial photographs contain details of terrain features, and shorelines can be observed

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and delineated with great precision (Fletcher et al. 2003; Liu et al. 2011), however, periodic overflights and aerial photograph analyses imply a high cost for updating coastlines (Guariglia et al. 2006; Zhao et al. 2008). The modern Light Detection And Ranging (LiDAR) technology (White and Wang 2003; Morton et al. 2005; Liu et al. 2007), Synthetic Aperture Radar (SAR) images (Yu and Acton 2004; Al Fugura et al. 2011), and video systems (Aarninkhof et al. 2003; Plant et al. 2007) have great potentials, but few data sets are available, making it difficult to infer changes over time. The repetitive coverage and multispectral nature of the satellite imagery is most suitable for mapping and updating the dynamic of the shorelines at regular intervals costeffectively and accurately (Guariglia et al. 2006; Kuleli 2010; Al-Hatrushi 2013). Therefore, mapping and detection of coastline changes from satellite images have become increasingly important over the recent decades, especially because remote sensing satellites provide digital imagery in infrared spectral bands where the land-water interface is well defined (Alesheikh et al. 2007; Durduran 2010; Cui and Li 2011). Furthermore, remote sensing data can be integrated with Geographical Information Systems (GIS) as an effective tool for analyzing and extracting more reliable and consistent information by using satellite imagery as a base data (Goodchild 2001; Durduran 2010; Cui and Li 2011). The total length of the Tunisian coastline is about 1,300 km of which 525 km is sandy, including 40 km of deltaic coastline (Paskoff 1988). There is dearth of studies on shoreline change assessment with remote sensing methods and GIS techniques in Tunisia. The notable exception of the works are from the Northwestern fringe (Halouani et al. 2007, 2011, 2013), Tunis Gulf (Saïdi et al. 2012; Louati and Zargouni 2013), Hammamet Gulf (Fathallah et al. 2010), Gabes Gulf (Bardi et al. 2011), and DJerba Island (Bouchahma et al. 2012; Bouchahma and Yan 2013). The present paper focuses on the recent (1972-2013) shoreline change of the Medjerda Delta coast using satellite image processing techniques and GIS tools especially aimed at estimating shoreline horizontal displacements, locating erosion and accretion segments, and clarifying the major natural and anthropogenic factors controlling coastline evolution.

Study area The delta of Medjerda River is a good example of a delta in a Mediterranean environment. Its development is fairly well known from geological and geomorphological studies, corroborated by archaeological data. The delta has formed during the last 5,000–6,000 years by the filling, from north to south, of a former bay drowned by the Versilian marine transgression. Human activity played an important role in its evolution.

Deforestation in ancient times considerably increased the solid load by the river, and public works (artificial levees, manmade cutoff, drainage, and diversion canals) completed during the present century have interfered with natural processes (Paskoff 1978). The Medjerda delta coast is located northwest of the Tunis Gulf on the northeastern part of the Tunisian Mediterranean littoral (Fig. 1). This deltaic coast is a very low-lying region and extends to about 40 km alongshore from Farina Cape to Gammarth Cape. The major coastal landforms are the long sandy shorelines, spit of Foum El Oued, lagoon of Ghar El Melh, and sebkhet Ariana. The beach sediments are mainly fine (D50, ~0.2 mm) to very fine (D50, ~0.1 mm) quartz sands, whereas the average nearshore (up to 5-m depth) slope is around 1 % (El Arrim 1996; Saidi et al. 2013). The bordering dunes are small and poorly developed with heights not exceeding 2 m (Oueslati 2004). The most important urban agglomerations along the study area are Ghar El Melh, Kalaat Landlous, and Raoued. The tide regime in the Tunis Gulf is semi-diurnal and micro-tidal with mean amplitudes of 12–30 cm (El Arrim 1996; Saïdi et al. 2012). The relative sea level rise in the study area during the last century has reached 11.5 mm/year of which 1.5 mm/year has been attributed to eustacy (Pirazzoli 1986; Calafat and Gomis 2009), and 10 mm/year to natural subsidence due to sediment compaction in the deltaic plain (Banque Mondiale 2011). Offshore of Tunis Gulf, the most prevailing and strongest wind waves are approaching from the northeastern direction, however those coming from southeast and east are non-negligible (Ben Charrada and Moussa 1997). Breaking obliquely on the shore, the prevailing waves generate a southward-flowing longshore current and a littoral sediment transport flux in that direction (Paskoff 1981; Oueslati 2004). The long-term (1972–2001) mean significant wave height and period are of 1.25 m and 4.5 s, respectively (wave data acquired from the KNMI/ERA-40 Atlas: http://www. knmi.nl/waveatlas). Along the beach between Farina Cape and the northeastern jetty of Ghar El Melh port, the average longshore sand transport rate, estimated using the empirical formulae of Central Laboratory for Hydraulics of France (LCHF), ranges between 30,000 and 40,000 m 3 /year (Paskoff 1981). Except the beach from Farina Cape to the northeastern jetty of Ghar El Melh port where the sediment supply is by longshore transport from eroding sandstone cliffs located on either side of Farina Cape and gully erosion on the Djebel Ennadhour slopes, most of the sediment is brought by the Medjerda River, the major Tunisian watercourse (Paskoff 1981; Oueslati 2004). The Medjerda basin extends from across the border in Algeria up to the Tunis Gulf. Its catchment covers 23,700 km2 of which 16,100 km2 is in Tunisia. The hydrological regime of the Medjerda is characterized by low daily flows that are disrupted by flash floods when it rains.

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Fig. 1 Location of the study area

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The mean daily runoff is equal to 32 m3/s whereas it exceeds 980 m3/s during floods (Bouraoui et al. 2005). Eight dams have been constructed on Medjerda and its tributaries (Zahar et al. 2008), namely: Nebeur and Beni M’tir in 1954, Laroussia in 1957, Lakhmess in 1966, Kasseb in 1968, Bou Heurtma in 1976, Sidi Salem in 1981, and Siliana in 1987 for hydroelectric generation, flood control, and irrigation. These dams intercepted sediments and, consequently, the present annual sedimentary discharge of Medjerda has been reduced by approximately 70 % compared with the natural condition from 17 × 10 6 ton (Pimienta 1959) to 5 × 106 ton (Ben Mammou 1998; Zahar 2001). The most important coastal engineering works in the study area are Ghar El Melh and Kalaat Landlous fishing ports built in 1974 and 1995, respectively (STUDI 2007a, b). The Ghar El Melh port especially is composed of a shipping area, which is an inland basin, an access channel connecting the shipping area with the Mediterranean Sea, and two jetties forming an entrance to the port. The northeastern and southwestern jetties have respective lengths of 300 and 200 m. In order to reduce sedimentation problems in the access channel and to maintain the navigation depth, the channel has been partially dredged five times from 1983 to 1991 (Oueslati 2004). The dredged sediments have been discharged into the Mediterranean Sea at depths of more than 10 m (Daoud 1993). Moreover, the northeastern jetty was extended to 487 m and two groins, which are 258 and 187 m long, respectively and 400 m apart from each other alongshore are built in 1993, aimed at preventing the navigation channel from clogging by littoral drift (Oueslati 2004).

from which shoreline change magnitudes are estimated along the study area. They are downloaded in GeoTIFF format at no cost from the US Geological Survey (USGS) Earth Explorer Website (http://www.earthexplorer.usgs.gov). These Landsat data constitute the largest useable database of mediumresolution images that is available now. All the scenes acquired pertain to summer season in good quality and are free of cloud (at least over the shorelines of interest) and sensor defects such as striping or banding. The MSS was a four-band sensor, with two visible (VIS) channels in the green and red wavebands and two near-infrared (NIR) channels. These channels, numbered 1–4, respectively, have a spatial resolution of 60×60 m. The TM wavebands are as follows: channels 1–3 cover the VIS spectrum, channel 4 has a wavelength range in the NIR, channels 5 and 7 cover the mid-infrared (MIR) while channel 6 is the thermal infrared (TIR) channel. The ETM + sensor detects reflected radiation from the Earth’s surface in the same seven bands as the TM and has an additional panchromatic band (channel 8), having a resolution of 15×15 m. The newest Landsat-8 satellite launched 11 February 2013 carries two instruments: the OLI and the Thermal Infrared Sensor (TIRS). The OLI instrument provides enhancement from prior ETM+sensor, with the addition of two new spectral bands: a deep blue VIS channel (band 1) and a new infrared channel (band 9). The TIRS instrument collects two spectral bands (bands 10 and 11) for the wavelength covered by a single TIR band on the previous TM and ETM + sensors. The scenes in the VIS, NIR, and MIR channels of TM, ETM+, and OLI sensors have a resolution of 30× 30 m. Methodology

Data and methodology Landsat imagery Eight scenes of Landsat-1 Multispectral Scanner (MSS), Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus (ETM+), and Landsat-8 Operational Land Imager (OLI) sensors, covering a 41-year period from 1972 to 2013 (Table 1) are used to extract shoreline positions Table 1 Landsat imagery used in the study

Sensor

Date of acquisition

Path/Row

MSS

11 August 1972

206/34

TM TM

25 June 1984 28 June 1985

191/34

TM

05 August 1987

ETM+

14 August 1999

ETM+

31 May 2001

TM

17 August 2003

OLI

11 July 2013

The research methodology includes (1) image pre-processing, (2) image processing leading to shoreline feature extraction, and (3) shoreline change rates estimation. IDRISI Selva 17.00 and ArcGIS 10.1 software are mainly used for the study. Image pre-processing The images pre-processing is the process of making them more suitable for a particular purpose. In this study, as the band ratioing is the main technique to perform, which leads to shoreline data extraction, the Landsat images are required to be geometrically matched (pixels to be compared are in the same area) and radiometrically consistent (the values of pixels refer to the same physical units). If these requirements are not met, the analyst can interpret as differences in ratios values, what might be in fact different locations or differences unrelated to the reflectance of land-cover types (Singh 1989; Song et al. 2001; Zhan et al. 2002). During the geometric correction especially, the resampling operation step may produce changes in the pixel values, and, consequently, can conduct to

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erroneous ratio. In order to reduce these undesirable pixel values alteration, the Landsat images pre-processing is going to begin by the radiometric correction. Radiometric correction A full radiometric correction would include subtraction of the atmospheric contribution, reduction of illumination, view angles and terrain effects, and sensor calibration (Lillesand et al. 2004; Mather and Koch 2011). First, the atmospheric correction is necessary because the effects of scattering increase inversely with wavelength, so the shorter-wavelength measurements experience more scattering than the longer-wavelength data. The computed ratio will thus be a biased estimate of the true ratio (Lillesand et al. 2004; Mather and Koch 2011). The dark object subtraction (Chavez 1996), a simple and widely utilized model, is selected to reduce the atmospheric effects, and the clean deep seawater of the Mediterranean Sea east of the study area was used as the dark object. Second, the Atmosc tool of IDRISI software, used to achieve the current radiometric correction in one step, combines the sun and view angle effects, and sensor calibration with the atmospheric correction. The needed parameters (offset/gain, sun elevation and satellite viewing angles, etc.) are included with the Landsat metadata documentation. Finally, with regard to terrain effects, it is known that, on the one hand, these effects are minimized in the situation of lowrelief areas (Gu and Gillespie 1998; Hantson and Chuvieco 2011), and on the other hand, the ratio show a much-reduced slope and aspect effects (Lillesand et al. 2004; Mather and Koch 2011). Geometric correction According to the metadata documentation, the MSS, TM, ETM+, and OLI images used in this study are ortho-rectified products. They are indeed in the World Geodetic System (WGS 84) datum and the Universal Transverse Mercator (UTM) projection system. The assessment of the images geometric quality by superimposing linear objects such as roads permitted to observe significant discrepancies particularly in the case of MSS images. To address this problem, it was necessary to georeference all the images. The most recent Survey of Tunisia toposheets of 1983 (1:25,000 scale in digital format) are utilized as base maps to georeference the Landsat-5 TM image acquired in 1987 into the UTM projection (UTM/Carthage/Zone 32 North), through image to map georeferencing. Then, the image of 1987 was considered as the master image from which the other scenes were georeferenced through image-to-image registration. During each georeferencing process, more than 40 welldistributed ground control points (GCPs) were selected carefully from permanent marks such as road intersections, corners of buildings and coastal structures. A first-order polynomial transformation model and nearest-neighbor image resampling (as no change occurs to the pixel values) are applied. The overall accuracy of the transformation, expressed as

the root mean square error (RMSE) for georeferenced images, was less than 0.5 pixels. It should be noted that in order to improve the images and help identify GCPs, linear stretching enhancement has been applied on the MSS images showing low contrast. Shoreline extraction Numerous semi-automatic and automatic methods are currently in use to extract shoreline from optical satellite imagery. In the case of Landsat data, supervised and unsupervised classifications (Guariglia et al. 2006; Ekercin 2007; Muslim et al. 2007; Kurt et al. 2010; Hereher 2011), thresholding (Frazier and Page 2000; Ryu et al. 2002; Yamano et al. 2006; Cui and Li 2011), and band ratioing (Winarso et al. 2001; Guariglia et al. 2006; Thao et al. 2008; Kulelei 2010; Cui and Li 2011) are among the most famous and simplest techniques. In the present study, the band ratioing is utilized to extract shoreline features from the Landsat imagery. In fact, such technique exploits infrared wavebands that are highly absorbed by water (even turbid water) and strongly reflected by vegetation and soil (Kelly et al. 1998; Alesheikk et al. 2007; Cui and Li 2011). The main peculiarities of this technique are ease of application, low time consumption, and the absence of operator intervention (Guariglia et al. 2006; Chand and Acharya 2010). After selecting the subsets of MSS, TM, ETM+, and OLI images covering the study area, four successive steps are followed to accomplish such extraction. The first step consists of ratios selection and calculation. In fact, the well-known ratio B5/B2 in the case of TM/ETM+data (Fig. 2) and the correspondent B6/B3 for OLI ones are used (Guariglia et al. 2006; Kulelei 2010; Cui and Li 2011). However, in the case of MSS image, three ratios B4/B2 (Winarso et al. 2001; Cui and Li 2011), (B3+B4)/B2 (Thao et al. 2008) and B4/(B2−B1) (Guargila et al. 2006) are tested, and the latest one is retained as it gives the best and quietest discrimination between land and sea. The reclassification is the second step; the ratios having values less than 1 are reclassified 1(water pixels), else 0 (land pixels) (Fig. 3). The third step is a raster-to-vector conversion, which once applied on the binary images, allows extracting especially the lines features (Fig. 4). The shoreline data particularly have a zagged appearance providing an unrealistic representation of the shoreline geometry. Finally, a 3-point moving average lowpass filter, a smoothing line technique, is used to reduce this kind of appearance without affecting the shoreline size (Fig. 5a, b). Shoreline change estimates Subdivision of the study area For convenience in shoreline change estimates, clarity in interpretation, analysis and presentation, the 40-km long littoral stretch under study has been

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maximum georeferencing error of ±45 m for the periods 1972–1987 and 1972–2013, and of ±30 m for the periods 1987–1999 and 1999–2013, have been considered in the following estimates of shoreline shift rates. Second, the short-term variability of shoreline position may be due to beach profile seasonal oscillations, meteorological and/or astronomic tide effects. On the one hand, the fact that the used Landsat scenes were acquired in the summer period and during calm sea conditions, the seasonal variability and meteorological tide effects are not accounted for in this study. On the other hand, Louati and Zargouni (2013) found that the maximum shift in shoreline position with regard to astronomic tide effect is oscillating between ±2.5 and ±9.5 m along the Tunis Gulf. Regarding these values and the spatial resolution of Landsat images going from 30×30 to 60×60 m, it is possible, as adopted in previous works (Guariglia et al. 2006; Dewidar and Frihy 2008; Kuleli 2010), to estimate shoreline change from Landsat images, without the interference of the tidal factor. Taking into account all the above considerations, the total error of ±3, ±2.5, ±2.1, and ±1.1 m/year for the periods 1972–1987, 1987–1999, 1999–2013, and 1972–2013, respectively may safely be linked to georeference process.

Fig. 2 Example of applying the ratio B5/B2 for Landsat-5 TM acquired in 1987

subdivided into five broad coastal sectors (CS1 to CS5; Fig. 1). This subdivision is based on the natural and artificial discontinuities alongshore. The CS1 is ~5.5 km long starting from Farina Cape to the northeastern jetty of Ghar El Melh port. The CS2 of ~5.6 km length extends between the southwestern jetty of Ghar El Melh port and the old mouth of Medjerda. The spit of Foum El Oued (CS3) has variable length over the study period; it ranged between 3 and 5 km. The CS4 is a 9.4-km-long section between the jetty of Kalaat Landlous fishing port and the current mouth of Medjerda. Finally, the ~15.1-km-long CS5 lies between the current mouth of Medjerda and the Gammarth Cape. Uncertainty in shoreline change The analysis of shoreline position change using satellite imagery has a range of error (Maiti and Bhattacahrya 2009; Kumar et al. 2010), and quantifying results that are statistically significant is critical to shoreline change studies (Addo et al 2011). In this study, the potential sources of errors are the images georeferencing and short-term variability of shoreline position. First, the RMSE in georeferencing process did not exceed 0.5 pixels. Hence, a

Transect-from-baseline approach The lines data are exported from IDRISI format to Shapefiles, and after removing the undesirable objects, the retained shoreline data are utilized as inputs to the Digital Shoreline Analysis System (DSAS 4.0) (Thieler et al. 2009) to measure the amount of shoreline change. The DSAS, a free extension for ArcGIS, is a useful set of tools to construct transects automatically and extract statistical results from a baseline and a set of historical shoreline data. A baseline is generated parallel to the general trend of the set of shorelines. It serves as the line origin of perpendicular transects directed towards the shorelines. The transect-frombaseline approach is based on the premise that if there was a shift in the position of a point on one shoreline to a corresponding position on the second shoreline, the distance associated with this translation of the shoreline is the measurement of shoreline change (Smith and Cromley 2012). In this study, the baselines were built onshore about 100 to 300 m landward by using the buffering technique of ArcGIS and editing tools of DSAS. In order to cover the most significant shoreline changes, the transects are set to be at a common 20 m spacing alongshore. At each transect, the end points method is used to estimate the rates of change between two overlaid shoreline data relative to intermediate periods (1972– 1987, 1987–1999, and 1999–2013). These rates are indeed estimated by determining the distance between two shoreline data and dividing it by the number of years between them (Dolan et al. 1991; Crowell et al. 1997). However, the linear regression (LR) method is chosen to estimate the change rates over the entire study period (1972–2013). It has been shown that LR, requiring at least four shoreline data (Fenster et al.

Author's personal copy Arab J Geosci Fig. 3 Reclassification of the ratio B5/B2 image

1993), is the most statistically robust method as it minimizes potential random error and short-term variability (Douglas and Crowell 2000; Genz et al. 2007; Dawson and Smithers 2010). Using this method, the rate of change is the slope of the least squares regression line fitted to all shoreline points (Thieler et al. 2003). The eight shoreline data are used when applying the LR method; the accuracy of calculated rates of change generally increases with the number of historic shorelines data used (Addo et al 2011). Finally, it is worth mentioning that, on the one hand, transects showing changes within the error range are excluded from the statistical appraisals of shoreline movements, and on the other hand, the coefficient (R2) in the LR is always not below 0.79.

Results and discussion Along every sector of the study area, the transect-based analysis shows space–time variability in coastline change. Changes along CS1 sector The changes in coastline position between 1972 and 2013 along CS1 sector between Farina Cape and the northeastern jetty of Ghar El Melh port are shown in Fig. 6. During 1972– 1987, the 0.8-km-long segment northward from the northeastern jetty of Ghar El Melh port manifested accretion; the respective mean and maximum shoreline advances were of

Author's personal copy Arab J Geosci Fig. 4 Vectorization of the reclassified image

7.1 and 11±3 m/year. This accretion is mainly due to the blockage of the southward longshore drift by this jetty. From 1987 to 1999, the accretion continued and characterized the segments northward (1.1 km length) and southward (long of 0.1 km) from the second groin, while rest of shoreline exhibited stability. Indeed, the average shoreline advancement was of 4.4±2.5 m/year with a maximum of 8.9±2.5 m/year immediately north of the second groin. This accretion has resulted from an excessive deposition of sediment trapped on the updrift side of the second groin that is gradually bypassed by sand. The accretion continued during 1999–2013 and characterized the 2-km-long segment northward from the first groin. The average and maximum progresses were in fact of 4.3 and 8.3±2.1 m/year, respectively. Over the whole study period

between 1972 and 2013, the 2-km-long segment northward from the northeastern jetty displayed accretion with mean and maximum rates of 3.4 and 5.8±1.1 m/year, respectively. Changes along CS2 sector When monitoring shoreline changes of CS2 sector between the southwestern jetty of Ghar El Melh port and the old mouth of Medjerda River, three stretches can be differentiated (Fig. 7). From 1972 to 1987, as the longshore sand transport is unidirectional southerly, erosion has taken place in the downdrift side of the southwestern jetty of Ghar El Melh port; the mean and maximum shoreline regressions attained −7.4 and −9.5±3 m/year, respectively. Toward the south, a 2-km-

Author's personal copy Arab J Geosci Fig. 5 Smoothing of the shoreline feature relative to the CS1 sector between Farina Cape and the northeastern jetty of Ghar El Melh port: a before smoothing and b after smoothing

long second stretch manifested accumulation resulting in an average shoreline progress of 9.4±3 m/year with a maximum of 13.1±3 m/year. Significant erosion occurred during the same period along the third stretch northward from the old mouth of Medjerda at an average shoreline withdrawal of −10.7±3 m/year with a maximum of −20.7±3 m/year. The principal cause of such severe erosion is the significant reduction sediment supply to this part due to the shift of Medjerda River course toward the south during the flood of March 1973 (Claude et al. 1977). Consequently, as highlighted in many studies of Mediterranean deltas (Li et al. 2000; Pranzini 2001; Sabatier et al. 2006), the abandoned delta lobe started to be destroyed by wave action and the shoreline migrated landward. From 1987 to 1999, the respective mean and maximum rates were of −5.3 and −11.4±2.5 m/year along the first

stretch, 4.8 and 6.4±2.5 m/year along the second one, and −10.2 and −24.3±2.5 m/year along the third one. Between 1999 and 2013, the respective average and highest changes were of −3.8 and −6.3±2.1 m/year along the first stretch, 4 and 6.3±2.1 m/year along the second one, and −9.5 and −23.8± 2.1 m/year along the third one. Over the whole study period, the respective mean and maximum rates were of −3.5 and −6.1± 1.1 m/year along the first stretch, 5.5 and 8.7±1.1 m/year along the second one, and −9 and −21±1.1 m/year along the third one. Changes along CS3 sector The spit of Foum El Oued (called Esshila by the local population) forms CS3 segment of the coast. During a flood in March 1973, the Medjerda River shifted its lowermost course.

Author's personal copy Arab J Geosci Fig. 6 Multi-temporal shoreline positions of the CS1 sector from Farina Cape to the northeastern jetty of Ghar El Melh port

The natural channel was abandoned, and the entire flow now passes through an artificial canal (Henchir Tobias canal) originally designed to evacuate flood discharge directly into the sea (Paskoff 1978). Both this natural shifting of Medjerda River and the construction of Ghar El Melh port disturbing the longshore sediment transport impacted the recurved spit of Foum El Oued, which has developed since the end of nineteenth century at the former mouth by the longshore drift of the river-borne sediment. Between 1972 and 1999, the average landward migration ranged between −27.3±2.5 and −41.3 ±3 m/year, with a maximum of −60.5±3 m/year. From 1999 to 2013, the spit has continued to recede by −29.6±2.1 m/year on average (Fig. 8). On the whole, the respective average and maximum retreats of the shoreline in CS3 section were of −38.4 and −42.6±1.1 m/year during 1972–2013. Changes along CS4 sector The shifts in shoreline position along CS4 sector between Kalaat Landlous port and the present mouth of Medjerda are given in Fig. 9. From 1972 to 1987, erosion (−3.9±3 m/year) occurred along a ~2.2-km-long segment to the north and along ~0.7-km-long segment to the south of the road to Kalaat Landlous. However, accretion took place immediately south of Kalaat Landlous port (long of ~0.25 km; 5±3 m/year) and north of the current mouth of Medjerda (~2.6 km length; 11.3 ±3 m/year). Between 1987 and 1999, while the segment to the south of Kalaat Landlous port continued to show accretion at 3.8±2.5 m/year, the segments on either side of the road to Kalaat Landlous experienced accelerated erosion leading shoreline retreat at −5.5±2.5 m/year on an average with a

maximum of −10.6±2.5 m/year. On the other hand, the southern part (~1.1-km length) of the segment north of the present mouth of Medjerda began to retreat by erosion at an average and maximum rates of -9.1 and −17.6±2.5 m/year, respectively while the remaining part continued to advance at a slower rate of 5.3±2.5 m/year. During the period 1999– 2013, the southern segment receded at −3.4±2.1 m/year, and the remaining part continued to advance at 3±2.1 m/year. However, the 0.7-km-long fringe in front of the tip of Foum El Oued spit manifested accretion (3.8±2.1 m/year). Over the whole study period, erosion characterized the segments on both sides of the road to Kalaat Landlous (−2.8±1.1 m/year) and immediately north of the present mouth of Medjerda (−4.1± 1.1 m/year), while the segments south of Kalaat Landlous port showed accretion (1.8±1.1 m/year) including the 3-km-long fringe at 0.7 km far from the current mouth of Medjerda (3.9±1.1 m/year). The major factors which led to shoreline recession on both sides of the road to Kalaat Landlous are the destruction of the bordering dunes and the vegetation that covers them due to frequent trampling by tourists, uncontrolled sand extractions from dunes and beaches (Bouhafa 1985; Oueslati 2004), and the offshore sediment loss during storms (Ayachi 2004). Oueslati (2004) attributed the accumulation trend at the fringe in front of the tip of Foum El Oued spit, during 1999–2013, to the protection provided by this spit against the breaking waves. Changes along CS5 sector Three segments are distinguishable when monitoring the shoreline dynamic of CS5 sector between the present mouth

Author's personal copy Arab J Geosci Fig. 7 Multi-temporal shoreline positions of the CS2 sector between the southwestern jetty of Ghar El Melh jetty port and the old mouth of Medjerda

Author's personal copy Arab J Geosci Fig. 8 Multi-temporal positions of the Foum El Oued spit

of Medjerda and Gammarth Cape (Fig. 10). Between 1972 and 1987, the first segment immediately south of the current mouth of Medjerda (0.5-km length) retreated by −6.2±3 m/ year on an average with a maximum of −8.4±3 m/year. This regression is due to the construction of Bou Heurtma and Sidi Salem dams in 1976 and 1981, respectively, the latter being the largest one in the Medjerda basin, traps approximately five million tons of sediment per year (Ben Mammou 1998). This significant decrease of sediment, representing almost 30 % of the total Medjerda discharge into the sea before its damming that began in 1954, and the continuous action of waves and the associated longshore currents led to shoreline erosion. The eroded sediment tends to be moved southward and deposited along a 2-km-long segment; the respective mean and highest shoreline advancements were of 15.2 and 23.1±3 m/year. The rest of the 13-km-long shoreline remained more or less stable. During 1987–1999, shoreline retreat in the first segment further intensified even over a longer stretch of 1.3 km when compared with the preceding period. The mean and maximum rates of retreat were of −9.4 and −23.4±2.5 m/year, respectively due to the construction of Siliana dam in 1986 among other reasons that exacerbated the impact of previous

Medjerda damming by Sidi Salem and Bou Heurtema. The second segment (1.7-km length) however showed slower accretion when compared with the previous period with an average and maximum rates of 4.8 and 6.7±2.5 m/year, respectively. From 1999 to 2013, similar trend continued in the first segment at a much reduced rate of retreat (−2.8±2.1 m/ year). This dramatic decrease is certainly due to significant increase in the solid load carried by the Medjerda to the sea during the floods occurred in May 2000, January–February 2003, January 2004, February 2005, January 2006, April 2009, November 2011, and February and March 2012. During the entire study period (1972–2013), the first segment retreated (−11.1±1.1 m/year), the second accreted (4.2± 1.1 m/year), and the third segment remained stable.

Validation of DSAS shoreline change The results obtained from DSAS analysis are validated by comparing the data on shoreline change available from in situ measurements and also calculated from aerial photographs and topographic maps, which indicated a good correlation.

Author's personal copy Arab J Geosci Fig. 9 Multi-temporal shoreline positions of the CS4 sector from the jetty of Kalaat Landlous port to the present mouth of Medjerda

Author's personal copy Arab J Geosci Fig. 10 Multi-temporal shoreline positions of the CS5 sector between the present mouth of Medjerda and Gammarth Cape

The maximum up-drift accretion (11±3 m/year) and down-drift erosion (−9.5±3 m/year) on both sides of Ghar El Melh port jetties, recorded between 1972 and 1987, are consistent with in situ measurements. Indeed, the shoreline advance relative to the pier head of the northeastern jetty, monitored from 1978 to 1993, was 14 m/year, whereas relative to the pier head of the southwestern jetty, the coastline retreated with −10 m/year on average during 1978–1990 (Oueslati 2004). With respect to the spit of Foum El Oued, Oueslati (2004), using aerial photographs and toposheets covering the period 1953– 2003, showed that the spit of Foum El Oued has moved landward at an approximate rate of −40 m/year. This value is very close to that estimated in this work between 1972 and 2013, which is of −38.4±1.1 m/year. Finally, based on in situ observations and aerial photographs spanning the period 1962–2003, Oueslati (2004) found that the segments on both sides of the road to Kalaat Landlous were receding at an average rate of −2 m/year. Therefore, the DSAS calculations yield a well-comparable value of −2.8±1.1 m/year from 1972 to 2013.

Conclusion The present research confirms the usefulness of image processing techniques and GIS tools applied on multi-temporal and multispectral Landsat images for assessment of the changes along the Medjerda delta coastline as the results obtained are fairly in agreement with those of in situ measurements or from the analysis of aerial photographs and topomaps. The deltaic coast of the Medjerda River has been subjected to a number of significant changes in the last four decades (1972–2013). Within this 40-km coastal strip, all possible trends have been observed, ranging from moderate accretion of 8.7±1.1 m/year to severe erosion of up to −42.6±1.1 m/ year, with some parts of the coast, however, remained stable. The predominant causes of erosion are both natural as well as anthropogenic including (1) significant reduction of sediment supply to the coast due to the natural shifting of Medjerda course and mouth, and effect of dam building on the Medjerda River and its tributaries, (2) the construction of Ghar El Melh port disturbing the shoreline stability by inducing downdrift erosion and updrift accretion on both sides of the port jetties,

Author's personal copy Arab J Geosci

and (3) the disruption of beach equilibrium due to the destruction of the bordering dunes and intense trampling of the associated vegetation by vacationers who frequent the region. The wave-induced lonsgshore currents redistributing sediments, relative sea level rise due especially to accelerated subsidence, and sand mining from dunes/beach are also contributing to the shoreline recession. Acknowledgments The authors sincerely thank the USGS for making available for free the Landsat data and the Digital Shoreline Analysis Software on its Websites.

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