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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES
Volume 7, No 3, 2017
© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0
Research article ISSN 0976 – 4380
Submitted on February 2016 published on February 2017 275
Spatio-temporal variation in Indian part of Sundarban Delta over the years
1990-2016 using Geospatial Technology Avinash Kumar Ranjan1, Vallisree Sivathanu2, Santosh Kumar Verma3, Lakhindar Murmu4,
Patibandla B. Sravan Kumar5
1- Centre for Land Resource Management, Central University of Jharkhand, Brambe 835205,
India
2- Department of Electronics and Communication Engineering, Birsa Institute of Technology,
Dhanbad 828123, India
3- Department of Environmental Science & Engineering, Indian Institute of Technology
(ISM), Dhanbad 826004, India
4- Department of Electronics and Communication Engineering
VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad 500090, India
5- School of informatics and computing, Indiana University, Indiana 47408, USA
avinashcuj.wings07@gmail.com
ABSTRACT
The Sundarban mangrove ecosystem is one of the world’s largest mangrove forest extended
over Bangladesh (62%) and India (38%) on the deltaic complex of rivers Ganga,
Brahmaputra, and Meghna. The Indian Sundarban Delta (ISD) covers 102 small islands
spread over 9630 km2, out of which 54 islands in 5370 km2 are having a population of 4.2
million (census 2011) and rest 48 islands spread across 4260 km2 are covered by Reserved
Forest (RF) with mangrove vegetation. There are incessant changes over the years in
Sundarban Delta due to natural and anthropological influences. In the present investigation,
an attempt has been made to detect and analyze the changes in mangroves and LU/LC
environment of ISD since last 15 years. High-resolution Remote Sensing (RS) satellite data
from 1990 to 2016 of equal intervals of ten years has been processed and analyzed with
Geospatial Information System (GIS) environment. Comparatively change detection in
LU/LC of ISD has been prudently studied over the years 1990, 2000, 2010 and 2016 by using
two image processing techniques: Normalized Difference Vegetation Index (NDVI) which is
used in detecting the temporal changes in vegetation and Maximum Likelihood Classification
(Supervised Classification) technique is used for Land Use/ Land Cover (LU/LC) analysis.
Keywords: Indian Sundarban Delta, Mangroves, Satellite Data, Remote Sensing & GIS,
NDVI, Maximum Likelihood Classification
1. Introduction
Geospatial technology/Geo-informatics technology basically comprises of RS, GIS and
Global Positioning System (GPS). Existing state Geospatial technology plays a vibrant role in
real-time applications by its specific capabilities to monitor natural resources and various
environmental disputes. With the advancement and availability of high resolution remotely
sensed satellite data, it becomes easier to bring up to date with the variation in LU/LC by
using GIS/Geographic Information Techniques (GIT) and GPS (Ranjan et al., 2016). Present
investigation focuses on the influence of 3S technology to detect the Spatio-temporal changes
in mangroves ecosystem as well as LU/LC of ISD since 1990 to 2016. The part of Sundarban
mangrove located in India is one of the world’s major deltas in Bengal estuarine province, in
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 276
the estuary area of Ganga-Brahmaputra and Meghna River. It is also well known for the
largest halophytic formation located on the coastline
(https://en.wikipedia.org/wiki/Sundarbans.). This was acknowledged as a “Biosphere
Reserve” in 1989 and “World Heritage site” in 1987 by United Nations Educational and
Scientific Cooperation and the International Union for Conservation of Nature (Pramanik,
2015). Coastal ecosystems have an important role in maintaining the wealth of species,
genetic diversity among them, store and recycle nutrients, maintaining pollution free
environment and also protect shorelines from erosion and storms (Raha et al., 2014). A
marine ecosystem helps in climate regulation and acts as a major source of oxygen and sink
for carbon. A mangrove ecosystem helps in maintaining the most significant biodiversity in
coastal regions of intertidal regions to mitigate the influence of tides (Thomas et al., 2014).
Now-a-days the coastal zone of the world is under stress due to spreading out of industries,
trade, business, tourism and subsequent human population growth and migration, as a result
water quality is deteriorating and mangroves are degrading (Mondal and Bandyopadhyay,
2014). In the coastal chapter of IPCC Assessment Report 4 (2007), the effect of climate
change and rise in global sea level rise is estimated to be 0.59 m in the 2090s (Pachauri and
Reisinger, 2007). The coastal systems are affected mainly due to the rise in sea levels,
temperatures, precipitation changes, large storm surges and an increase in ocean acidity.
Human activities had a continuous impact on the coastal regions because of rapid
urbanization and growth of megacities at the cost of coastal resources. The GMSL (Global
Mean Sea Level) is anticipated to rise to 0.28-0.98 m in 2100; however, the actual rise in the
local sea level could be greater than the projected value owing to regional variations and local
factors (Thomas et al., 2014). The main factor considered is the relative rise in sea level rise
between the GMSL rise induced by the climatic changes regional variations and any other
non-climate sea level changes for the assessments of coastal impacts, adaptation and
vulnerability (IPCC, 2014) (Pachauri and Meyer, 2015). Following are the natural causes that
are taken into account: coastal erosion, loss of landmass, breach of embankments,
biodiversity, sea levels rising, etc. Apart from these, the vulnerable island ecosystem is highly
impacted by the human interferences. Lack of security, infrastructure, and environmental
pollution which are caused by tourists, and other degradation occurred due to human
activities are in need of immediate attention (Lakshmi and Edward, 2010). For the above
reasons, it is essential to study the outcome of a sea-level rise in coastal regions. The coastal
environment is a natural and valuable resource which undergoes transformation continuously.
The shoreline of this vibrant system can either be advanced or declined which is influenced
by various meteorological, biological, anthropogenic and geological factors (Valerio et al.,
2012). Sand dune or salt marsh erosion occurs as a natural process of the working of the
wider coastal system, which internally allows it to adjust to changes in sediment or energy
caused by natural or anthropogenic factors. On the other hand, the continuous reduction in
coastal landforms (for example dunes, mudflats or marshes) leads to deterioration.
The present study aims to understand the variation in mangroves and LU/LC pattern, as well
as to identify the major forcing parameters affecting the ISD ecosystem since 1990 to
2016.The USGS developed a structure for land use/land cover classification for employing it
with remotely sensed satellite data in the mid-1970s which are still implemented today
(Anderson et al., 1976). Proper knowledge about the spatial land cover is needed for accurate
planning, and management of natural resources (Zhu, 1997). The spatial land cover
information provides a valuable contribution for various agricultural, hydrological, ecological
and geological models. Precise and latest land cover information is required for studying
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 277
about any natural hazard (i.e.: landslide hazard zonation) (Gupta et al., 1999; Saha et al.,
2002). Satellite remote sensing imagery acts as a feasible source for obtaining accurate
information about the land cover at all scales (local, regional and global) owing to synoptic
view, repetitive coverage and map-like format (Csaplovics, 1998; Foddy, 2002). The Land
use of any area occurs because of human controls on the land resources in a systematic
manner. The equilibrium of nature is maintained by having all kinds of land such as wetland,
forestland, waste land, cultivable land etc. in a balanced manner (Vink, 1975). The physical
appearance of earth’s surface is described by the Land cover, while land use describes the
land right of economically using it (Ranjan et al., 2016). Land use/land cover changes can
occur locally or place dependent, where augmentation occurs, which indicates attention on a
global scale (Sherbinin et al., 2002; Lambin and Geist, 2006). Humans are shifting land cover
continuously all the way through the consent of reinforcement of land for their cultivation
and livestock (Sherbinin et al., 2002). The human activities impact on land has grown
massively in the last two centuries, shifting all-encompassing landscapes, thereby eventually
affecting the earth's nature. The results of these include intensified agriculture, reduced forest
land, biodiversity loss, vast land degradation and soil erosion (Pellika et al., 2004). Few
coastal areas are highly sensitive having valuable ecological areas with greater biodiversity
and high productivity. Owing to this, the population in coastal zones increases rapidly leading
to industrialization and urbanization (Clarke, 1996). So it is crucial to study the LU/LC
changes in coastal areas to appreciate and assess the environmental consequences due to such
changes (Santhiya et al., 2010: Giri et al., 2005).
2. Study area
The present investigation extend over the eastern shore of India in the southern part of West
Bengal, spatially located between latitude 21° 13´ to 22°40´ North and longitude 88° 05´ to
89° 06´ East as presented in Figure 1. It is placed near 100 km southeast of Kolkata and
spreads across two districts: North 24-Parganas (6 blocks) and South 24-Parganas (13 blocks)
(Chatterjee et al., 2015). Out of the 102 islands in Sundarban region, 48 islands in the
southernmost region are declared as RF which is prohibited for human settlement (Pramanik,
2015). The 3500 km long embankment protects the rest densely populated 54 islands from
the incursion of saline water during high tide. Though the deltaic inter-tidal region is very
rich in biological resources, inhabitants of the area are very poor (Chatterjee et al., 2015).
Despite the unfavorable physical environment, high salinity and crustal subsidence, the area
can still generate high production of sustainable biological resources if properly managed
(CSE Report 2012). Concentrated areas are dominated by a cyclone and prone to flood, the
tides of research area are semi-diurnal which varies in different regions during different
seasons (Chatterjee et al., 2015). These tides are side by side influent by the sea at the Hugli
river entrance, also via the Ganga and Brahmaputra estuaries (Pramanik, 2015).
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 278
Figure 1: Geographical location of Indian Sundarban Delta (Area of Interest)
3. Materials and methodology
3.1 Data used
Based on the availability of satellite imagery, four multispectral data of ISD were
downloaded from United State Geological Service (USGS) and Global Land Cover Facility
(GLCF) which provides the satellite data free of cost (http://glovis.usgs.gov/,
http://glcfapp.glcf.umd.edu:8080/esdi/search). Topographic maps obtained from the library of
Texas were used at 1: 250000 scale (http://www.lib.utexas.edu/map.ams/india/). Four satellite
data of years 1990, 2000, 2010 & 2016 were used; data of 1990 and 2010 belongs to Landsat
TM, whereas 2000 and 2016 belong to Landsat ETM+ as shown in Table 1. Care was taken
that all the data belong to the same period.
Table 1: Data used in present study
Year Satellite Data Spatial Resolution Data Source
1990 Landsat TM 30 m GLCF
2000 Landsat ETM+ 30 m GLCF
2010 Landsat TM 30 m GLCF
2016 Landsat ETM+ 30 m USGS
3.2 Methodology
In present study, Area of Interest (AOI) has been extracted from toposheets as well as Google
Earth by manual digitizing it which is shown in Figure 1. Throughout the entire research
work numerous systematic steps have carried out as presented in Figure 2.
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 279
Figure 2: Methodology adopted in present study
3.2.1 Image processing
All image processing and pre-processing is done using ERDAS IMAGINE 9.2 and ArcMap
10 softwares. Layer stacking of individual band in single image file was done by ERDAS
IMAGINE, then False Colour Composite (FCC), extraction of the study area and further
process for NDVI and Maximum Likelihood Classification were completed by using GIS
environment (ArcMap 10). Google earth operation was accomplished using Elshayal Smart
GIS software.
3.2.2 Image classification
Till date, a number of change detection methods were developed using conventional image
differencing, normalized difference vegetation index, image ratio, principal component
analysis, multi-date image classification, post-classification comparison, manual onscreen
digitization etc. (Lillesand and Kiefer, 1999). In this study, two techniques; NDVI (Indices)
and Maximum Likelihood Classification (Supervised Classification) has been used in
detecting the temporal changes in vegetation and LU/LC classification respectively. In
supervised classification, Maximum Likelihood Classification (MLC) method was adopted
due to better reliable results of mangrove zonation. Supervised classification technique
considers a set of raster bands and creates a classified raster for the same reflectance as output.
The supervised classification involves training areas for every category and the training area
is used to express spectral reflectance patterns/signature of each LU/LC category (Lillesand
and Kiefer, 1999). During the MLC classification, spectral profiles of various LU/LC features
are also noted as shown in Figure 3.
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 280
Figure 3: Spectral profile of LU/LC Features
However, LU/LC was taken in five classes namely; mangroves, sand deposition, water body,
vegetation/agricultural land and bare land/others grounded on Google Earth visit of the
research area. The number of the test points/pixels was derived based on the thumb rule that
these should be at least ten times the total number of classes (Ranjan et al., 2016). But in the
present study, 10 test points have been taken for better accuracy. As there are five LU/LC
classes, so the total sample size was computed to be 5*10*5=250 pixels, and the number of
pixels for each class was determined by using ratio calculation. All the five LU/LC classes
were assigned a rank in the ascending order of their area, each rank was divided by the sum
of the ranks, i.e. 15, and to conclude it was multiplied by the total number of pixels, i.e. 250
as shown in Table 2. The training sample was collected on-screen.
Table 2: Sample size of test point
Classes Rank Sample Size
Mangroves 1 16.67
Waterbody 2 33.33
Sand Depositions 5 83.33
Vegetation/Crop land 3 50
Bare Land/ Others 4 66.67
Total 15 250
3.2.3 Mapping of vegetation
Analysis of vegetation density and canopy structure or patches of greenness on land, spectral
vegetation attributes are used. NDVI is the most common and important ratio indices for
vegetation, it eases to compare the images over the time to detect the changes in ecological
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 281
condition. NDVI basically works on the principle of the rate of difference between Red (R)
band and Near Infrared band (NIR) as shown in following equation (Lillesand and Kiefer,
1999; Jenson, 2006).
Generally, NDVI value lies between -1 to +1, where 0 indicates the bare land, negative values
shows the presence of water and the positive value indicates the vegetation density. In present
research NDVI maps of 1990, 2000, 2010 and 2016 are generated using Landsat satellite
imagery and classed based on the reflectance value.
3.2.4 LU/LC change detection
The variation in LU/LC utilization was analyzed with the help of prepared maps of various
years, by adopting supervised classification. In supervised classification, MLC techniques
have been implemented, all these processes are done by Erdas Imagine and ArcGIS
Softwares. By using Elshayal Smart GIS software a small area is selected to demonstrate the
LU/LC variation within the ISD region as shown in Figure 4 ([a] & [b] belong to the same
location and [c] & [d] another location of the different time period). From these Google Earth
images of 2002 and 2016, it is clearly identified that LU/LC of ISD is gradually changing
over the time. To explore the temporal LU/LC variation, at approximately ten year’s interval
from 1990 to 2016 GIS techniques have been used with remote sensing data and these data
are tabulated also.
Figure 4: Google earth images of two different location showing LU-LC variation
NDVI= NIR-R/NIR+R
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 282
4. Results and discussion
Sundarbans ecosystem located in the climatic hotspot is susceptible to rise in sea level (Ardil
and Wolff, 2009; Ellison and Stodart, 1991). Other than sea level fluctuations, it is highly
influenced by Monsoonal floods, storm surge, drainage, cyclones, and Salinization. The
major factors which influence the dynamics of mangrove ecosystems are the sea level
fluctuations, salinization and related events (Kebede et al., 2010; Roy et al., 2011). These
factors are primarily connected with soil, slope, habitat stratigraphy and salinity regimes that
can alter the mangrove systems (Uddin et al., 2013). Managing natural resources in coastal
regions is dependent on the mangrove population and association with it (Biswas et al., 2009;
Church and White, 2006; Datta et al., 2012). The rise in sea level and salinity may induce
edaphic changes, soil and salinity changes, tidally dominated mud flat, ground water
fluctuations and their quality. The dynamics and adjustment of mangrove forests are also
affected by the soil slope in coastal areas. Sundarban has faced many challenges like cyclones,
high flood events and inaccessible terrain conditions as part of sustainable mangrove
management. However, humans carry out harmful activities such as farming, timber
collection, and honey collection for their livelihood which is not favorable conditions for
their sustainability (Ardil and Wolff, 2009). Also, the mangrove covers are damaged by rural
people in the last few decades due to the lack of management initiatives (Roy et al., 2011;
Islam, 2011). However, the Aila cyclone (25 may 2009) and tsunami (havoc) had lead to
flooding, landslide, bank erosion, loss of human lives and property which in turn caused the
destruction and defragmentation of mangrove territory in intertidal areas (Haq, 2010; Gilman
et al., 2007; IUCN Report, 1989, Gilman et al., 2006). The exemplary growth of shrimp
farming in the northern part of Indian border has influenced the deforestation activities
(Figure 5). Wikramanayake 1998 reported that the mangrove habitat is destructed in
Sundarban area due to the unsustainable growth of shrimp farms (Semeniuk, 1993). Islam
suggests that the administration system is insufficient for the ecosystem maintenance and
generally people are dependent on the mangrove ecosystem economically (Roy et al., 2011).
Figure 5: LU/LC map representation of ISD
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 283
With the help of remote sensing data and GIS environment, it is quite tough to accurately
estimate the mangroves ecosystem as the spectral reflectance of mangroves, agricultural land,
vegetation, forest cover etc. have somewhat identical spectral profile. In the present research,
four satellite images of the different years has been processed in GIS environment to prepare
LU/LC map as shown in Figure 5.
On evaluating these maps, it is noted that mangroves ecosystem has been gradually
decreasing at the rate 27.25 km2 per year and it got converted into other features. In 1990,
mangroves and vegetation/cropland area were 2046.50 km2 and 2008.12 km2 where in 2000,
mangroves area decreased to 1924.49 km2 whereas vegetation/cropland area increased to
2293.20 km2. Such as in 2010 and 2016 vegetation/crop land measures to 2203.19 and
1888.70 km2 which show the mixed status (loss and gain) with respect to time, it may due to
seasonal variations. But mangroves area is continuously decreased; in 2010 it reduced to
1577.37 km2 and 1337.84 km2 in 2016 which is a great percentage of mangroves
deterioration. This drastic change may due to classification error or seasonal variation (as the
overall accuracy is 82% in the year of 2016, as shown in accuracy assessment Table 5 [D]).
The most interesting thing can be noted that as the area of mangroves decreased when the
area of bare land/others increased as shown in Table 3. In 1990 mangroves cover was
2046.50 km2 where the bare land/others area was 267.04 km2, but in 2000 mangroves area
comes down slightly to 1924.49 km2 and bare land also decreased drastically due to
increment in vegetation/cropland area which is 2293.20 km2 (2008.19 km2 in 1990). Later in
2010 mangroves area has come down to 1577.37 km2 with radical increment in bare
land/others area which is 791.80 km2 when compared to previous time period 347.12 km2
mangroves cover area was decreased where 583.40 km2 area of bare land/others got increased.
Losses of mangroves cover area between 2000 and 2010 are very high which is quite difficult
to admit, this extreme variation may due to classification error or seasonal variation (because
user accuracy is less which is 90% and 80% in respective years as given away in accuracy
assessment Table 5 [B] and [C]). Incessantly in 2016 mangroves area are detected in 1337.84
km2, reasonably bare land/ others area increased to 1251. 21 km2 relatively others features i.e.
analyzed as mix variation (increased and decreased both) in 1990 water body estimated as
2843.46 km2 likely in 2000, 2010 and 2016 it is estimated as 2749.88, 2645.92 and 2789.74
km2 respectively (such slight variation may occur due to variation in mangroves area or due
to classification/seasonal error). Hereafter soil/sand deposition has also mix variation in 1990
it was projected as 255.78 km2 relatively in 2000, 2010 and 2016 it was 363.55, 292.54 and
271.98 km2.
Table 3 Area of LU/LC features over the years
LU/LC
Classes
1990
2000
2010
2016
Area in
km2
Area
in %
Area in
km2
Area
in %
Area in
km2
Area
in %
Area in
km2
Area
in %
Mangroves 2046.50 27.58 1924.49 25.53 1577.37 21.01 1337.84 17.74
Vegetation/
crop land
2008.12 27.05 2293.20 30.42 2203.19 29.33 1888.70 25.05
Waterbody 2843.46 38.32 2749.88 36.47 2645.92 35.23 2789.74 37.00
Sand
deposition
255.78 3.45 363.55 4.82 292.54 3.89 271.98 3.61
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 284
Bare land/
others
267.04 3.60 208.40 2.76 791.80 10.54 1251.21 16.60
From the Table 3, it can be easily analyzed that mangroves decreased from 27.58% to
17.74% which is 0.37% of total area per year, whereas vegetation/ agricultural land has mix
variation, in 1990 it was 27.05% after that in 2000, 2010 and 2016 it varied to 30.42, 29.33
and 25.05% respectively. Likely water body and sand/soil deposition has continual mix
variation (sometimes it increase and sometimes decreased). But it is quite exciting to analyze
the variation of bare land/others features which is gradually increased till date at the rate of
0.5% per year. In 1990 bare land/ others was 3.60% which increased to 16.60% in 2016, but
in the time period of 1990-2000 it decreased 0.84% which may due to classification error (as
presented in accuracy assessment table, the user's accuracy for bare land/ others is 88.89% as
shown in Table 5 (B). after that it has regularly increased. The variation of different features
over the years can be simply understood by interpreting the graph as shown in Figure 6.
Figure 6: Graphical representation of LU/LC variation
Pramanik MK (2015) has also revealed that mangroves ecosystem is gradually diminishing as
well as bare land/others are steadily increasing due to various natural or human factors.
Pramanik MK states that mangroves area gradually decreases from 20375.2 km2 (44%) to
13272.3 km2 (31 %) and bare land increases from 1507.8 km2 (2.86 %) to 3724.7 km2
(7.12%) during the study period between 1975 to 2014, owing to natural causes like rising in
sea level, salinization, anthropogenic disturbances (livelihood collection, shrimp farming etc.)
and constant land reclamation. Categories like water body, agriculture, and sand deposition
have nearly remained. It is quite interesting to see that in the present study from 1990 to 2016,
mangroves are diminishing at the rate of 0.37% per year while as per the study of MK
Pramanik from 1975 to 2014, mangroves ecosystem diminished at the rate of 0.33% per year
which is approximately close to each other.
4.1 Change detection in vegetation indices
NDVI technique is used to detect the change in the land use to demarcate the mangrove
dynamics during the years 1990 to 2016. Four NDVI maps were prepared and visually
interpreted to appreciate the density of vegetation/ mangrove as well as the destruction
caused owing to sea level variation. The classes of NDVI are classified in five categories;
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 285
Waterbody, Sand deposition/ flooded area, bare soil, less dense vegetation and Very dense
vegetation with respect to their values as shown in Table 4.The changes are higher along the
river and coastal boundaries due to great proportions of bank erosion. The NDVI value varied
fairly from 1990 to 2016 as shown in Figure 7. But after 1990 the value has slightly
decreased owing to positive modifications in the human population, regular deterioration of
mangrove/ vegetation and the agricultural lands conversion into bare land and urbanized
areas. However, positive changes are noticed in specific areas like Chulkati Island, Lothian,
protected forests and vegetal river banks. However, the NDVI value of less dense vegetation
and very dense vegetation has decreased gradually as shown in represented map (Figure 7).
In 1990 value of less dense vegetation and very dense vegetation were 0.21-0.39 and 0.39-
0.69 where it varied to 0.15-0.29 and 0.29-0.58 in 2000. Although in 2010 it comes to 0.13-
0.30 and 0.30-0.60 while it’s varied to 0.19-0.41 and 0.4-0.63 in 2016. Very dense vegetation
is generally analyzed in South-Eastern (SE) region of the study area where the less dense
vegetation was detected in North-Western (NW) region. But in the year of 2016 less dense
vegetation was detected in the Northern region, this may occur due to classification error or
seasonal variation. Moreover, innermost movement of mangrove forest and defragmentation
of mangroves lead to declining NDVI values.
Figure 7: NDVI map of study area
In 1990, NDVI value is more in north-western part and lowered in mangrove regions owing
to defragmentation of mangroves. Furthermore, NDVI (2016) values do not vary
considerably from 1990. But, the pattern and condition of healthy upper layer mangrove
characteristics are different in different classification periods. Therefore, more healthy areas
in 1990 are different from the least healthy areas of 2000, 2010 and 2016. As discussed above,
the lack of a various set of satellite imageries for each time period, the different image
acquisition spells of different period and the difference in the tidal inundation degree of
different satellite images confine comparison of complete values of canopy closure layers.
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 286
However, the canopy closure layer has been reduced in different time periods of 1990, 2000,
2010, and 2016 (Figure 7). The positive value of NDVI slowly decreases due to the
increasing flooded areas and barren lands. The value of NDVI over 0.40 indicates the dense
mangrove cover area that significantly changes over the periods, where the degraded land,
barren lands are gradually increased over different time periods (Table 4). This may signify
that the mangroves are totally influenced by climate change and their related effects over the
periods.
Table 4: NDVI value with remarks
LU/LC NDVI Value
1990 2000 2010 2016
Waterbody -0.63 to -0.37 -0.64 to -0.31 -0.48 to -0.28 -0.55 to -0.24
Flooded area/ sand
deposition -0.37 to -0.09 -0.31 to -0.07 -0.28 to -0.08 -0.24 to -0.06
Bare soil -0.09 to 0.21 -0.07 to 0.15 -0.08 to 0.13 -0.06 to 0.19
Less dense
vegetation 0.21 to 0.39 0.15 to 0.29 0.13 to 0.30 0.19 to 0.41
Very dense
vegetation 0.39 to 0.69 0.29 to 0.58 0.30 to 0.60 0.41 to 0.63
5. Accuracy assessments
The accuracy assessment is essential to validate the image classification results and a number
of methods have been developed for this process. For accuracy assessment validation, an
Error matrix has prepared with the help of classified and pre-classification satellite imagery
(Banko, 1998), using a sample of 10 randomly selected pixels within each class that was
collected on-screen by an experienced interpreter. The spatial data accuracy is well-defined
by the United States Geological Survey USGS, 1990 as: "Accuracy assessment or
authentication is a vital step in the treatment of remote sensing data. It governs the statistics
value of the resultant data. Productive application of geo-data is conceivable if the data
quality is well-known. The accuracy of any map may be verified by paralleling the locations
of points with matching locations as determined by surveys of a higher accuracy. As a
consequence, accuracy assessment is essential for the judgment if they performed
classification which corresponds with the nature aspects. Without an accuracy assessment,
the output or results are of little value (Adam et al., 2013). The percentage of overall
accuracy of the four LU/LC datasets during the years 1990, 2000, 2010, 2016 are 86, 90, 82
and 82 respectively as shown in Table 5.
Table 5: Error matrix over the years for LU/LC
(A) Year- 1990
LU/LC
Classes Mangroves
Vegetation/
Cropland Waterbody
Sand
Deposition
Bareland/
Others Total
User's
Accuracy
(%)
Mangroves 8 1 0 0 0 9 88.89
Vegetation/
Cropland 2 9 0 0 0 11 81.82
Waterbody 0 0 10 0 0 10 100
Sand
Deposition 0 0 0 8 2 10 80
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 287
Bareland/
Others 0 0 0 2 8 10 80
Total 10 10 10 10 10 50
Producers
Accuracy (%) 80 90 100 80 80
Overall
Accuracy (%) 86
(B) Year- 2000
LU/LC
Classes Mangroves
Vegetation/
Cropland Waterbody
Sand
Deposition
Bareland/
Others Total
User's
Accuracy
(%)
Mangroves 9 1 0 0 0 10 90
Vegetation/
Cropland 1 9 0 0 0 10 90
Waterbody 0 0 10 0 0 10 100
Sand
Deposition 0 0 0 9 2 11 81.81
Bareland/
Others 0 0 0 1 8 9 88.89
Total 10 10 10 10 10 50
Producers
Accuracy (%) 90 90 100 90 80
Overall
Accuracy (%) 90
(C) Year- 2010
LU/LC Mangroves Vegetation/
Cropland Waterbody
Sand
Deposition
Bareland/O
thers Total
User's
Accuracy
(%)
Mangroves 8 2 0 0 0 10 80
Vegetation/
Cropland 2 8 0 0 0 10 80
Waterbody 0 0 10 0 0 10 100
Sand
Deposition 0 0 0 7 2 9 77.78
Bareland/
Others 0 0 0 3 8 11 72.73
Total 10 10 10 10 10 50
Producers
Accuracy (%) 80 80 100 70 80
Overall
Accuracy (%) 82
(D) Year- 2016
LU/LC Mangroves Vegetation/
Cropland Waterbody
Sand
Deposition
Bareland/O
thers Total
User's
Accuracy
(%)
Mangroves 7 1 0 0 0 8 87.5
Vegetation/
Cropland 3 9 0 0 0 12 75
Waterbody 0 0 10 0 1 11 100
Spatio-temporal variation in Indian part of Sundarban Delta over the years 1990-2016 using Geospatial
Technology
Avinash Kumar Ranjan, Vallisree Sivathanu, Santosh Kumar Verma, Lakhindar Murmu, Patibandla B.
Sravan Kumar
International Journal of Geomatics and Geosciences
Volume 7 Issue 3, 2017 288
Sand
Deposition 0 0 0 7 2 9 77.78
Bareland/
Others 0 0 1 3 8 12 66.67
Total 10 10 10 10 10 50
Producers
Accuracy (%) 95 95 95 95 95
Overall
Accuracy (%) 82
6. Conclusion
Managing intertidal mangrove ecosystem has been a foremost problem owing to climatic
complication and contemporary human interventions. As demonstrated, 3S Technology or
Geo-informatics Technology provides significant information about mangrove dynamics
changes and existing status. Satellite images are used to determine vegetation/forest cover
and change in mangrove forest cover by employing careful image processing methods as
presented in current research. The deforested area is basically identified by maximum
likelihood algorithm but the partially deteriorated area is quite difficult to identify. Also, sub
pixel classification process and NDVI differencing technique are employed the
transformations of forest cover in the Indian part of Sundarban. The lowland coastal regions
and higher population with larger mangrove compactness are maximum vulnerable because
there are minor social and financial adaptation etc. Thus, the sustainability of mangroves is
typically reliant on locational physical characteristics and durable thoughts along with
commercial deeds. The study discloses that mangroves are one of the most vulnerable
ecosystems which are on the verge of destruction owing to continuous anthropogenic stresses
in coastal areas and climatic variability. The present study revealed that mangroves are
diminished at the rate of 0.37% which is 9.84 km2 due to various climatic and anthropogenic
factors. This drastic change in mangroves area is quiet large so it is grim to come clean, since
mangroves ecosystem follows a cyclic process (it may due to classification error as discussed
in result and discussion section). Whereas the vegetation and agricultural area are converted
into bare land, built up the area and flooded area etc. Vegetation/ agricultural area detected a
fair variation (loss and gain with different time period). Whereas bare land/ others increased
at the rate of 0.50% which indicates that the vegetation/ agricultural area got converted into
bare land/others. An anthropogenic deed has created more threat to mangroves when
compared to sea level rise. However, it may encompass a considerable quantity of projected
loss of mangroves in future. The increase in global temperature and amplified concentration
of CO2 are probably to raise the productivity of mangrove wetlands, change in the timing of
flowering and fruiting, and immigration of mangrove classes into upper latitudes. Conversely,
cropland and shrimp farming are identified as major factors for the destruction of mangroves
and thereby increased the intensity of coastal disasters.
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Sravan Kumar
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