Shoreline Anomaly Mapping Using Multi-Temporal Remote Sensing - The Recent Updates after the December 2004 Tsunami in Phang Nga, Thailand

Transcription

1 Shoreline Anomaly Mapping Using Multi-Temporal Remote Sensing - The Recent Updates after the December 2004 Tsunami in Phang Nga, Thailand 1 Daroonwan KAMTHONKIAT, Department of Geography, Faculty of Liberal Arts, Thammasat University. THAILAND dawan@tu.ac.th Phone: / Fax: Aneak SAIWANRUNRKUL, Mangrove Administrative Division 2, Krabi Province. THAILAND - Phone: / Fax: - 3 Shunichi KOSHIMURA, Disaster Control Research Center, Graduate School of Engineering, Tohoku University. JAPAN koshimura@tsunami2.civil.tohoku.ac.jp, Phone: / Fax: Masashi MATSUOKA, GEO Grid Research Group, Information Technology Research Institute National Institute of Advanced Industrial Science and Technology (AIST). JAPAN m.matsuoka@aist.go.jp Phone: / Fax: ABSTRACT The shoreline or coastline extraction, a part of coastal zone monitoring is an important task in sustainable development and environmental protection in the tsunami affected area. In this paper, multi-temporal remote sensing images from ASTER data were analyzed for mapping the dynamics of shoreline in Phange Nga Province. The simple density slice was applied on Near Infrared band of satellite images to extract water and land boundary. The analyzed shorelines of each year were compared and monitored its shape. Most of the swept away beaches had recovered. While the erosion at Ban Thung Dap, Ko Phrathong in Khura Buri District and Ban Pak Koh in Takua Pa District is still active in different shape. KEY WARDS: Shoreline, the 2004 Tsunami, Multi-temporal Remote Sensing, ASTER, Phang Nga 1. Introduction Between 9.40 and a.m. local time on 26 December 2004, the first Indian Ocean Tsunami caused by the two strongest earthquakes had the magnitude of 9.3 and 7.3 on the Richter scale induced tsunamis hit the Andaman Coast (954 kilometers in length) including 6 provinces in the Southern Thailand; Phang Nga, Phuket, Satun, Krabi, Ranong and Trang. The highest death toll, damaged housing and left-homeless victims was from Phang Nga, 78.3 % of the total 5,395 casualties including foreigners and unidentified bodies (DDPM, 2008 and UNEP, 2005). After the catastrophe, a survey on damages and different geological changes caused by the Tsunami has been conducted through the Department of Mineral Resources, Ministry of Natural Resources and Environment. Beyond the heavy toll on human lives, it has been reported that severe damage has been inflicted on ecosystems such as mangroves, coral reefs, forests, coastal wetlands, vegetation, sand dunes and groundwater. In Phang Nga Province, the Tsunami carried sand sediments pilled up on the shore, created new 1

2 beaches and made river mouth shallow. In some areas, water courses, underwater sand bars and coastal sand dunes were transformed due to changes in the slope and nature of the sand (ONEP, 2006). It is crucial to identify the location of the shoreline and the changing position of this boundary through time, not only before and after Tsunami events but up-to-date information of shoreline are also of elemental importance to coastal scientists, engineers, and managers. The location of the shoreline can provide information in regard to shoreline reorientation adjacent to structures and beach width and volume, and it is used to quantify historical rates of change. In addition, an analysis of shoreline information is required in the design of coastal protection, to calibrate and verify numerical models, to develop hazard zones, to formulate policies to regulate coastal development, and to assist with legal property boundary definition and coastal research and monitoring (Boak and Turner, 2005). Due to the fact that the Tsunami affects wide areas, it is absolutely necessary to use Remote Sensing (RS) and Geographic Information Systems (GIS) technologies for obtaining an overview of the area which was affected by the tsunami and to map the anomaly or variance of the shoreline along Phang Nga beaches, comparing before and after the 2004 Tsunami (Koshimura and Kayaba, 2010, Vu T et al., 2007, Koshimura and Takashima M., 2006 and Phillips- Born et al., 2005). 2. Objective The goal of this study is to use multi-temporal RS and GIS to map the shorelines of Phang Nga Province. The anomaly of shorelines which was affected by the tsunami is reported and discussed. 3. Study Area The analysis was framed on the western coast area of Phang Nga Province covers 4 districts; Takua Thung, Tai Muang, Takua Pa and Kuraburi as shown in Figure 3. Khuraburi Takua Pa Tai Muang Takua Thung Figure 3 Study area : Phang Nga Province, Southern Thailand 2

3 The topography of Phang Nga consists mainly of hilly and mountainous area range from the north to the south and coastal area along the Andaman Sea. The coastal area is approximately kilometers. Physical characteristics of the coastal area in Phang Nga province comprise of estuary and narrow gulf. This makes water mass flow through a shallow bathometry and increases the higher degree of damage. 3. Materials and Methods 3.1 Materials The necessary materials are consists of digital maps, software, satellite images and others as followings Digital maps Landuse Map of Phang Nga provinces, 2000 at the scale 1:50,000 from the Land Development Department (LDD) Landuse Map of Phang Nga provinces, 2007 at the scale 1:25,000 from the Land Development Department (LDD) Topographic map of Phang Nga provinces, 2003 at the scale 1:50,000 from the Royal Thai Survey Department (RTSD) Software; ENVI 4.6, ArcView 3.2 and ArcGIS9.2, Others; handheld Global Positioning System (GPS), camera, etc., Satellite images; ASTER and LANDSAT The spatial analysis is performed using high performance optical sensor, ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) from GEO Grid, the National Institute of Advanced Industrial Science and Technology (AIST), Japan. ASTER has 14 bands from visible to thermal infrared region; 4 bands in visible and near infrared (VNIR) with 15 meters spatial resolution, 6 bands in short wave infrared region (SWIR) with 30 meters spatial resolution, and 5 bands in thermal infrared (TIR) with 90 meters spatial resolution. However the limited cloud free ASTER images were limited in 2004, LANDSAT (name indicating Land + Satellite) scene which acquired by the ETM+ (Enhanced Thematic Mapper Plus) sensor in February 2005 distributed for research purpose by the Global Land Cover Facility ( was another option as listed in Table 1. Table 1 Satellite Platform (No. of scene) ASTER (12 scenes) LANDSAT7 ETM+ (1 scene) The Analysis Images; ASTER and LANDSAT Wavelength, Spatial and Acquisition Date Resolution (meter) VNIR, 15 m 07 March 2003 VNIR, 15 m 31 December 2004* VNIR, 15 m 26 January 2006 VNIR, 15 m 6 February 2010 Panchromatic, 15 m and VNIR, 30 m 24 February 2005* Acquisition Time (GMT) 04:03:14 04:03:23 04:03:32 03:56:02 03:56:11 04:01:15 04:01:23 04:01:32 04:02:45 04:02:54 04:03:03 Acquisition Time (Local Time: am) 11:03:14 11:03:23 11:03:32 10:56:02 10:56:11 11:01:15 11:01:23 11:01:32 11:02:45 11:02:54 11:03:03 Tide (Estimated) N/A N/A Medium High 03:35:12 10:35:12 N/A Note: * Since ASTER image on 31 December 2004 has rather poor (cloud covered and covered just a fraction of west coast of Phang Nga), LANDSAT ETM+ were substituted at the poor locations as a date. 3

4 The acquisition time lag between ASTER and LANDSAT is about a half of hour as mentioned in the above table. The selected images for shoreline mapping were acquired during December March, covering a dry season Thailand. In Phang Nga Province, tides are semidiurnal with two high and low tides in 1 day, with an average tidal range of 1.55 meter (Choowong et al., 2008). Heights of water predicted in meters above the lowest low water by the Hydrographic Department, Royal Thai Navy showed that water heights at 2 stations in Phang Nga; Ao Tap Lamu, Tai Muang District and Khuraburi, Kuraburi District. Table 2 presents the heights of water on the 2 nd February, The lowest water always appear around 06:35 in the morning and evening while the highest water appear around a half hour after midnight and behind a half pass noon time. Table 2 Heights of Water at the Same Date of Satellite Acquisition Date Ao Tap Lamu Khuraburi Time Height (meter) Time Height (meter) 2 February, (ASTER) Source: Heights of water predicted in meters above the lowest low water, 2010 Figure 2 presents the fluctuation of water height within a day on the 26 th January, The heights of water predicted in meters above the lowest low water at 11 am are about the medium height at 1.2 meter. In this study, the medium tide is therefore assumed for tidal condition in the images. It should be noted that a different water height at the acquisition data and time of satellite image might induce an error of shoreline identification around 1-2 meters in horizontal. It should be noted that different tides on the acquisition dates of the multitemporal images might affect the position of shoreline less than 2 meters. Heights of water in meters Tides in 24 hour on 26th January 2006 Ao Tab Lamu Station, Phang Nga Hours Figure 2 Water heights within a day on the 26 th January, (Source: Hydrographic Department, Royal Thai Navy, 2010) 4

5 3.2 Methods In reality, the shoreline is not easy to identify in the nature in contrast to the coastline, which is based on a clear morphological shift between the shore and the coast. Therefore, to analyze shoreline variability and trends, a functional definition of the shoreline is required (Boak and Turner, 2005). Technically, shoreline is the intersection between the mean high water line and the shore. The line delineating the shoreline on Nautical Charts (Sea Maps) approximates this Mean High Water Line see also the coastal profile in Figure 3 below (Mangor, 2008). Figure 3 The Coastal Profile (modified from Mangor and Karsten, 2008) It was the fact that the specific definition chosen is generally of lesser importance than the ability to quantify how a chosen shoreline indicator relates in a vertical/horizontal sense to the physical land water boundary. In this study, shoreline detection technique applied to visibly discernible shoreline features is image processing or semi-automatic interpretation from satellite data. The selected geo-coded ASTER and LANDSAT images were coregistered or adjusted to the correct scale (15 m spatial resolution). Extraction of shoreline on specifically tidal flats has rarely been investigated in depth. The reflectance of flats tidal mud is affected by various parameters, including the particle size, the moisture content, the local slope, and the turbidity of seawater. For extracting of the waterline on tidal flat areas, selecting an appropriate band or combination of bands is very important. Figure 4 present the area of Laem Prakarang (Coral Reef Point/Cape) Tambon Kuk Kak, Takua Pa District, in an individual band, ratio vegetation index (Normalized Difference Vegetation Index - NDVI) and the false color composite, the sharpest boundary between water and land is clearly presented in NIR. ASTER RED ASTER - Green ASTER NIR ASTER - NDVI ASTER False Color Composite Figure 4 Band Comparisons of ASTER Data 5

6 Beyond manual digitization, several techniques have been reported in the literature for the derivation of the coastline position from satellite images (Chen and Rau, 1998, Frazier and Page, 2000, Ryu et al., 2002, Braud and Feng, 1998, Boak, E.H. and Turner, I.L., 2005 and Phillips-Born, et al., 2005). The most common are density slice using single or multiple bands and multi-spectral classification, both supervised and unsupervised classification (Soille and Bagli, 2003). In this study, the spectral distribution in Near Infrared band of ASTER and LANDSAT were observed for identifying the slice ranges. In the analyzed ASTER and LANDSAT ETM+ images, two ranges of value 0-50 and were sliced. Figure 5 presents the histogram of ASTER (Band 3N: NIR) acquired on 07 March 2003, the overall histogram exhibits two peaks and a valley of water (the left peak) and land (the left peak), namely, a bimodal shape (Liu and Jezek, 2004). The slicing method was applied to the analysis images to extracted 2 classes of water and land as shown in Figure 6. Water Land Figure 5 The Bimodal Histogram ASTER 2003 ASTER 2004 LANDSAT 2005 ASTER 2006 ASTER 2010 Sliced Image Figure 6 Multi-temporal Satellite Data and A Sample of Density Sliced Image 6

7 The results of density slice were performed post-classification clump or substituted the isolated pixels of inland water to the class of land. The results were then exported to GIS format for smoothing and improving the edge/boundary between water and land. 4. Results Since the 2 years restoration of Thailand s natural resources and environment after the 2004 Tsunami has been reported by the Office of Natural Resources and Environmental Policy and Planning, Ministry of Natural Resources and Environment, Thailand (2006) and some related studies (Choowong et al., 2008, Srivicahi et al., 2007), the analysis in this study will not emphasis on the 2006 data. In the results, the anomaly of shorelines in 2003, and 2010 were observed using some referenced points such as roads nearby the coast. 1.Laem Pakarang 4. Bang Lut Beach 3.Thap Tawan Beach 2.Klong Phru Sai Roads Figure 7 Comparisons of Shorelines at Laem Pakarang and Thap Tawan, Takua Pa District 6.Ban Nok Na 7.Ban Pak Ko 5.Ko Kor Khao Roads Figure 8 Comparisons of Shorelines at Ko (Islands) named Kor Khao, Takua Pa District 7

8 9.Ban Thung Dap 10.Ban Pak Chok Figure 9 Comparisons of Shorelines at Ko (Islands) Phrathong, Khura Buri District The distances between the referenced points to the shorelines of 2003, and 2010 were listed in Table 3 corresponded to the presented in Figure 7-9. Table 3 Distances between the Referenced Points to the Extracted Shoreline Locations Name (Direction of Measurement)) 1. Laem Pakarang, Ban Pramong (NW to the sand dune) 2. Klong Phru Sai, Ban Bang Khaya (E-W) 3. Thap Tawan Beach, Ban Thai Mai (E-W) * 4. Bang Lut Beach, Ban Bang Pling (E-W) Distance from the referenced point to the Extracted Shoreline (meters) Referenced Point Road (-673) 495 (-178) Road (-60.5) 234 (+88) Road 68 27(-41) 190 (+122) Road (-40) 272 (-22) 5.Ko Kor Khao (E-NW) Ban Kor Kor Khao 1, (-352) 942 (-70) 6.Ban Nok Na (E-W) Road 1,465 1,360 (-105) 1,515 (+50) 7.Ban Pak Ko (E-W) Pier Ban Pak Ko (-47) 295 (-155) 8.Bam Nam Khem (E-W) Pier Ban Nam Khem (-85) 203 (-59) 9a. Ban Thung Dap, Ko Phrathong (N-S) Ban Thung Dap (-17) 191 (-44) 9b.Ban Thung Dap, Ko Phrathong (E-W) Ban Thung Dap (-44) 421 (-76) 10.Ban Pak Chok, Ko Phrathong Pier Ban Pak Chok (-41) 832 (+59) Note: + / - are the subtracted distance of to 2003 and 2010 to

9 5. Conclusions After the 2004 Tsunami, the mouths of rivers and lagoons along the coast of Phang Nga were extended by the strong wave, such as Kong Phru Sai in Ban Bang Khaya, Klong Khuek Khak in Ban Khuek Khak, Ban Thung Wa Nok and Ban Bang Niang in Takua Pa District and BanThung Wa, Ban Nai Rai and Ban Khuan in Tai Muang District. Although a strip sand dune at Laem Pakarang was blown away after the disaster, it filled back more than 50% in 2010 but in different shape. In Table 10, the most serious erosions (entirely damaged) more than 100 meters from the shoreline were occurred at No.1, No.5 and No.6. While other observed locations were also serious with the level of erosion between meters from the shoreline. In 2010, these locations were recovered and filled back the swept away beaches. The area of beaches at No.2, No.3, No.6 and No.10 were extended beyond the shoreline of On another hand, the erosion has been removing the beach/land at No. 7, No.9a and No.9b. The shoreline at these locations is keep changing, For further study, the existing methods of automatic shoreline extraction using remote sensing and other sources of data are challenging to apply for large area analysis (Ryu, et al., 2002, Bagli and Soille, 2003, Di, et al., 2003, Ouma and Tateishi, 2006). 6. Acknowledgements Sincere thanks go to the financial supported by the project ID 08E52010a which is hosted by Tohoku University, supported by the New Energy and Industrial Technology Development Organization (NEDO) of Japan. The author also appreciated the support from the GEO Grid, the National Institute of Advanced Industrial Science and Technology (AIST) for supporting the ASTER data during this research. 7. References Alesheikh,A.A., Ghorbanali, A., Nouri, N., Coastline Change Detection Using Remote Sensing. International Journal of Environ. Sci. Tech., 4 (1): 61-66, 2007 ISSN: Bagli, S., Soille, P., Morphological Automatic Extraction of Coastline from Pan-European Landsat TM Images. Proceedings of the Fifth International Symposium on GIS and Computer Cartography for Coastal Zone Management, October 2003, Genova. Italy Boak, E.H. and Turner, I.L., Shoreline Definition and Detection: A Review. Journal of Coastal Research, 21(4), West Palm Beach (Florida), ISSN Braud, D. H., and Feng. W., Semi-automated Construction of the Luisiana Coastline Digital Land/Water Boundary Using Landsat Thematic Mapper Satellite Imagery. Technical Report , Department of Geography & Anthropology, Luisina State University. Luisiana Applied Oil Spill Research and Development Program, OSRAPD, Chen, L. C., and Rau, J. Y., Detection of Shoreline Changes for Tideland Areas Using Multitemporal Satellite Images. International Journal of Remote Sensing, 19 (17): , Frazier, P. S., and Page, K. J., Water Body Detection and Delineation with LANDSAT TM Data. Photogrammetric Engineering and Remote Sensing, 66 (12): , December Choowong, M., Phantuwongraj S., Charoentitirat T., Chutakositkanon V., Yumuang S., Charusiri P., Beach recovery after 2004 Indian Ocean tsunami from Phang-nga, Thailand. Geomorphology (2009) 104, Publisher : Elsevier (Available online at DDPM, Final Report, Project Strengthening Thailand s Capacity on Tsunami Warning, This FY Cooperative project was undertaken by Department of Disaster Prevention and Mitigation (DDPM), Ministry of Interior, Thailand In collaboration with Asian Disaster Reduction Center (ADRC), 28 March

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