International Journal of Remote Sensing Applications (IJRSA)

Editor-in-Chief: Eman M. Ghoneim
Frequency: Quarterly
ISSN Online: 2226-4353
ISSN Print: 2226-4361
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Shoreline Change Mapping Using Remote Sensing and GIS--Case Study: Bushehr Province

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Author: Ali Kourosh Niya, Ali asghar Alesheikh, Mohsen Soltanpor, Mir Masoud Kheirkhah zarkesh

Abstract: In both developed and developing countries, the coastal zone is likely to undergo the most profound change in the near future. More than 60 percent of the world's population lives within 60 km of the coast. By the turn of the century two-thirds of the population (3.7 billion) in developing countries have occupied the coast. Consequently, unless careful environmental management and planning are instituted, severe conflicts over coastal space and resource utilization are likely, and the degradation of natural resources will close development options. In addition to the population pressure, the world’s coastal areas and small islands are highly vulnerable to climate change. Low-lying delta, barrier coasts, low-elevation reef islands, and coral atolls are especially sensitive to the rising sea level, as well as to changes in rainfall, storm frequency, and intensity. Inundation, flooding, erosion, and saltwater intrusion are only a few of the potential impacts of climate change. Iran, connected to Caspian Sea in its north and to Persian Gulf and Gulf of Oman in its south, has totally about 5700 kilometers(scale 1:25000)coastlines and this country has the largest coastline in the Persian Gulf. A part of this coastline is located in Bushehr Province. For coastal zone monitoring, coastline extraction in various times is a fundamental work. Coastline, defined as the line of contact between land and a body of water , as one of the most important linear features on the earth's surface, holds a dynamic nature; therefore, coastal zone management requires the information about coastline changes. The main objective of this research was to estimate the coastline changes for a period of 1990 to 2005 using RS and GIS. In this research, TM satellite data, dated 1990, were compared with ETM+ satellite data of 2000 and 2005 in order to deduce changes. Different image processing techniques have been carried out to enhance the changes from 1990 to 2005. Band math, band ratio, supervised and unsupervised classification, post classification, band selection and masking were applied using GIS software. In this research, coastlines of the study area were extracted using satellite imagery. These changes were perpetual. However, the coastline has been changed significantly from 2001 to 2005. These great changes have happened as a consequence of development of the south Pars exclusive zone of energy (asaloyeh). A new approach was employed for coastline extraction, for which a histogram threshold together with band ratio techniques was utilized. In order to assess the accuracy of the results, they have been compared with ground truth observations. The accuracy of the extracted coastline has been estimated as 1.2 pixels (pixel size=30 m).

Keywords: Coastline Extraction; TM & ETM+sensors; Histogram Thresholding; Band Ratios; Remote Sensing



References:

[1] Alesheikh, et al., 2007, “Generation the coastline change map for Urmia Lake by TM and ETM+ imagery”

[2] Aplin, P. (2004), Remote sensing land cover: Progress in Physical Geography, vol. 28, pp. 283-293

[3] Birkett, C., and Mason, I. (1995), A new global lakes database for remote sensing programmed studying climatically sensitive large lakes. Journal of Great Lakes Research, vol.21 (3), pp. 307-318

[4] Birkett, C.M. (1994), Radar altimetry-a new concept in monitoring global lake level changes. Eos Trans. AGU, vol. 75 (24), pp. 273–275

[5] Birkett, C.M., Mertes, L.A.K., Dunne, T., Costa, M. and Jasinski, J. (2002), Altimetric remote sensing of the Amazon: Application of satellite radar altimetry. JGR, vol. 107 (D20), pp. 8059

[6] Braud, D. H. and Feng, W. (1998), Semi-automated construction of the Louisiana coastline digital land/water boundary using Landsat Thematic Mapper satellite imageries. Department of Geography & Anthropology, Technical Report Series 97-002

[7] Chen, L.C. and Shyu, C.C, (1998).”Automated extraction of shorelines from optical and SAR images”

[8] Cracknell, A. P. (1999), Remote sensing techniques in estuaries and coastal zones- an update. International Journal of Remote Sensing, vol. 19(3) pp.485-496.

[9] Curran, P.J. (1985), Principles of remote sensing, London Scientific and Technical Group, Essex, England, pp.282

[10] Dalton, J.A. and Kite, G.W. (1995), A first look at using the TOPEX/Poseidon satellite radar altimeter for measuring lake levels. Proceedings of the International Workshop on the Application of Remote Sensing in Hydrology , Saskatoon, Canada, NHRI Symp. 0838-1984, No. 14, Kite, G.W., Pietroniro, A. and Pultz, T.D. (Eds.), 105–112.

[11] DeWitt H., JR. Weiwen Feng, “Semi-Automated Construction of the Louisiana Coastline Digital Land-Water Boundary Using Landsat TM Imagery”

[12] Duker, L. and Borre, L. (2001), Biodiversity conservation of the world's lakes: a preliminary framework for identifying priorities. LakeNet Report Series Number 2. Annapolis, Maryland USA

[13] eCognition User Guide (2003), http://www.definiens-imaging.com.

[14] Ekstrand, S. (1994), Assessment of forest damage with Landsat TM: correction for varying forest stand characteristics. Remote Sensing Environ no.47, pp. 291-302

[15] ERDAS IMAGIANE 8.7 (2003), ERDAS Field Guide™, Fifth Edition, Revised and Expanded, ERDAS, Inc., Atlanta, Georgia

[16] Frazier, P. S., and Page, K.J. (2000), Water body detection and delineation with Landsat TM data. Photogrammetric Engineering and remote Sensing, vol. 66 (12), pp. 1461-1467

[17] Ghosh, S.K. and Shankar, B. (2000), Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. International Journal of Remote Sensing, vol. 21 (11), pp. 2269-2300

[18] Hall, O. and Hay, G. (2003), Multiscale object-specific approach to digital change detection. International Journal of Applied Earth Observation and Geoinformation, vol. 4, pp. 311-327

[19] http://oceanservice.noaa.gov/mapfinder/products/tsheets/welcome.html

[20] http://www.pecad.fas.usda.gov/cropexplorer/global_reservoir/gr_regional_chart.cfm?regionid=metu®ion=&reservoir_name=UrmiaJabbarlooye

[21] http://www.worldlakes.org

[22] Jupp, D.L.B., (1988), “Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters”. Proceeding of the symposium on remote sensing of coastal zone, Gold Coast, Queensland, September 1988, IV.2.1-IV.2.19

[23] Rasuly A. A., et al., (2010), “Monitoring of Caspian Sea coastline changes using object-oriented techniques”, International Conference on Ecological Informatics and Ecosystem Conservation

[24] S. Pirasteh, (2006), “Investigation of Kouhestak-Karian Coastline changes using GIS”, Map Asia 2006.

[25] S. Pirasteh, (2006), “Change detection of coastal zone in Persian Gulf (1970 to 2002): Using topography map and remotely sensed data”, Map Asia

[26] Winarso G., (2001). The potential application remote sensing data for coastal study. Singapore. 22nd Asian Conference on Remote Sensing. Singapore. Refer from: http:// gisdevelopment.net/aars/acrs