Journal of Shipping and Ocean Engineering 2 (2012) 238-243 D DAVID PUBLISHING Shoreline Change Prediction Model for Coastal Zone Management in Thailand Siriluk Prukpitikul, Varatip Buakaew, Watchara Keshdet, Apisit Kongprom and Nuttorn Kaewpoo Geo-Informatics and Space Technology Development Agency, Bangkok 10210, Thailand Abstract: The prediction of shoreline erosion is vital for coastal management. This study aims to utilize geo-informatics technology to increase accuracy of a shoreline prediction model along two study sites in Samutprakarn province and in Prachuabkirikhan province. Predicting coastline change using remote sensing together with GIS (geographic information system) is a spatio-temporal technology, which can continuously provide perspectives of coastal areas. Due to a long term of operational period of LANDSAT satellite, it is useful to enhance accuracy of prediction model. LANDSAT-5 TM images acquired during 1999-2009 were used to produce historical shoreline vectors. Physical data were modified to be input data of digital shoreline analysis system. The model was validated. Linear regressions were applied in order to derive equations of erosion magnitude. The result presents that averaged erosion and accretion rate along Samutprakarn province was 22.30 meters/year and 2.94 meters/year, respectively. On the other hand, the average rate of coastal erosion along Prachuabkirikhan province was much lower, being 2.48 meters/year while the accretion rate was approximately 4.11 meters/year. The predicted shoreline change at Samutprakarn province in 2019 is about -132.69 ± 0.758 meters while at Prachuabkirikhan is 40.58 ± 0.0012 meters. In conclusion, this prediction model focused the changing of shoreline in long term and accuracy of the model could be improved by increasing number of shorelines vectors, transect intervals and resolution of satellite images. Clearly, the model is flexible and can be applied in other particular areas for coastal zone management in Thailand. Key words: Shoreline change, coastal prediction model, geo-informatics technology. 1. Introduction Thailand s coastline is about 2,600 kilometers along the Gulf of Thailand and the Andaman Sea. The Gulf of Thailand coastline is 1,650 kilometers long, covering 17 provinces while the Andaman coastline is 950 kilometers long, bordering six provinces. There are over 11 million people living in coastal regions. Therefore, the coastal regions are significant in terms of residential, industrial and commercial aspects while serving as areas for tourism as well as habitats for marine species. However, anthropogenic interruptions resulting from coastal developments and urban expansions may cause an imbalance to occur along the coastline. Shoreline may be severely eroded, causing a great loss for coastal land, governmental and Corresponding author: Siriluk Prukpitikul, M.Sc., research fileds: remote sensing and GIS application in marine and coastal zone (water quality and coastal erosion monitoring). E-mail: siriluk2000@yahoo.com. residential properties, as well as traditional livelihood. During the last 10 years, coastal erosion has chronically invaded Thailand s coastline. Erosion severity was more than 1,200 rai (1 rai = 1,600 square meters) in some locations (Fig. 1). Mitigation measures are urgently required. However, the proper mitigation measures can only be established when science-based information is available for coastal managers. Predicting coastline change using remote sensing together with GIS (geographic information system) is one of the most potential methods since this spatio-temporal technology can continuously provide perspectives of coastal areas. This article demonstrates how to utilize such approach for coastal erosion. Two case studies were presented. The first study site was at Tambol Lamfapa, Prasamutjaydee District, Samutprakarn province as shown in Fig. 2A, and the second study location was at Tambol Klongwan, Muang District, Prachuabkirikhan province in Fig. 2B.
Shoreline Change Prediction Model for Coastal Zone Management in Thailand 239 distributed throughout the image. Image rectification utilized second-order polynomials equation and set to be less than one pixel. Nearest neighbor was used as a re-sampling method. The output images had a resolution of 25 25 meters. A near-infrared band was used for a shoreline-extracting process. Shoreline classification was exported to shapefiles in order to verify accuracy of the shoreline by using physical, oceanographic and tidal data. 2.1 Model Description Fig. 1 Historical coastal erosion in Thailand [1]. (A) (B) Fig. 2 Study sites. 2. Methodology In order to investigate shoreline change situation along the Gulf of Thailand, a long-term data set comprising of LANDSAT-5 TM composite band (RGB: 321) in 1999-2009, GCP (ground control point), tidal data from Hydrographic Department, and topographic maps from the Royal Thai Survey Department were processed. Pre-processing consisted of geometric correction and image enhancement. The image enhancement was undertaken by an image-to-image technique. An ortho-image was applied as the reference with at least 15 GCPs being DSAS (digital shoreline analysis system) was applied. This model was a dynamically-segmented linear model with a 4D (four dimensional) system, including x, y, z and t (time). The output contained a distance measurement which was later used to compute a rate of change along each transect. A baseline was created from shoreline buffering by using a 20-meter interval. A calculation of shoreline change was done using an estimated distance of shoreline movement and the rate of change. A summary of shoreline movement was undertaken and a prediction of future shoreline positions was carried out [2]. Historical shorelines were taken from LANDSAT-5 TM during 1999-2009. An extrapolation of a constant rate-of-change was applied to predict future shoreline position [3]. EPR (end point rate) and LRR (linear regression) were calculated [2, 4, 5]. The EPR is calculated by dividing the distance of shoreline movement by the time elapsed between the earliest and latest measurements. The major advantage of the EPR is its ease of computation and minimal requirement for shoreline data [6]. The LRR statistics can be determined by fitting a least squares regression line to all shoreline points for a particular transects. The overall steps are summarized in Fig. 3. 2.2 Model Validation An essential process of developing a model is to validate its accuracy. A shoreline vector from
240 Shoreline Change Prediction Model for Coastal Zone Management in Thailand Fig. 4 A model validation at Tambol Laemfapa, Samutprakarn province. Fig. 3 Procedures in a shoreline prediction model. LANDSAT-5 TM in 2009 was used as a baseline and shoreline vectors in 1999-2007 were utilized for modelling. In Samutprakarn province, we found that the correlation between the predicted and the actual data was 0.5718 and the accuracy was 57.18% (Fig. 4). However, in Prachuabkirikhan province, the correlation between the predicted (a red line in Fig. 5) and the actual shoreline (a yellow line in Fig. 5) was low, being about 0.1362. The accuracy was 13.62%. The underlying reason was that there were a lot of coastal structures in such area, including two piers, 12 detached breakwaters, a large breakwater, and jetties at a nearby river mouth. The study site at Tambol Klongwan was further divided into zone A and B in order to investigate impacts of such coastal structures. A comparison of shorelines in zone A featured 100% accuracy. Unlike zone B where coastal structures were constructed in 2007, the accuracy was 7.02 % (Fig. 5). Thus, the accuracy of the prediction model depended on coastal characteristics, existing coastal structures and the number of historical shoreline vectors. Fig. 5 A model validation at Tambol Klongwan, Prachuabkirikhan province.
Shoreline Change Prediction Model for Coastal Zone Management in Thailand 241 3. Results and Discussion An averaged erosion and accretion rate along Samutprakarn province was 22.30 meters/year and 2.94 meters/year, respectively (Fig. 6A). Coastal landuse along Samutprakarn province was mainly aquaculture and degraded mangrove forests. Such coastal characteristics accelerated coastal erosion, which was not accounted for in the model developed. On the other hand, the average rate of coastal erosion along Prachuabkirikhan province was much lower, being 2.48 meters/year while the accretion rate was approximately 4.11 meters/year (Fig. 6B). The model has objectives to forecast shoreline movement as well as its rate of change. At Samutprakarn province, shoreline was eroded severely. A linear regression found a fitted equation, being y = -21.175x + 42619.67. This equation implied that an average rate of coastal erosion was 21.175 meters per year and the minus sign indicated erosion moved inland. The coastline in 2019 was predicted to recede about -132.69 ± 0.758 meters (Fig. 7). Similarly, an equation derived from the linear regression along Prachuabkirikhan province was y = 0.0193x + 1.633. The slope of the equation was 0.0193 meters per year. The average distance of shoreline position in year 2019 was predicted to be 40.58 ± 0.0012 meters (Fig. 8). In 2019 along Samutprakarn province, coastal erosion would be drastic with an erosion magnitude of 200-700 meters (Fig. 9A). Areas that would be likely to experience the erosion were categorized based on the predicted severity (Fig. 9B). Fig. 7 The linear regression at Samutprakarn province. Fig. 8 The linear regression at Prachuabkirikhan province. (B) Fig. 6 Shoreline change rate at (A) Samutprakarn province and (B) Prachuabkirikhan province. Fig. 9 (A)The distance from baseline, (B) shoreline position in year 2019 at Samutprakarn province.
242 Shoreline Change Prediction Model for Coastal Zone Management in Thailand Future shoreline along Prachuabkirikhan province would be moderately eroded. The erosion magnitude was predicted to be between 21.12 and 128.4 meters (Fig. 10A). There would be no coastline movement (Fig. 10B). Accuracy of the model could be improved by increasing number of shorelines vectors, transect intervals and resolution of satellite images. Some limitations in this study were: (1) number of transects had to be less than 600 data sets, (2) a baseline should be rechecked for accuracy, and (3) the information about shoreline behavior provided by additional shorelines was neglected in cases where more than two shorelines were available. Thus, changes in sign or magnitude of the shoreline movement trend, or a cycle of behavior might be missed. However, this was the first step in developing an effective model to predict overall coastal changes, which was particularly important for coastal erosion management along the coast of Thailand in the long term. 4. Conclusions Development of shoreline change prediction model processed in two areas: (1) Prachuabkirikhan province where the change is represented due to man-made activities and (2) Samutprakarn province which is representative of the coastal changes resulting from natural causes. Running the model to analyze the current shoreline situation of these two areas presents that Samutprakarn province has greater erosion rate but less accretion rate compared to Prachuabkirikhan province. Result from model indicates the predicted shoreline situation in 2019 at Samutprakarn province which is still remaining serious problem if there is no proper prevention. In contrast to Prachuabkirikhan province, the predicted shoreline is in a lower changing due to a coastal prevention structure. The results of this study demonstrated that the shoreline change in critical area should be resolved urgently by responsibility agencies. In summary, this prediction model focused on the changing of shoreline in long Fig. 10 (A) The distance from baseline, (B) shoreline position at Prachuabkirikhan province in year 2019. term. However, this is the initial prediction model that needs further development and must take into account to include land use change that cause changing in coastal zone. In addition, the model is flexible to enhance accuracy with high resolution satellite image and cost-effective collection in the field. It is a powerful tool for investigation overall changing and can be applied in other particular areas for coastal zone management in Thailand. Acknowledgments This work is sponsored by Geo-Informatics and Space Technology Development Agency. The authors are also thankful to Dr. Cherdvong Saengsupavanich from Kasetsart University for writing advice. References (B) [1] GISTDA (Geo-Informatics and Space Technology Development Agency), Satellite appraisal and protection for coastal erosion along the coast of Thailand, in: International Meeting on CU Solving Coastal Erosion Problem in Thailand, Bangkok, Thailand, Nov. 30-Dec. 2, 2009. [2] E.R. Thieler, E.A. Himmelstoss, J.L. Zichichi, T.L. Miller, Digital Shoreline Analysis System (DSAS) Version 3.0: An ArcGIS Extension for Calculating Shoreline Change, U.S. Geological Survey open-file report 2005-1304, 2005. [3] D.W. Owens, Coastal management in North Carolina, APA Journal 51 (3) (1985) 322-329.
Shoreline Change Prediction Model for Coastal Zone Management in Thailand 243 [4] R.X. Li, J.K. Liu, Y. Felus, Spatial modeling and analysis for shoreline change detection and coastal erosion monitoring, Marine Geodesy 24 (2001) 1-12. [5] E.A. Himmelstoss, DSAS 4.0 Installation Instructions and User Guide, Digital Shoreline Analysis System (DSAS) Version 4.0: An ArcGIS Extension for Calculating Shoreline Change, U.S. Geological Survey open-file report 2008-1278, 2009. [6] T. Nguyen, J. Peterson, L. Gordon-Brown, P. Wheeler, Coastal change predictive modeling: A fuzzy set approach, World Academy of Science, Engineering and Technology 48 (2008) 468-473.