Int J Appl Earth Obs Geoinformation 68 (2018) 238–251 Contents lists available at ScienceDirect Int J Appl Earth Obs Geoinformation journal homepage: www.elsevier.com/locate/jag 55-year (1960–2015) spatiotemporal shoreline change analysis using T historical DISP and Landsat time series data in Shanghai ⁎ ⁎ 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, China 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 Hengsha Island. 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 Yangtze 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 Chongming Island, 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).