Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques

8/18/2019 Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Eq…

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564 Raju Aedla et al. / Aquatic Procedia 4 (2015) 563 – 570

1. Introduction

Coastal zones are one of the most complicated ecosystems with a large number of living and non-living

resources. Coastal zones are exposed to a series of dynamic natural processes like coastal erosion, accretion,

sediment transport, environmental pollution, and coastal development that usually causes changes in long and short

term spans. Coastal zones are complicated ecosystems with a large number of living and non-living resources by

Constanza et al. (1997). Coastal zones are major socio-economic environment in worldwide and these coastalchanges impacts on loss of life and property, security of harbors, change of the coastal socio-economic environment,

and decrease of coastal land resources. So, coastal zone monitoring is a significant task in national development and

environmental protection, in which, extraction of shoreline is the fundamental study of necessity by Rasuly et al.

(2010). Shoreline is considered as one of the most dynamic processes in coastal area by Bagli and Soile (2003);

Mills et al. (2005) and it is the physical interface of land and water by Dolan et al. (1980). Shoreline is formed by a

number of geological factors such as interaction, sediment deposition of rivers and oceans, various weather and sea

conditions, as well as the frequent human social and economic activities by Boak and Turner (2005). The shoreline

is one of the 27 features recognized by IGDC (International Geographic Data Committee) by Li et al. (2001). The

location of the shoreline provides the data in respect to shoreline reorientation adjacent to structures by Komar

(1998) and beach width and volume by Smith and Jackson (1992), and it is used to quantify historical rates of

change by Dolan et al. (1991); Moore (2000). The extraction of shoreline is useful for several applications like

coastline change detection and coastal zone management, and this task is difficult, time consuming, and sometimes

impossible for entire coastal system when using traditional ground survey techniques by Cracknell (1999).Due to the preference and large effort involved in manual detection, quite a few automatic shoreline detection

methods have been proposed. Advanced remote sensing and geographical information system (GIS) techniques are

overcoming the difficulties in detecting shoreline position and shoreline change analysis. Several techniques have

been developed to extract shoreline and change detection from satellite imagery such as, image enhancement, multi-

temporal data classification and comparison of two independent land cover classifications, density slice using single

or multiple bands, and multi-spectral classification, both supervised and unsupervised (like ISODATA, Principle

Component Analysis (PCA), Tasseled Cap, NDWI) by Mas (1999); Frazier and Page (2000); Ryu et al. (2002);

Braud and Feng (1998); Kuleli (2010); Kuleli et al. (2011); Zheng et al. (2011); Bouchahma and Yan (2012). Along

with image classification methods, various thresholding based techniques have been proposed by Bayram et al.

(2008); Jishuang and Chao (2002); White and Asmar (1999); Yamayo et al. (2006). In addition, image processingalgorithms such as pre-segmentation, segmentation and post-segmentation have been proposed for automatic

extraction of coastline from remotely sensed images by Liu and Jezek (2004); Mason and Davenport (1996); Di etal. (2003).

In automatic shoreline extraction task, general-purpose edge detection and image segmentation techniques are

not enough, because of lack of constant, sufficient intensity contrast between land and water regions and resulting

complexity in separating shoreline edges from other object edges by Liu and Jezek (2004). Considerable contrast

exists between land and water masses will generate continuous and clear shoreline. With this knowledge, the present

study proposed a complete automatic shoreline extraction method from satellite imagery by using clipped histogram

equalization based contrast enhancement and thresholding based techniques.

Histogram Equalization (HE) is a well-known indirect contrast enhancement method, where histogram of the

image is modified. Because of stretching the global distribution of the intensity, the information laid on the

histogram or probability distribution function (PDF) of the image will be lost. To overcome the drawbacks of HE

method, several HE-based techniques have been proposed. Based on the modification of input image histogram, the

techniques are categorized into Bi-Histogram Equalization, Multi-Histogram Equalization and Clipping or Plateau

HE methods by Raju et al. (2013a). Bi-HE methods by Kim (1997); Wang et al. (1999); Chen and Ramli (2003a);Chen and Ramli (2004); Sengee et al. (2010); Zuo et al. (2012) are preserving the brightness and enhance contrasts

of the images up to certain limit and showing over-enhancement with annoying artefacts in the image. Multi-HEmethods by Wongsritong et al. (1998); Chen and Ramli (2003b); Sim et al. (2007); Wadud et al. (2007); Ibrahim

and Pik Kong (2007); Menotti et al. (2007); Kim and Chung (2008); Wadud et al. (2008); Sheet et al. (2010); Khan

et al. (2012) providing well brightness preserving without introducing any undesirable artefacts, but sacrifices the

contrast enhancement in the image.

Clipping histogram equalization methods by Yang et al. (2003); Wang et al. (2006); Nicholas et al. (2009); Kim

and Paik (2008); Ooi et al. (2009); Ooi and Isa (2010); Liang et al. (2012) are superior in controlling the


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enhancement rate, brightness preserving and avoiding over amplification of noise in the image. Contrast

enhancement techniques emphasize the small or suppressed objects and object edges, resulting high positional

accuracy of coastline through automatic detection.

The present study was carried out with a view to develop an automatic shoreline extraction method using

clipped histogram equalization based contrast enhancement for enhancing coastal pixels and thresholding techniques

for segment water and land regions. DSAS software and multi-temporal IRS-P6 and IRS-R2 data has been used for

the analysis of shoreline changes of Netravati-Gurpurrivermouth area, Mangalore Coast, West Coast of India.

The present paper is organized in five sections. Section 1 gives brief introduction of coastal zone, shoreline

changes and existing automatic coastline detection methods. Section 2 explains the selected study area and section 3

describes the data used and methodology developed for automatic shoreline extraction. Section 4 demonstrates the

application of developed method through results and discussion and finally, concluding technical remarks are presented in section 5.

2. Study Area and Data Products

Netravati-Gurpurrivermouth area, a stretch along Mangalore Coast from Talapady in the South and

Tannirbhavi beach in the North, along the West Coast of India is the study area. The study area lies between

12˚45'26''–12˚53'25'' North latitude and 74˚47'00''-74˚53'00'' East longitude as shown in Figure 1.

Netravati and Gurpur rivers are originate in the Western Ghats, flows westward, takes almost 90˚ turn near the

cost and then flows parallel to the coast either southward or northward, before joining the Arabian Sea at Mangalore by Dwarakish (2001). Bengre at North and Ullal at South are two active submerged sand spits attached to mainland

developing infront of the confluence of rivermouth.

Fig. 1. Location map of the study area

Total 16 km length of coastline including rivermouth and 5 km width (1 km offshore and 4 km onshore from

shoreline) covering an area of 80 km2 is considered as a study area to predict shoreline changes in and around the

rivermouth. The rivermouth is unstable because of the large carrying capacity of Netravatiriver compared to that of

Gurpur river discharges lot of sediments into Arabian Sea. The climate is tropical and the mean daily temperature

recorded so far is 37˚C. The average annual rainfall is 3954 mm of which 87% is received during the southwest

monsoon (June to September) by Murthy et al. (1988).Geometrically corrected and orthorectified IRS P6 LISS – III

2005, 2007, 2010 and IRS R2 LISS – III 2013 pre-monsoon (January to May) remotely sensed satellite data set have

been used for shoreline change studies of Netravati-Gurpurrivermouth, West Coast of India. The specifications ofsatellite data used in the study are provided in Table 1.

Table 1. Specifications of satellite data used in the study

Sl No. Satellite & Sensor Acquired Date Path/Row Resolution (m)

01 IRS-P6 LISS-III 2005-01-05 97/64 23.5

02 IRS-P6 LISS-III 2007-12-21 97/64 23.5

03 IRS-P6 LISS-III 2010-01-03 97/64 23.5

04 IRS-R2 LISS-III 2013-01-23 97/64 23.5


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3. Methodology

The proposed automated shoreline extraction method has been developed using ERDAS Imagine 9.2 from

geometrically rectified single band (near-infrared) grey-scale 8-bit (intensity value range between 0 and 255)

satellite images. At near-infrared (NIR) wavelengths, water appears dark in the image because of its strong

absorbance and mainly vegetation or exposed soil areas appear brighter because of their strong reflectance. The

complete methodology of the present study is shown as flow chart in Figure 2. The present study adopted ModifiedSelf-Adaptive Plateau Histogram Equalization with Method threshold (Modified SAPHE-M), a clipped histogram

equalization based contrast enhancement method to enhance coastal features.

Fig.2. Flow chart of automated shoreline extraction algorithm fromsatellite image

Fig.3. Flow chart of Contrast Enhancement method based on

Clipped Histogram Equalization

3.1. Modified Self – Adaptive Plateau Histogram Equalization with Mean Threshold (Modified SAPHE-M)

Modified SAPHE-M, is a modified method of Self-Adaptive Plateau Histogram Equalization (SAPHE)

proposed by Wang et al., 2006, to enhance the main objects and supress the background for infrared images.

Modified SAPHE-M, which consists of five steps (Raju et al., 2013b);

1. Smoothen the input image histogram with 3-neighbour Median filter

2. Fond the local maximum and global maximum values

3. Selected the optimal mean plateau value

4. Modified the histogram according to mean plateau value and equalize the histogram

5.

Normalized the image brightness

In Modified SAPHE-M, the original histogram h(x) was obtained from the input image, for 0 ≤ x ≤ L-1.

Histogram h(x), was filtered by using a median filter of 3-neighbour (i.e. a median filter of size 1Χ7 pixels), to

reduce the fluctuation and also to remove some empty bins inside the histogram. A new congregation histogram

{h(x) |0 ≤ x ≤ J} was formed based on non-empty bins in the filtered histogram. Where, J was the number of nonzero

units in filtered histogram.

Local maximum values and global maximum value of h(x) were found by applying differential operation to

h(x) as shown in Eq. (1);

h'(x)=h(x) – h(x-1), for 1≤ x≤ J (1)

A sub-congregation {h(xi )} or histogram local maximum values h(xi ), were found by using the Eq. (2) and Eq.(3);

|h'(x)|


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Probability Density Function (PDF) was found from hmod (x) and then cumulative density function (CDF), c(x),

was determined from the PDF. The transformation function, f(x) was obtains the final output image from the Eq. (5)

and then normalizes the image for brightness preserving. The developed contrast enhancement method for satellite

images is illustrated in Figure 3.

f(x)= (5)3.2. Thresholding

Mean (μ) and Standard Deviation (σ) from local maximum and local minimum values of contrast enhanced

satellite image histogram were calculated using f rom Eq. 8 to Eq. 11. μ+2σ and μ-2σ were treated as maximum

threshold value (T MAX ) and minimum threshold value (T MIN ) respectively. If f(i,j) was the intensity value of the image

pixel at (i,j), and T MAX and T MIN were locally adaptive maximum and minimum threshold values, the output image

g(i,j) after thresholding operation (6) is;

(6)

The pixels with intensity value higher than maximum threshold were coded as 0 (land pixels), pixels intensity between minimum and maximum threshold were coded as 255 (sand pixels) and the pixels with intensity value

lower than minimum threshold were coded as 0 (water pixels).Region grouping and labeling was performed using a

‘grass fire’ concept, where the image was scanned in a row-wise manner, and a ‘fire’ was set at the first pixel of an

image object. The water pixels were coded as 0s in g(i,j) and grouped and labeled as individual image objects. In

next step, the land pixels were grouped and labeled into individual objects and coded as 255s. After these two

stages, only two large continuous land and ocean objects were appeared in the image. The small image objects,

which were not belongs to shoreline were dissolved into the land and ocean areas were removed by Region of

Interest (ROI) method by Parker (1997). Single or multiple regions or objects were detached from the image using

ROI method. The morphological image operations, image dilation and erosion were used to generalize the jagged

boundaries of image objects and making the coastline morphologically smoother by Parker (1997). The smoothed

shoreline was highlighted with Robert’s edge operator by Thieler et al. (2005) and outlined shorelines were

converted to vector maps. The vector maps of IRS P6 LISS-III (2005, 2007 and 2010) and IRS R2 LISS-III (2013)

were carried into DSAS to calculate the rate of shoreline movement and changes.

DSAS casts a number of transects perpendicular from a baseline and records the intersection position between

transect and each shoreline. DSAS automatically generated several statistical methods, such as Shoreline Change

Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), Linear Regression Rate (LRR), Weighted

Linear Regression (WLR) and Least Median of Squares (LMS). In the present study, shoreline changes were

estimated using two statistical approaches such as End Point Rate (EPR) and Linear Regression Rate (LRR). The

EPR was calculated by dividing the distance of shoreline movement by the time elapsed between the earliest and

latest measurements at each transect. LRR was used to express the long-term rates of shoreline change.

4. Results And Discussion

DSAS generated 800 transect that were oriented perpendiculars to the baseline at 30 m spacing along 16 km

length of Netravati-Gurpurrivermouth.Shoreline change rates have been calculated using DSAS software with twodifferent statistical techniques such as EPR and LRR. Baseline is constructed 300 m distance from latest 2013

shoreline and total 533 transects are generated with 20 m spacing along 16 km stretch of study area. Most substantial

changes have been observed at Netravati-Gurpurrivermouth. Bengre spit, northern sector of Netravai-

Gurpurrivermouth is under accretion and Ullal spit, souther n segment is under erosion. For complete analysis, the

study area is divided into 5 regions. Region A, Thannirbhavi Beach, northern part of rivermouth covers transects

from 1 to 130 and transects from 131 to 156 in Bengre Sand Spit, termed as Region B. Region C, Ullal Sand Spit

southern part of rivermouth covered by transects from 177 to 230. Ullal Beach from transects 231 to 421 is labeled

as Region D and finally, transects from 422 to 523 in Someshwara Beach is considered as Region E. The resulted


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shoreline change rate assessed at each region with respect to transect was plotted for the study area is shown in

Figure 4. The detailed transect and shoreline change trends in all the five regions of the study area are given in Table

2.

Fig.4. The resulted shoreline change rates (erosion/accretion) using EPR and LRR

From Figure 4, Region A, Tannirbhavi Beach, from transects 1 to 130 do not show much change in shoreline and

average shoreline change rate is 1.5 m/yr (EPR) and 1.41 m/yr (LRR) from 2005 to 2013. In Tannirbhavi Beach, at

transect 40 and 59 maximum shoreline accretion of 3.27 m/yr (EPR) and 3.04 m/yr (LRR) and average erosion rate

is -0.96 m/yr (EPR) and -0.89 m/yr (LRR). Region B, Bengre Sand Spit, northern part of rivermouth from transects

131 to 156 is under accretion and average accretion rate is 2.96 m/ye (EPR) and 3.07 m/yr (LRR). The maximumaccretion rate is 8.51 m/yr (EPR) and 8.69 m/yr (LRR) at transect 144. The sediments discharges from Netravati and

Gurpur rivers are moving towards North due to wave action in Southwest direction and currents from South to

North. Due to circulation of water, calm area is created on the Northern sector of rivermouth (Bengre Sand Spit) and

more sand is deposited from transects 140 to 145 as shown in Figure 4. The average shoreline accretion rate in this

area is 7.26 m/yr (EPR) and 7.41 m/yr (LRR).

Table 2.Shoreline change trends in study area

RegionA B C D E

Tannirbhavi Beach Bengre Sand Spit Ullal Sand Spit Ullal Beach Someshwara Beach

transect 1-130 131-156 177-230 231-421 422-523

Number of transect 130 26 54 191 102Transect length (m) 700 700 700 700 700Baseline distance from

coastline (m)

300 300 300 300 300

Average Accretion

(m/yr)

1.50 (EPR)

1.41 (LRR)

2.95 (EPR)

3.07 (LRR)

----

----

1.53 (EPR)

1.58 (LRR)

1.62 (EPR)

1.56 (LRR)

Average Erosion (m/yr)-1.00 (EPR)-0.83 (LRR)

--------

-0.56 (EPR)-0.59 (LRR)

-2.41 (EPR)-2.35 (LRR)

-1.25 (EPR)-1.18 (LRR)

Max. accretion (m/yr)(transect)

3.27 (EPR)

3.04 (LRR)(40 and 59)

8.51 (EPR)

8.69 (LRR)(144)

--------

3.77 (EPR)

3.90 (LRR)(272)

2.75 (EPR)

2.67 (LRR)(450)

Max. erosion (m/yr)

(transect)

-2.74 (EPR)

-2.38 (LRR)(68)

----

----

-4.31 (EPR)

-4.25 (LRR)(188)

-5.66 (EPR)

-5.74 (LRR)(419)

-4.29 (EPR)

-4.35 (LRR)(422)

Region C, Ullal Sand Spit, the southern sector of Netravati-Gurpurrivermouth is undergoing erosion and

average shoreline erosion rate is -0.56 m/yr (EPR) and -0. 59 m/yr (LRR) from transects 177 to 230. Due to high

concentration of wave energy on the Ullal side, indicating the predominant movement of sediments from Netravati-

Gurpur rivers towards North and more deposition in Bengre Spit.Region D, from transects 231 to 421 covers Ullal Beach and shows less accretion and erosion rates because of

sea wall constructed along the coastline. The average shoreline change rate from 2005 to 2013 in Ullal Beach is -

0.49 m/yr (EPR) and -0.44 m/yr (LRR). From transects 292 to 297 and 327 to 421 observed shoreline erosion and

the shoreline change rate is -2.34 m/yr (EPR) and -2.27 m/yr (LRR). From transects from 231 to 291 and 298 to 421,

shoreline accretion is perceived and the average shoreline accretion rate is 1.57 m/yr (EPR) and 1.63 m/yr (LRR).

Someshwara Beach, region E from transects 422 to 523 shows less regular changes in accretion and erosion

rates because of low energy concentrated wave actions. The average shoreline change rate in region E is 0.48 m/yr

and 0.44 m/yr. From transects 422 to 434 and 456 to 489, shoreline erosion is observed and average shoreline

-8

-6

-4

-2

0

24

6

8

10

0 50 100 150 200 250 300 350 400 450 500 550 R a t e o f C h a n g e

( m / y r )

Transect EPR LRR

A - Thannirbhavi BeachB - Bengre Sand SpitC - Ullal Sand Spit

AB C D

E

Ri v e r m o u t h


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change rate is -1.30 m/yr (EPR) and -1.26 m/yr (LRR). The shoreline accretion is perceived from transects 435 to

455 and mean shoreline accretion rate is 1.6 m/yr (EPR) and 1.42 m/yr (LRR). The average accretion rate from

transects 490 to 519 is 2.36 m/yr (EPR) and 2.32 m/yr (LRR). From transects 524 to 543, in Someshwara Beach

shows shoreline accretion and mean shoreline change rate is 0.85 m/yr (EPR) and 0.79 m/yr (LRR).

5. Conclusions

The present study provides the automated shoreline extraction method from satellite images using contrastenhancement and thresholding based techniques. The developed contrast enhancement method based on Modified

Self-Adaptive Plateau-Histogram Equalization with Mean Threshold (Modified SAPHE-M) improved significant

contrast enrichment of coastal edges and coastal objects for clear recognition and delineation. The thresholding

operation, in combination of mean (μ) and standard deviation (σ) has efficiently segmented the land and water

regions. Region of interest method is perfectly removed unwanted objects from ocean and land regions and

morphological image operations are fine smoothed the shoreline by adding and removing pixels. End Point Rate

(EPR) and Linear Regression Rate (LRR) statistical methods are shown more substantial shoreline changes at

Netravati-Gurpurrivermouth.

Bengre Sand Spit (region B), northern sector of rivermouth is under sediment deposition and maximum

shoreline accretion rate is 8.51 m/yr from EPR and 8.69 m/yr from LRR at transect 144. The Tannirbhavi Beach

(region A), has shown not much change in shoreline and average shoreline change rate is 1.5 m/yr (EPR) and 1.41

m/yr (LRR). The southern segment of Netravati-Gurpurrivermouth, Ullal Sand Spit is undergoing erosion due to

high concentration of wave energy on Ullal side and average shoreline erosion rate is -0.56 m/yr (EPR) and -0.59

m/yr (LRR). Maximum shoreline erosion rate in region C is -4.31 m/yr (EPR) and -4.25 m/yr (LRR) at transect 188.

Ullal Beach, due to construction of sea wall, not much change in shoreline and average accretion rate is 1.53

m/yr (EPR) and 1.58 m/yr (LRR). The average erosion rate in Ullal Beach is -2.41 m/yr (EPR) and -2.35 m/yr

(LRR). The average accretion rate in Someshwara Beach is 1.62 m/yr (EPR) and 1.56 m/yr (LRR) and average

erosion rate is -1.25 m/yr (EPR) and -1.18 m/yr (LRR).

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Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques