2013
DOI: 10.1007/s11852-013-0251-6
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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… Show more

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Cited by 125 publications
(72 citation statements)
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References 24 publications
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“…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.…”
Section: Resultsmentioning
confidence: 71%
See 1 more Smart Citation
“…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.…”
Section: Resultsmentioning
confidence: 71%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Modification To the Tidal Channel Network And Quantified Lanmentioning
confidence: 99%
“…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.…”
Section: Single Imagementioning
confidence: 99%
“…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).…”
Section: Introductionmentioning
confidence: 99%