Coastal Morphological Changes: Assessing Long-Term Ecological Transformations Across the Northern Bay of Bengal

Environmental Challenges 1 (2020) 100001

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Environmental Challenges

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Coastal morphological changes: Assessing long-term ecological transformations across the northern Bay of Bengal

Kabir Uddin a,∗, Nishanta Khanal a,b, Sunita Chaudhary a, Sajana Maharjan a,

Rajesh Bahadur Thapa a a International Centre for Integrated Mountain Development, Kathmandu 44600, Nepal b Spatial Informatics Group, 2529 Yolanda Ct. Pleasanton, CA 94566, USA

a r t i c l e i n f o a b s t r a c t

Keywords: Major rivers from the Himalayas carry a high volume of sedimentation, and deposit it across the Bay of Bengal Remote sensing in Bangladesh. This has caused signiﬁcant changes in the morphology of the bay, including the development of

Landsat islands across the bay area. However, few studies have been carried out on the morphological changes, especially

GEE the development of new islands across the northern Bay of Bengal. This study, therefore, aimed to assess the Monitoring coastal morphological changes and ecological succession of the newly formed islands of the bay area. We used Island formulation

Ecological succession state of the art cloud computing technologies, using the Google Earth Engine (GEE) platform. Publicly available

Coastal morphology annual composites of Landsat 8, Landsat ETM+, and TM data from 1989 to 2018 were used for analysis. The findings showed significant changes in the morphology of the coastal area over a period of 30 years. There was a 1.15% increase in land area between 1989 and 2018. New islands were formed across the bay, and a few old islands disappeared between 1989 and 2018. The majority of the offshore islands developed in the estuary of the Meghna River. Among the quickly grown islands, Bhashan Char, Char Nizam, Jahajerchar, and Urir Char are prominent. Initially, the islands appeared as barren areas without any vegetation, but different types of vegetation have been observed growing on the newly formed islands recently. The findings of this study are important for the conservation and development planning of newly formed islands.

1. Introduction respectively. As such, the deposition of sediments in the floodplains and bay area create fan-like formations, called deltas. These formations turn Coastal morphology is a natural process which structures and re- into raised landforms gradually over time ( Al et al., 2018 ; Datta and structures the coastal zone over space and time. In this natural pro- Deb, 2012 ), and manifest in different shapes and sizes, ranging from cess, the rainfall, sea-level rise, ocean waves, erosion, sedimentation, low line flat beaches to undulating high hills through natural coastal wind, and tides directly interact with materials to build coastal islands geomorphodynamic processes ( Paul and Rashid, 2017 ). ( Brammer, 2014 ; Coleman et al., 2020 ). Rainfall, especially during mon- Human activities also play an essential role in forming and reform- soons, plays a crucial role in depositing sedimentation along the bay ing lands along coastal zones ( Kaliraj et al., 2017 ; Li et al., 2017 ). The and coastal areas. Monsoon rain, which is often of high intensity, trig- extraction of sand and groundwater along river beds and coastal ar- gers sedimentation in rivers and estuaries, especially in mountain re- eas, overfishing, and infrastructure development activities have notable gions with rugged terrain, and a high volume of sediment is carried impacts ( Hoque et al., 2018 ). Fluvial systems and river damming also downstream to the floodplains and bay areas ( Klatzel and Murray, 2009 ; contribute to physical changes of the coastal setting by interrupting the Mishra et al., 2019 ; Uddin et al., 2016 ). The average global estimate of regular sediment supply to deltas ( Nienhuis et al., 2020 ; Tamura et al., the annual sediment load into the world’s oceans varies from 15 to 30 2010 ). Similarly, the production and transportation of sedimentation is billion tons ( UNEP, 2019 ). Sediment in the Ganges, Brahmaputra, and shaped by different land cover and land use alterations and management Meghna basins, shared by Bhutan, India, Nepal, and China, is variable, practices both upstream and downstream ( Kidane and Alemu, 2015 ; and has been estimated to range from 402 to 710 million tons per year Uddin et al., 2018 ), contributing to substantial physical transformations for the Brahmaputra River, and from 403 to 660 million tons per year of the coastal landscape ( Nienhuis et al., 2020 ). For instance, an inte- for the Ganges River ( Rahman et al., 2018 ). The Ganges and Brahmapu- grated watershed management approach with integrated forest, agricul- tra rivers in Bangladesh transport 316 and 721 million tons of sediment, ture soil, and water conservation and development practices has been

∗ Corresponding author. E-mail address: [email protected] (K. Uddin). https://doi.org/10.1016/j.envc.2020.100001 Received 23 October 2020; Received in revised form 12 November 2020; Accepted 15 November 2020 2667-0100/© 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

Fig. 1. Location of the study area: (a) mountain originated primary river flow out to the northern Bay of Bengal, which forms the largest riverine delta in the world. (b) Study area map comprised of the Meghna estuary and Northern Bay of Bengal, represented by Landsat satellite natural-color images. reported to significantly reduce the impact of erosion and sedimenta- cused on island monitoring, without a clear overview of island formation tion processes ( Teka et al., 2020 ; Kidane and Alemu, 2015 ). Global cli- along the coastal zone of northern Bay of Bengal ( Donchyts et al., 2016 ; mate change is also directly and indirectly contributing to changes in Hay, 2013 ; Kench et al., 2018 ; Kidane and Alemu, 2015 ; Nienhuis et al., the highly dynamic and fragile coastal landscape system. Climate abnor- 2020 ; Pekel et al., 2016 ). There is a poor understanding of gains and mality factors are driving changes in regular coastal waves and wind, losses of land and water area along the delta, and its contribution to storm surges, and flooding ( Anderson et al., 2012 ; Gargiulo et al., 2020 ; shaping modern delta morphology. Ł abuz, 2015 ; Reguero et al., 2015 ). For instance, the faster melting of We have, therefore, attempted to present the first comprehensive na- ice is causing unusual inundations around coastal regions, especially in tional level assessment to analyze the morphological changes that have small island and low-lying cities ( IPCC, 2018 ). occurred across the northern Bay of Bengal over 30 years (1989–2018). Bangladesh is a low-lying country at high risk of inundation and The following specific objectives guided our study: submersion with sea-level rise. Around 40 percent of productive land in • Describe how GEE-based methods and remote sensing data can be southern Bangladesh is predicted to become submerged due to sea level used for morphological change assessments across the Bay of Bengal. rise by the year 2080 ( Yu et al., 2010 ). The coastal landscape and mar- • Examine the spatial and temporal changes along the coastal land and itime zone of the country situated along the head of the Bay of Bengal water area of the northern Bay of Bengal. is also highly vulnerable to tropical cyclones, and subject to significant • Analyze the number of islands formed along the bay area of morphological changes through regular erosion and sediment deposi- Bangladesh. tion. The rivers originating from the Himalayas carry around one bil- • Examine changes on the newly formed islands in terms of vegetation. lion tons of siltation and deposit this in the Bay of Bengal every year, resulting in significant changes in the morphology of the coastal areas In doing so, we aimed to discuss the implications of morphologi- ( Islam, 2019 ). The island development process, with the formation of cal changes, and recommend actions for conservation and development new islands over the years along the coastal zones of the country, has planning. The outcomes of the study are used to project formulations for been significant ( Abdullah et al., 2019 ). The coastal zone of the Meghna future land availability, and consider new openings for a vastly more estuary of Bangladesh is considered to be one of the most morphologi- nuanced adaptation opportunity for Bangladesh nations. The paper is cally dynamic areas, undergoing significant changes in land formations structured in five sections. Following this introduction, we introduce ( Paul and Rashid, 2017 ). For instance, Charland, which is an area lo- the study area in Section 2 , and provide details on the tools, data use, cated near the estuary of the Meghna River at the confluence of the and methods adopted in Section 3 . The results are described in Section 4 , two main Himalayan rivers of the Ganges and the Brahmaputra, has the discussion in Section 5 , and the conclusion in Section 7 . reported new island formations across the bay ( Abdullah et al., 2019 ; Brammer, 2014 ; Jakobsen et al., 2002 ). 2. Study area However, there are few studies on the geomorphological changes caused by the formation of islands at the regional and national scales. Bangladesh is located in the southern part of the foothills of the Hi- In Bangladesh, recent efforts have been made to map out the morpho- malayan mountain region and the northern edge of the Bay of Bengal, logical changes along the delta. Abdullah et al. (2019) mapped out with the boundary falling between 20° 34 ′ N to 26°38 N and 88° 01 ′ the dynamics of land gain and loss in the Bangladesh coast. Similarly, N to 92° 41 ′ E, and with an area of 147,570km 2 . A total of 310 rivers Brammer (2014) mapped the coastal regions and sea-level rise to de- and tributaries flow across the country. The Brahmaputra, Jamuna, Kar- velop Disaster Risk Reduction (DRR) strategies for coastal flooding. nafuli, Meghna, Padma, Surma, and Teesta rivers are the major rivers Ciavola et al. (2015) used Landsat images from 1978, 1989, 2001, 2006, of Bangladesh ( Fig. 1 ). About 50% of the country is within 7 m of the and 2014 to develop a land cover map for Sandwip island. For change mean sea level, and most of the country is on a delta plain within the monitoring in Sandwip island, Emran et al. (2016) used Landsat images influence of the Padma, Jamuna, and Meghna rivers ( BBS, 2018 ). Out and remote sensing techniques. Similarly, the coastline position changed of the 64 districts of Bangladesh, the coastal zone accommodates 19 im- between 1989 and 2010 for Hatiya Island in Bangladesh was monitored portant districts situated across the northern Bay of Bengal. The districts using discrimination of Landsat image indices ( Ghosh et al., 2015 ). How- occupy 32% of the total land area and accommodate around 26% of the ever, these studies are often based on expert qualitative assessment fo- total population of the country ( BBS, 2012 ). Although many islands are K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

Fig. 2. An overall methodological framework applied to assess the coastal morphological changes and ecological transformation across the northern Bay of Bengal.

losing land area, there has been an overall net gain in the land area and WRS Row 044 to 045 for our selected study area. Landsat can boast due to the regular acceleration process in other parts of those islands constant image bands and a long historical archive, which ensures the ( Alam and Uddin, 2013 ). Out of around 71 islands in Bangladesh, half repetitive acquisition of observations over the Earth’s landmass, coastal of them are situated in the unstable Meghna estuary ( Jakobsen et al., boundaries, and coral reefs, with minimal variability between images 2002 ). The study area was chosen considering the active geomorpho- ( Hagenaars et al., 2018 ; Naseer and Hatcher, 2004 ). Landsat satellites logical processes over time, and island instability and reformation in mostly capture data in the visible, near-infrared, short wave infrared, the area ( Brammer, 2014 ). According to the Köppen climate zone, the and thermal infrared spectral bands. The training samples for mapping study area, Bhola, has a tropical savanna climate (Aw) with an average were collected using Collect Earth Online (CEO). Collect Earth Online rainfall of 2424 mm ( Climate-Data.Org, 2019 ). is a free customized tool which allows high-resolution satellite image viewing for land cover training and validation plot labeling by interpre- 3. Data and methods tation. Following systematic sampling with a grid of 2 km width, 2000 reference points were collected in total. 3.1. Tools and data 3.2. Image analysis The overall methodology for assessing the coastal morphological changes and ecological transformation across the Bay of Bengal In order to identify the yearly land and water cover of the Meghna is outlined in Fig. 2 . We used Google Earth Engine (GEE), which estuary on the coast of Bangladesh, yearly composite Landsat 5 with the has a gradual accusation in satellite images has occurred in an Thematic Mapper (TM) sensor, Landsat 7 with the Enhanced Thematic economics demand in online-based storage. Earth Engine Code Edi- Mapper (ETM + ) sensor, and Landsat 8 with Operational Land Imager tor ( https://code.earthengine.google.com/ ) cloud-based smart analysis (OLI) satellite images from 1989 to 2018 were created for each year. platform that enables planetary-scale extensive collections of satellite Landsat is a constellation of the world’s longest-serving satellites, run- imagery data sets ( Midekisa et al., 2017 ; Phongsapan et al., 2019 ; ning from 1972 till today, as a joint program of USGS and NASA. The Saah et al., 2020 ; Xie et al., 2019 ). GEE satellite imagery is publicly Landsat satellites observe the same spot of the earth’s surface once every available at no direct charge for the processing of radiometric and ge- two weeks. These data were acquired through the commonly used high ometric corrections. GEE is a state of the art cloud-based image pro- power planetary-scale raster analysis utilities of Google Earth Engine cessing platform capable of processing vast areas and performing Earth (GEE). GEE has archived Landsat scenes that have undergone multiple surface mapping promptly in three ways (a) archive of satellite images procedures, such as computing the sensor radiance, top-of-atmosphere in the cloud; (b) online computational resources facility; and (c) acces- (TOA) reflectance, surface reflectance (SR), cloud score, and cloud-free sibility of smart analytical tools ( Donchyts et al., 2016 ; Hansen et al., composites. The detailed methodology used to prepare the Landsat an- 2013 ; Pekel et al., 2016 ). Google archieves a large amount of remote nual composite is described by Khanal et al. (2020) . Briefly, to create the sensing satellite data, including Landsat series from around the world, yearly image composite for 30 years for the study area, we used Landsat and provides image analysis functionality for various thematic areas and surface reflectance dataset ( n = 3592 images) from GEE archives Landsat spatial scales ( Gorelick et al., 2017 ; Uddin et al., 2019 ). GEE is a robust surface reflectance data eliminated the shadow and cloud and per-pixel online tool with a high-performance computational capacity that can saturation mask ( Saah et al., 2020 ). Finally, annual image of Landsat classify a large number of Landsat scenes without downloading them. composites were prepared by taking the median of these datasets after More importantly, it can be used to map the land and water areas at removing clouds and shadows. Median was taken for each year so that a larger spatial scale and over a long period of time ( Cao et al., 2020 ; the composite image would be representative for the year while being Jacobson et al., 2015 ; Murray et al., 2019 ). consistent in terms of intra-year seasonal variation. Annual data include Remote sensing is one of the most accurate and convenient methods median values at 80th and 20th percentiles. These image statistics on used for mapping and monitoring. Remote sensing can use a variety of each Landsat image band were kept to generate a harmonized annual historical satellite imageries which is freely available on different on- composite of multi-band imagery and saved in an image collection as an line platforms. Across the northern Bay of Bengal, the coastal and is- earth engine asset. Then, the Land Water Mask (LWM) ( Eq. (1) ), Nor- land changes and their impact on the ecological transformation were malized Difference Vegetation Index (NDVI) ( Eq. (2) ), Spectral Vegeta- monitored using the Landsat satellite images from three missions The- tion Index (SVI) ( Eq. (3) ), and Global Vegetation Moisture Index (GVMI) matic Mapper (TM), Enhanced Thematic Mapper (ETM), and Landsat ( Eq. (4) ) were calculated using image composites. The index results were 8 Data Continuity Mission (LDCM), through the Google Earth Engine then exported for the next step of mapping. (GEE) platform. The Landsat TM, ETM, and LCDM image scenes cover 𝑛𝑖𝑟 𝑙𝑤𝑚 = ∗ 100 (1) an area of around 185 km x 185 km, with a spatial resolution of 30 m 𝑔𝑟𝑒𝑒𝑛 + 0 . 0001 which observed Earth surface for more than 30 years. These Landsat 𝑛𝑖𝑟 − 𝑟𝑒𝑑 images fit in the worldwide reference system (WRS) Path 136 to 138 𝑛𝑑𝑣𝑖 = (2) 𝑛𝑖𝑟 + 𝑟𝑒𝑑 K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

Fig. 3. Box and whisker plot based on the indices values of LWM, NDVI, SVI and GVMI for land and water bodies.

To assist in mapping, 2000 reference data were collected across 𝑠𝑤𝑖𝑟 − 𝑛𝑖𝑟 𝑠𝑣𝑖 = (3) land and waterbodies. The points were first generated systematically 𝑠𝑤𝑖𝑟 + 𝑛𝑖𝑟 throughout the study region and visually interpreted using Collect Earth ( 𝑛𝑖𝑟 + 0 . 01) − ( 𝑠𝑤𝑖𝑟 + 0 . 02) Online ( Saah et al., 2019 ). The points used to derive the interpretation 𝑔𝑣𝑚𝑖 = (4) ( 𝑛𝑖𝑟 + 0 . 01) + ( 𝑠𝑤𝑖𝑟 + 0 . 02) were sampled on the resulting image with LWM, NDVI, SVI and GVMI bands. For the four bands, a box and whisker plot was created for each Where nir represents the spectral values of the near infrared band, green of them and compared, to identify the index with the best inter-class represents the spectral values of green band, red represents the spectral separability ( Fig. 3 ). Since the dataset contained two labels —land and values of the red band, and swir represents the values of shortwave in- water —the LWM values intended to be used for segregating these two frared band in the Landsat imagery. classes were expected to have a bimodal distribution. In order to find LWM is widely used for land cover mapping purpose as it can be the best threshold, Otsu’s thresholding method was used ( Otsu, 1979 , conducive in distinguishing waterbodies from landmasses. LWM values 1980 ; Vala and Baxi, 2013 ). range from 0 to 255, but indices values in water usually range from 0 Otsu’s method was used to perform automatic image thresholding, to 50 ( Uddin et al., 2015 , 2019 ). On the other hand, NDVI values can and it considers a dataset in gray level values. Since our dataset has range from negative 1 to positive 1, and the value of the index for the one set of values, it can be considered the same. The algorithm works waterbody is usually negative, while that for plants will be generally by minimizing intra-class variance, defined as the weighted sum of the high positive values ( Huete and Jackson, 1987 ). SVI has been referred variances of two classes. to different names such as land surface water index (LSWI), normalized 𝑡 𝜎 𝑡 𝜔 𝑡 𝜎 2 𝑡 𝜔 𝑡 𝜎 2 𝑡 difference water index (NDWI), and normalized difference moisture in- 𝜔 ( ) = 0 ( ) 0 ( ) + 1 ( ) 1 ( ) (5) dex (NDMI), etc. ( Ji et al., 2011 ). SVI can be calculated as the normalized 𝜔 𝜔 𝜎2 𝜎2 difference between shortwave infrared (SWIR) and near-infrared (NIR) where 0 and 1 are the probabilities, while 0 and 1 are the variances bands. GVMI is appropriate for recognizing water and vegetation con- of the two classes (1 and 2), separated by threshold t. tent. GVMI is found to be positively correlated to surface moisture due For the two classes, minimizing intra-class variance is equivalent to to plant ( Ceccato et al., 2002 ). maximizing inter-class variance ( Otsu, 1979) . K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

2 2 2 Char Nizam has 92.36% of the area in common between automatically σb (t) = σ − σω (t) ( ) ( ) and manually digitized maps. 2 2 = ω0 μ0 − μT + ω1 μ1 − μT [ ] 2 = ω0 (t) ω1 (t) μ0 (t) − μ1 (t) (6) 4.2. Spatial and temporal changes in the coastal land and water area along the northern Bay of Bengal , where ω0 μ0 + ω1 μ1 = μT and Signiﬁcant changes were observed in the morphology of the coastal

ω0 + ω1 = 1 (7) area along the Bay of Bengal over a period of 30 years. In 1989, the land area was only 56.06% (28,835 km 2 ), while the water area was 43.94% The threshold t that yields maximum σ2 is the desired threshold. b (22,600 km 2 ). In 2018, the land area increased to 57.21% (29,426 km 2 ); The threshold derived from this method was fine-tuned manually an increase of 1.15% (590 km 2, Fig. 5 ). Further changes were observed for results that aligned better with the visual interpretation. Upon in- from 1999 to 2009. The land area in 1999 and 2009 was 56.49% and specting the results based on thresholding of different indices ( Fig. 3 ), 56.68%, respectively, with a total increase of 0.19%. The island refor- the LWM provided the best set of results. Therefore, this final threshold mation tendency showed that the new land area increased every year range, from 0 to 54, was then applied to LWM from the years 1989 to by an average of 0.038 (20 km 2 ) along the coastal region of Bangladesh 2018 for the water area and 55 to 255 for the land area mapping. In the (Fig. annex −1). GEE, the procedure was accomplished by the code “updateMask(lwm. gte (54))". Through this process, pixels with prominent land or water 4.3. New island formation along the northern Bay of Bengal can be labeled. This resulted in binary raster datasets representing land and water from the years 1989 to 2018. Formation of new islands in the coastal areas along the Bay of Ben- Using the resulting land and water layer, coastal morphological gal was observed. In the western areas, new islands were observed but changes were examined. This included studying the changes in land no significant area loss within the study period was noted. In Meghna and water pixels, which provide us an indication of the land erosion estuary, significant morphological changes were observed, with the for- and sedimentation trends. A smaller subset of the study region, consist- mation of many new islands. Similarly, Bhashan Char, a newly formed ing of Bhashan Char, Char Nizam, Hatiya, Jahajerchar (New Sandwip island, was observed in 2012 with an area of 1.40 km 2 . The area of the Island), Nijhum Dwip, Sandwip, and Urir Char, was inspected for areas Char was significantly increased from 13.33 km 2 in 2013 to 41.78 km 2 with an NDVI greater than 0.3, which showed the ecological changes in 2018. Similarly, an island known as Char Nizam was observed with happening on the islands. Furthermore, the change in distance of the an area of 0.07 km 2 , which later increased to 20.55 km 2 in 2018. Jaha- (island) from the coast of the mainland was also measured. jerchar island, with an area of 14.49 km 2 in 1998, increased to a land size of 158.68 km 2 by 2018. Most notably, the Urir Char, an island with

4. Results an area of just 29.56 km 2 in 1989, tripled in size by 2018 to an area of 88.68 km 2 . 4.1. Threshold determination for mapping Significant changes in landforms were observed due to both erosion and siltation. The analysis showed that most of the changes occurred in Using the Landsat TM, ETM + and OLI and the algorithm, yearly time the eastern part of the study region. Moreover, the analysis showed that series coastal area maps for 30 years were prepared. Each map, consist- the erosion for Bhola, Hatiya, and Manpura island occurred in the north- ing of two classes —land and waterbodies —is presented in Fig. 4 . The ern part, while the erosion for Sandwip Island occurred in the southern possibility of GEE and Landsat-based automated analysis chains pro- part of the island. The formation of one of the two islands towards the vided coastal morphological information that could be used in coastal north can be explained by it breaking off from another island due to area planning. During the land and waterbodies mapping for the de- erosion, but the map itself presents no such evidence for this new island termination of coastal morphological changes, optimum LWM, NDVI, at the south of another island. These patterns, which are also supported SVI, and GVMI index values were used for the appropriate separation by the map in Fig. 6 , provide an overview of changes in the land and of waterbodies and land; derived optimal LWM indices for waterbodies water area, showing which areas have undergone frequent and recent are significantly distinct from each other, as observed in the Fig. 3 . The changes. The changed areas were coastal areas towards the eastern side, LWM mean value for the waterbodies area was 54.14, with a range be- while the western side of the study region remained mostly unchanged tween 33.33 to 97.43 and a standard deviation of 13.18. For land area, ( Fig. 6 ). the LWM value was from 80.64 to 352.17, with a standard deviation of 45.07. The GVMI and NDVI values for land area were within the range 4.4. Vegetation changes observed in the newly formed islands 31.84 to 126.25 and –0.08 to 0.67, respectively. In the waterbodies, the mean NDVI value was –0.25, and the SVI value was –0.58. The identi- Vegetation growth was observed on the newly formed islands over fied optimal ranges of LWM values can be applied for the automation of a period of 30 years. At the initial stage, the islands were generally land and waterbodies mapping, using developed indices to produce the muddy barren areas which gradually changed to grassland and trees coastal maps. over time. A comparison of ecological transformation processes for the For accuracy assessment, the classified map for 2018 used 500 ref- seven vital islands of the coastal area of Bangladesh, based on the proxy erence points randomly collected from the 2018 Sentinel-2 images. The of vegetation growth Landsat NDVI data, is presented in Fig. 7 . The overall accuracy of the 2018 coastal area map was found to be 98.4%, study revealed that the overall vegetation greening process increased with overall producer’s accuracy 98.57%, overall user’s accuracy 98.14, based on the spatiotemporal changes in NDVI, although some areas were a kappa value of 0.81, standard error kappa of 0.02, and a 95% con- submerged in the sea. fidence interval between 0.77 and 0.85 (annex Table 1). Besides the Among Jahajerchar, Urir Char, Char Nizam, Bhashan Char, Hatiya, accuracy assessment, the 2018 Landsat automatically mapped seven is- Nijhum Dwip and Sandwip islands, the highest annual mean NDVI was lands areas, which were superimposed for comparison with on-screen 0.469 in Nijhum Dwip, and the lowest NDVI value was –0.25 in Char digitized island boundaries from the 2018 Sentinel-2 images. The com- Nizam. For Jahajerchar island, the mean annual NDVI for the year 1989 parison result shows the automatically and manually digitized island was 0.06, which gradually increased to 0.20 by 2018 ( Fig. 7 ). This study area have a high degree of similarity (Fig. annex −1). The comparison showed that the NDVI value was positive in Jahajerchar for almost all of two methods for Hatiya Island shows that 99.59% was shared, with the years except 1992. In terms of NDVI value change, Char Nizam 1.57% commission and 0.41% omission. The same approach shows that showed a rapid increase between 1989 and 2018. In 1989, the NDVI K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

Fig. 4. Annual land and waterbodies map between 1989 and 2018, used to determine erosion and acceleration of the coastal region, derived from cloud-free Landsat imagery. K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

Fig. 5. Long term changes in land and waterbody areas between 1989 and 2018.

Fig. 6. The erosion and acceleration process contributed to land alteration: dark green represents regained land area that is unchanged, light green shows gradual reformation into a new island area, and pink represents lost land area into waterbodies. value was –0.25, which means the island was underwater. The same is- ume of sediment from the Himalayas ( Al et al., 2018) , and the land size land’s NDVI value reached 0.34 in 2018. Similarly, changes in the NDVI is continuously increasing. Due to the annual deposition of sediment, value for Bhashan Char island showed the same situation as for Char the study shows the formation of new islands along the coastline. How- Nizam island. Regarding the stability of the ecosystem, Hatiya island ever, the process of erosion is also significant ( Brammer, 2014 ), making was largely unchanged between 1989 and 2018. This study did not find the country highly vulnerable to coastal erosion. Moreover, Bangladesh any negative NDVI values in Hatiya, and from 2013 onwards the NDVI does not have any policies in place to manage these newly formed is- value remained above 0.35. Based on the 1989 and 2018 NDVI values, lands, which are highly fragile ( Hoque et al., 2018 ). There is a need for this study determined that Nijhum Dwip and Sandwip islands always policies and actions to halt erosion and manage the deposition of sedi- had healthy vegetation, as the average NDVI values were above 0.20 ment along the coastline; this has also been echoed by Brammer (2014) , ( Fig. 8 ). and Al et al. (2018) . Our study provides up-to-date, consistent, and high quality data on the morphological changes along the coastline, and this 5. Discussion could support planning of policies and actions for better management of the coastal areas. The quantified patterns of Bangladesh’s coastal morphological Our results show prevailing evidence of changes in the coastal mor- changes and island temporal dynamics provide an ideal basis for novel phology, including island expansion across the northern Bay of Bengal approaches to coastal land use planning and management. This study of Bangladesh over the past 30 years. From our assessment, it can be provides a detailed overview of the morphological changes that have observed that the land mass in the coastal areas of the study region occurred in the northern Bay of Bengal area of Bangladesh over the span is highly dynamic, and there is a lot of shifting of soil, also noted by of 30 years, from 1989 to 2018. The results show a total net gain in land Abdullah et al. (2019) and Hoque et al. (2018) . The presented method area of about 591 km 2 , with an increase of 19.7 km 2 per year. Similar was successful in producing results with high accuracy, and the use of results are reported by Abdullah et al. (2019) in Bangladesh and else- Landsat scenes provided an outlook of the changes occurring in the study where by Teka et al. (2020 ). This means Bangladesh receives a high vol- region. Thus, the methodology confirmed that it is applicable to use GEE K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

Fig. 7. Comparative plant spectral response with vegetation in terms of ecological succession at Jahajerchar, Urir Char, Char Nizam, Bhashan Char, Hatiya, Nijhum Dwip, and Sandwip, between 1989 and 2018. with freely available satellite images to detect coastal morphological In particular, the sediment source and tidal waves and processes pro- changes and island formation, prepare historical land cover maps, and vide the most compelling reasons for the physical changes documented assess ecological transformations over time for the vast coastal region of in the islands ( Brown and Nicholls, 2015 ), most notably the expansion Bangladesh. The functional methodology provides a cost-effective and of the majority of islands, and their locational adjustments over the flexible way to obtain the spatial information needed to raise public past 30 years. It has been claimed that the adaptation experience of is- consciousness and support coastal planning and decision-making for im- lands in Bangladesh to date has been deprived . Recognition of the land proved coastal island management in remote coastal zones. resources will continue through the next century, capturing the chal- Significant changes in coastal islands throughout Bangladesh are lenges of past and current patterns of adaptation. Management of new considered to have been influenced mostly by environmental rather than islands could contribute to the overall land gain of the country. This direct anthropogenic factors, also noted elsewhere ( Bera et al., 2017 ). has implications for both conservation and development outcomes, also K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

Fig. 8. A focused window around the Jahajer char, Bhashan Char and Sandwip islands, with vegetation succession.

noted by Brown and Nicholls (2015 ). Newly formed islands are partic- the country could deal with and adapt to the impacts of climate change. ularly crucial for a country like Bangladesh, with a small land size and Newly formed islands, as shown by this study, could help the country high population density. The land could be used for sustainable growth adapt to the sea level rise triggered by climate change. However, fur- ( Hoque et al., 2018 ; Sarker et al., 2018 ), as land availability is directly ther research on the impacts of climate change is required. It is also linked with food production, economic prosperity, and overall human essential to know the spatial distribution of tidal influence and tempo- development. Abdullah et al. (2019) and Hoque et al. (2018) reported rary changes in tidal waves across the island developed. Focusing only that integrated coastal and freshwater systems contribute to food secu- on changes island and its reformation seeks to a limitation on proper rity and livelihoods for more than 160 million people in Bangladesh. coastal management recommendation ( Cao et al., 2020 ; Murray et al., This study is equally crucial from a conservation perspective. As 2019 ). The research could be focused on the analysis of the impacts of newly formed islands are not suitable for agriculture in the early years climate change, especially the impact of the volume of runoff and sedi- of formation, our study, showing the status of vegetation growth, could mentation from the Himalayan rivers on coastal areas. be used for conservation planning. The land could be protected from human disturbance to promote natural succession. The vegetation growth 6. Key recommendations and plantation of native species on barren islands could support biodiversity. Islands are generally rich in biodiversity, supporting endemic In the coastal areas of Bangladesh accretion and erosion remain nat- and threatened species ( Iftekhar and Takama, 2008 ; Reddy et al., 2004 ; ural processes; nevertheless, the processes may be altered when human Sarker et al., 2019 ). Vegetation growth will further protect the land from activities exacerbate them. In this regard, the following are the short coastal erosion, and hence contribute to stabilizing the newly formed is- and long term recommendations to decrease erosion and sedimentation lands ( Abdullah et al., 2019 ; Adnan et al., 2020 ). in the widespread coastal region, which align well with the recommen- This study is also timely in view of climate change. Bangladesh, as dations of the Government of Bangladesh: a low-lying country, is highly vulnerable to climate change, especially sea level rise. With the rise of temperature in the Hindu Kush Himalayas • Establishing remote sensing-based operational coastal and island ( Sharma et al., 2019 ), the rate of sedimentation, with faster snowmelt sub-annual monitoring and projection of future morphological and high run-off, could be higher. However, if actions are taken in time, changes. K. Uddin, N. Khanal, S. Chaudhary et al. Environmental Challenges 1 (2020) 100001

• Based on the developed monitoring tools and scientiﬁc ﬁndings, Supplementary materials identify the most erosion-prone areas, where settlements will be eroded soon, and prioritize the allocation of a stable zone for re- Supplementary material associated with this article can be found, in settlement for those victims. the online version, at doi: 10.1016/j.envc.2020.100001 . • As the detailed analysis shows vegetation growth on the new islands,

take action to protect the coastal area from erosion and manage the References newly formed islands sustainably. Abdullah, H.M. , Muraduzzaman, M. , Islam, I. , Miah, M.G. , Rahman, M.M. , Rahman, A. , • Strong upstream-downstream linkages require regional coopera- Ahmed, N. , Ahmed, Z. , 2019. Spatiotemporal dynamics of new land development tion.. in Bangladesh coast and its potential uses. Remote Sens. Appl.: Soc. Environ. 14, 191–199 . Adnan, M.S.G. , Abdullah, A.Y.M. , Dewan, A. , Hall, J.W. , 2020. The eﬀects of changing

7. Conclusions land use and ﬂood hazard on poverty in coastal Bangladesh. Land Use Policy 99, 104868 .

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