Coastline change detection using remote sensing

Alesheikh, Ali Asghar; Ghorbanali, A.; Nouri, Naser

doi:10.1007/bf03325962

Cited by 356 publications

(178 citation statements)

References 3 publications

(3 reference statements)

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“…1 Although it would be possible to extract shorelines manually from the satellite images (White et al 1999), adopting an automated shoreline extraction procedure has several advantages in terms of efficiency and consistency in recognising former shoreline positions for complex and dynamic coasts such as Bangladesh. A Band ratio has been shown to be an efficient approach to automated shoreline identification (Alesheikh et al 2007). In this method, Band-5 of a Landsat image is divided by Band-2 using the Band Math tool in ENVI ® 4.5, resulting in three classes: water, which is assigned a pixel value of zero (0) accuracy (Frazier and Page 2000), and was the preferred method adopted in this study.…”

Section: Methodsmentioning

confidence: 99%

Rates of shoreline change along the coast of Bangladesh

2013

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Bangladesh, at the confluence of the sediment-laden Ganges and Brahmaputra Rivers, supports an enormous and rapidly growing population (>140 million in 2011), across low-lying alluvial and delta plains that have accumulated over the past few thousand years. It has been identified as one of the most vulnerable places in the world to the impacts of climate change and sea-level rise. Although abundant sediment supply has resulted in accretion on some parts of the coast of Bangladesh, others are experiencing rapid erosion. We report a systematic assessment of rates of shoreline change over a 20-year period from 1989 to 2009, using Landsat satellite images with pixel resolution of 30 m on the ground. A Band ratio approach, using Band-5 divided by Band-2, discriminated the water line on images that were largely cloud-free, adequately registered, and at comparable tidal stages. Rates of shoreline change were calculated for >16,000 transects generated at 50 m intervals along the entire mainland coastline (>1,100 km) and major islands, using the End Point Rate (EPR) method in the Digital Shoreline Analysis System (DSAS) extension in ArcGIS®. Erosion characterises most of the seaward margin of the Sundarbans in western Bangladesh. Retreat rates of up to 20 m/yr are typical, with little evidence that local devastation of the mangrove fringe by Cyclone Sidr in November 2007 had resulted in uncharacteristic long-term rates of retreat where it made landfall. Erosion exceeded accretion in the Barguna Patuakhali coastal zone, most of which eroded at up to 20 m/yr, but with truncation of the southern tip of the Patharghata Upazila at up to 100 m/yr. In Bhola, erosion at rates of up to 120 m/yr were observed along much of the coast, but in the Noakhali Feni coastal zone, similar rates of erosion were balanced by rapid accretion of the main promontory by more than 600 m/yr. Rates of change were more subdued in the Chittagong and Cox's Bazar coastal zones of southeast Bangladesh. Islands in the Meghna estuary were especially dynamic; Hatiya Island accreted along some of its shoreline by 50 km2 between 1989 and 2009, but lost 65 km2 through erosion elsewhere, resulting in the island moving south. Similar trends were observed on adjacent islands. The overall area changed relatively little across the entire coastline over the 20-year period with accretion of up to 315 km2, countered by erosion of about 307 km2.

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Section: Methodsmentioning

confidence: 99%

Rates of shoreline change along the coast of Bangladesh

2013

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“…One of the best sources for land use studies is the Landsat series of satellites. Landsat data are useful for land surface mapping, change detection and monitoring (Cohen and Goward 2004;Alesheikh et al 2007;Hansen et al 2008;Masek et al 2008;Cetin 2009). Landsat data are particularly applicable for land use classification on a regional scale because of their lower cost, longer history and higher frequency of archives than other remote sensing data sources (Rozenstein and Karnieli 2010).…”

Section: Introductionmentioning

confidence: 99%

A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran

Mohammady

Moradi

Zeinivand

et al. 2014

Int. J. Environ. Sci. Technol.

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Land use classification is often the first step in land use studies and thus forms the basis for many earth science studies. In this paper, we focus on low-cost techniques for combining Landsat images with geographic information system approaches to create a land use map. In the Golestan region of Iran, we show that traditional supervised and unsupervised methods do not result in sufficiently accurate land use maps. Therefore, we evaluated a synthetic approach combining supervised and unsupervised methods with decision rules based on easily accessible ancillary data. For accuracy assessment, confusion matrices and kappa coefficients were calculated for the maps created with the supervised, unsupervised and synthetic approaches. Overall accuracy of the synthetic approach was 98.2 %, which is over the 85 % level that is considered satisfactory for planning and management purposes. This shows that integration of remote sensing data, ancillary data and decision rules provides better classification accuracy than traditional methods, without significant additional use of resources.

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“…Image enhancement was the combination of bands needed to make clearer the boundary between land and water that will ease the process of digitization coastline. For Landsat TM and ETM+, the combination of histogram threshold and band ratio techniques is proposed by using band 2, 4, and 5 to separate water and land directly [2]. Other approach is to use single band thresholds such as band 5 or band 7 of Landsat TM and ETM+ to extract water-land interface because they are useful in clear water conditions [15].…”

Section: Methodsmentioning

confidence: 99%

“…The area of this study focused in Bengkalis island, because the shoreline has significantly changed recently due to land use change and coastal hydrodynamic. The area of Bengkalis Island is about 900 km 2 , of which 665 km 2 is covered by peat more than 1 m thick [1].…”

Section: Introductionmentioning

confidence: 99%