Detecting shoreline changing trends using principle component analysis in Sagar Island, West Bengal, India Ismail Mondal, Jatisankar Bandyopadhyay & Sangeeta Dhara
Spatial Information Research ISSN 2366-3286 Spat. Inf. Res. DOI 10.1007/s41324-016-0076-0
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Author's personal copy Spat. Inf. Res. DOI 10.1007/s41324-016-0076-0
Detecting shoreline changing trends using principle component analysis in Sagar Island, West Bengal, India Ismail Mondal1 • Jatisankar Bandyopadhyay1 • Sangeeta Dhara1
Received: 19 September 2016 / Revised: 3 December 2016 / Accepted: 5 December 2016 Korean Spatial Information Society 2016
Abstract Sagar coastline is a major attraction site for tourist and also source of income for the local peoples. However shoreline has been changing due to erosion. The shoreline position is difficult to predict but the trend of erosion or accretion can be determinate by statistical techniques. The study aims to assess the shoreline changes and prediction in Sagar Island, a delta of Ganges, situated in West Bengal, India. This study sought to find the trend of shoreline changes and factors. Shoreline can be detected by using PCA and non-directional edge techniques from Landsat images. The shoreline mapping of Sagar Island during (1975–2015) using geospatial techniques. The present study focuses the shoreline change and in future prediction from satellite derived multi-temporal Landsat MSS, Landsat TM, Landsat ETM?, Landsat OLI data using GIS; it is used to determinate or to estimate the change rate of shoreline in Sagar Island by End Point Rate, and Linear Regression models. Keywords Shoreline changing trends PCA Sagar Island
Electronic supplementary material The online version of this article (doi:10.1007/s41324-016-0076-0) contains supplementary material, which is available to authorized users. & Ismail Mondal
[email protected] 1
Department of Remote Sensing and GIS, Vidyasagar University, Midnapore 721102, West Bengal, India
1 Introduction Shoreline change is measured as a major problem in delta regions all over the world. Shoreline is defined generally as a connection between land and water [1] which is dynamic in nature. The landform dynamics of shoreline areas are a major concern to the researchers, [2]. Dynamic nature of shoreline shoreline makes it difficult to delineate how much area is accredited or eroded by time. There are several factors which is the main causes for the shoreline changes, example- Sea level rise, storm surges, wind tide, wave tide: long term and short term changes caused by hydrodynamic change of river, sea level rise, geomorphological changes (barrier island formulation, and other factor (seismic waves storm) natural process and human activities are responsible for coastline changes. Anthropogenic activities, wind waves, tide and waves made also changes the shoreline position frequently. The backward movement of land is called erosion and the forward movement of land is called accretion due to these natural factors. Most of the Sundarban coastal regions are affected due to erosional movement [3]. Continuous erosion of shoreline area is responsible for economic and natural resource of environment. The shoreline change of Sagar Island during (1975–2015) of Satellite imageries one the method to detecting the Principal component analysis for digital shoreline mapping. The Sagar Island of the across the shoreline influencing by marine dynamic to effect the land and huge amount sediment are loss for the tidal fluctuation. The detection of the changing landforms along shoreline area and its various influencing geomorphological factors and Accuracy Assessment techniques of Land use/Land cover mapping of Sagar Island of different year. To find out the applicability of a combined technique of satellite image analysis and statistics in the prediction of future shoreline position. The principle component analysis for detecting
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actual shoreline position using Remote Sensing and GIS Techniques. The multi resolution satellite images are to be used to determinate shoreline position during different time of past and present. The detection of the changing landforms along shoreline area and its various influencing geomorphological factors and Accuracy Assessment techniques of Land use/Land cover mapping will also be used.
2 Materials and methods 2.1 Study area Sagar Island (Fig. 1) is located in Indian Sundarban delta which is a part of the Ganga–Brahamaputra–Meghna basin Sundarban is one of the largest mangrove forest, which is situated in the part of both India and Bangladesh. Sundarban is situated between 21370 2100 N to 21520 2800 N and 88010 4600 E to 8890 2500 E which is spread over south eastern part of North 24 Parganas and South 24 Parganas. The island is only 6.5 m above sea level [4] and is connected to the mainland by a ferry service across the Muriganga river total 102 islands are situated in Indian Sundarbans. Among them 54 islands are human inhabitant and the oddment of 48 islands covered with mangrove forest [5]. It is bounded by the Hoogly river in North West, Muri-ganga in east and Bay of Bengal in the south. The maximum width and length of this island are 12 and 30 km respectively and it stretched from north to South direction. In 1951 the Sagar Island covers an area of 286 sq km but in 2015 due to extensive rate of erosion it goes down into 239 sq km. The island is ravaged by tropical cyclones and influenced daily by tidal fluctuations. Being a tide-dominated deltaic island, it is geomorphically very active and environmentally very sensitive. The morphology of this island is affected by natural coastal processes, man-made structures like seawalls and jetties, and storm waves. 2.2 Data and methodology The Multi resolution and multi date satellite data of Landsat series over the study area (Path/Row- 148/45 for Landsat TM and ETM? and Path/Row- 148/45 for Landsat MSS and Path/row- 138/45) for Landsat OLI have been taken for shoreline mapping. At first, geometric correction was done for all four images. Then radiometric correction was carried out for each of the bands of satellite images. It includes two steps: (1), converting the DN values into radiance values (2) converting the radiance values into reflectance values. The Landsat MSS image of 1975 was considered as the base map for the entire study and it was resample into 30 m to match the spatial resolution of Landsat MSS, Landsat TM, Landsat ETM?, Landsat OLI images.
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2.3 Shoreline extraction Automatic shoreline delineation is a complex process due to the presence of water saturated zone at the land water boundary [6]. Describes several techniques of shoreline detection… in present study the shorelines were identified and delineated by the processing of the NIR bands using ‘PCA’ and segmentation by ‘non-directional edge enhancement’ techniques. In present study, involved different sensors, only NIR band which is common of all satellite images, was chosen for multi-spectral images as like as ISO DATA techniques involve various bands in different sensors. Indicates a part of the image from that shoreline position to be identified (b) and (D) indicates shoreline positions obtained without and with applying PCA. The application of PCA gives a single shoreline position. Uncertainties in some portions were observed in the image. To remove uncertainties in order to map continuous shoreline positions other proxies were carried out manually in different stages. Then shoreline pixels were extracted and the image was converted into binary image. Finally, the raster binary image was converted into vector image and the shoreline boundary was extracted. Then digitise them. Finally continuous shoreline positions during different periods (1975, 1990, 2000 and 2015) were drawn (Fig. 2) and It is used to determine or to estimate the change rate of shoreline in Sagar Island by End Point Rate (EPR), and Linear Regression (LR) models (Tables 1, 2). 2.4 Accuracy assessment of LULC classes As previously mentioned, accuracy assessment of the classified image is an important step in image classification. The quality of a thematic map from a satellite image is determined by its accuracy. A classification accuracy assessment was performed on the 2015 Landsat OLI image and an assessment report was obtained having an error matrix, accuracy totals and a kappa statistics. An overall classification accuracy of 79.53% and a Kappa coefficient (overall kappa statistics) of 0.7465 was achieved. All the remaining LULC classes were having their accuracies above 50%. Overall Classification Accuracy = 79.53% Overall Kappa Statistics = 0.7465.
3 Result and discussion 3.1 LULC change trend from 1975 to 2015 The trend analysis of the Sagar Island reveals a change in size of the LULC over the 40 years period of the study (Table 4). Agricultural (mono-crop) land faced the most positive change while crop land experienced the most
Author's personal copy Detecting shoreline changing trends using principle component analysis in Sagar Island, West…
Fig. 1 Location map of the study area
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the agricultural (mono-crop) land confronted negative change. Similarly, settlement with homestead orchard, representing 9.08% made the positive change in 2015. The wetland converted to aquaculture land is observed in present study. Other LULC classes such as crop land has made positive change (0.55%) from 1975. mangrove vegetation amount has decreased than 1975 (-1.04%) and agricultural (mono-crop) land also made the negative change converted to settlement and crop land respectively.
3.2 Post-classification comparison and change detection
Fig. 2 SLR change between 1975 and 2015
negative change. The results also indicate that from 1975 to 2015, the major LULC conversions that have been taken place from 1975 to 2015 within the Sagar Island. The diagonal of this (Table 3) shows that the LULC portions of total area from 1975 to 2015 is 35,851,500 sqmt and is the study area. What is the most evident in the results is that,
Change detection analysis encompasses a broad range of methods used to identify, describe and quantity differences between images of the some scene at different times or under different conditions. Numerous tools have been used independently or in combination as part of a change detection analysis. Change detection menu after a straight forward approach to measuring changes between a pair of images that represent the initial stage and final stage. The change detection statistics for classification images average used to compute difference map for image [7] of many methods (i.e., Image overlay, change vector analysis, principal component analysis, image rationing) that are available for change detection in land use land cover, post classification comparison was used in this satellite images of 1975 and 2015 (Fig. 3). In this technique, images of different dates are firstly classified and labelled individually. Using unsupervised classification, the classified images were then compared and changed areas extracted. Since the errors in the individual classified images could be reflected in the final
Table 1 Showing the shoreline change rates according to LR model of the littoral cells of LC 1 to LC 6 Lc1
Lc2
Lc3
Lc4
Lc5
Lc6
According To LR (linear regression) model Shoreline change rate (M/year)
4.64
8.255714
2.364286
7.035556
-16.6067
-2.2725
Erosion (M/year) Accretion (M/year)
-6.99 6.301429
0 8.255714
-3.76333 -0.50191
-11.695 12.38714
-23.1514 6.3
-11.5 0.803333
Minimum rate of erosion (M/year)
0
0
-2.43
-6.91
-0.93
0
Maximum rate of erosion (M/year)
-6.99
0
-5.33
-16.48
-30.25
-11.5
Minimum rate of accretion (M/year)
1.4
5.4
2.62
2.54
4.49
0.18
Maximum rate of accretion (M/year)
13.04
15.63
10.58
23.95
8.11
1.19
No of transects
8
7
7
9
9
4
Erosion transects
1
0
4
2
7
1
Accretion transects
7
7
3
7
2
3
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Author's personal copy Detecting shoreline changing trends using principle component analysis in Sagar Island, West… Table 2 Showing the shoreline change rates according to EPR model of the littoral cells of LC 1 to LC 6 Lc1
Lc2
Lc3
Lc4
Lc5
Lc6
According To EPR (end point rate) model Shoreline change rate (M/year)
5.41375
9.045714
2.152857
8.457778
-13.9244
-2.05
Erosion (M/year)
-9.55
0
-1.5375
-6.675
-20.3643
-12.51
Accretion (M/year)
6.187143
9.045714
7.073333
12.78143
5.743333
1.436667
Minimum rate of erosion (M/year)
0
0
-0.79
-1.17
-9.63
0
Maximum rate of erosion (M/year)
-9.55
0
-2.5
-12.18
-29.91
-12.51
Minimum rate of accretion (M/year)
0.64
4.82
2.77
2.89
1.11
0.62
Maximum rate of accretion (M/year)
12.77
16.36
11.56
24.38
9.56
2.31
No of transects
8
7
7
9
9
4
Erosion transects
1
0
4
2
7
1
Accretion transects
7
7
3
7
2
3
Table 3 Trend analysis LULC 1975–2015
LULC Classes LULC
1975 Area(Hec)
2015 Area(Hec)
Deep water
5706.87
4752.72
Mangrove vegetation
1516.63
Settlement
2377.62
Sand bar
5711.814664
-4.94466
2377.62
-1.04203
2377.62
418.5
4280.56
-0.05046
716.85
3453.69
-0.59208
831.094
9.085973
Agricultural land
4280.56
4387.68
45.486
Agricultural (mono crop) land
3453.69
2359.89
155.952
Road Wet land
Total % of LULC Classes
4131.54
428.218
Tidal creek
Table 4 LULC conversersion from 1975 to 2015
630
(1975–2015)
0.553857 -5.66757
45.486
58.86
505.22
0.069275
155.952
233.46
18907.96266
0.401508
Sl. no.
Area over changed
Area (sqmt)
Percentage (%)
1
Deep water to shallow water
8,619,300
2
Tidal creek to shallow water
162,900
0.454374294
3
Tidal creek to mangrove swamp
117,900
0.328856533
4
Mangrove vegetation to agriculture land
2,727,000
7.606376302
5
Mud flat to shallow water
486,900
1.358102171
6
Mud flat to mangrove swamp
532,800
1.486130287
7
Wet land to crop land
8
Wet land to aquaculture land
9
Agriculture mono crop to settlement
10
Agriculture mono crop to agriculture land Total area
35,851,500
100
24.0416719
19,800
0.055227815
671,400
1.872724991
8,081,100
22.54047948
14,432,400
40.25605623
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land representing 7.60% of the area. Similarly, 14,432,400 sqmt of agricultural (mono-crop) land and were converted to agriculture land representing 40.26%. The conversion of mud flat to mangrove swamp area is 532,800 sqmt (representing 1.48%). The LULC classes such as wet land converted to aquaculture land is 671,400 sqmt (1.87%). The agricultural (mono-crop) land converted to settlement with homestead orchard 8,081,100 sqmt (22.54%). Furthermore the other LULC conversions are agricultural (mono-crop) land to crop land 14,432,400 sqmt (40.26%) mud flat to shallow water 486,900 sqmt (1.36%), wetlands to crop land 19,800 sqmt representing 0.055% in this area.
4 Conclusion and outlook
Fig. 3 Change detection map (1975–2015)
change of image, post-classification requires an individual classified images to be as exact as possible. Postclassification comparison has been used, e.g., to detect: mangrove vegetation to crop land, agricultural (monocrop) land to crop land, conversion and changes in general land use, wetlands and settlements with homestead orchards. The summary of the major LULC conversions that have been taken place from 1975 to 2015 within the Sagar Island is in the diagonal of this study (Table 3). What is most evident in the results is that, mangrove vegetation made the change of 2,727,000 sqmt to crop
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The study revealed the massive erosion process occurred in the Sagar Island. Southern, south western and eastern parts of the area are mainly undergone high rate of erosion. Accretion has been seen only in a small portion of south western and in some eastern part of the Sagar Island. Long term shoreline change shows severe erosional activities, with erosional hotspot location at Dhablat (Fig. 4). The validated results indicate that longer time interval can be used for the estimate of shoreline positions. Various reasons are responsible for the erosion of Sagar Island. Mainly bank erosion in Muriganga estuary is responsible for the erosion of the eastern part of the Island while the Southern part of the Island is mainly eroded due to ocean activity. Future shoreline is difficult to predict because it is the most dynamic geomorphic feature of the earth’s surface. But the trend of shoreline change based on the future shoreline prediction can be done using multi-temporal satellite data which may help to reduce the loss of property. The Future shoreline of Sagar Island has been predicted using EPR and LR Model for the Year 2025 and 2050. The shoreline change rate of M/year of LC1 4.64, LC2 8.25, LC3 2.36, LC4 7.03, LC5 16.60 and LC6 -2.27 (Table 1) meter per year is based on LR Model. According to EPR model shoreline changes are M/year of LC1 5.41, LC2 9.04, LC3 2.15, LC4 8.45,
Author's personal copy Detecting shoreline changing trends using principle component analysis in Sagar Island, West…
Fig. 4 Field Photo, Dhablat extensive sea erosion (Hotspot Zone)
LC5 -13.92 and LC6 -2.05 (Table 3) meter per year respectively. The derived coastal change units were verified and 70% accuracy in change detection was observed [8, 9]. This mapping proved that the Muriganga and Saptamukhi River is shifted from its actual baseline channel [10].
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6.
7.
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