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UNIVERSITY OF VISVESVARAYA COLLEGE OF ENGINEERING
DEPARTMENT OF CIVIL ENGINEERING
A Internship Work on
BANKLINE CHANGES ALONG BRAHMAPUTRA RIVER IN
MAJULI ISLAND
Under the guidance of Presented by
Dr. M .INAYATHULLA SANDESHA K M (P25UV22T111005)
Professor, Dept. of civil Engineering, UVCE Department of Civil Engineering, UVCE
Bengaluru Bengaluru

CONTENTS
 INTRODUCTION
 OBJECTIVES OF THE STUDY
 REVIEW OF THE LITRATURE
 STUDY AREA
 DATA PRODUCTS
 METHODOLOGY
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
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INTRODUCTION
• Majuli faces critical erosion
challenges, including both
bankline and surface soil
erosion, primarily exacerbated
by annual monsoon floods
when the Brahmaputra swells

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• At the start of the 20th century, Majuli covered an area of
approximately 1,250 square kilometers; however, due to
severe erosion, its size has decreased to about 352 square
kilometers by 2014.
• The Brahmaputra and Barak River with more than 50
numbers of tributaries feeding them, causes the flood
devastation in the monsoon period each year.
• The Digital Shoreline Analysis System (DSAS) is a tool
for quantifying shoreline changes using historical and
current data.

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•Natural: Hydrological variations, geological
properties, climatic conditions, vegetation cover.
•Anthropogenic: Land use changes, deforestation,
construction activities.
Factors influencing bankline changes include:

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The following are the objectives of the study,
 Detect and quantify bankline changes around
Majuli Island using DSAS.
 Analyze the spatial and temporal patterns of
bankline changes.
OBJECTIVES OF THE STUDY

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LITERATURE REVIEW
Goswami et al. (2018) conducted a geospatial assessment of erosion and
channel migration patterns along the Brahmaputra River in Majuli Island,
Assam, India.
•The primary objective of the study was to analyze the occurrences of
erosion and the migration of river channels over a 40-year period from 1976
to 2017.
•The authors utilized geospatial technology combined with unmanned aerial
vehicle (UAV) remote sensing to assess changes in the river's
geomorphology.

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•They mapped areas affected by erosion and deposition, revealing
that approximately 85.23 square kilometers had been eroded while
135.38 square kilometers had experienced sediment deposition.
•The study highlighted that continuous bankline erosion threatens
agricultural lands and local communities, with predictions indicating
that Majuli could disappear within 10-15 years if current trends
persist.

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STUDY AREA
•Majuli Island is situated in the Brahmaputra River in Assam, India,
covering approximately 880 km², making it the largest river island
globally.
•The island lies between 26°45' and 27°12' N latitude and 93°39' and
94°35' E longitude, characterized by a complex network of
distributaries and channels.
•Majuli's landscape consists of low-lying floodplains prone to
flooding and erosion, with alluvial soil primarily composed of silt,
clay, and sand.

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•The island is ecologically significant and culturally important
but faces severe bank erosion, leading to substantial land loss
and community displacement over recent decades.
•The study area considered between Shivsagar and itanagar.
•Understanding bankline changes is crucial for effective
management and conservation strategies to mitigate erosion
risks.

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STUDY AREA
Fig. 1a.Study area

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DATA PRODUCTS:
Local acquisition
date
Sensor Space craft
27/05/2011 TM Landsat-5
30/11/2015 OLI-TIRS Landsat-8
The Landsat satellite imagery from USGS Earth Explorer for
the year 2011 and 2015 are downloaded for the study area.
The product details are shown below

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METHODOLOGY
Data Collection (Landsat
Satellite Images)
Preprocessing
Unsupervised
Classification
Image Post-Processing
Bankline and baseline
Extraction
DSAS Implementation
Analysis
Detection of bankline
changes

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 Acquire Landsat satellite images covering different years to capture temporal
changes in banklines around Majuli Island.
 Choose relevant Landsat images based on cloud cover, seasonality, and image
quality.
 All images are georeferenced to a coordinate system UTM 46N for accurate
spatial analysis.
 Unsupervised classification methods applied to distinguish water bodies and
sediments from land in the Landsat images.
 Image post-processing steps applied to refine classification results, improve
Bankline delineation, and enhance the overall quality of the classified images.

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 The raster layer converted into a polygon vector layer to facilitate further
processing.
 The polygon layer converted to a polyline layer, ensuring that the outlines of the
polygons are represented as lines.
 Buffer command in ArcGIS used to create a baseline from the extracted
banklines. This involves setting an appropriate buffer distance to generate a
smoothed and continuous baseline for analysis.
 Install and configure the DSAS extension within ArcGIS.

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 Import the extracted bankline and baseline data into DSAS for further
analysis.
 Define transects perpendicular to the extracted banklines to facilitate
measurement of bankline changes.
 DSAS tool used to calculate bankline change metrics such as erosion rates
and deposition rates.

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RESULTS AND DISCUSSIONS
•The analysis of bankline changes along Majuli Island from
2011 to 2015 reveals significant patterns of erosion and
deposition, utilizing Landsat images and the Digital
Shoreline Analysis System (DSAS).
•The right bank experienced an erosion of 10.46 km² and a
deposition of 16.39 km², indicating a net gain in landmass.

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•Conversely, the left bank showed an erosion of 4.32 km²
and a deposition of 2.55 km², suggesting a net loss of land.
•The higher rate of deposition on the right bank is attributed to
sediment transport dynamics in the Brahmaputra River, where
sediments are likely deposited more effectively.
•The left bank's lower deposition rates is due to stronger
erosional forces or reduced sediment accumulation.

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•Understanding these changes is essential for managing land
resources and mitigating erosion risks on Majuli Island.
•The findings provide valuable insights for planning
interventions to protect vulnerable areas and promote
sustainable land use practices.
•Future studies should incorporate more recent data and
advanced modeling techniques to better predict future bankline
changes and inform policy decisions.

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Fig. 2.Banklines of Majuli Island

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1 15 29 43 57 71 85 99 113127141155169183197211225239253267281295309323337351365379393407421435449463
-1000
-500
0
500
1000
1500
Transect ID
NSM
Fig . 5.NSM vs. Transect ID (Left bankline)

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1 12 23 34 45 56 67 78 89 100111122133144155166177188199210221232243254265276287298309320331342353364375386397
-2500
-2000
-1500
-1000
-500
0
500
1000
1500
Transect ID
NSM
Fig. 7. NSM vs. Transect ID (Right bankline)

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Table 1 Erosion and Deposition
Erosion and deposition (in km2 )
2011-2015
Rightbank Leftbank
Erosion Deposition Erosion Deposition
10.46 16.39 4.32 2.55

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CONCLUSIONS
 The analysis shows significant spatial variability in
shoreline changes across both left and right banklines of
Majuli Island.
 By using the landsat images of 2011, 2015 years
analysis has been done.

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Main conclusions are
 In Left bank erosion was about 4.32km2
and the deposition
was about 2.55km2
.
 In Right bank erosion was about 10.46km2
and the deposition
was about 16.39km2
in different zones.
 This analysis will be useful for the authorities for Riverbank
protection works.

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REFERENCES
 Ahammed KKB, Pandey AC (2019) Shoreline morphology changes along the Eastern
Coast of India, Andhra Pradesh by using geospatial technology. J Coast Conserv
23(2):331-353. https://doi.org/10.1007/s11852-018-0662-5
 Ahammed KKB, Pandey AC (2022) Assessment and prediction of shoreline change
using multi-temporal satellite data and geostatistics: A case study on the eastern coast
of India. J Water Clim 13(3):1477-1493. https://doi.org/10.2166/wcc.2022.270
 Ahmed I, Das N,Debnath J, Bhowmik M (2018) Erosion induced channel migration
and its impact on dwellers in the Lower Gumti River, Tripura, India. Spat Inf Res
26:537-549.https://doi.org/10.1007/s41324-018-0196-9

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 Akay SS, Ozcan O, BalıkŞanlı, F. (2022) Quantification and visualization of flood-
induced morphological changes in meander structures by UAV-based monitoring. Eng Sci
Technol an Int J 27:101016. https://doi.org/10.1016/j.jestch.2021.05.020
 Ali PY, Narayana AC (2015) Short-term morphological and bankline changes at Trinkat
Island, Andaman and Nicobar, India Punjab Province of Pakistan. J Space Technol
V(1):15-21.
 Anam B, Khan JA, Munir BA (2015) Assessment of river evolution and channel
migration rates in the Fluvial Regime of based assessment of river dynamics of
Brahmaputra river in India. J Water Resource Prot 4(2):63-72.
https://doi.org/10.4236/jwarp.2012.42008
 Baki ABM, Gan TY (2012) Riverbank migration and island dynamics of the braided
Jamuna River of the Ganges–Brahmaputra basin using multi-temporal Landsat images.
Quat Int 263:148-161.

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 Bera R, Maiti R (2019) Quantitative analysis of erosion and accretion (1975–2017)
using DSAS—A study on Indian Sundarbans. Reg Stud Mar Sci 28:100583.
https://doi.org/10.1016/j.rsma.2019.100583

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