spatio-temporal variation in indian part of sundarban delta …€¦ · ·...

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

[email protected]

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


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Sravan Kumar



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).


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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.


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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.


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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


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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


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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


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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


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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;


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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.


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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


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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


Technology


Sravan Kumar



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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spatio-temporal variation in indian part of sundarban delta …€¦ · ·...

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