Int J Appl Earth Obs Geoinformation 68 (2018) 238–251

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Int J Appl Earth Obs Geoinformation

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55-year (1960–2015) spatiotemporal shoreline change analysis using T historical DISP and Landsat time series data in ⁎ ⁎ Gang Qiaoa,1, Huan Mia,1, Weian Wanga, , Xiaohua Tonga, , Zhongbin Lib, Tan Lia, Shijie Liua, Yang Honga,c a College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, b Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, United States c School of Civil Engineering and Environmental Sciences, The University of Oklahoma, Norman, OK 73019, United States

ARTICLE INFO ABSTRACT

Keywords: Shoreline change has been an increasing concern for low-lying and vulnerable coastal zones worldwide, espe- Spatiotemporal shoreline change cially in estuarine delta regions, which generally have significant economic development, large human settle- DISP ments and infrastructures. Thus, long time-series shoreline change data are useful for understanding how Landsat shorelines respond to natural and anthropogenic activities, as well as for providing greater insights into coastal Image series protection and sustainable development in the future. For the first time, this study analyzes 55 years of spa- Shanghai tiotemporal shoreline changes in Shanghai, China, by integrating the historical Declassified Intelligence Satellite Photography (DISP) and Landsat time series data at five-year intervals from 1960 to 2015. Twelve shorelines were interpreted from DISP and Landsat images. The spatiotemporal changes in the shorelines were explored at five-year intervals within the study period for the Shanghai mainland and islands. The results indicate that shorelines in Shanghai accreted significantly over the last 55 years, but different accretion patterns were ob- served in Chongming Dongtan. The rate of shoreline change varied in different areas, and the most noticeable expansions were Chongming Beitan, Chongming Dongtan, Hengsha Dongtan, and Nanhuizui. The length of the entire shoreline increased by 25.7% from 472.6 km in 1960 to 594.2 km in 2015. Due to the shoreline changes, the Shanghai area expanded by 1,192.5 km2 by 2015, which was an increase of 19.9% relative to its 1960 area. The Digital Shoreline Analysis System (DSAS) was used to compute rate-of-change statistics. Between 1960 and 2015, 10.6% of the total transects exceeded 3 km of Net Shoreline Movement (NSM), with a maximum value of approximately 20 km at eastern . The average Weighted Linear Regression Rate (WLR) of the Shanghai shoreline was 52.2 m/yr from 1960 to 2015; there was 94.1% accretion, 3.1% erosion, and 2.8% with no significant change. In addition, the driving forces of the shoreline changes were also explored in detail. Compared with natural factors, such as relative Sea Level Rise (SLR) and the reduction in sediment loading from the River, anthropogenic activities that include land reclamation and channel projects are the primary causes of the shoreline changes in Shanghai.

1. Introduction coastal ecological and socioeconomic development (Rahman et al., 2011). Thus, monitoring changes in coastal regions is important for Global mean sea level increased by approximately 210 mm from the national development and environmental protection (Rasuly et al., late 19th century to the beginning of the 21 st century (Church and 2010). White, 2011), and it is predicted to increase by 450 mm to 820 mm by Shoreline has been recognized as among 27 important features by the end of the 21 st century (Church et al., 2013). Sea level rise (SLR) the International Geographic Data Committee (IGDC) (Kuleli et al., will pose great threats to low-lying and vulnerable coastal regions and 2011). Changes to shorelines are of great concern in many coastal areas, islands with large populations and substantial infrastructures (Arkema such as atolls and estuaries. Eroded shorelines caused by accelerated et al., 2013; Johnston et al., 2014), and it will have negative impacts on SLR pose great threats to atolls, such as Maui (Genz et al., 2007), Oahu

⁎ Corresponding authors. E-mail addresses: [email protected] (G. Qiao), [email protected] (H. Mi), [email protected] (W. Wang), [email protected] (X. Tong), [email protected] (Z. Li), [email protected] (T. Li), [email protected] (S. Liu), [email protected] (Y. Hong). 1 Both authors contributed equally to this work, and should be considered co-first authors. https://doi.org/10.1016/j.jag.2018.02.009 Received 29 March 2017; Received in revised form 17 January 2018; Accepted 11 February 2018 0303-2434/ © 2018 Elsevier B.V. All rights reserved. G. Qiao et al. Int J Appl Earth Obs Geoinformation 68 (2018) 238–251

(Romine et al., 2009), Majuro Atoll (Ford, 2012) and Wotje Atoll (Ford, and 100 km, respectively. Chongming, Changxing, Hengsha and Jiu- 2013). By contrast, shoreline changes in estuarine regions and deltas, duansha are the main islands of Shanghai and were formed by sediment such as in the Nile River (White and El Asmar, 1999), Mississippi River deposition along the Yangtze River. In 2014, Shanghai’s population of (Blum and Roberts, 2009), Yellow River (Cui and Li, 2011) and Yangtze permanent residents exceeded 24 million people (Central Intelligence River (Chu et al., 2013), are more complicated. Shoreline change is a Network, 2015). dynamic process (Mills et al., 2005). Analyzing the spatiotemporal From 1959 to the 1980s, the U.S. launched several reconnaissance dynamics of shorelines in coastal areas and exploring the drivers of satellite programs that acquired a large number of images (U.S. shoreline changes are essential to understanding how those shorelines Geological Survey, USGS, 2015). For the sake of national security, these respond to natural and anthropogenic effects. satellite images were classified for many years, until President Clinton As the most prosperous metropolis in China, Shanghai is highly declassified the first batch of satellite imagery in 1995 (USGS, Declas- developed and densely populated and is well known for its ecological sified Satellite Imagery − 1), which we refer to as DISP-1. A second and economic functions. It is located in the Yangtze Estuary (Feng et al., batch, herein referred to as DISP-2, was declassified in 2002 (USGS, 2014) and is highly susceptible to SLR because of its low elevation and Declassified Satellite Imagery − 2). In this study, we used KH-2, KH-4A, lack of resources to mitigate such threats (Cui et al., 2015; Tian et al., and KH–4 B from DISP-1 and KH-9 from DISP-2. Details of the images 2010). Many studies have investigated shoreline changes around used in this study are given in Table 1. Shanghai’s coastal regions and the Yangtze Estuary. For example, using Cloud-free Landsat TM/ETM+/Operational Land Imager (OLI) Landsat images from 1987 to 2010, Li et al. (2014) found that the images (paths 38 and 39, row 118) with 30 m spatial resolution from shoreline at Dongtan, which is located at the center of the Yangtze 1985 to 2015 were used. The 2015 images were acquired by Landsat Estuary on , generally experienced a decreasing rate OLI. The remaining images were from Landsat TM (1985–1995) and of change over the entire study period, and they attributed that de- ETM+ (2000–2010). creasing rate of shoreline change and the net accretion in Dongtan to sediment discharge at Datong Station. Chu et al. (2013), using multi- 3. Methodology temporal remote sensing data from Landsat Multi spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) Fig. 2 shows the proposed methodology for shoreline change ana- from 1974 to 2010 at intervals of approximately eight years, suggested lysis used in this study. The proposed approach consists of three main that the area of the Yangtze subaerial delta increased by 667 km2; they steps: (1) image preprocessing for both DISP and Landsat data to obtain found a net progradation rate of 18.5 km2/yr, with the greatest pro- the orthophoto series, (2) shoreline interpretation, accuracy evaluation gradation occurring on the eastern shore of Chongming Island and and shoreline bias analysis between DISP and Landsat derived shor- Nanhui bank. By using 4 periods of Landsat images, Feng et al. (2015) elines to eliminate their inconsistencies, and (3) shoreline modeling to found that the Shanghai shoreline experienced drastic change, pro- analyze the spatiotemporal changes over the 55 years. Each step of the grading 551.7 km2 in its coastal region from 1979 to 2008. Another proposed approach is elaborated in the following sections. study (Ding and Li, 2014) used shorelines extracted from SAR images to suggest seaward movement in Shanghai from 1993 to 2005. 3.1. Image preprocessing For shoreline change analysis, most existing studies used Landsat images after the 1970s; no earlier datasets have been utilized. In ad- To correctly analyze reliable shoreline changes, geometric correc- dition, the study areas in previous research were often confined to re- tion and geographic registration were applied to the satellite images. latively small areas rather than the entire Shanghai coastal region. Collected with panoramic cameras through slit scanning, DISP images However, it is important to understand all of the interactions between conform to the deformation characteristics of panoramic cameras (Sohn SLR, river sediment discharge and human activities. The aim of this et al., 2002); the ratio of maximum elevation difference between the study is to further develop existing work. We employed time series of ground and datum plane and the altitude of the satellite is too small historical Declassified Intelligence Satellite Photography (DISP) and because of the flat study area. The relief displacement induced by the modern Landsat satellite images to systematically explore the spatio- elevation difference is therefore negligible (Bayram et al., 2004). In temporal changes of shorelines throughout the entire Shanghai coastal cases where the imaging parameters or information regarding the region from 1960 to 2015. The DISP images span 1960–1980, and the sensor orbits and ephemerides were missing, we applied a partition Landsat images span 1985–2015. In total, 12 shorelines with five-year third-order polynomial model (Mi et al., 2014) to geometrically rectify intervals were interpreted. Hereafter, the 12 intervals are referred to as the DISP images to an orthophoto map that was in gauss projection Periods. Although some previous studies revealed that local regions coordinates generated using 5-m resolution SPOT satellite images col- were undergoing accretion (Chu et al., 2013; Li et al., 2014) over lected in 2001. All the DISP images were resampled to 30 m spatial shorter periods, the results of this research have greatly extended those resolution to match the resolution of Landsat images. The Landsat conclusions both spatially and temporally. Results show that the entire images were also geometrically registered to the same orthophoto base Shanghai shoreline increased about 25.7% (121.6 km) in length, map using affine transforms after radiometric calibration and atmo- leading to 19.9% expansion in area (1192.5 km2) during the past spheric correction. Resulting Root Mean Square Errors (RMSEs) of one 55 years. We documented the evolution process of the Jiuduansha Is- pixel or less were achieved for both the check points and ground control land from 1960 to 2015, and its shoreline and area changes were also points in all of the images. analyzed. 3.2. Shoreline interpretation 2. Study site and dataset Shorelines are usually identified using specifi c indicators, such as Shanghai is located on the eastern coast of China, with the Pacific wet-dry lines, high or low water marks, mean high water lines, or ve- Ocean to the east, and Provinces to the west, and getation lines (Kumar et al., 2010; Maiti and Bhattacharya, 2009; Bay to the south, which opens into the Sea. Romine et al., 2009). Shorelines around Shanghai can be divided into Shanghai is at the confluence of the Yangtze and Qiantang Rivers and two categories: artificial shorelines and muddy shorelines. Artificial extends from 30°40′ N to 31°53′ N and 120°51′ E to 122°12′ E(Fig. 1). shorelines have regular shapes and obvious boundaries between land As part of the Yangtze River Delta, the study area is approximately 4 m and sea and therefore can easily be identified throughout most of the above mean sea level and has an area of 6340.5 km2. It is elongated in coastal region. Muddy shorelines are more difficult to identify because shape, with maximum north-south and east-west dimensions of 120 km they are influenced by the tides and have blurred boundaries.

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Fig. 1. Location map of Shanghai, China. (a), (b): Location of Shanghai, China. (c): Overview of the study area in a Landsat 7 ETM+ SLC-on image taken in 2000 with standard false color format.

Table 1 Selected information of and shoreline uncertainty from DISP and Landsat imagery used in this study. Note: PAN denotes Panchromatic; MS denotes MultiSpectral.

Category Image type (Spectrum) Acquisition date Spatial res. (m) Shoreline uncertainty (m)

Georef. Interpre. Total

DISP-1 KH-2 (PAN) 12/07/1960 7.5 2.5 1.7 8.1 KH-4A (PAN) 08/23/1965 2.75 1.5 2.8 4.2 KH–4 B (PAN) 12/06/1970 1.8 1.4 2.8 3.6 DISP-2 KH-9 (PAN) 11/21/1974 3.5 0.9 2.5 4.4 KH-9 (PAN) 09/12/1980 4 2.6 7.3 8.7 Landsat TM (MS) 11/20/1985 30 6.8 8.0 31.8 TM (MS) 12/04/1990 30 6.8 8.0 31.8 TM (MS) 11/16/1995 30 6.7 8.0 31.8 ETM+ SLC-on (MS) 12/23/2000 30 10.1 8.0 32.7 TM (MS) 11/27/2005 30 8.0 8.0 32.1 TM (MS) 12/27/2010 30 8.3 8.0 32.2 OLI (MS) 01/23/2015 30 7.7 8.0 32.0

In this research, shorelines of these two types are interpreted se- both DISP and Landsat images, with a consistent scale and a set of parately. For artificial shorelines with stable geolocations and relatively procedures, the vegetation lines were manually digitized as muddy regular shapes, different automatic extraction algorithms were em- shorelines using ArcGIS 10.0 software (ESRI, Redlands, CA, USA). The ployed to the panchromatic DISP and multi-spectral Landsat images. muddy shorelines are mainly located in Chongming Dongtan in the For DISP images, an object-oriented classification method was used to Nature Reserve, where the vegetation lines are not influenced by automatically extract the boundaries between land and sea (Qiao et al., human activities (see Section 5.3). In this study, we also selected the 2013; Rasuly et al., 2010). Image segmentation was first achieved by Landsat images in winter (Table 1) under a similar vegetation state, as using Edge (Jin, 2012) and Full Lambda Schedule (Robinson et al., did by Ford (2012) and Li et al. (2014). The interpretation results were 2002) algorithms with empirical scale levels (e.g., 50 and 90, respec- checked by different operators to reduce human-induced errors. tively), and a rule-based feature extraction method that employed features, such as the texture and spatial relationship, was then applied 3.3. Accuracy assessment and bias analysis with experimental thresholds (e.g., a texture range smaller than 10), followed by a post-processing procedure that includes automatic edge Generally, natural factors, such as the seasonal cycle of erosion and tracing of the land-sea, edge smoothing, and manual editing. For deposition, vegetation growth, typhoons, and storms, affect the shore- fi ff Landsat images, the Modi ed Normalized Di erence Water Index line spatial location. In addition, the rectification of remotely sensed (MNDWI) (Xu, 2006) was utilized with empirical thresholds to obtain imagery and shoreline extraction processes introduce errors during the shorelines. The muddy shorelines are characterized by strong cur- shoreline change modeling. To obtain statistically robust shoreline vature, water/land boundaries and other complex shores, which often changes, two types of uncertainties, i.e., positional uncertainties and complicate the delineation (Pardo-Pascual et al., 2012). Therefore, for measurement uncertainties (Thieler and Danforth, 1994), should be

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Fig. 2. Conceptual flowchart in this study: (a) image preprocessing for DISP and Landsat data; (b) shoreline interpretation, accuracy assessment and bias analysis; (c) shoreline modeling and change analysis over the 55 years; (d) driving forces of Shanghai shoreline change. taken into account. There are five potential sources of uncertainty in series. Prior to calculating the statistics, we first created a buffer for the aerial photographs: digitizing error, pixel error, seasonal error, rectifi- 1960 shoreline and manually edited it to generate a gentle baseline, cation error and tidal fluctuation error (Romine et al., 2009). This study then cast a transect perpendicular to this baseline (at a user-defined only considers three of them, i.e., digitizing error, pixel error and rec- spacing) that intersected the shoreline to establish measurement points. tification error, because seasonal and tidal fluctuation errors are re- Finally, we computed the statistics from the distances between the levant only to unconsolidated beaches and do not affect the extraction baseline and each intersection point on the transects that provided lo- of relatively stable vegetation and anthropic features (Ford, 2012). We cation and time information. calculate the pixel error using the spatial resolution of the images, the The rate-of-change statistics generated automatically by the DSAS georeferencing error using the standard deviation of the shoreline po- include Net Shoreline Movement (NSM), End Point Rate (EPR), and sition from rectification, and the interpretation error using the standard Weighted Linear Regression Rate (WLR), which are described in deviation of the shoreline position from repeated digitization of the Table 2. Transects were cast at a 50 m interval along the baseline, and a same section of coast by multiple operators (Romine et al., 2009). The confidence interval of 2σ (95.5%) was applied when calculating re- total shoreline error is derived from the above three errors (Table 1). gression-based shoreline change rates. Here WLR is determined by fitting a least-squares regression line to 3.4. Shoreline modeling all shoreline points for a particular transect (Thieler et al., 2009). The regression line is placed so that the sum of squared residuals is mini- We employed the Digital Shoreline Analysis System (DSAS) (Thieler mized. The linear regression rate is the slope of the line. The weight (w) et al., 2009), an ArcGIS extension developed by the USGS, to compute is defined as a function of the variance in the uncertainty of the mea- rate-of-change statistics for the shorelines derived from the image surement (e)(Genz et al., 2007):

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Table 2 shoreline feature is used to calculate a weight. Description of the statistics used in this study (from Thieler et al., 2009).

Statistic Description Unit 4. Results

NSM The total distance between the earliest and most recent m In all, 12 Periods of shorelines interpreted from DISP and Landsat shorelines for each transect. image series are superimposed in Fig. 3. As the KH-4A image in 1965 EPR Calculated by dividing the NSM by the number of years m/yr between the oldest and the most recent shoreline. did not cover the northwest part of Chongming Island, the shoreline WLR Determined by fitting a least-squares regression line to all m/yr was determined from the KH-2 image in 1960. Some smaller islands shoreline points for a particular transect, placing greater (area smaller than 10 km2 in 2015) with complicated shoreline shapes emphasis on shorelines with positional uncertainty were not considered in this study. Fig. 4 shows the entire shoreline length and Shanghai area changes w = 1/(e2 ), (1) induced by shoreline changes over the study period recorded by the remote sensing images. Shoreline lengths mainly increased over the where e = shoreline uncertainty value. The uncertainty of the study period, with some minor decreases, presenting a steady-increase-

Fig. 3. Shorelines interpreted from historical satellite images of Shanghai from 1960 to 2015. The base map is the Shanghai boundary in 1960. (a) to (f) are enlarged regions with distinctive shoreline changes.

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For the alluvial islands, the increase in the area of CMI, CHI and JDI was statistically significant. Similarly, along the ML shoreline, the re- gion of the Airport-Nanhuizui-north shore of Hangzhou Bay also accreted. Fig. 5b shows the change in area computed for the dif- ferent sub-regions at each Period. Of all of the sub-regions, CMI had the greatest increase in area, 553.5 km2, which represented 53.6% of the entire increase, followed by ML with 363.4 km2, CHI with 202.0 km2, and JDI 73.6 km2. The change in area for Shanghai is temporally variable. For example, the maximum increase occurred between 2000 and 2005 (148.7 km2), whereas the minimum occurred between 1985 and 1990 (55.0 km2), with an average of 108.4 km2.

4.2. Net shoreline movement from 1960 to 2015 Fig. 4. The whole shoreline length and Shanghai area changes over the study period. We calculated shoreline changes in the direction of the transects decrease-increase pattern (shown as the blue line in Fig. 5), thus up- over the research period. Herein NSMs between 1960 and 2015 are dating the increase-decrease pattern reported by Feng et al. (2015) from discussed in detail, as shown in Fig. 6. 1979 to 2008. We found that the entire Shanghai shoreline length re- As seen in Fig. 6, accretion was the main change mode over the past mained relatively steady before 1980, and increased during half century. Of the 8017 transects that intersect both the 1960 and 1980–2000, then decreased from 2000 to 2010, and increased again in 2015 shorelines, 10.6% had NSM values exceeding 3 km, 87.9% had – 2015. The decrease was mainly attributed to the extension and merge of values of 0 3 km, and only 1.5% transects had negative NSM values, some fragmentary shorelines in Chongming Island and Changxing Is- which indicates that a small amount of erosion occurred during the land. The shoreline length values ranged from 472.6 km to 594.2 km, a study period. The shoreline accretion with NSM exceeding 3 km is 25.7% increase from 1960 to 2015. The Shanghai area induced by particularly apparent in the southeast of ML, north and east of CMI, and shoreline changes showed a monotone increase curve during that north and east of CHI (Fig. 6). In eastern Hengsha Island, the maximum period. Compared to the initial Shanghai area in 1960, approximately NSM value was approximately 20 km. Of the 11 Intervals shown in – 1,192.5 km2 of new land was added by 2015, a net increase of ap- Fig. 6b, the largest NSM frequencies appeared in 0 0.5 km in the po- proximately 19.9%. sitive sides, and apparent negative values showing erosion could also be Four sub-regions with different characteristics will be examined in observed, especially from 1974 to 2005, whereas the total shoreline – detail (Fig. 5a): 1) Shanghai Mainland (ML), 2) the first generation erosions were very small during the 1960 1965 study period. Besides, ff ff alluvial island, Chongming Island (CMI), 3) the second generation al- the NSM values of di erent Periods also showed di erent changes, as in luvial islands, Changxing and Hengsha Islands (CHI), and 4) the third Fig. 6. For example, the negative NSM value in the 11 Periods showed generation island, Jiuduansha Island (JDI). Only four Periods of shor- an increase-decrease trend, suggesting the corresponding erosion trend elines were obtained for JDI from 2000 to 2015 due to its late emer- during these Periods. gence. In the following subsections, shoreline changes in the entire region of Shanghai and four sub-regions are examined in detail with the 4.3. Weighted linear regression rate of shoreline changes from 1960 to 2015 aid of DSAS in terms of NSM, area changes caused by shoreline changes, and WLR statistics. Linear regression rate-of-change statistical approaches that in- corporate all available data regardless of changes in trend or accuracy are widely applied to high temporal resolution shorelines to obtain 4.1. Shanghai area changes caused by shoreline changes from 1960 to 2015 more reliable results (Maiti and Bhattacharya, 2009; Ford, 2013). WLR is used in this study for statistical analysis. The Confidence Interval of We calculated the Shanghai area changes caused by the shoreline Weighted Linear Regression (WCI) is calculated by multiplying the changes in different Periods. Shanghai’s land area is maintained by standard deviation of the slope with a 2σ confidence interval (Thieler Yangtze inputs and Yangtze- interactions. Shoreline et al., 2009). change has resulted in a landmass increase since 1960, as shown in Fig. 7 shows an example of a shoreline WLR that was determined by Fig. 5. plotting the shoreline positions with respect to time. The shoreline

Fig. 5. Shanghai area expansion caused by shoreline changes in different Periods from 1960 to 2015. The reference area is the 1960 Shanghai area interpreted in this research.

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Fig. 6. NSM distribution and statistics over the study period. Note that because Jiuduansha Island appeared after 2000, its NSM was calculated between 2000 and 2015. measurement points with smaller positional-uncertainty values had Table 3 more influence on the regression calculation because of the weighting WLR statistics of shoreline change from 1960 to 2015 within each sub-region. The sta- component in the algorithm. The slope of the regression line is the rate tistics for JDI were computed from 2000 to 2015, and the statistics for SH were estimated by ML, CMI and CHI. (204.6 m/yr). The shoreline uncertainty has been exaggerated 100 times for visual effect. Shoreline Average WLR Accretion (%) Erosion (%) No significant The WLR statistics of shoreline change for each sub-region during Region rates (m/yr) change (%) the research period are shown in Table 3. The sign of the WLR value ML 43.7 92.2 0.1 7.7 (slope of the regressed line) at each transect indicates accretion (posi- CMI 70.9 97.5 2.5 0 tive numbers) or erosion (negative numbers) shoreline changes, and CHI 19.4 93.7 6.3 0 zero means that no significant change occurred. The percentage of the JDI 74.7 85.9 14.1 0 three types is the ratio of the number of corresponding transects to the SH 50.9 94.6 2.2 3.2 total number of transects in each sub-region. The results show that 94.6% of transects exhibited net shoreline accretion, 2.2% had erosion, newest sub-region, had 14.1% of the erosional transects, which was the and 3.2% had no significant change, with an average WLR rate of largest percentage. 50.9 m/yr over the study period. Within the three largest sub-regions, The WLR rates for the transects along shorelines in each sub-region over 90% of the transects showed net shoreline accretion. CMI, with an are presented in Fig. 8. Overall, shoreline change along the coast was average WLR rate of 71.2 m/yr and representing 97.5% of transect spatially variable, with distinct areas of erosion and accretion, but the accretion, exhibited a particularly apparent accretionary trend. JDI, the

Fig. 7. Example of a shoreline WLR determination from this study.

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Fig. 8. WLR rates of transects cast at 50-m intervals along shorelines in different sub-regions from 1960 to 2015. The 2σ confidence intervals are included on the WLR series. The transects are numbered clockwise from the starting point. Note that the WLR rates for JDI are from 2000 to 2015 and the abscissa has been lengthened for visual effect. prevailing mode of shoreline change was accretion. WLR rates were South Branch and South Channel interrupted the sediment supply calculated along 3180 transects within ML, and they ranged from 0 to (Fig. 10(I)), which indicates that anthropogenic activities can con- 144 m/yr, with an average rate of 43.7 m/yr. The WLR rates of the CMI solidate channel boundaries as well as prevent shoreline accretion. shoreline were greater than 100 m/yr for 33% of transects, which were With transect numbers 1201–2100, zone B is at the junction of the located in the western, northern and eastern parts of CMI. In the Yangtze Estuary and Hangzhou Bay, has positive EPRs and was char- southern part of CMI, there was an average WLR of 12 m/yr, indicating acterized by significant accretion and a rich sediment supply. The lar- almost no accretion. CHI was consistent with ML and CMI and was gest increase in EPR was 876 m/yr, which occurred during 2000–2005, predominantly characterized by accretion. At a 2σ confidence interval, when the construction of Dishui Lake and Lingang New City caused 51.2% of JDI transects exhibited erosion, with a minimum WLR rate of considerable reclamation (Li et al., 2010; Fig. 10(IV)). This significant −28.2 m/yr. However, JDI as a whole still exhibited accretion. change is consistent with the observation reported in Ding and Li (2014). The maximum EPR value during 2010–2015 was 693 m/yr due to the eastward expansion of Pudong International Airport. The 5. Discussion shoreline of zone C remained comparatively stable, with EPRs less than 200 m/yr along all of the shoreline transects. From 1970–1974, there 5.1. Analysis of spatiotemporal shoreline changes from 1960 to 2015 was notable shoreline accretion induced by industrial activities. The shoreline change along ML was spatially variable, with evident areas of fi In this section, EPR is used to analyze the rates of change at ve- accretion and slight erosion in some areas of zone C during 1980–1985, year intervals and to explore the spatiotemporal shoreline changes from 1985–1990, and 2000–2005. 1960 to 2015. All four sub-regions were analyzed, and the causes for As the first-generation alluvial island in Yangtze Estuary, CMI is those changes were investigated. highly sensitive to sediment from the Yangtze River. To further assess First, it should be noted that the entire Shanghai shoreline lengths the spatial pattern of shoreline change, CMI is divided into Chongming extracted in this study are comparable to those from previous studies at Island South (CMIS) and Chongming Island North (CMIN). A total of corresponding periods. For example, the comparison of shoreline length 3115 transects were used to compute the EPRs for each Period (Fig. 9b). values from four approximate years interpreted by Feng et al. (2015) CMIS, with transect numbers 0–1500, had obvious shoreline movement and this study shows similar shoreline states, e.g., 451.7 km in 1979 vs. to the northwest during 1970–1974, with an average EPR of 944 m/yr 457.8 km in 1980, 517.2 km in 1992 vs. 512.2 km in 1990, 525.3 km in and a maximum EPR of 1119 m/yr. There were no significant changes 2001 vs. 568.7 km in 2000, and 513.9 km in 2008 vs. 544.7 km in 2010. after that point. That trend may have been due to the trivial deposits The apparent larger values in 2001/2000 and 2008/2010 from this caused by high flow speeds as well as dredging and other anthropogenic paper may be due to the addition of JDI shoreline. disturbances. Due to sedimentation and reclamation (Fig. 10(I)), the ML is located at the southern Yangtze Estuary and northern CMIN shorelines expanded rapidly during 1960–1965, 1974–1980, fi Hangzhou Bay. We further classi ed ML into three zones, which are 1980–1985, 2000–2005, and 2010–2015, and the maximum EPR value – indicated by A CinFig. 9a, based largely upon geographical location reached 1000 m/yr. North Brach narrowed because of the land expan- fl and factors that in uenced shoreline changes. Zone A is situated in the sion, as illustrated in Fig. 10. – southern Yangtze Estuary, with transect numbers 0 1200. The EPRs of CHI is a second-generation alluvial island that has experienced shoreline change were moderate because engineering works in the

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Fig. 9. Shoreline change rates indicated by EPR at each Period from 1960 to 2015. (a) Shanghai Mainland; (b) Chongming Island; (c) Hengsha and Changxing Islands; (d) Jiuduansha Island. continuous accretion over 50 years. Fig. 9c shows the shoreline change different time and shoreline interpretation standards. In this study, we EPRs. As seen, net shoreline accretion and erosion occurred in CHI. In analyzed shoreline changes in the three main shoals of JDI. particular, engineering structures, such as the Qingcaosha Reservoir We would like to highlight the EPRs of JDI after 2000 at five-year (Fig. 10(II)) and Yangtze Estuary Deepwater Channel Regulation Pro- intervals (Fig. 9d). Due to the adjacency relation (see Figs. 11(I) and ject (hereafter Deepwater Channel Project, Fig. 10(III)), constructed in 11(III)), the Deepwater Channel Project has changed the morphology of the Estuary caused a dramatic seaward expansion of the shoreline on JDI dramatically since 2000 by providing abundant sediment from the northwestern Changxing Island and eastern Hengsha Island. The dredged channel. Specifically, it leads to erosion of the southern Qingcaosha Reservoir, which was built in the northern part of Shangsha (Song et al., 2015). Nevertheless, the evolution pattern of JDI Changxing Island from 2006 to 2010 and has an area of 67.2 km2 (SEH, is mainly characterized by constant accretion, and the maximum ERP 2015), caused the EPR value to increase to 4160 m/yr during values from 2000 to 2015 were 400 m/yr along Shangsha and 328 m/yr 2010–2015 in some parts of Changxing Island. The Deepwater Channel along Zhongsha and Xiasha. Project implemented from 1998 to 2010 dredged a shipping channel In the following two subsections, different shoreline accretion pat- that was 92.9 km long, 12.5 m in depth and 350–400 m in width (China terns could also be observed. For example, in contrast to the expediting Harbor, 2015; Wan et al., 2014). The shoreline of eastern Hengsha Is- shoreline expansion in the entire Shanghai region, supplemented by the land was greatly extended due to the huge reclaimed quantities of silt sediment transportation from the Deepwater Channel Project, deceler- (Li, 2007). ating shoreline extension was observed in Chongming Dongtan, which JDI is the youngest alluvial island, is located between the South and was dominated by the reduced sediment discharge from the Yangtze North passages of the Yangtze Estuary, and consists of three shoals: River. Shangsha, Zhongsha and Xiasha (Fig. 11). Initially, Zhongsha and Xiasha were two separate shoals, but since 2000 they were integrated 5.2. Driving forces of shanghai shoreline change due to riverine and marine processes as well as human interventions. Because of the large quantity of sediment that entered the estuarine In estuarine delta regions, long-term shoreline changes are generally region, a new shoal was formed, as shown in the upper left portion of affected by both natural and anthropogenic activities (Pilkey and the Landsat images from 2010 to 2015 (Fig. 11). The area of JDI in- Cooper, 2004; Syvitski et al., 2009; Rahman et al., 2011). Natural terpreted by this study in 2010 (57.9 km2) and that from Chu et al. factors, such as wind, wave, tide, riverine sediment supply, and relative (2013) (49 km2) is comparable, and the difference may be due to SLR, can result in erosion and/or accretion over different temporal and

246 G. Qiao et al. Int J Appl Earth Obs Geoinformation 68 (2018) 238–251

Fig. 10. Channel structure, farms, and estuarine engineering projects around the Yangtze Estuary: (I) land reclamation over the past half century (the filled regions); (II) Qingcaosha Reservoir in the North Channel; (III) Deepwater Channel Project along the North Passage (white lines); (IV) Dishui Lake and Lingang New City in Nanhuizui. Note: II, III, and IV are from Landsat 8 OLI images acquired on March 12, 2015.

Fig. 11. Evolution of Jiuduansha Island between 1960 and 2015.

247 G. Qiao et al. Int J Appl Earth Obs Geoinformation 68 (2018) 238–251

Fig. 12. Record of monthly mean sea level at Lvsi station (PSMSL ID 979) near Shanghai from 1961 to 2015 (after Zhou et al., 2013). Note: In gen- eral, there were 12 monthly mean values each year in Lvsi station, whereas the data gaps before 1970 and between 1995 and 2000 resulted in a data integrity of 82.7%. The minimum and maximum sea level registers ap- peared in Jan 1968 (RLR: 6509 mm) and Oct 2012 (RLR: 7387 mm), re- spectively.

spatial scales. In this paper, we mainly analyze the riverine sediment consistent with the reconstruction of the Three Gorges Dam Project supply and relative SLR because they are permanent natural driving (1998–2009) (Dai et al., 2014). forces for shoreline dynamics (Mann and Westphal, 2014). In contrast to the decreasing trend of the sediment discharge, the Data from the Lvsi tide station (32.08°N, 121.37°E), the nearest site pattern of the accretion area of two contiguous Periods is accelerating to Shanghai that has long-term monthly tidal data (from 1961 to 2015), in Fig. 13, and this difference suggests that compared with natural were used to examine SLR for Shanghai in this study (after Zhou et al., factors, anthropogenic activities could possibly exert a greater impact 2013). Fig. 12 shows the recorded sea level change at the Lvsi tide on land dynamics as well as shoreline changes. The key human activ- station. We use the Revised Local Reference (RLR) as a datum, which at ities that influenced the Shanghai shoreline changes over the past half each station is defined to be approximately 7000 mm below mean sea century include early land reclamation projects using reclaimed farms level, a convention established to avoid negative numbers in the re- and recent major engineering projects conducted using large ma- sulting RLR monthly and annual mean values. The resulting SLR rate chinery. The reclamation farm is a special semi-military unit estab- was 5.5 mm/yr (PSMSL, 2015), and the SLR amount was approximately lished by the state or collectiveness for reclaiming the intertidal zone 297 mm, which represented a negative effect on the continuous ex- using labor force beginning from 1950s. The Shanghai Local Chronicles pansion of the shoreline during the study period. (Office of Shanghai Local Chronicles, 2017) reported that there were In Shanghai, land dynamics (shoreline change) are mainly affected totally 62 reclamation activities performed by the reclamation farms by sediment supply (Xie et al., 2013). Fig. 13 shows a comparison of the with 379,853 people in Shanghai during 1954–1990, and the reclaimed annual changes of sediment discharge at the Datong Gauging Station on land area was about 518.8 km2, which is close to the 599.0 km2 docu- the Yangtze River (the nearest gauging station to Shanghai, and the mented by the remote sensing images during 1960–1990 in this study, location is shown in Fig. 1) from 1960 to 2015 and the accretion area of considering the possible contribution from other forms of shoreline contiguous Periods in Shanghai interpreted by remote sensing images. changes interpreted from images. Besides, Li et al. (2007) recorded A clear decreasing tendency could be observed for the sediment dis- reclaimed land area of 988 km2 during 1950–2005 in Shanghai, con- charge, from 0.45 * 109 ton/yr in 1960–1985 to approximately 0.10 * sistent with the interpreted 950.2 km2 in this study. Fig. 10 shows the 109 ton/yr after 2005, which represents a decrease of approximately distribution of the major reclamation farms around the Shanghai 78%. This decrease was mainly due to the over 50,000 dams and re- coastal regions during the study period, which is consistent with the servoirs built along the Yangtze River basin over the past 60 years. In distribution of the shoreline expansion. particular, the sharpest decrease occurred during 1999–2009, which is The change in the net accretion area of the two contiguous Periods

Fig. 13. Sediment discharge at the Datong Gauging Station (location refers to Fig. 1) from 1960 to 2015 and the accretion area of the two contiguous Periods in Shanghai interpreted in this research.

248 G. Qiao et al. Int J Appl Earth Obs Geoinformation 68 (2018) 238–251 in Fig. 13 accords with land reclamation activities affected by historical (1978), the accretion area of two contiguous Periods in 1965–1980 is events at different epochs. For example, the drop and rise in 1970 and relatively stable, accordant with the annual sediment discharge in this the following decade were consistent with the movements of ‘educated period. However, in 1985, there was a surge in the accretion area; this youth to go and work in countryside’ that provided large amounts of sudden change was caused by a historical event, the Tuanjiesha Re- labor force in 1970s. The left image of anthropogenic activities in claiming Project (1979.3–1982.6), which connected Tuanjiesha Island Fig. 2d shows a typical case of a land reclamation project carried out on with the Chongming Dongtan mainland. The evolution of Tuanjiesha a traditional farm. The drops in 1990 and 1995 were a result of the Island from 1965 as well as this Project is shown in Fig. 14a. From declines in both sediment discharge and the labor force. The reduction 1990, the accretionary area of the two contiguous Periods gradually in farmland in suburbs after the Pudong opening-up in 1990 raised decreased along with the degressive sediment discharge, showing a requests for new land resources. The contradictory phenomenon be- very high dependency. This general decrease trend in accretion rate in tween the reduced sediment discharge and the rising rate of net ac- Chongming Dongtan is also reported by Li et al. (2014) during cretion area after 1995 was attributed to two reasons. On one side, the 1987–2010. The net increase in area from 1985 to 2010 (67.3 km2)in Deepwater Channel Project started in 1998, and the subsequent main- this study can be validated by the result from Li et al. (2014) (66.0 km2) tenance efforts (Fig. 10(III)) provided more sediment input to the re- given the different remote sensing data sources. clamation activities, making up the decreased amount evocable by se- The consistency of the accretion area and the input of the sediment diment discharge; one typical example is the evolution of JDI (Fig. 11). discharge suggest that, although the two reclamation farms appeared in The Deepwater Channel Project included three stages, from 1998 to 1966 and 1991, the shoreline evolution is primarily dominated by the 2002, 2002–2005, 2005–2011, respectively (China Dredging natural sediment discharge from the Yangtze River. The Chongming Association, 2017), and the total dredging engineering quantity was Dongtan Nature Reserve prevents modern construction projects as well about 423 billion m3. On the other side, with the development of both as sediment input generated by the Deepwater Channel Project. economy and technology, the large machinery based modern major engineering projects (the right image of anthropogenic activities in 6. Conclusions Fig. 2d) have greatly speeded up the reclamation process, examples included Lingang New City construction (Fig. 10(IV)), reclamation in In this research, for the first time, 55-year spatiotemporal shoreline Hengsha Dongtan and the expansion project of the Pudong Interna- changes in Shanghai were studied by integrating the time series data of tional Airport. The construction of Lingang New City reclaimed DISP images from 1960 to 1980 and Landsat images from 1985 to 2015, 2 131.1 km land during 2000–2005 in this research, very close to the both at five-year intervals. The high sampling frequency of the data 2 133.7 km measured by Ding and Li (2014) during 1999–2005. Zhao used in this study enabled a detailed analysis of Shanghai shoreline et al. (2015) reported that the Hengsha Dongtan reclaimed land area of changes over the past half century. The delineated shorelines in dif- 2 2 81.7 km during 2003–2015, in consistent with the result 76.4 km in ferent periods were quantitatively analyzed using several evaluation this study during 2005–2015. indicators, i.e., Net Shoreline Movement (NSM), shoreline length and resultant area changes, End Point Rate (EPR), and Weighted Linear 5.3. Chongming Dongtan: sediment discharge dominated shoreline evolution Regression Rate (WLR). In addition, the natural and anthropogenic drivers of shoreline change in Shanghai were also explored. Compared with the entire Shanghai coastal region discussed above, The results demonstrated that although the spatiotemporal shore- the evolution of Chongming Dongtan was less dominated by human line changes in Shanghai are complicated, i.e., both accretion and activities. Fig. 14b shows a comparison similar to Fig. 13 of the annual erosion can be observed, accretion was the dominant shoreline change sediment discharge at Datong Gauging Station and the Periodical (five- mode during the past 55 years in Shanghai, especially in Chongming year) area accretion in Chongming Dongtan, and the evolution of Beitan, Chongming Dongtan, Hengsha Dongtan, and Nanhuizui. Over Chongming Dongtan at every five years is illustrated in Fig. 14a. the entire study period, Shanghai shorelines increased 25.7% in length, It can be observed that the two broken lines in Fig. 14b are generally and the resulting land area caused by shoreline change increased by consistent. The changes in accretion area in Chongming Dongtan can be 19.9%. Compared with natural factors (e.g., SLR and reduced sedi- divided into three stages: 1965–1980, 1985, and 1990–2015. Due to the mentation), anthropogenic activities, such as land reclamation and limited effect of human activities before the ‘Reform and Opening-up’ channel engineering, were the major drivers of all of the Shanghai

Fig. 14. (a) Evolution of and (b) annual sediment discharge in Chongming Dongtan from 1960 to 2015. Note: the red rectangles in (a) represent reclamation farms, and the purple stars represent reclaiming activities over time. The annual sediment discharge in (b) was at Datong Gauging Station (see Fig. 1). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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