Rates of shoreline change along the coast of Bangladesh

Abstract

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.

Projected changes in area of the Sundarban mangrove forest in Bangladesh due to SLR by 2100

“…Rahman et al (2011) have analysed Landsat images and for the period from 2000 to 2010 showed a net total erosion of 93.5 ± 0.4 km 2 for the entire Sundarbans (i.e., including the Indian Sundarbans): about half (48.56 km 2 ) corresponds with the Bangladeshi Sundarbans. Sarwar and Woodroffe (2013) reported that erosion rates of up to 20 m/year are typical along Fig. 2 SLAMM is able to reproduce observed wetland area losses by Rahman et al (2011) for the period 2000-2010.…”

“…There are several processes and process interactions that might influence mangrove area changes but which are not considered in this model, such as the positive feedback between sedimentation and mangrove growth (e.g., Gilman et al 2008). The assumption of uniform net subsidence (i.e., balance of subsidence and sedimentation) for the entire study area results in SLAMM's failure to reproduce the localized mangrove accretion relative to the observed period 2000(Sarwar and Woodroffe 2013. The GDTR is also assumed uniform and constant for the entire study area and simulated period.…”

Widespread infilling of tidal channels and navigable waterways in the human-modified tidal deltaplain of southwest Bangladesh

“…Most bank line changes in the Sundarbans over this time have taken place along the exposed coast, with lateral migration (i.e., limited net shoreline erosion) occurring within the tidal channels themselves (Allison, 1998;Sarwar and Woodroffe, 2013;Small et al, in prep). The Sundarbans tidal deltaplain thus appears to have, on average, remained in a relatively steady state in terms of tidal exchange and the import of sediment to offset relative sea-level rise (Rogers et al, 2013;Brammer, 2014, Giri et al, 2007.…”

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

“…Sarwar and Woodroffe (2013) used band ratio approach on Landsat TM and Landsat ETM images, using Band-5 divided by Band-2 to discriminate the water line on images. While Lira and Taborda (2014) also used band ratio of Band-4 (Red) divided by to extract water/land boundary from Landsat 8 image.…”

“…For the satellite image processing techniques, methods used are band rationing (Sarwar and Woodroffe, 2013;Tarmizi et al, 2014;Lira and Taborda, 2014), edge detection (Al Fugura et al, 2011;Zhang et al, 2013;Wang and Allen, 2008), thresholding (Rigos et al, 2014), segmentation (Al Fugura et al, 2011;Shu et al, 2010;Zhao et al, 2012;Semenov et al, 2016), wavelet (Yu et al, 2013), cellular automata (Feng et al, 2014), raster color slicing (Tarmizi et al, 2014) and Normalized Difference Water Index (NDWI) (Choung and Jo, 2016). On the other hand, satellite image classification techniques (Lipakis and Chrysoulakis, 2005) were classified into supervised classification such as Maximum Likelihood (Muslim et al, 2006;Sekovski et al, 2014;Rokni et al, 2015), Parallelepiped (Sekovski et al, 2014), Minimum Distance (Sekovski et al, 2014), Mahalanobis Distance (Tarmizi et al, 2014;Sekovski et al, 2014), Neural Network (Rokni et al, 2015), Support Vector Machines (Rokni et al, 2015;Yousef and Iftekharuddin, 2014) and unsupervised classification such as ISODATA (Tarmizi et al, 2014;Sekovski et al, 2014).…”