See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/255700349 Medium resolution satellite imagery as a tool for monitoring shoreline change. Case study of the Eastern coast of Ghana Conference Paper  in  Journal of Coastal Research · March 2013 DOI: 10.2112/SI65-087.1 CITATIONS READS 17 298 3 authors: Philip Jayson-Quashigah Kwasi Appeaning Addo University of Ghana University of Ghana 5 PUBLICATIONS   52 CITATIONS    67 PUBLICATIONS   494 CITATIONS    SEE PROFILE SEE PROFILE Sosthenes Kufogbe University of Ghana 5 PUBLICATIONS   86 CITATIONS    SEE PROFILE Some of the authors of this publication are also working on these related projects: Deltas, Vulnerability & Climate Change: Migration & Adaptation (DECCMA) View project Coastal and marine resources management in the ECOWAS region View project All content following this page was uploaded by Philip Jayson-Quashigah on 19 May 2014. The user has requested enhancement of the downloaded file. Medium resolution satellite imagery as a tool for monitoring shoreline change. 511 Medium resolution satellite imagery as a tool for monitoring shoreline change. Case study of the Eastern coast of Ghana Philip-Neri Jayson-Quashigah†, Kwasi Appeaning Addo‡ and Kufogbe Sosthenes Kodzo§ †Coastal and Continental Shelf Processes ‡ Department of Marine and Fisheries §Department of Geography and Resource Project, Department of Marine and Sciences, University of Ghana, P.O. Box Development, University of Ghana, www.cerf-jcr.org Fisheries Science, University of Ghana, LG 99 Accra, Ghana. kappeaning- P.O.Box LG 59, Accra, Ghana. Box BT 99 Accra, Ghana. addo@ug.edu.gh skufogbe@ug.edu.gh pnjquashigah@gmail.com ABSTRACT Jayson-Quashigah, P-N., Appeaning Addo, K. and Kufogbe, S.K., 2013. Shoreline monitoring using medium resolution satellite imagery, a case study of the eastern coast of Ghana. In: Conley, D.C., Masselink, G., Russell, P.E. and O’Hare, th T.J. (eds.), Proceedings 12 International Coastal Symposium (Plymouth, England), Journal of Coastal Research, Special Issue No. 65, pp. 511-516, ISSN 0749-0208. www.JCRonline.org Shoreline change analysis provides important information upon which most coastal zone management and intervention policies rely. Such information is however mostly scarce for large and inaccessible shorelines largely due to expensive field work. This study investigated the potential of medium resolution satellite imagery for mapping shoreline positions and for estimating historic rate of change. Both manual and semi-automatic shoreline extraction methods for multi- spectral satellite imageries were explored. Five shoreline positions were extracted for 1986, 1991, 2001, 2007 and 2011 covering a medium term of 25 years period. Rates of change statistics were calculated using the End Point Rate and Weighted Linear Regression methods. Approximately 283 transects were cast at simple right angles along the entire coast at 200m interval. Uncertainties were quantified for the shorelines ranging from ±4.1m to ±5.5m. The results show that the Keta shoreline is a highly dynamic feature with average rate of erosion estimated to be about 2m/year ±0.44m. Individual rates along some transect reach as high as 16m/year near the estuary and on the east of the Keta Sea Defence site. The study confirms earlier rates of erosion calculated for the area and also reveals the influence of the Keta Sea Defence Project on erosion along the eastern coast of Ghana. The research shows that shoreline change can be estimated using medium resolution satellite imagery. ADDITIONAL INDEX WORDS: Coastal Erosion, Landsat, ASTER, DSAS, Keta Sea Defence Project. INTRODUCTION of regional shoreline dynamics (Blodget et al., 1991; Chu et al., Coastal zones are facing intensified natural and anthropogenic 2006). disturbances including sea level rise, coastal erosion, over Remote sensing techniques provide a synoptic vision of the exploitation of resources among others. Over 70% of the world’s Earth that is not possible to obtain other than by exhaustive and beaches are experiencing coastal erosion and this presents a expensive field evaluations. Data from remote sensors allow serious hazard to many coastal regions (Appeaning Addo et al., analysis of a region with sufficient accuracy in an efficient, rapid 2008). According to Zhang (2010), awareness of the quality of and low-cost way (Berlanga-Robles and Ruiz-Luna, 2002). It also global coastal ecosystems being adversely impacted by multiple helps in analysing areas that are poorly accessible or rapidly driving forces has accelerated efforts to assess, monitor and changing (Chu et al., 2006). The use of remote sensing data is mitigate coastal stressors. Monitoring temporal–spatial changes of therefore increasingly becoming a more effective option for coastal environments can help understand among others, the monitoring shorelines. Over the years, geomorphologists, spatial distribution of erosion hazards, predicting their oceanographers and geologist have developed interpretation keys development trend and supporting the mechanism research on for mapping coastline geomorphic features using aerial coastal erosion and its countermeasures. photographs; however, few studies of this type have used images The shoreline, which is defined as the position of the land-water generated by remote sensing orbital instruments (Kawakubo, interface at one instant in time (Gens, 2010) is a highly dynamic 2011). Though the use of aerial photographs tends to be effective feature and is an indicator for coastal erosion and accretion. The in this case, the frequency of acquisition, cost and coverage processes of erosion and accretion affect human life, cultivation presents a challenge (Appeaning Addo et. al., 2008). Furthermore, and natural resources along the coast. Rapid shoreline changes can the spectral range of these sources is minimal and may introduce create catastrophic social and economic problems along populated errors in shoreline interpretation (Alesheikh et al., 2007). strands. Design of viable land-use and protection strategies to On the other hand, multispectral remote sensing satellites reduce potential loss is necessary and this requires comprehension provide digital imagery in various spectral bands, including the near infrared where the land-water interface is well defined. ____________________ Furthermore this approach has advantages: less time consuming, DOI: 10.2112/SI65-087.1 received 07 December 2012; accepted 06 inexpensive to implement, large ground coverage, and the March 2013. © Coastal Education & Research Foundation 2013 Journal of Coastal Research, Special Issue No. 65, 2013 512 Jayson-Quashigah, et al. capability for repeat data acquisition and monitoring (Van and Bihn, 2008). The principal limitation of satellite images is arguably their low spatial resolution when compared to photographs taken from aircraft (Kawakubo, 2011). Coastal Zone of Ghana According to Armah and Amlalo (1998), Ghana’s coastal zone represents about 6.5% of the land area of the country, yet houses 25% of the nation’s population. This small strip of land now hosts 80% of the industrial establishments in Ghana. Over 70% of the shoreline of 550km is sandy. Coastal erosion, flooding and shoreline retreat are serious problems along the coast. According to Ly (1980) the eastern coast has been identified as the most erodible stretch with rates as high as 4m/year prior to the construction of the Akosombo Dam on the River Volta. The construction of the Dam in the early 1960’s has supposedly reduced sediment supply to this coast offsetting the balance between the sediment lost to longshore drift and replenishment. Erosion rates increased reaching as high as 8m/year around 1970. There have been interventions such as the Keta Sea Defence Project (KSDP) which involved stabilization of the shoreline with break water and groynes, construction of a flood control structure Figure 1. Location of the Study Area (after Ly, 1980; Ghana and land reclamation from the lagoon. The KSDP was completed Survey Department) in 2004 (GLDD, 2001). These among others have influenced the accretion and erosion patterns along this coast. about 3m. They normally arrive from the direction between south This paper explores the analysis of shoreline change using and south west with an average period of 10.91s which may reach medium resolution satellite imagery including Landsat TM, a maximum of 19.68s (Svašek Hydraulics, 2006). Tides are semi- Landsat ETM+ and ASTER imagery. Data for the study spans diurnal with an average range of about 1m (Appeaning Addo, over a period of 25years and covers the period before and after the 2009). The tidal currents caused by this tides are weak. construction of the KSDP. Erosion and accretion patterns are compared before and after the sea defence to determine the Data and processing influence of the KSDP on rates. Five imageries including Landsat and Aster (Table 1) were used for analysing shoreline change. The imageries span a period METHODS of 25 years and were acquired from the United States geological Study Area survey, earth resources observation and science centre. The coastal zone of Ghana is generally divided into three The Landsat TM data was resampled using nearest neighbour st sections, the western, central and eastern based on the and 1 order polynomial transformation to 15m. For the Landsat geomorphology (Ly, 1980) (Figure 1). The Eastern coast, which ETM+ data, the panchromatic band with a resolution of 15m was is about 149km, stretches from Aflao (Togo Border) in the East to used to sharpen the six multispectral bands to obtain a new image the Laloi Lagoon west of Prampram. The shoreline studied covers at 15m. The Gram-Schmidt pan sharpening algorithm in ENVI about 52 km of this stretch, from the eastern side of the Volta which is based on principal component analysis was used. estuary to Blekusu east of Keta. The area falls between latitudes The ASTER VNIR bands were already at 15m but were 5º25' and 6 º 20' North and between longitude 0 º 40' and 1 º 10' acquired at L1A (raw data), hence the need for geometric East. The landscape consists of a large shallow lagoon (Keta correction/rectification. The VNIR bands were co-registered Lagoon complex) surrounded by marshy areas with a sandbar (Image to image) to the Landsat 2001 ETM+ data using 30 (sand spit) separating the lagoon from the Gulf of Guinea and a visually interpreted Ground Control Points (GCP). The total root number of creeks along the coast. The sand spit is very narrow; mean square (RMS) error was 0.35m. barely more than 2.5km at its widest point with a general elevation up to 2m above mean sea level (Awadzi et al., 2008; Boateng Shoreline Extraction 2009). The dry wet/boundary which approximates the high waterline The geology of the area is soft and generally comprises (HWL) was extracted using semiautomatic and manual methods. quaternary rocks and unconsolidated sediments made up of clay, Previous studies used the HWL as the shoreline proxy for change loose sand and gravel deposits (Akpati, 1978). The Volta River analysis in Ghana (Appeaning Addo et al., 2008; Appeaning System, the main source of sediment supply to this basin, consists Addo, 2009; Ly, 1980). Band ratio between the mid infrared (band of a larger drainage basin, broad delta plain, narrow shelf, steep 5) and the green (band 2) was used to identify the water-land upper slope, and a large basin floor. Recent mapping of the sea boundary for the Landsat images except the 2011 image due to the bed topography reveals the presence of numerous canyons gaps in the data. This was used so as to reduce the level of (valleys) from the shelf all the way to the deepwater (Manu et al., subjectivity in delineating the shoreline. These were edited and 2005). used for change analysis The ASTER and Landsat ETM+ 2011 Two types of wave approach this coast, the seas generated by were however directly digitized. the weak, local monsoon and the swell generated by storms in the southern part of the Atlantic Ocean. Average wave height for the area between 1997 and 2006 is 1.39m but may reach a height of Journal of Coastal Research, Special Issue No. 65, 2013 Medium resolution satellite imagery as a tool for monitoring shoreline chnage. 513 Table 1. Imagery Characteristics uncertainty. 15m was used as scale/resolution uncertainty for all Data Path/Row Date Bands Resolution Level images. The tidal range (1m) of the area was negligible and therefore Landsat 192/56 1986- 6MS 30m L1T was not accounted for as a source of uncertainty due the TM 01-13 resolution of the imagery used. A total shoreline positional error Landsat 192/56 1991- 6MS 30m L1T for each epoch (Ex) was therefore calculated using the following TM 01-03 equation: Landsat 6MS 30m L1T 2 2 2= (   ) (3) Ex Es Ep Er ETM+ 192/56 2001- 1 Pan 15m 01-30 Where Es is the error occurring from scale difference, Ep is the ASTER 192/56 2007- 3VNIR 15m L1A photogrammetric error and Er is the registration error. This 11-06 approach carries the assumption that component errors are Landsat 6MS 30m L1T normally distributed (Dar and Dar, 2009). ETM+ 192/56 2011- 1 Pan 15m The total uncertainties were used as weights in the shoreline 01-10 change calculations. The values were annualised to provide error (Et) estimation for the shoreline change rate at any given transect and expressed as: 2 2 2 2 2 (4) E = (E E E   ) / Shoreline Analysis t 1 2 3 E4 E5 The shoreline positions were compiled and managed in ArcGIS 9.3. The Digital Shoreline Analysis System (DSAS 4.2) where E1, E2,... E5 are the total shoreline position error for the developed by the USGS in 2010 (Himmelstoss, 2009) was used various years and T is the 25 years period of analysis. for rate estimation. The DSAS uses measurement baseline method to calculate rate of change statistics for a time series of shorelines. The maximum annualised uncertainty using best estimate for The baseline is constructed to serve as the starting point for all this study is ±0.44m/year. transects cast by the DSAS application. Transects were cast at simple right angles from the baseline at 200m interval. Historic rates of shoreline change were then RESULTS AND DISCUSSION calculated at each transect using end point rate (EPR) and weighted linear regression (WLR). In all, 7 shoreline positions were extracted for change The EPR was employed where only two shoreline positions detection (Figure 2). Change rates were calculated for the period were available as was the case for the period between 2001 and between 1986 and 2007 and then for 1986 to 2011(Figure 3). The 2007. The EPR is calculated by dividing the distance between the results show that there have been significant changes along the shorelines by the number of years that have elapsed. entire coast for the 25 years period under study. For the period between 1986 and 2011, about 40% of transects were ignored due R  Dm/T (1) to the gap in the 2011 shoreline positions. This affected change rates especially near the estuary. However, overall rates show little Where R is the rate, Dm is the distance in meters between the two variation when compared. The averages of the calculated rates are dates and T is the period between the two shoreline positions. shown in Table 2. For the entire period the WLR method was used for Overall rates ranges from -12m/year to 18m/year where calculating the rates. The method was also used to calculate negative values represent erosion and positive values represent changes for periods between 1986 and 2001 (period before the accretion (Figure 3). Using the 1986 to 2007 results as a reference, KSDP) and between 2001 and 2011 (the period during and after about 45% of the entire shoreline experienced erosion while the the KSDP). Both periods had more than two shoreline positions remaining have mostly accreted. mapped. In computing WLR, more reliable data thus shoreline Accretion rates range from 0.1m/year to 19m/year with an positions with smaller uncertainty, are given greater emphasis or average 2.50m/year while erosion rates were between 0.1 to weight towards determining a best-fit line. The slope of the 9.30m/year with an average of 2.38m/year. Both rates are regression line between the shoreline positions at each transect is significantly high. reported as the change rate (equation 2). The Keta area has been much accreting with rates reaching about 18m/year while the area between Keta and Blekusu has been eroding with rates at an average of 3.50m/year with some sections y  mx b (2) recording as high as 9m/year. Near the estuary there are extreme Where y = distance from baseline; m = slope (rate of change) and cases of erosion and accretion over the period and rates are as high b = y-intercept. as -11m/year and 17m/year respectively. For the entire shoreline, For this study, uncertainties were quantified using estimates erosion and accretion rates average at 2m/year. based on studies such as Crowell et al. (1991) and Moore (2000) The period before the KSDP (1986 to 2001) revealed that erosion and Hapke et al., (2010). Additional errors, which were associated dominates the entire shoreline (Figure 4) with about 70% of the with the imagery used for this study, were estimated. Four main cast transects recording erosion. Erosion rates range from 0.1 to sources of error were identified to account for the uncertainties. 15.40 m/year with an average of 3m/year and accretion rates Errors resulting from image registration, digitization of the ranging from 0.1m/year to 21m/year with an average of shoreline, position of HWL and differences in resolution were 5.90m/year. The higher erosion rates occur between Keta and considered. Resampling the 1986 and 1991 images from 30m to Blekusu and around Atorkor and Anyanui while the area between 15m did not add any spatial information but rather added to the Journal of Coastal Research, Special Issue No. 65, 2013 514 Jayson-Quashigah, et al. Table 2. Average erosion and accretion rates Period Erosion Accretion (m/year) (m/year) 1986-2011 1.91 2.04 1986-2007 2.38 2.77 1986-2001 3.10 5.17 2001-2011 4.52 5.59 2001-2007 4.68 10.04 Figure 2. Extracted Shorelines Keta and Anloga have experienced significant accretion. Close to the estuary there is evidence of both erosion and accretion over the period. Here erosion rates were as high as15m/year and accretion rates also at a high of 14m/year. These high rates have led to the destruction of coastal dwellings within the period. It is estimated Figure 4. Erosion and Accretion Rates between 1986 and 2001 that about 70% of the Keta Township now lies under sea values were threatened. This led to the initiation of the KSDP (Fiadzigbey, 2005). Beaches as well as ecological and aesthetic which was completed in 2004. The project involved among others the establishment of groynes, revetments and beach nourishment. The period between 2001 and 2011 which is considered the period after the KSDP shows a reversal of situations with the (a) entire coast experiencing more accretion (about 80%) (Figure 5). However, erosion rates have remained high ranging from 0.1 to 17m/year with an average as high as 4.50m/year. Accretion rates also were high ranging from 0.1 to 26m/year and an average of 5.6m/year. The area between Keta and Blekusu (down drift of the KSDP) and the area near the estuary (up drift of the KSDP) remain high points of erosion over this period with rates reaching as high as 16m/year. This trend reveals the fact that, such ‘ad hoc’ management interventions like the KSDP classically tend to stabilise the shoreline at the protected section and aggravate the situation elsewhere along the shoreline (“knock-on effects”). Overall it is evident that most areas that experienced erosion between 1986 and 2001 have accreted between 2001 and 2007. The immediate vicinity of the Keta Township continues to accrete as well as areas around the estuary with values reaching 17m/year. (b) Most portions of the Cape have also accreted. Erosion trends According to Ly (1980), erosion rates along the eastern coast have increased after the construction of the Akosombo dam in 1962. The rates reached as high as 8m/year as compared to the 4m/year high rates before the construction. The result of this study reveals high rates of erosion along the entire coast for the period under study from 1986 to 2011 thus an average rate of about 2m/year ±0.44m. This confirms the high rates of erosion in the area. The period before the construction of the KSDP marked intense erosion along the entire coast with rates reaching as high as 15m/year and an average of about 3.10m/year for the area near Figure 3. Rates of Change (a) 1986-2007 and (b) 1986- Keta and the Volta estuary. This has led to destruction of many 2011 Journal of Coastal Research, Special Issue No. 65, 2013 m/yr m/yr m/yr Medium resolution satellite imagery as a tool for monitoring shoreline chnage. 515 coastal facilities and homes especially at Keta. It also confirms the assertion that the Cape has been retreating since the construction of the Akosombo Dam (Boateng, 2009). (a) (a) (b) (b) Figure 6. Destruction caused by sea erosion at (a) Blekusu and (b) Atorkor Figure 5. Erosion and Accretion Rates (a) 2001-2007 CONCLUDING REMARKS (b) 2001-2011 Results of this study have been useful in revealing the trends in shoreline change along the eastern coast of Ghana. Although As part of efforts to curb the situation, the Keta Sea Defence aerial photographs are traditionally the main sources of data for project was initiated with work beginning in 2001. Thereafter, the shoreline monitoring, the study has shown that medium resolution rates indicate more accretion to the west of the site and erosion to multi-spectral satellite imagery can be used to map and monitor the east. Erosion rates remain high averaging 4.52m/year while the large and dynamic shoreline this coast. The approach could accretion rates were also as high as 5.50m/year. The high erosion also be replicated along the entire coast. rates in the east have led to the destruction of houses at Blekusu The findings generally confirm the high rates reported for this and its surrounding communities (Figure 6a). This situation area after the construction of the Akosombo Dam. Average confirms the knock-off effects by hard coastal protection erosion rates are estimated to be 2m/year with the sections to the measures. extreme east and west experiencing higher rates. Previous studies Further down to the west (near the estuary) erosion rates had estimate erosion rates be 1.13m/year ±0.17 for the Accra shoreline also increased leading to the destruction of homes and schools. As (Appeaning et al., 2008) and Ly (1980) place estimates for the well the road linking Anyanui in the far west to Keta was eastern coast between 4-8m/year. The current rates reflect this completely cut off at Atorkor (Figure 6b). Efforts are underway to general trend. protect this area from further erosion. The comparison of rates before and after the KSDP reaveals Natural factors such as the high energy of waves in the area, the structure is currently playing a role in the erosion and soft geology and the orientation of the shoreline as well as sea accretion patterns in the area. Erosion is now taking place down level rise account for the high erosion in these areas. Topographic drift (Blekusu and beyond). However, the shoreline around Keta mapping of the sea bed around the estuary has also revealed and Cape St. Paul has been experience accretion since the canyons offshore which causes waves to break at higher speed and completion of the KSDP. This study confirms the ‘knock-on increase erosion in the area. These are however aggravated by effects’ of ad hoc coastal hard protection along the coast of Ghana human factors such as the construction of the Akosombo Dam on and supports the call for shoreline management planning (SMP). the Volta Lake in 1965 which led to reduction in sediment supply to the area, ad hoc interventions such as the Keta Sea Defence ACKNOWLEDGEMENT Project, sand mining and mangrove harvesting and development We are grateful to the USGS for making DSAS and satellite close to the shore (leading to land squeeze). imagery available for this work. We also thank Scott Mitchell and Doug King of Geography and Environmental Studies Department, Journal of Coastal Research, Special Issue No. 65, 2013 m/yr m/yr 516 Jayson-Quashigah, et al. Carleton University, Canada for their contribution. We are also Dar, I.A. and Dar, M.A., 2009. Prediction of Shoreline Recession grateful to the University of Ghana for funding support. 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