Coastline information extraction based on the tasseled cap transformation of Landsat-8 OLI images

Accepted Manuscript Coastline information extraction based on the tasseled cap transformation of Landsat-8 OLI images Chao Chen, Jiaoqi Fu, Shuai Zhang, Xin Zhao PII:

S0272-7714(18)30613-9

DOI:

https://doi.org/10.1016/j.ecss.2018.10.021

Reference:

YECSS 6011

To appear in:

Estuarine, Coastal and Shelf Science

Received Date: 28 July 2018 Revised Date:

16 October 2018

Accepted Date: 29 October 2018

Please cite this article as: Chen, C., Fu, J., Zhang, S., Zhao, X., Coastline information extraction based on the tasseled cap transformation of Landsat-8 OLI images, Estuarine, Coastal and Shelf Science (2018), doi: https://doi.org/10.1016/j.ecss.2018.10.021. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Coastline information extraction based on the tasseled cap transformation of Landsat-8 OLI images

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Abstract: As a dynamic belt between land and oceans, coastline provides rich information on land-

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ocean interactions. Sensitive to climate and anthropogenic influences, the changing coastline affects

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intertidal mudflat resources and the coastal environment. In this study, the greenness and wetness

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components of the tasseled cap transformation (TCT) were used to extract coastline information. Due

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to the high total suspended sediment content that leads to the failure of traditional method, sea-

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waterbody information extraction was initially carried out by TCT. After considering the characteristics

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of coastline in remote sensing images and coastline morphology in the natural world, the coastline with

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shorter length was eliminated and the intermittent coastline was connected based on the coordinate

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geometry description (such as length, distance, and direction). Finally, the results of the coastline

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information extraction were superimposed on the original images to evaluate accuracy. The

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experimental results indicated that the proposed method was more effective in clearly delineating the

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land-ocean boundary. The producer’s accuracy and user’s accuracy were 0.95 and 0.91, respectively,

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and the length extraction error was -2.16%. Therefore, the proposed method was more successful for

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coastline information extraction in the area with high sediment concentration.

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Keywords coastline information, tasseled cap transformation, accuracy assessment, Landsat-8, OLI

Chao Chen a, *, Jiaoqi Fu a, Shuai Zhang b, Xin Zhao c, *

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1. Introduction

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* Corresponding author: [email protected]

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a. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, Zhejiang 316022, China b. School for the Environment, University of Massachusetts – Boston, Boston, MA 02125, USA c. Taishan College of Science and Technology, Shandong University of Science and Technology, Tai’an, 271019, China

The coastline is one of the most rapidly changing landforms of coastal areas (Mujabar and

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CHANDRASEKAR, 2011; Ghosh et al., 2015). It changes constantly because of the rising sea level

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due to natural conditions, such as estuary sedimentation and global warming, and the influence of

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human activities such as reclamation and ocean engineering (Ouma and Tateishi, 2006). The coastline

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is not only an important land resource, but also serves as a significant basic geographic information

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source. The position of coastline usually changes following the ebb and flow of the tides (Berberoglu

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and Akin, 2009). Therefore, quick and accurate measurements of dynamic coastline changes are of

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great significance to coastal management, sea level change research, environmental protection, and

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sustainable coastal development (Liu et al., 2013).

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Usually, the coastline information is obtained through field surveys (Liu et al., 2013). Although the

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accuracy is high, executing a large-scale coastline survey was costly and very time consuming.

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Additionally, it is limited by the geographical conditions, and thus measuring the inaccessible areas is

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ACCEPTED MANUSCRIPT difficult. Remote sensing (RS) technology provides timely, large-scale information of coastlines that is

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not possible with field surveys (Roelfsema et al., 2013; Karpatne et al., 2016; Tilakasiri, 2017), making

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it a popular method of coastline information extraction. At present, there are two methods to extract

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coastline information from remote sensing images: visual interpretation and automated extraction

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(Kuenzer et al., 2014; Li and Gong, 2016). Visual interpretation is simple and has high accuracy, but

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because of its slow speed and heavy workload, it cannot provide rapid data over a large area. Compared

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with visual interpretation, automatic interpretation is faster and more efficient, but suffers from spectral

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confusion and error. Complex post-processing operations are usually needed in such conditions.

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The methods of automatic interpretation of the coastline information from remote sensing images

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mainly include edge detection-based method, index analysis-based method, threshold segmentation-

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based method, region growing-based method, neural network-based method, and sub-pixel method

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(Ouma and Tateishi, 2006; Ruiz et al., 2007; Li and Damen, 2010; Rasuly et al., 2010; Pardo-Pascual et

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al., 2012; Li et al., 2013; Chen et al., 2014; Mala and Sridevi, 2016; Namikawa et al., 2016; Brewin et

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al., 2017). The edge detection-based method can detect the positions of step change of gray value by

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the spatial relationship of coastline with Roberts, Prewitt, Sobel, Laplace, Canny and other operators.

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The method is simple and efficient. Based on the analysis of the spectral characteristics of ground

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objects, the index analysis-based method generally uses the normalized differential vegetation index

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(NDVI), normalized difference water index (NDWI), and modified normalized difference water index

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(MNDWI) to separate land and water. The physical meaning of such method is quite clear. A similar

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properties with similar gray value defines the precondition for the threshold segmentation-based

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method, which is best suited for images with strong contrast between the target and background. The

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region growing-based method clusters pixels with similar properties, providing more accurate

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classification data. The neural network-based method simulates the structure and function of the human

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neural system, clustering land and water by sample training in order to extract coastline information.

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The sub-pixel method is based on the different spectral responses of water and land in the multispectral

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bands, and attempts to determine the most probable position of the instantaneous coastline (or the

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interface between water and land). Each of the above methods has its own pros and cons. The edge

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detection-based method may be subject to interferences and has poor continuity of coastline

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(Namikawa et al., 2016). The index analysis-based method is easily saturated, and coastal water may be

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easily identified as “land,” affecting the accuracy of coastline extraction (Chen et al., 2014; Namikawa

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et al., 2016). The threshold segmentation-based method is easily influenced by substances with a

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similar spectrum (Rasuly et al., 2010). For the region growing-based method, it is difficult to select an

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appropriate growth rule, and it is not suitable in places with large local variance (Li et al., 2013; Brewin

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et al., 2017). The neural network-based method is more complex and requires purer samples (Mala and

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Sridevi, 2016). The accuracy of the sub-pixel method is better than that of pixel-level methods.

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Recently, further research has been proposed to detect coastline changes using successive coastline

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data obtained at the subpixel level (Ruiz et al., 2007; Pardo-Pascual et al., Li and Gong, 2016).

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To overcome these limitations and consider the influence of suspended sediment concentration on

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the optical properties of water and the actual change of the coastline, we used a coastline extraction

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method described in this paper. After analyzing and comparing the advantages, disadvantages, and

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ACCEPTED MANUSCRIPT applicability of traditional methods, we proposed a method based on the tasseled cap transformation of

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remote sensing images. This study mainly refers to the following research contents: (1) The threshold

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values are selected by the histogram peak method based on the tasseled cap transformation (TCT) in

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order to obtain sea-waterbody information, which could weaken the influence of suspended sediment

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during extraction. (2) In order to obtain non-segmented coastline information, elimination of coastline

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with shorter length and reconnection of intermittent coastline are required with the support of

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coordinate geometry description of coastline. (3) Simulation experiments are conducted to verify the

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accuracy and validity of the results.

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2. The tasseled cap transformation

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2.1 Principle of the tasseled cap transformation

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The tasseled cap transformation (TCT) is a conversion of the original bands of an image into a new

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set of bands with defined interpretations that are useful for vegetation mapping (Crist and Cicone,

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1984c; Crist, 1985; Chen et al., 2012; Singh et al., 2016). The coefficients used to create the tasseled

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cap bands are derived statistically from images and empirical observations and are specific to each

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imaging sensor. Each tasseled cap band is created by the sum of image band 1 times a constant, plus

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image band 2 times a constant, etc. The mathematical expression of tasseled cap transformation is as

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follows:

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u = RT x + r

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(1)

where u represents the value of pixels of different bands after tasseled cap transformation; R is the

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coefficient of the TCT; x represents the value of pixels of different bands; r assures that the elements of

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vector u are always positive.

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The TCT is a useful tool to compress spectral data into a few bands with minimal information loss,

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while still being associated with scene characteristics. Three axes including brightness, greenness, and

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wetness were generated (Scott et al., 2003; Li and Tang, 2013). Firstly, a rotation is defined to separate

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the vegetation from non-vegetated features by maintaining the orthogonal property between brightness

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and greenness components. Then, rotating the greenness and wetness components orthogonally

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separated the water from vegetation features. Finally, orthogonal rotation between brightness and

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wetness components was implemented in order to separate water from vegetation and non-vegetation

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features (Zhang et al., 2002). Brightness – the first feature of TCT – is a weighted sum of all the bands,

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the variation that affects the interpretation of one image the most. It is typically associated with bare or

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partially covered soil, natural and man-made features, and varies in topography (Crist et al., 1986).

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Greenness is a measure of the contrast between the near-infrared band and the visible bands due to the

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scattering of infrared radiation, which results from the cellular structure of green vegetation and the

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absorption of visible radiation by plant pigments. Soil reflectance curves (soil signatures) are

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represented by higher values in brightness components, but expressed lower for greenness values. The

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third component is orthogonal to the first two components and is associated with soil moisture, water,

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and other moist features (Dave, 1981; Crist and Cicone, 1984a; Crist and Cicone, 1984c; Crist and

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Kauth, 1986, Liu and Liu, 2009).

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Figure 1 shows the approximate location of selected classes in the Transition Zone View (TZV) of

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ACCEPTED MANUSCRIPT TCT. Turbid water and clear water are represented by higher wetness values and lower greenness

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values than other classes, and the wetness values of turbid water is higher than clear water. Forest cover

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is represented by higher greenness values, and man-made materials with both lower greenness and

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wetness values. All these results are consistent with those obtained in the Tasseled Cap plane, and

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provide further support to the claim that the plane of ground objects is in fact equivalent to the Tasseled

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Cap plane (Dave, 1981; Crist and Cicone, 1984c). This allows us a) a better understanding of scene

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class properties, b) to more clearly express the physical scene characteristics in the plane of ground

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objects, and c) to more efficiently characterize ground objects as a reference point.

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Figure 1. Scene classes in the Transition Zone view of TCT

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The tasseled cap transformation has been widely used in the remote sensing community. Compared

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to PCA, TCT can compress multi-spectral data into a few bands that can be directly linked to physical

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scene characteristics and are easier to understand. It originated from the Landsat multi-spectral scanner

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(MSS), which was first launched in 1972, and is now widely applied to modern sensors, such as

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Landsat 4 TM, Landsat 5 TM, Landsat 7 ETM+, JERS-1 OPS, ASTER, MODIS, Spot 5, IKONOS,

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Quickbird 2, CBERS-02 and HJ-1 (Huang et al., 2002; Scott et al., 2003; Li et al., 2011; Chen et al.,

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2012; Arnett et al., 2014; Baig et al., 2014; Liu et al., 2015). However, TCT is sensor-dependent,

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subject to seasonal changes, and varies by geographic locations of images. For a new sensor or a new

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application (different set of relevant scene classes), a reworking of the TCT is required, starting from

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the image analysis. Therefore, it is also necessary to export the TCT parameters of new images from

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the Landsat-8 satellite periodically.

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Table 1 shows the TCT coefficients of reflectance data for Landsat-8 Operational Land Imager top

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of atmosphere (OLI TOA) (Arnett et al., 2014). Since the ninth Landsat band (Band 9, 1360-1390 nm)

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is used to detect cirrus but results in very little information on land use and land cover, it is not used in

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the derivation of the TCT coefficients. All seven multi-spectral bands have positive loadings on the

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brightness component. The near infrared band (Band 5, 845-885 nm), first shortwave infrared band

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(Band 6, 1560-1660 nm), and second shortwave infrared band (Band 7, 2100-2300 nm) are more

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important for brightness and wetness components. The red band (Band 4, 630-680 nm) and the near

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infrared band (Band 5) are more important for the greenness component. The coastal band (Band 1,

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433-453 nm) in all the seven bands contributes the least for brightness component, and less for the

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greenness component but more for wetness component than the blue, green, and red bands (Band 2,

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450-515 nm; Band 3, 525-600 nm; Band 4, 630-680 nm).

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Table 1. The TCT coefficients for Landsat-8 OLI TOA reflectance Landsat-8 OLI TCT Brightness Greenness Wetness

Band 1 (Coastal) 0.0312 -0.1674 0.2665

Band 2 (Blue) 0.0528 -0.2056 0.2311

Band 3 (Green) 0.1153 -0.2319 0.1700

Band 4 (Red) 0.2225 -0.3802 0.1048

Band 5 (NIR) 0.3372 0.8246 -0.4790

Band 6 (SWIR 1) 0.6440 -0.0102 -0.5847

Band 7 (SWIR 2) 0.6364 -0.2264 -0.5142

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The overall workflow (Figure 2) was divided into four steps: a) performing radiometric calibration to

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obtain the reflectance data at each pixel of the study area (Section 3.1), b) modeling with wetness and

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greenness after TCT to extract sea-waterbody information (Section 3.2), c) extracting coastline

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information based on coordinate geometry description, such as length, direction, and distance between

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different segments (Section 3.3), and d) performing accuracy assessment by qualitative and quantitative

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evaluation (Section 3.4).

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Figure 2. Flow chart of coastline information extraction

3.1 Pre-processing

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Radiometric calibration was carried out using ENVI (ENVI, 2011). The digital numbers in the raw

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data were converted to the top of atmosphere (TOA) reflectance via calibration parameters.

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3.2 Extraction of sea-waterbody information using TCT

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3.2.1 Selection of threshold value

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According to the physical meaning of the TCT, the second and third components are characterized

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by surface water content. The more water contained in the surface, the higher the wetness and lower the

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greenness. Considering the influence of suspended sediment concentration in the near shore, the

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appropriate thresholds of wetness and greenness were selected by the histogram peak method to obtain

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the initial sea-waterbody information.

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In many digital image-processing methods, histogram peak method is desirable for selectively

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ACCEPTED MANUSCRIPT applying digital image processing to identifiable targets in the image. For this purpose, it is known to

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select threshold values between targets based on the location of corresponding peaks in the histogram

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of the digital image (Sun et al., 2018). The location of peaks were detected in the wetness or greenness

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component histograms as shown in Figure 3.

Figure. 3 The location of peaks in the histogram of wetness component or greenness component

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In this study, the threshold value of wetness and greenness are expressed as the following formula: 0.5*PWmax , PWmax ≥ 0 Twetness =  -1.5*|PWmax |, PWmax < 0 1.5*PGmin , PGmin ≥ 0 Tgreenness =  -0.5*|PGmin |, PGmin < 0

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where Twetness and Tgreenness are the threshold values of wetness and greenness, respectively; PWmax is the

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maximum peak in the wetness component histogram; PGmin is the minimum peak in the greenness

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component histogram, and || indicates to take the absolute value.

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3.2.2 Extraction of initial sea-waterbody information

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The initial sea-waterbody information extraction was carried out with a specific formula as follows:

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1, I wetness ≥ Twetness and I greenness ≤ Tgreenness I water =  0, other

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where Iwater is the initial sea-waterbody information; Iwetness and Igreenness are the wetness component and

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greenness component after the TCT, respectively; and Twetness and Tgreenness are the threshold values of

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wetness and greenness using the histogram peak method selected from the wetness and greenness

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components, respectively.

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3.2.3 Extraction of final sea-waterbody information

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The initial sea-waterbody information involves both the ocean and high-water content objects such

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as lakes and rivers. Contrastingly, ships can create holes in initial waterbody information. In order to

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determine the final sea-waterbody information and extract coastline information more accurately, the

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water bodies surrounded by land (e.g., lakes, rivers) and the holes caused by ships must be filled during

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post-processing. Considering the differences between lakes, rivers, holes, and oceans in area and shape,

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mathematical morphology of initial sea-waterbody information was performed.

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Mathematical morphology is a mathematical tool for image analysis according to morphology. As a

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branch of image interpretation, mathematical morphology was established on the foundation of the

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ACCEPTED MANUSCRIPT integral geometry work of Metheron and Serra at Mines Paris Tech, France, in 1964 (Serra and Soille,

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2012). Its basic idea is to use certain structuring elements to measure and extract the corresponding

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shapes or objects in images to realize the visual interpretation and target recognition. This method has

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been widely used in image processing, pattern recognition, and computer vision. There are four basic

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operations in mathematical morphology analysis, namely erosion, dilation, opening, and closing (Serra

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and Soille, 2012). With the external filtering function, dilation can fill the smaller (compared to the size

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of structuring elements) gaps in images and smooth the object’s outline. Using the internal filtering

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function, erosion can eliminate the small fragments in an image and eventually shrink the image.

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Opening can smooth the image, eliminating tiny objects at the edge such as “burrs,” or remove the

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isolated pixel or blocks in an image. Closing has the effect of image filtering, filling up small holes and

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gaps in an image and filtering nearby objects with a smoothed boundary. The above are four basic

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operations and can be combined in different ways to generate new operations for morphology analysis.

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In this study, opening and closing with circular structuring elements were selected for post-

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processing and obtaining final sea-waterbody information. According to the basic principle of

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mathematical morphology, opening operation connects the land separated by lakes and rivers, and

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closing can fill up the holes caused by ships. The method provided final sea-waterbody information

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with high accuracy, avoiding any changes to the original boundaries of the sea-water bodies.

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3.3 Extraction of coastline information based on coordinate geometry description

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Using feature knowledge can significantly improve the accuracy of target recognition and

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information extraction, and therefore, ground target must be associated with one or more feature

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knowledge.

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As a linear feature, the coastline information is obtained based on the coordinate geometry

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description of coastline. To obtain the marginal information of the sea-water as the initial coastline

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information, sea-waterbody information undergoes vectorization (Kennedy, 2013). According to the

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characteristics of coastline on remote sensing images, combining the actual situations of continuous

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distribution of coastline – the length (L), direction (D) and distance between different segments (A)

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(initial coastline information) – are calculated, and the information of coastline is extracted based on

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coordinate geometry description.

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The formula for setting the length threshold value to eliminate the influence of the high-water

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content surface feature on the results, such as in the tidal-flat area, is as follows:

1, Li ≥ LT i Flag retain = 0, Li < LT

(i = 1, 2,3,..., N )

(4)

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where Flagiretain indicates whether to reserve the coastline, i; 1 for reservation; 0 for deletion; N is the

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total quantity of initial coastlines; Li is the length of the initial coastline, i; and LT is the length

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threshold value.

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According to previous studies, LT usually accounts for 95% of total length. All initial coastlines were sorted in descending order of length, and LT is the length of initial coastline I:

LT = LI ( I = INT (0.95* N ))

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where INT represents round numbers; and N is the total quantity of initial coastlines.

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When length is less than threshold value LT defined by Equation (4), the corresponding coastlines are deleted, and other coastlines are left for subsequent operations. Due to the influence of mixed pixels and the water content of shallows being inconsistent, the initial

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coastline is intermittent. In order to obtain complete coastline information, those coastlines must be

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reconnected. Taking into account the continuity of the coastline, this research firstly counts the

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direction of each section of the coastline relative to the X-axis, and the distance to the other coastline.

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Afterwards, setting the threshold value to determine whether the adjacent coastline satisfies the

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connecting conditions.

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1, Dm − n ≤ DT and m− n Flag connect = 0, other

Am − An ≤ AT

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(m, n = 1, 2, 3,..., N )

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Flagm-nconnect represents whether to connect coastline m and n, 1 for connect, 0 for not connect; Dm-n is

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the distance, and can be obtained by calculating the minimum distance between the pixels on coastline

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m and n; Am and An are the angles relative to the X-axis, and can be obtained by fitting the direction of

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coastline m and n according to the location of pixels; DT and AT are the threshold values of distance and

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angle, respectively. Based on experience, DT and AT usually take 10 pixels and 5°, respectively.

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3.4 Accuracy assessment

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Accuracy assessment was carried out by qualitative and quantitative evaluation. By definition,

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qualitative evaluation is the assessment of an image produced by visual observation with the support of

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feature knowledge. Quantitative evaluation is the assessment of an image using statistical parameters,

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and was generally based on two factors: non-positional and positional accuracy. Qualitative evaluation

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was conducted mainly on two factors: location and shape. In terms of positional accuracy, producer’s

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accuracy (PA), user’s accuracy (UA), errors of omission (OE), and errors of commission (CE) were

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calculated based on the extraction and reference results. For the non-positional accuracy, the lengths of

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coastline in resulting images were calculated and compared with the length of the real coastline.

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Producer’s accuracy is the accuracy of a map from the view of the mapmaker (the producer). It

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indicates how often the physical features on the ground are correctly shown on the classified map or the

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probability that certain area on the ground is classified as a given land cover (Fu et al., 2008; Brewin et

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al., 2017). User’s accuracy is the accuracy from the view of the map user (not the mapmaker). The

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user’s accuracy reflects how often the features on a map are actually present on the ground and is

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referred to as reliability. The equations for the producer’s accuracy and user’s accuracy are as follows:

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ο’ ∩ ο  η1 = ο  η = ο’ ∩ ο  2 ο’

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(7)

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where η1 and η2 are the producer’s accuracy and user’s accuracy, respectively; and O and O’ are the

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reference data obtained by visual interpretation for a type of surface feature and the data extracted for

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that type of surface feature, respectively.

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Errors of omission refer to the reference sites that were omitted from the correct class in the

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classified map (Brewin et al., 2017; Win et al., 2017). The errors of omission and commission

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complement the producer’s and user’s accuracy, respectively. The equations for these errors are as

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follows:

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η3 =1-η1  η 4 =1-η 2

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where η3 and η4 are the errors of omission and errors of commission, respectively; and η1 and η2 are the

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producer’s accuracy and user’s accuracy calculated by Equation (7).

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4. Study Area and Data

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4.1 Study area

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Our study area was the Yangtze River Estuary (YRE), which is located in the east of the Yangtze

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River Basin. With a length of ~6.3×103 km, the Yangtze River is recognized as the third longest river

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in the world and the fourth largest river in terms of its water discharge and sediment load (Fu et al.,

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2008; Wei et al., 2017). Due to the influence of the sediment load from the upper stream, the river has

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been expanding seaward for the past hundred years.

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The YRE, located in the eastern region of China (30°10′ N - 30°40′ N, 120°55′ E - 123°00′ E),

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extends from the upstream non-flood tidal boundary to the downstream subaqueous delta, where the

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interaction of fresh and saline water occurs (Figure 4). As a branching estuary, the YRE is

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characterized by a tree-tier bifurcation with four openings to the margin of East Sea. First, the YRE is

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divided into the North and South Branches by Chongming Island. Then, the South Branch is separated

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into the North and South Bays by Changxing Island, and the South Bay is further divided into the

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North and South passage by an intertidal region named Jiuduansha.

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Figure 4. The study area.

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The YRE is Yangtze River’s lower, tide-affected section, which provides key services for global

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ecosystems and local economic growth. The linear length of the YRE coastline is ~172 km, and the

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wetland in the study area is affected by combined interactions between the river and ocean.

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4.2 Data

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ACCEPTED MANUSCRIPT In this case study, the Landsat-8 OLI (Operational Land Imager) images of the YRE and its

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surrounding area with a 30 meter spatial resolution were used. Landsat-8, launched on February 11,

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2013, is the 8th in the series of Landsat satellites, making the continuity of the Landsat earth

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observation mission possible and providing a long time-series availability of Landsat images for

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regional application research (Baig et al., 2014). Landsat-8 OLI images with a moderate frequency (16

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days) and medium resolution (30 m) are potentially more useful than other data sources for coastline

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extraction (Kuenzer et al., 2014). The specifications of Landsat-8 OLI are given in Table 2.

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Table 2. The specifications of Landsat-8 OLI Spectral range (µm) 0.433 – 0.453 0.450 – 0.515 0.525 – 0.600 0.630 – 0.680 0.845 – 0.885 1.560 – 1.660 2.100 – 2.300 0.500 – 0.680 1.360 – 1.390

Spatial resolution (m) 30 30 30 30 30 30 30 15 30

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Band name Coastal Blue Green Red NIR SWIR 1 SWIR 2 Pan Cirrus

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Band number 1 2 3 4 5 6 7 8 9

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The image was acquired on July 23, 2017. It is in 12-bit quantization and is a subset of our study

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area of 7381 rows × 6441 columns (Figure 5). The geographic coordinates of this place are

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approximately 121º54' E and 31º35' N. It can be seen from Figure 5 that the research area covers the

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sea, lakes, river, urban areas, wetland and other objects. The wetland is covered by vegetation and

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water. Due to the influence of upwelling and streaming of the Yangtze River, amounts of suspended

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sediment in the immediate offshore area cause the sea near the shore to show a slight yellow or light

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green color, while the sea further from the land is blue. Lakes and rivers on the land are relatively clear,

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illustrating dark tones.

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Figure. 5 Pseudo-color composite image by Landsat 8 OLI TOA reflectance in the study area dated 23

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July 2017.

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5. Results

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5.1 Coastline extraction

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The TCT of Landsat-8 OLI images, as a linear rotation of the reflective bands, include components.

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Figure 6 illustrates the vast majority of data variability being captured in the first three features, with

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the coefficients for these features, as they currently exist, shown in Table 1. As can be seen from Figure

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6a and 6b, the waterbody has higher wetness values in wetness component and lower green values in

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greenness component. This can lay the foundation for extraction of sea-waterbody information.

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(a) Greenness

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(b) Wetness

Figure. 6 Results after the TCT of Landsat-8 OLI images

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The peak values were initially detected from wetness and greenness components, then threshold

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values of wetness and greenness were calculated using Equation (2). In this case, the maximum peak

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value of the wetness component and minimum peak value of the greenness component are 0.0396 and -

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0.4880, respectively, so the threshold value of wetness and greenness are 0.0198 and -0.2440,

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respectively.

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The sea-waterbody information is shown in Figure 7. We can see from Figure 7a that it not only

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contains the final waterbody information but also that of lakes, rivers, and other small water areas.

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According to the characteristics of mathematical morphology, opening and closing operations with

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circular structuring element were applied to the initial waterbody information. The method not only

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removed the influence of the waterbody from small areas such as lakes and rivers, but also filled the

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holes created by ships, and kept the sea boundaries well preserved (Figure 7b).

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(a) Initial sea-waterbody information

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Figure 7. Resulting images of sea-waterbody information using the proposed method

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（b）Final sea-waterbody information

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Sea-waterbody information was vectorized to obtain the initial coastline information (Figure 8a). It

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is evident that the initial coastline information contains some shorter line segments, which is mainly

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due to the elimination of small water areas on the image by the mathematical morphology approach.

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Moreover, the inconsistency of water content in the shallow area makes the water information difficult,

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resulting in the coastline interruption.

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Considering the geometry characteristics of the coastline on the remote sensing image, and the shape

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of the coastline in the actual situation, the coastline information was extracted based on coordinate

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geometry description. In one scenario, we set the length threshold value to eliminate the coastline with

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shorter length. In contrast, connection of the intermittent coastline after counting the distance and

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direction between the coastlines became possible. The result of the coastline information is shown in

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Figure 8b.

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(a) Initial coastline information

(b) Final coastline information

Figure 8. Resulting images of coastline information using the proposed method

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The accuracy of sea-waterbody information can lay the foundation for coastline extraction. In order

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to verify the validity and applicability of the proposed method, comparisons with the NDWI-based

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method, MNDWI-based method and multi-bands-based method were carried out. The normalized difference water index (NDWI) and modified NDWI (MNDWI) are band-ratio

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approaches using two multispectral bands, which are widely used to extract waterbody information.

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NDWI is taken from the green and near infrared bands and MNDWI is taken from the green and

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middle infrared bands (Wei et al., 2017). The multi-bands-based method uses more than two bands to

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perform operations to highlight waterbody information. The equations for these methods are expressed

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as follows:

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I sea − waterbody

( ρ g -ρ nir ) / ( ρ g +ρ nir ) > TNDWI  = ( ρ g -ρ mir ) / ( ρ g +ρ mir ) > TMNDWI  ( ρ g +ρ r ) − ( ρ nir +ρ mir ) > Tmulti −band

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(8)

where Isea-waterbody is sea-waterbody information. ρg, ρr, ρnir and ρmir are reflectance data of green band,

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red band, near infrared band and mid-infrared band such as OLI band 3, band 4, band 5, and band 6,

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respectively. TNDWI, TMNDWI and Tmulti-band are the thresholding values for distinguishing between sea-

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waterbody information and other ground objects by NDWI-based method, MNDWI-based method and

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multi-bands-based method, respectively.

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The resulting images of sea-waterbody information using the NDWI-based method, MNDWI-based

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method and multi-bands-based method are shown in Figure 9. The thresholding values of resulting

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images using NDWI, MNDWI and multi-bands are 0.65, 0.72 and 0, respectively (Xu, 2006; Zhao et

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al., 2017). Figure 9 demonstrates that the sea-waterbody information was excessively extracted due to

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influences from clear water such as river and lake, while the waterbody with large suspended sediment

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near the shore was not extracted.

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(a) NDWI-based method

(b) MNDWI-based method

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(c) Multi-bands-based method

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Figure 9. Resulting images of sea-waterbody information using the index analysis-based method

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The resulting coastline information extracted from the NDWI-based method, MNDWI-based method

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and multi-bands-based method are shown in Figure 10. It is noted that the boundary of larger lakes and

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longer rivers were also extracted as coastline.

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(a) NDWI-based method

(b) MNDWI-based method

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(c) Multi-bands-based method

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Figure 10. Resulting images of coastline information using the index analysis-based method

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5.2 Accuracy Assessment

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5.2.1 Qualitative evaluation Qualitative evaluation was done by comparing the results with the original remote sensing images by

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visual interpretation. For waterbody information, the qualitative evaluation mainly compares the

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resulting images with visual interpretations based on image location and sea range. As can be seen

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from Figure 8, the land and sea boundaries are obvious, and the method can effectively remove the

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influence of waterbody in small areas such as lakes and rivers. By comparing resulting images with the

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original source images, the qualitative evaluation showed that the extracted coastline has complete

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boundaries and an accurate location in close accordance with a visual interpretation, and the shape of

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coastline is regular and continuous.

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5.2.2 Quantitative evaluation

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(1) Positional accuracy evaluation

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The producer’s accuracy, user’s accuracy, errors of omission, and errors of commission of the

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resulting images were calculated and demonstrated in Table 3.

The PA and UA of the extracted waterbody information were higher than 0.85, indicating that

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extracted waterbody contains large sea areas. The PA and UA of the extracted coastline information

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was above 0.90, which means that the extracted coastline information accounts for both a large

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proportion of the actual coastline information with fewer omission errors, and a large proportion of the

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extraction results. For the NDWI-based, MNDWI-based methods and multi-bands-based method, the

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results cannot distinguish between turbid water and clear water, the PA and UA were lower than the

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proposed method results. In other words, users can obtain useful information from the recognition

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results of the proposed method.

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Table 3. Positional accuracy evaluation of waterbody information and coastline information

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Methods Sea-waterbody information Proposed method Coastline information Sea-waterbody information NDWI-based method Coastline information Sea-waterbody information MNDWI-based method Coastline information Sea-waterbody information Multi-bands-based method Coastline information

PA 0.87 0.95 0.79 0.85 0.76 0.80 0.73 0.82

UA 0.86 0.91 0.82 0.87 0.80 0.82 0.75 0.79

OE 0.13 0.05 0.21 0.15 0.24 0.20 0.27 0.18

CE 0.14 0.09 0.18 0.13 0.20 0.18 0.25 0.21

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(2) Non-positional accuracy evaluation

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The lengths of each coastline obtained by the proposed method and index analysis-based method are

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shown in Table 4. The result obtained by the proposed method was consistent with the true coastline

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determined by visual interpretation from the original remote sensing images according to interpretation

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signs constructed by spectral, texture, geometry and spatial relationship characteristics; the error was -

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2.16%. “True” here means the water boundaries that not influenced by wind flows, water currents, mud

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flats, dune juxtapositions or shadows, among other factors that may influence the true edge information

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extraction. Due to the influence of larger rivers and larger lakes, the errors from the NDWI-based

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method, MNDWI-based method and multi-band-based method were 24.13%, 9.76% and 35.22%,

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respectively.

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Table 4. Non-positional accuracy evaluation of coastline information Lengths (km) 1044.39 1021.78 1296.38 1146.30 1412.26

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Error (%) -2.16 24.13 9.76 35.22

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Methods True coastline Proposed method NDWI-based method MNDWI-based method Multi-bands-based method

(1) Error analysis

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The spatial resolution specifies the pixel size of satellite images covering the earth surface, which

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dictates the accuracy of information extraction. For feature extraction, the higher the spatial resolution

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of the remote sensing images, the higher the accuracy of the map produced by remote sensing images.

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Therefore, information richness acquired by interpreting remote sensing images varies with spatial

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resolution.

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Spatial resolution is a key factor for evaluating the quality of interpretation results, and the

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interpretation accuracy can be calculated quantitatively by spatial resolution of remote sensing images

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(Latifovic and Lothof, 2004). The interpretation accuracy of point target, line target, and plane target

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and the spatial resolution of remote sensing images can be represented by the following formula:

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1 P =3P ' = P '' =2 2a 3

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(8)

where P, P’, and P’’ are the interpretation accuracy of point target, line target and plane target,

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respectively; a is the spatial resolution of remote sensing images.

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The above analysis shows that there is uncertainty in the extraction of line target from remote

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sensing images, and the accuracy is related to spatial resolution of remote sensing images. In this case,

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the spatial resolution of remote sensing image is 30 meters, so the accuracy of the coastline information

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is 28.28 meters.

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(2) Tidal correction

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Compared with conventional survey methods, remote sensing technique has the advantage of low

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cost and repeatable observation for large-scale coastline extraction. However, the results obtained from

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remote sensing images is the instantaneous coastline at the time of satellite observation. A coastline is

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defined as a linear intersection of the coastal land with the surface of a waterbody, while the waterline

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is defined as the highest possible water level (Niedermeier et al., 2005; Silva et al., 2018). The Yangtze

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River Estuary (YRE) is located in the eastern subtropical monsoon climate region with distinctive

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seasons. Owing to the variation of topography, a symmetric/irregular asymmetric semi-diurnal tide is

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observed outside/inside the river mouth. Therefore, tidal correction should be performed after

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extracting instantaneous coastline.

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The tide tables of the Wusong port located in the study area were checked and compared with

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satellite imaging time, as shown in Figure 11. It can be seen from the figure that the coastline obtained

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from remote sensing images is close to the lowest possible water level.

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Figure 11. Tidal curve of Wusong port, Shanghai, on July 23, 2017.

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7. Conclusions

The tasseled cap transformation compresses the de-correlated bands of remote image information

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into fewer bands associated with the physical characteristics of the land surface. Greenness and wetness

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bands are important components mainly used to define the water content of ground objects. In this

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paper, a method of coastline information extraction was developed based on the tasseled cap

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transformation, which was applied to extract coastline information in sea-water with high total

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suspended sediment content. Following image pre-processing, sea-waterbody information extraction,

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coastline information was extracted based on coordinate geometry description. The experiment was

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carried out in the Yangtze River Estuary, China, to demonstrate and validate this method. Based on the

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qualitative and quantitative evaluations, the proposed methods of sea-waterbody information extraction

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and coastline information extraction are accurate, and the validity and practicality of the methods has

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been verified.

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The main results are as follows:

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(1) The proposed method based on the tasseled cap transformation can be used to extract sea-

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waterbody information with high total suspended sediment content. The experimental results indicate

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that the sea-waterbody information was accurate, and the boundaries between sea and land were clear.

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Thus, the initial results provide a foundation for coastline information extraction.

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(2) Geometry characteristic of coastlines was analyzed. Following the overall technical coastline

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information extraction, the length, distance, and directions were applied at different stages in order to

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guide the research to reduce the influence of ships, lakes, and rivers and ensure accuracy and continuity

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of the results. The limitations of this study include: (1) the tasseled cap transformation coefficients are related to

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the sensor and richness of ground objects in the images, so it is very important to develop the

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appropriate tasseled cap transformation coefficients of specific coastal regions; (2) selecting the

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optimal time of mathematical morphology to remove the influence of lakes, rivers, and ships on the

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sea-waterbody information extraction is critical for future research; (3) feature knowledge is needed to

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obtain accurate coastline extraction.

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Acknowledgment

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We would like to thank the anonymous reviewers for their constructive comments and suggestions.

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This work was supported by National Natural Science Foundation of China (Grant No. 41701447);

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Zhejiang Provincial Natural Science Foundation of China (Grant NO. LY16F010011); Science

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Foundation of Zhejiang Ocean University (Grant NO. Q1502).

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The tidal data and Landsat data are provided by the Maritime Safety Administration of the People’s

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Republic of China, the United States Geological Survey, and National Aeronautics and Space

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Administration.

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