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

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Aquatic Procedia 4 ( 2015 ) 563 – 570

INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques

Raju Aedlaa*, Dwarakish G Sb, D Venkat Reddyc

aDepartment of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka (NITK), Surathkal, Mangalore, India bDepartment of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka (NITK), Surathkal, Mangalore, India cDepartment of Civil Engineering, National Institute of Technology Karnataka (NITK), Surathkal, Mangalore, India

Abstract

The shoreline change extraction and change detection analysis is an important task that has application in different fields such as development of setback planning, hazard zoning, erosion-accretion studies, regional sediment budgets and conceptual or predictive modeling of coastal morphodynamics. Shoreline delineation is difficult, time consuming, and sometimes impossible for entire coastal system when using traditional ground survey techniques. Recent advances in remote sensing and geographical information system (GIS) techniques are overcoming the difficulties in extraction of shoreline position and detection of shoreline changes. In the present paper, an automatic shoreline detection method using histogram equalization and adaptive thresholding techniques is developed. The shoreline of Netravati-Gurpur rivermouth area along Mangalore coast, West Coast of India have been extracted from Indian Remote Sensing Satellite (IRS P6) LISS-III (2005, 2007 and 2010) and IRS R2 LISS-III (2013) satellite images using developed automatic shoreline detection method. The delineated shorelines have been analyzed using Digital Shoreline Analysis System (DSAS), a GIS Software tool for estimation of shoreline change rates through two statistical techniques such as, End Point Rate (EPR) and Linear Regression Rate (LRR). The Bengre spit, Northern sector of Netravati- Gurpur river mouth is under accretion an average of 2.95 m/yr (EPR) and 3.07 m/yr (LRR) and maximum accretion obtained is 8.51 m/yr (EPR) and 8.69 m/yr (LRR). Southern sector, the Ullal spit is under erosion an average of -0.56 m/yr (EPR) and -0.59 m/yr (LRR). ©© 20152015 Published The Authors. by Elsevier Published B.V. byThis Elsevier is an open B.V. access article under the CC BY-NC-ND license (Peerhttp://creativecommons.org/licenses/by-nc-nd/4.0/-review under responsibility of organizing committee). of ICWRCOE 2015. Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords:Histogram equalization, thresholding, shoreline, remote sensing, digital shoreline analysis system

* Corresponding author. E-mail address: [email protected]

2214-241X © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi: 10.1016/j.aqpro.2015.02.073 564 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 coastal changes 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 processing algorithms 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 et al. (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-HE methods 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 Raju Aedla et al. / Aquatic Procedia 4 ( 2015 ) 563 – 570 565 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 of satellite 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 23.5 566 Raju Aedla et al. / Aquatic Procedia 4 ( 2015 ) 563 – 570

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 Modified Self-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 from Fig.3. Flow chart of Contrast Enhancement method based on satellite image 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)|0, h'(x+1)<0 (3)

Where, 0 ≤ x ≤ J, 1 ≤ i ≤ Nmax and Nmax was the number of local maximum values. The global maximum value h(xk) was found out from h(xi).Meannhk, was derived from sub-congregation {h(xi)| k ≤ i ≤ Nmax }. Then, the evaluated hk, was the plateau threshold value (i.e. T). The modified histogram hmod(x) with the threshold value could be generated by Eq. (4); ሺሻǡሺሻ ൑ h (x) = ൜ (4) mod ǡ Raju Aedla et al. / Aquatic Procedia 4 ( 2015 ) 563 – 570 567

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.

െͳሻǤܿሺݔሻܮሺ f(x)=ቔ ቕ (5) ܿሺܮെͳሻ

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 from Eq. 8 to Eq. 11. μ+2σ and μ-2σ were treated as maximum threshold value (TMAX) and minimum threshold value (TMIN) respectively. If f(i,j) was the intensity value of the image pixel at (i,j), and TMAX and TMIN were locally adaptive maximum and minimum threshold values, the output image g(i,j) after thresholding operation (6) is;

Ͳ ሺǡ ሻ ൐ ሺǡ ሻ ൌቐʹͷͷ ൏݂ሺǡ ሻ ൐ (6) Ͳ ሺǡ ሻ ൏ 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 two different 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, southern 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 568 Raju Aedla et al. / Aquatic Procedia 4 ( 2015 ) 563 – 570

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.

10 A-Thannirbhavi Beach 8 B-Bengre Sand Spit 6 C-Ullal Sand Spit 4 2 0 (m/yr) Rivermouth -2 0 50 100 150 200 250 300 350 400 450 500 550 Rate of Rate of Change -4 A E -6 B C D Transect -8 EPR LRR 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 maximum accretion 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 A B C D E Region 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 54 191 102 Transect length (m) 700 700 700 700 700 Baseline distance from 300 300 300 300 300 coastline (m) Average Accretion 1.50 (EPR) 2.95 (EPR) ---- 1.53 (EPR) 1.62 (EPR) (m/yr) 1.41 (LRR) 3.07 (LRR) ---- 1.58 (LRR) 1.56 (LRR) -1.00 (EPR) ---- -0.56 (EPR) -2.41 (EPR) -1.25 (EPR) Average Erosion (m/yr) -0.83 (LRR) ---- -0.59 (LRR) -2.35 (LRR) -1.18 (LRR) 3.27 (EPR) 8.51 (EPR) 3.77 (EPR) 2.75 (EPR) Max. accretion (m/yr) ---- 3.04 (LRR) 8.69 (LRR) 3.90 (LRR) 2.67 (LRR) (transect) ---- (40 and 59) (144) (272) (450) -2.74 (EPR) -4.31 (EPR) -5.66 (EPR) -4.29 (EPR) Max. erosion (m/yr) ---- -2.38 (LRR) -4.25 (LRR) -5.74 (LRR) -4.35 (LRR) (transect) ---- (68) (188) (419) (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 Raju Aedla et al. / Aquatic Procedia 4 ( 2015 ) 563 – 570 569 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 contrast enhancement 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). 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