water

Article Continuously Tracking the Annual Changes of the Hengsha and Changxing at the River from 1987 to 2016 Using Landsat Imagery

Nan Xu 1, Dongzhen Jia 2,*, Lei Ding 3 and Yan Wu 4

1 Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, ; [email protected] 2 School of Earth Science and Engineering, Hohai University, 211100, China 3 Nanjing Hydraulic Research Institute, Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport, Nanjing 210029, China; [email protected] 4 Dahua Surveying & Mapping Co., Ltd., Shanghai 200136, China; [email protected] * Correspondence: [email protected]; Tel.: +86-133-9091-4608

Received: 5 December 2017; Accepted: 2 February 2018; Published: 8 February 2018

Abstract: The evolution of estuarine islands is potentially controlled by sediment discharge, tidal currents, sea level rise, and intensive human activities. An understanding of the spatial and temporal changes of estuarine islands is needed for environmental change monitoring and assessment in estuarine and coastal areas. Such information can also help us better understand how estuarine islands respond to sea level rise in the context of global warming. The temporal changes of two estuarine islands in Shanghai near the Yangtze River Estuary were obtained using Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper) images from 1987 to 2016 on an annual scale. First, a composite image was generated by using the multi-temporal Landsat images for each year. Then, a modified normalized difference water index (MNDWI) was applied to the annual estuarine maps using a threshold segmentation method. Finally, we obtained the temporal changes of the estuarine islands in Shanghai during the period 1987–2016. The results suggest that (1) Landsat TM/ETM+ images can be used for estuarine island mapping and change detection; (2) the two estuarine islands have expanded significantly during the past three decades; (3) human activities are the main driving factor that caused the expansion of the estuarine islands; and (4) the sea level can also partly explain the change in the estuarine islands. This study demonstrates that Landsat data are useful for determining the annual variations in the land area of two estuarine islands in Shanghai during the past 30 years. In the future, other factors and their contributions to estuarine island changes should be further investigated.

Keywords: estuarine islands; Yangtze River Estuary; Landsat data; remote sensing; Shanghai

1. Introduction The evolution of estuarine islands can be shaped by sediment discharge, sea level rise, and tidal currents [1,2]. Intensive human activities near coastal areas can greatly change the morphologies of estuarine islands by the process of land reclamation [3–5]. Such coastal projects are common in coastal zones, particularly in coastal China, which has experienced rapid economic development since China’s economic reform and opening-up in 1978 [6–10]. As the Chinese economic center, Shanghai undoubtedly needs new space for further economic development in the future [11]. Changxing Island and are the second and third largest islands in Shanghai, which serves port, coastal industry, high-end service, and tourism functions. Because of intensive human activities around the two islands [12], timely and accurate information on the temporal changes of estuarine islands is

Water 2018, 10, 171; doi:10.3390/w10020171 www.mdpi.com/journal/water Water 2018, 10, 171 2 of 17 essential for deepening our understanding of how the coastal zone is changing and evaluating the impact of human activities. This information can also be used as the basis for sustainable development and environmental protection for Changxing Island and Hengsha Island. Therefore, it is important to track the coastline dynamic of these islands over long time periods. To date, various remotely sensed data have been widely used for mapping and detecting changes in coastal zones because of their relatively low cost compared with traditional approaches [13–17]. In this study, Landsat imagery with a 30-m spatial and 16-d temporal resolution was selected as the main data source for tracking the temporal change of estuarine islands [18]. Unlike previous studies [19–22], this study aims to obtain an annual map of the estuarine islands and track their temporal changes at an annual scale rather than an interval of several years, which can provide more temporally detailed information on the estuarine islands’ variations. Conventional methods that map changes in the estuarine islands over several-year intervals cannot detect more short-term changes [23]. Such changes are better characterized, with many observations using the method that was developed in this study. In addition, many coastline indicators have been proposed in previous studies [24]. For each year, Landsat time series images were firstly stacked together to generate a median image [25] after the removal of clouds and shadows. Based on the annual median image, the waterline [26] as the boundary between the estuarine islands and the surrounding water was then defined as the coastline on an annual scale. Finally, the annual land area of the estuarine islands was extracted from the annual median image. The main objective of this study was to continuously track the dynamics of Changxing Island and Hengsha Island from 1987 to 2016 using a 30-m Landsat 4/5 Thematic Mapper (TM) and Landsat-7 (EMT+) Top of Atmosphere (TOA) reflectance product with the Google Earth Engine (GEE) [27]. Then, annual maps of the two estuarine islands were produced. Based on the generated data, a comprehensive description of the annual variations of the estuarine islands was presented, and the impact of human activities was investigated.

2. Study Area and Data

2.1. Study Area The study area included Changxing Island and Hengsha Island, which are located near the Yangtze Estuary (Figure1). Both islands were formed by sedimentation from the Yangtze River (also known as the Changjiang River), which, at 6380 km long, is the longest river in Asia and the third longest river in the world. The Yangtze runs west to east from the Anhui Province to the Province and finally flows into the Sea at Shanghai. The annual mean water discharge and sediment discharge of the river are 905.1 × 109 m3/year and 0.43 × 109 ton/year during 1950–2000 [28]. The Yangtze River Estuary is characterized as a mesotidal estuary in terms of tidal range [29]. are regular semidiurnal out of the mouth, and non-regular semi-diurnal inside. The mean (and maximum) tidal range is 2.66 m (4.62 m) at Zhongjun near the mouth, and decreases up-estuary to 2.43 m (3.96 m) at Gaoqiao and 2.21 m (4.48 m) at in the South . In the background of climate change, the sea level around Shanghai has risen at a rate of 3 mm/year over the past three decades [30]. Since the construction of the Three Gorges Dam (TGD) in 2003, the suspended sediment discharge from the upstream into the estuary has been reduced from 0.43 × 109 ton/year during 1950–2000 to less than 0.15 × 109 ton/year up to now [28]. Shanghai, which features a humid subtropical climate with a mean annual air temperature of 17.1 ◦C and a mean annual precipitation of 1166.1 mm, is located in the Yangtze . The largest island in Shanghai, Chongming is an alluvial island at the mouth of the Yangtze River in eastern China, and it was 1267 km2 in 2010. Combined with the Changxing and Hengsha Islands, it forms Chongming County, the northernmost area of the provincial-level municipality of Shanghai. The islands Changxing and Hengsha are the second and third largest islands in Shanghai. Changxing Island lies between Chongming and Shanghai in the southern channel of the Yangtze opposite to the Water 2018, 10, 171 3 of 17 mouth of the Huangpu, the major river of central Shanghai. Hengsha Island lies in Changxing’s east and is connectedWater 2018, 10 to, x FOR the PEER mainland REVIEW and other islands. Changxing Island first emerged from3 the of 16 Yangtze in 1644, and it began to be reclaimed for agricultural purposes in 1843. Hengsha Island first emerged from thein Yangtze1644, and init began 1858, to and be reclaimed it began for to agricultur be reclaimedal purposes for agricultural in 1843. Hengsha purposes Island infirst 1886. emerged Now, both Changxingfrom Islandthe Yangtze and Hengshain 1858, and Island it began are to inhabitedbe reclaimed islands for agricultural with developed purposes in agricultural, 1886. Now, both freshwater ChangxingWater 2018, 10 ,Island x FOR PEERand HengshaREVIEW Island are inhabited islands with developed agricultural, freshwater3 of 16 fishing, and marine fishing industries. fishing, and marine fishing industries. in 1644, and it began to be reclaimed for agricultural purposes in 1843. Hengsha Island first emerged from the Yangtze in 1858, and it began to be reclaimed for agricultural purposes in 1886. Now, both Changxing Island and Hengsha Island are inhabited islands with developed agricultural, freshwater fishing, and marine fishing industries.

Figure 1. Geographical location of Changxing Island and Hengsha Island, Shanghai, China. Figure 1. Geographical location of Changxing Island and Hengsha Island, Shanghai, China. 2.2. Data

2.2. Data To studyFigure the 1. Geographicaltemporal changes location of ofthe Changxing two estuarin Islande islands, and Hengsha time seriesIsland ,of Shanghai, Landsat China. TM (Thematic ToMapper) study the and temporal ETM+ (Enhanced changes Thematic of the two Mapper) estuarine Top of islands, Atmosphere time (TOA) series reflectance of Landsat data TM from (Thematic 1987 to 2016 was used based on Google Earth Engine cloud platform. The spatial resolution of Mapper)2.2. and Data ETM+ (Enhanced Thematic Mapper) Top of Atmosphere (TOA) reflectance data from Landsat TM/ETM+ images is 30 m and the temporal resolution is 16 days. The study area was covered To study the temporal changes of the two estuarine islands, time series of Landsat TM (Thematic 1987 toby 2016 the satellite was used track based of path on 118 Googleand row Earth38. A total Engine of 323 cloud standard platform. Level 1 Terrain-corrected The spatial resolution (L1T) of Mapper) and ETM+ (Enhanced Thematic Mapper) Top of Atmosphere (TOA) reflectance data from Landsatproducts TM/ETM+ were acquired images for is this 30 mscene. and Specifical the temporally, 123 TM resolution images during is 16 1987–1999 days. Theand 200 study ETM+ area was 1987 to 2016 was used based on Google Earth Engine cloud platform. The spatial resolution of coveredimages by the during satellite 2000–2016 track of were path used 118. The and annual row 38.distribution A total of th 323e Landsat standard imagery Level and 1 Terrain-correctedthe annual Landsat TM/ETM+ images is 30 m and the temporal resolution is 16 days. The study area was covered percentage of cloud cover of the Landsat imagery were presented in Figure 2a,b, respectively. The (L1T) productsby the satellite were track acquired of path for118 and this row scene. 38. A Specifically,total of 323 standard 123 TM Level images 1 Terrain-corrected during 1987–1999 (L1T) and number of Landsat images during 1987–2016 varies from 4 to 17 with no significant trend (R2 = 0.0068, 200 ETM+products images were during acquired 2000–2016 for this scene. were Specifical used.ly, The123 TM annual images distribution during 1987–1999 of the and Landsat 200 ETM+ imagery p-value = 0.67). Also, the cloud cover during 1987–2016 varies from 4 to 17 with no significant trend and theimages annual during percentage 2000–2016 of were cloud used cover. The annual of the distribution Landsat imageryof the Landsat were imagery presented and the in annual Figure 2a,b, (R2 = 0.0078, p-value = 0.64) in the cloud cover was found from 1987 to 2016. respectively.percentage The numberof cloud cover of Landsat of the Landsat images imagery during were 1987–2016 presented varies in Figure from 2a,b, 4 to respectively. 17 with no The significant 2 trend (Rnumber2 = 0.0068 of Landsat, p-value images = 0.67 during). Also, 1987–2016 the cloud varies cover from 4 during to 17 with 1987–2016 no significant varies trend from (R = 4 0.0068, to 17with no p-value = 0.67). Also, the cloud cover during 1987–2016 varies from 4 to 17 with no significant trend R2 p significant(R2 = trend 0.0078, ( p-value= 0.0078, = 0.64) in-value the cloud = 0.64) cover in was the found cloud from cover 1987 was to 2016. found from 1987 to 2016.

Figure 2. Detail information on the Landsat imagery used in this study. (a) The annual distribution of the Landsat imagery; (b) The annual percentage of cloud cover of the Landsat imagery.

Figure 2. Detail information on the Landsat imagery used in this study. (a) The annual distribution of Figure 2.theDetail Landsat information imagery; (b) onThe the annual Landsat percentage imagery of clou usedd cover in this of the study. Landsat (a) imagery. The annual distribution of the Landsat imagery; (b) The annual percentage of cloud cover of the Landsat imagery.

Water 2018, 10, 171 4 of 17

3. Methods GEE combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Top of Atmosphere (TOA) calibrated Landsat 5 (TM) and 7 (ETM+) reflectance data that are available from the GEE platform were used for this study. A simple cloud score function (i.e., ee.Algorithms.Landsat.simpleCloudScore()) was used to avoid the influence of clouds and shadows on the Landsat data. A reduction function (i.e., imageCollection.reduce()) provided by GEE was applied to generate the annual Landsat time series from 1987 to 2016. For each pixel, the output is composed of the median value of all the Landsat images within each year at that location, which can effectively remove the noise caused by outliers and poorly removed edges of clouds [25]. For estuarine islands mapping, such median-composing method can minimize the short-term coastal changes associated with sea level variabilities, wave run-up, sedimentary seasonal variations, and coastal storms [18]. Note that the pixels in the Landsat 7 SLC-off gaps were not included in the generation of the annual Landsat images owing to their non-value characteristics. It means that the pixels contaminated by clouds, shadows, and Landsat 7 SLC-off gaps were not used for the calculation. All annual Landsat images were generated based on GEE platform. The following operations performed for these downloaded annual Landsat images based on MATLAB are described below. A land-water classification was the basic step that was performed during the estuarine island area extraction. The MNDWI has been widely used for land-water classification [31–34], and it was used in this study. MNDWI = ((Green − MIR))/((Green + MIR)) (1) in which MIR is a middle infrared band (band 5 for Landsat TM/ETM+ data) and Green is the green band (band 2 for Landsat TM/ETM+ data). The MNDWI was designed to enhance water features by composing various bands from multi-spectral remote sensing images. Based on the annual MNDWI image, a threshold segmentation method was performed to separate the land and water. For this process, it was critical to determine the threshold for the MNDWI image [35,36]. The frequency distribution of the annual MNDWI image was then counted. A 5-point moving average filter was applied to reduce the noise of the frequency distribution. The distribution formed two peaks, which represent the land and water around the estuarine islands. The threshold corresponding to the valley between the two peaks was determined and applied as the optimal threshold to separate the land and water (Figure3). This method has a high effectiveness for land-water classification because of the significant spectral difference between the land and water areas. The MATLAB function “imclose” was used to avoid the spatial discontinuity of the land area around the boundary between the land and water in some regions. This function performs a closing operation with a structuring element. In this study, a square structuring element whose kernel size is 9 pixels was used in the experiments (Figure4c). The MATLAB function “imfill” was applied to automatically fill the inland water of the estuarine islands in each land-water classification map (Figure4d). This function fills holes in the input binary image (i.e., land represents 1 and water represent 0). The inland waters proposed herein were treated as land, since they are disconnected from the ocean. Finally, a 30-m resolution dataset of the multi-year estuarine island areas was generated for further calculations. Note that the TM images from 1987 to 1999 and the ETM+ images from 2000 to 2016 were utilized for this study because of data availability. Annual maps of the two estuarine islands from 1987 to 2016 were obtained using the above-mentioned method based on GEE and MATLAB. To quantitatively investigate the temporal changes, the island area was selected as an indicator to characterize the temporal change of an island, as it is a fundamental property of an island. Specifically, annual area time series for each estuarine island and the combined total of the islands were calculated. For each island and the total, a linear regression model was adopted to estimate the long-term change rates of the land area variations. To further assess the temporal changes during different sub-periods, we visually divided the total Water 2018, 10, 171 5 of 17

study period into several intervals based on the annual area time series. Two types of tipping points were considered in this study. The first one is the jumps in annual estuarine islands area. The second one is the jumps in the first-order derivative of annual estuarine islands area, which was used to represent the trend change in annual estuarine islands area. For each sub-period, the linear regression method was employed to investigate and analyze the rate of change in land area of each estuarine 2 islandWater 2018 and, 10 the, x FOR total. PEER The REVIEWR and p-value for each linear regression were also computed. 5 of 16 Water 2018, 10, x FOR PEER REVIEW 5 of 16

Figure 3. An example of determining an optimal threshold from the frequency distribution of an Figure 3. An example of determining an optimal threshold from the frequency distribution of an Figureannual 3. AnMNDWI example map of of determining the estuarine an islands optimal in thresh1990. Noteold from that the frequency distributiondistribution hasof anbeen annual MNDWI map of the estuarine islands in 1990. Note that the frequency distribution has been annualsmoothed MNDWI with map a 5-pointof the estuarine moving islandsaverage in filter. 1990. TheNote threshold that the frequencycorresponds distribution to the valley has been of the smoothed with a 5-point moving average filter. The threshold corresponds to the valley of the MNDWI smoothedMNDWI with frequency a 5-point distribution moving wasaverage dete rminedfilter. The as the threshold optimal correspondsthreshold. to the valley of the frequency distribution was determined as the optimal threshold. MNDWI frequency distribution was determined as the optimal threshold.

Figure 4. An example of generating an annual map of the estuarine islands in 1990. (a) The MNDWI Figure 4. An example of generating an annual map of the estuarine islands in 1990. (a) The MNDWI Figureimage from 4. An 1990; example (b) Land-water of generating classification an annual map map of after the estuarinethe thresholding islands insegmentation; 1990. (a) The ( MNDWIc) Land- image from 1990; (b) Land-water classification map after the thresholding segmentation; (c) Land- imagewater classification from 1990; (b )map Land-water after the classification closing operation; map after (d) Land-water the thresholding classification segmentation; map after (c) Land-water the filling water classification map after the closing operation; (d) Land-water classification map after the filling classificationoperation (i.e., map the final after results); the closing In (b operation;–d), the yellow (d) Land-water area represents classification the estuarine map islands. after the In fillinga, the operation (i.e., the final results); In (b–d), the yellow area represents the estuarine islands. In a, the operationlight area represents (i.e., the final the water results); body, In (andb–d the), the dark yellow area represents area represents the land the area. estuarine islands. In a, light area represents the water body, and the dark area represents the land area. the light area represents the water body, and the dark area represents the land area. Annual maps of the two estuarine islands from 1987 to 2016 were obtained using the above- mentionedAnnual maps method of basedthe two on estuarine GEE and islandsMATLAB. from To 1987 quantitatively to 2016 were investigate obtained the using temporal the above- changes, mentionedthe island method area was based selected on GEE as an and indicator MATLAB. to characterize To quantitatively the temporal investigate change the temporalof an island, changes, as it is a thefundamental island area was property selected of anas anisland. indicator Specifically, to characterize annual areathe temporal time series change for each of anestuarine island, islandas it is anda fundamentalthe combined property total ofof thean island.islands Specifically, were calculated. annual For area each time island series and for theeach total, estuarine a linear island regression and themodel combined was totaladopted of the to islandsestimate were the long-termcalculated. change For each rates island of theand land the total,area variations.a linear regression To further modelassess was the adopted temporal to changes estimate during the long-term different changesub-periods, rates weof thevisually land dividedarea variations. the total Tostudy further period assessinto theseveral temporal intervals changes based during on differentthe annual sub-pe areariods, time we series. visually Two divided types theof tippingtotal study points period were intoconsidered several intervals in this study. based The on first the oneannual is the area jump times in annualseries. estuarineTwo types islands of tipping area. Thepoints second were one consideredis the jumps in this in the study. first-order The first derivative one is the of jumpannuals in estuarine annual estuarineislands area, islands which area. was The used second to represent one is thethe jumps trend changein the first-order in annual derivative estuarine ofislands annual area. estuarine For each islands sub-peri area,od, which the linear was used regression to represent method thewas trend employed change into annualinvestigate estuar andine analyze islands thearea. rate For of each change sub-peri in landod, area the oflinear each regression estuarine islandmethod and wasthe employed total. The to R investigate2 and p-value and for analyze each linear the rate regression of change were in landalso computed.area of each estuarine island and the total. The R2 and p-value for each linear regression were also computed.

Water 2018, 10, 171 6 of 17

Water 2018, 10, x FOR PEER REVIEW 6 of 16

4. ResultsWater 2018, 10, x FOR PEER REVIEW 6 of 16 4. Results 4.1. Estuarine4. Results Island Area Extraction 4.1. Estuarine Island Area Extraction The4.1. Estuarine estuarineThe estuarine Island island island Area mapping Extraction mapping results results were were producedproduced from from the the MNDWI MNDWI images. images. Figure Figure 5 shows5 shows the extracted estuarine islands (including Changxing Island and Hengsha Island) for each year in the the extractedThe estuarine estuarine island islands mapping (including results Changxing were produced Island from and the Hengsha MNDWI Island) images. for eachFigure year 5 shows in the period from 1987 to 2016. In this figure, the background is the areas observed as permanent ocean area theperiod extracted from 1987estuarine to 2016. islands In this (including figure, theChangxing background Island is andthe areasHengsha observed Island) as for permanent each year oceanin the duringperiodarea 1987–2016. during from 1987–2016.1987 For to each 2016. For sub In each this image sub figure, image in Figure the in ba Figu6ckground, yellowre 6, yellow andis the and blue areas blue represent observed represent theas the permanent dynamic dynamic ocean ocean area and estuarinearea duringand estuarine islands 1987–2016. islands area For for area each each for sub year.each image year. Figure in Figure Figu6 demonstratesre 6 6, demonstrates yellow and the blue the annual annualrepresent variations variations the dynamic in in the the ocean areas areas of Changxingareaof Changxing and Island estuarine Island (blue islands line),(blue area Hengshaline), for Hengsha each Island year. Island Figure (yellow (yel 6 demonstrateslow line), line), and and the the the total annual total (red (red variations line) line) from from in the 1987 1987 areas toto 2016. 2 2 The average,of2016. Changxing The maximum, average, Island maximum, (blue and minimumline), and Hengsha minimum total Island areas total (yel wereareaslow were 226.6 line), 226.6 km and2 km, 283.5the, 283.5total km km(red2 (in (in line) 1990), 1990), from and and 1987 181.2181.2 to km2 2 (in 2015),2016.km respectively.(in The 2015), average, respectively. maximum, The average, The and average, maximum, minimum maximum, total and areas minimum and were minimum 226.6 area km of area Changxing2, 283.5 of Changxingkm2 (in Island 1990), Island were and 150.5181.2were km2, 2 2 2 km150.52 2(in km 2015),, 193.0 respectively. km (in 1990), The2 andaverage, 114.6 maximum, km (in 2015), and respectively.minimum area The of average, Changxing maximum, Island were and 193.0 km (in 1990), and 114.6 km (in 2015), respectively.2 The average,2 maximum, and2 minimum area minimum2 area of Hengsh2 a Island were 76.12 km , 90.5 km (in 1990), and 66.6 km (in 2015), of Hengsha150.5 km Island, 193.0 were km 76.1(in 1990), km2, and 90.5 114.6 km2 km(in1990), (in 2015), and respectively. 66.6 km2 (in The 2015), average, respectively. maximum, and minimumrespectively. area of Hengsha Island were 76.1 km2, 90.5 km2 (in 1990), and 66.6 km2 (in 2015), respectively.

Figure 5. Spatio-temporal distributions of the estuarine islands from 1987 to 2016 with a 30-m spatial Figure 5. Spatio-temporal distributions of the estuarine islands from 1987 to 2016 with a 30-m spatial Figureresolution 5. Spatio-temporal and an annual scale,distributions which show of the the estuarin expansione islands of Changxing from 1987 Islandto 2016 and with Hengsha a 30-m Island.spatial resolutionThe yellow and ancolor annual represents scale, land, which and showthe blue the color expansion represents of ocean. Changxing Note that Island land-ocean and Hengsha maps were Island. resolution and an annual scale, which show the expansion of Changxing Island and Hengsha Island. The yellowlimited colorin the representsarea of land land, and dynamics and the blueocean color area. representsBackground ocean. represents Note the that permanent land-ocean ocean maps area. were The yellow color represents land, and the blue color represents ocean. Note that land-ocean maps were limited in the area of land and dynamics ocean area. Background represents the permanent ocean area. limited in the area of land and dynamics ocean area. Background represents the permanent ocean area.

Figure 6. Annual variations in the areas of Changxing Island (blue line), Hengsha Island (yellow line), Figureand the 6. total Annual combined variations island in thearea areas (red ofline) Changxing from 1987 Is landto 2016. (blue line), Hengsha Island (yellow line), Figure 6. Annual variations in the areas of Changxing Island (blue line), Hengsha Island (yellow line), and the total combined island area (red line) from 1987 to 2016. and the total combined island area (red line) from 1987 to 2016.

Water 20182018,, 1010,, 171x FOR PEER REVIEW 7 of 1716 Water 2018, 10, x FOR PEER REVIEW 7 of 16 The spatial distribution of the frequency observed as land data in the period of 1987–2016 was The spatial distribution of the frequency observed as land data in the period of 1987–2016 was usedThe to characterize spatial distribution the spatiotemporal of the frequency pattern observed of the estuarine as land islanddata in areas the period (Figure of 7). 1987–2016 The center was of used to characterize the spatiotemporal pattern of the estuarine island areas (Figure7). The center of usedthe estuarine to characterize islands the had spatiotemporal land frequency pattern values of cl thosee estuarineto 30, which island signify areas permanent (Figure 7). land The coveragecenter of the estuarine islands had land frequency values close to 30, which signify permanent land coverage thewith estuarine a higher islandselevation. had The land areas frequency with major values va clriations,ose to 30, which which had signify land frequencypermanent values land coveragebetween with a higher elevation. The areas with major variations, which had land frequency values between 0 with0 and a 30, higher were elevation. found in theThe northwest areas with region major of va Chriations,angxing which Island had and land the frequencyeastern region values of Hengshabetween and 30, were found in the northwest region of Changxing Island and the eastern region of Hengsha 0Island. and 30, Some were variations found in werethe northwest also observed region around of Changxing the western Island region and theof Changxing eastern region Island. of HengshaThe area Island. Some variations were also observed around the western region of Changxing Island. The area Island.that was Some observed variations to be wereland 100%also observed of the time around was 173.5 the western km2, or region 57.6% of Changxingthe entirety Island.of the estuarine The area that was observed to be land 100% of the time was 173.5 km22, or 57.6% of the entirety of the estuarine thatislands. was Over observed the last to be three land deca 100%des, of a the significant time was expansion 173.5 km ,(97.7 or 57.6% km2 )of of the the entirety estuarine of theislands’ estuarine land islands. Over the last three decades, a significant expansion (97.7 km2) of2 the estuarine islands’ land has islands.has occurred, Over theand last the three majority deca ofdes, the a landsignificant expans expansionion occurred (97.7 in kmthe )northern of the estuarine region of islands’ Changxing land occurred, and the majority of the land expansion occurred in the northern region of Changxing Island hasIsland occurred, and the and eastern the majorityregion of of Hengsha the land Island expans (Fionigure occurred 8). The in total the changenorthern in regionthese two of Changxing regions in and the eastern region of Hengsha Island (Figure8). The total change in these two regions in 2007 and Island2007 and and 2008 the is eastern 68.8 km region2, accounting of Hengsha for 70.4% Island of (F theigure land 8). expansion The total duringchange the in theseperiod two of 1987–2016. regions in 2 2 20082007Both and isFigures 68.8 2008 km 5 is and ,68.8 accounting 6 km suggest, accounting forobvious 70.4% for regional of 70.4% the land ofdiff the expansionerences land expansion in the during temporal during the period changesthe period of 1987–2016. of ofthe 1987–2016. estuarine Both FiguresBothislands. Figures5 and6 5 suggest and 6 suggest obvious obvious regional regional differences differences in the temporal in the temporal changes changes of the estuarine of the estuarine islands. islands.

Figure 7. Spatial distribution of the frequency observed as ocean for the estuarine islands in the period Figure 7. Spatial distribution of the frequency observed as ocean for the estuarine islands in the period Figureof 1987–2016 7. Spatial with distribution a resolution of ofthe 30 frequency m. Backgr observedound represents as ocean the for permanentthe estuarine ocean islands area. in the period of 1987–2016 with a resolution of 30 m. Background represents the permanent ocean area. of 1987–2016 with a resolution of 30 m. Background represents the permanent ocean area.

Figure 8. Change in the area of the estuarine islands between 1987 and 2016. The blue areas indicate Figure 8. Change in the area of the estuarine islands between 1987 and 2016. The blue areas indicate Figureland expansion 8. Change from inthe 1987 area to 2016. of the Background estuarine islands represents between the unchanged 1987 and 2016. ocean The area blue in 1987 areas and indicate 2016. land expansion from 1987 to 2016. Background represents the unchanged ocean area in 1987 and 2016. land expansion from 1987 to 2016. Background represents the unchanged ocean area in 1987 and 2016. 4.2. Coastal Island Area Temporal Change 4.2. Coastal Island Area Temporal Change 4.2. CoastalTo quantitatively Island Area Temporalinvestigate Change the temporal changes of the estuarine islands, annual variations in To quantitatively investigate the temporal changes of the estuarine islands, annual variations in the landTo quantitatively areas of Changxing investigate Island the and temporal Hengsha changes Island over of the the estuarine past 30 years islands, are presented annual variations (Figure 9). in the land areas of Changxing Island and Hengsha Island over the past 30 years are presented (Figure 9). the landOur areas results of Changxingsuggest that Island both and islands Hengsha experienced Island over a remarkable the past 30 yearsexpansion are presented during (Figurethe study9). periodOur despite results sea suggest level rise that induced both islands by climate experienced change [30]. a remarkable Changxing expansion Island expanded during fromthe study 116.4 periodkm2 to 190.8despite km sea2 with level a risenet landinduced increase by climate of 74.3 change km2 and [30]. a change Changxing rate ofIsland 2.5 km expanded2/year (R from2 = 0.73, 116.4 p- km2 to 190.8 km2 with a net land increase of 74.3 km2 and a change rate of 2.5 km2/year (R2 = 0.73, p-

Water 2018, 10, x FOR PEER REVIEW 8 of 16 value < 0.001). Similarly, the area of Hengsha Island increased from 66.8 km2 to 90.2 km2 with a net land increase of 23.4 km2 and a change rate of 0.8 km2/year (R2 = 0.56, p-value < 0.001). These statistically significant upward trends indicate that Changxing Island and Hengsha Island expanded during the last 30 years. In total, the area of the two islands increased from 183.2 km2 to 280.9 km2 with a change rate of 3.3 km2/year (R2 = 0.71, p-value < 0.001). Moreover, a certain amount of correlation in the annual variations of the two islands was observed (R2 = 0.83, p-value < 0.001), which reflects some consistency between the variations of the two estuarine islands. Both Changxing Island and Hengsha Island show different variations at various time periods between 1986 and 2016 (Figure 9). To further uncover the characteristics of temporal change, the variations in the land area of the estuarine islands were divided into four sub-periods (1987–1992, 1993–1998, 1999–2008, and 2009–2016 for Changxing Island and 1987–1992, 1993–1998, 1999–2007, and 2008–2016 for Hengsha Island). The land area change rate and average value for each of the four sub-periods were calculated for each estuarine island (Figure 10). For Changxing Island, the change rates of the land area during four sub-periods were 0.7 km2/year, −4.2 km2/year, −0.4 km2/year, and 0.5 km2/year in temporal order. For Hengsha Island, the change rates of land area during the four sub-periods were −0.3 km2/year, −2.5 km2/year, −0.1 km2/year, and 0.5 km2/year in temporal order. In the first sub-period, the two islands displayed different change rates with slight variations. Both of the two islands exhibited significant land shrinkage during the second sub-period. In the third sub- period, both islands stayed at a relatively steady level and experienced slight land shrinkage. A gradual land expansion was observed for the two estuarine islands in the last sub-period.

Furthermore,Water 2018, 10, 171 we observed a declining trend in the land area of the estuarine islands in 2003 when8 of 17 the Three Gorges Dam (TGD) was put into operation (Figure 9).

Figure 9. AnnualAnnual variations variations in in the the land land areas areas of of ( (aa)) Changxing Changxing Island Island and and ( (bb)) Hengsha Hengsha Island Island during during 1987–2016. The red line represents the linear regressionregression line for each sub-period.

Our results suggest that both islands experienced a remarkable expansion during the study period despite sea level rise induced by climate change [30]. Changxing Island expanded from 116.4 km2 to 190.8 km2 with a net land increase of 74.3 km2 and a change rate of 2.5 km2/year (R2 = 0.73, p-value < 0.001). Similarly, the area of Hengsha Island increased from 66.8 km2 to 90.2 km2 with a net land increase of 23.4 km2 and a change rate of 0.8 km2/year (R2 = 0.56, p-value < 0.001). These statistically significant upward trends indicate that Changxing Island and Hengsha Island expanded during the last 30 years. In total, the area of the two islands increased from 183.2 km2 to 280.9 km2 with a change rate of 3.3 km2/year (R2 = 0.71, p-value < 0.001). Moreover, a certain amount of correlation in the annual variations of the two islands was observed (R2 = 0.83, p-value < 0.001), which reflects some consistency between the variations of the two estuarine islands. Both Changxing Island and Hengsha Island show different variations at various time periods between 1986 and 2016 (Figure9). To further uncover the characteristics of temporal change, the variations in the land area of the estuarine islands were divided into four sub-periods (1987–1992, 1993–1998, 1999–2008, and 2009–2016 for Changxing Island and 1987–1992, 1993–1998, 1999–2007, and 2008–2016 for Hengsha Island). The land area change rate and average value for each of the four sub-periods were calculated for each estuarine island (Figure 10). For Changxing Island, the change rates of the land area during four sub-periods were 0.7 km2/year, −4.2 km2/year, −0.4 km2/year, and 0.5 km2/year in temporal order. For Hengsha Island, the change rates of land area during the four sub-periods were −0.3 km2/year, −2.5 km2/year, −0.1 km2/year, and 0.5 km2/year in temporal order. In the first sub-period, the two islands displayed different change rates with slight variations. Both of the two islands exhibited significant land shrinkage during the second sub-period. In the third sub-period, both islands stayed at a relatively steady level and experienced slight land Water 2018, 10, 171 9 of 17 shrinkage. A gradual land expansion was observed for the two estuarine islands in the last sub-period. Furthermore, we observed a declining trend in the land area of the estuarine islands in 2003 when the Three Gorges Dam (TGD) was put into operation (Figure9). Water 2018, 10, x FOR PEER REVIEW 9 of 16 Water 2018, 10, x FOR PEER REVIEW 9 of 16

Figure 10. Change rates (a) and average values (b) of land area in Changxing Island and Hengsha Figure 10. Change rates (a) and average values (b) of land area in Changxing Island and Hengsha Figure 10. Change rates (a) and average values (b) of land area in Changxing Island and Hengsha IslandIsland duringduring differentdifferent sub-periods.sub-periods. Island during different sub-periods. In addition, our results indicate some obvious sharp changes in the land area of the estuarine islands.In addition, Specifically, our sharp results changes indicate in someland area obvious were sharp observed changes in 1993 in andthe land2008 for area Changxing of thethe estuarineestuarine Island islands.and in 1993 Specifically, Specifically, and 2007 sharpsharp for Hengsha changeschanges Island inin landland (Figure areaarea werewe 9).re Including observed inthe 1993 significant and 2008 land for Changxing shrinkage of Island both andislands in 1993 that andoccurred 2007 forduring Hengsha the second Island sub-period, (Figure9 9).). Including Includingland area thechangethe significantsignificant maps for landland the shrinkageshrinkage four sub-periods ofof bothboth islandswere produced that occurred (Figure during 11). From the second 1993 to sub-period, 1994, land land expansion area changechange occurred maps in forthe thethenorthwest fourfour sub-periodssub-periods region of wereChangxing produced Island (Figure and the 11).11). eastern From From 1993 region 1993 to to 1994,of 1994, Hengsh land land aexpansion expansionIsland. From occurred occurred 1994 into inthe1998, the northwest considerable northwest region region land of ofChangxingshrinkage Changxing was IslandIsland observed and and the in theeastern the eastern eastern region region region of Hengsh of of Hengsha Hengshaa Island. Island. Island, From From 1994and 1994slightto 1998, to land 1998, considerable shrinkage considerable landwas landshrinkageobserved shrinkage inwas the wasobserved northwest observed in regionthe in theeastern of eastern Changxing region region of Is ofHengshaland. Hengsha Combing Island, Island, the and and satellite slight slight images,land land shrinkage shrinkage the changes was observedduring 1993–1994 in the northwestnorthwest and 1994–1998 region ofof were ChangxingChangxing natural Island.Is land.and mainly Combing controlled the satellite by images,the hydrodynamic the changes duringconditions 1993–19941993–1994 of the coastal and and 1994–1998 regions.1994–1998 wereFrom were natural 2007 natural to and 2008, mainlyan a significantd mainly controlled landcontrolled by expansion the hydrodynamic by wasthe observedhydrodynamic conditions in the ofconditionseastern the coastal region of the regions. of coastal Hengsha From regions. Island. 2007 From toFrom 2008, 2007 2008 a to significant to200 2008, a9, significant a land significant expansion land land expansion was expansion observed was was observed in observed the eastern in the in regioneasternthe northwestern ofregion Hengsha of Hengsharegion Island. of Island.Changxing From 2008From Island. to2008 2009, to 200 a significant9, a significant land land expansion expansion was was observed observed in the in northwesternthe northwestern region region of Changxing of Changxing Island. Island.

Figure 11. Dramatic changes of estuarine islands during four periods: (a) 1993–1994; (b) 1994–1998; Figure(c) 2007–2008; 11. Dramatic and (d changes) 2009–2009. of estuarine Regions islandswithin yellowduring ellipsesfour periods: well present (a) 1993–1994; the obvious (b) 1994–1998; change of Figure 11. Dramatic changes of estuarine islands during four periods: (a) 1993–1994; (b) 1994–1998; (estuarinec) 2007–2008; islands. and Background (d) 2009–2009. represents Regions the within unchanged yellow ellipsesocean area well during present four the periods. obvious change of (c) 2007–2008; and (d) 2009–2009. Regions within yellow ellipses well present the obvious change of estuarine islands. Background represents the unchanged ocean area during four periods. estuarine islands. Background represents the unchanged ocean area during four periods. Furthermore, Figure 12 displays the high-resolution images that are available on Google Earth, whichFurthermore, clearly document Figure 12the displays land reclamation the high-resolutio in the ncoastal images regions. that are availableThe first onrow Google records Earth, the whichimplementation clearly document of the Hengsha the land east reclamation reclamatio in then projectscoastal regions.(phase III). The These first projectsrow records started the in implementation2007 in order to of provide the Hengsha a suitable east shoallocation reclamatio for a deepwatern projects (phaseharbor III).site These and new projects space started for the in 2007development in order ofto Shanghai provide Citya suitable [3]. Our location results fo indicater a deepwater that a land harbor expansion site and of 16.56new spacekm2 occurred for the developmentfrom 2008 to of2009, Shanghai which City is co [3].nsistent Our results with theindicate area thatreport a edland for expansion these projects, of 16.56 17.34 km 2 kmoccurred2, in a fromprevious 2008 study to 2009, [37]. which It is an is example consistent of landwith exthepansion area report induceded forby thesereclamations projects, around 17.34 kmthe 2,study in a previousareas during study 1987–2016. [37]. It is Thean example second rowof land shows expansion the Qingcaosha induced byReservoir, reclamations which around is located the studyat the areas during 1987–2016. The second row shows the Qingcaosha Reservoir, which is located at the

Water 2018, 10, 171 10 of 17

Furthermore, Figure 12 displays the high-resolution images that are available on Google Earth, which clearly document the land reclamation in the coastal regions. The first row records the implementation of the Hengsha east shoal reclamation projects (phase III). These projects started in 2007 in order to provide a suitable location for a deepwater harbor site and new space for the development of Shanghai City [3]. Our results indicate that a land expansion of 16.56 km2 occurred from 2008 to 2009, which is consistent with the area reported for these projects, 17.34 km2, in a previous studyWater [ 372018]., It10 is, x anFOR example PEER REVIEW of land expansion induced by reclamations around the study areas10 during of 16 1987–2016. The second row shows the Qingcaosha Reservoir, which is located at the lower reaches oflower the separation reaches of mouth the separation of the south mouth and of norththe south channels and north of the channels Changjiang of the River.Changjiang To resolve River. water To shortagesresolve inwater Shanghai, shortages the Shanghaiin Shanghai, Municipal the Shangh Governmentai Municipal decided Government to build the decided Qingcaosha to build Reservoir the Qingcaosha Reservoir as a water source in 2006 [38]. Subsequently, this project started construction as a water source in 2006 [38]. Subsequently, this project started construction in June 2007 and was in June 2007 and was put into operation in June 2011. The changes documented by the satellite images put into operation in June 2011. The changes documented by the satellite images can be related to the can be related to the human activities. Those changes tracked by high-resolution images agree with human activities. Those changes tracked by high-resolution images agree with the results derived the results derived from the Landsat data, which could also be used as a validation of our proposed from the Landsat data, which could also be used as a validation of our proposed method of using method of using Landsat data. The expansion of estuarine islands after execution of the Qingcaosha LandsatReservoir data. is Thealso expansion consistent of with estuarine the observ islandsed after narrowing execution of ofthe the u Qingcaoshapper North ReservoirChannel [29]. is also consistentSpecifically, with a thelarge observed part of narrowingthe upper ofNorth the upperChannel North narrowed Channel from [29 7.1]. Specifically, km to 4.3 km a large after part the of theconstruction upper North of Channellevees. narrowed from 7.1 km to 4.3 km after the construction of levees.

Figure 12. High-resolution images from Google Earth that illustrate the sharp changes in land area Figure 12. High-resolution images from Google Earth that illustrate the sharp changes in land area that occurred in 2007 and 2008. The first row (a,b) and the second row (c,d) show the land reclamation that occurred in 2007 and 2008. The first row (a,b) and the second row (c,d) show the land reclamation in Hengsha Island and Changxing Island, respectively (regions within red ellipses). in Hengsha Island and Changxing Island, respectively (regions within red ellipses). Unlike many traditional methods for tracking estuarine islands at multi-decadal time scales, this studyUnlike continuously many traditional monitored methods the temporal for tracking change of estuarine two estuarine islands islands at multi-decadal with a yearly frequency, time scales, thiswhich study can continuously provide more monitored temporally the detailed temporal information change about of two estuarine estuarine islands islands variations. with a yearlyThe frequency,traditional which approach can provide is simple more to conduct temporally but not detailed always information effective. One about problem estuarine is that, islands during variations. their Themoments traditional of observation, approach is corresponding simple to conduct images but have not to always be at the effective. same water One problemlevel to minimize is that, during the theirimpact moments of tidal of observation,inconsistencies. corresponding Another problem images is havethat the to beimages at the need same to water be obtained level to without minimize theclouds impact and of shadows. tidal inconsistencies. Even under ideal Another conditions, problem only is thatchanges the imagesthat occur need between to be obtainedtwo images without can cloudsbe detected; and shadows. it is difficult Even underto capture ideal changes conditions, that occur only changes during the that time occur between between two two images, images which can be detected;may be it important. is difficult For to capture instance, changes abrupt thatchanges occur that during occurred the time in 1993, between 2007, two and images, 2008 cannot which be may bedetected important. using For conventional instance, abrupt methods changes that use that interv occurredals of inseveral 1993, years. 2007, andMoreover, 2008 cannot this method be detected can usingavoid conventional the effects of methodsthe Landsat that 7 SLC use (Scan intervals Line ofCorr severalector)-off years. gaps Moreover, caused by thisthe failure method of the can Scan avoid Line Corrector (SLC) in Landsat 7. Such method takes full advantage of all available pixels with good the effects of the Landsat 7 SLC (Scan Line Corrector)-off gaps caused by the failure of the Scan Line quality at a pixel level, while only Landsat images with a lower cloud percentage were used at a scene Corrector (SLC) in Landsat 7. Such method takes full advantage of all available pixels with good level in many conventional studies. quality at a pixel level, while only Landsat images with a lower cloud percentage were used at a scene level4.3. in In manyfluencing conventional Factors Analysis studies. The results of the influencing factors analysis conducted on human activities, sediment discharge, and sea level are presented in Figure 13. In this study, the human activity mainly indicates the land reclamation in coastal zones around Hengsha Island and Changxing Island, which can directly reshape the coastline position of estuarine islands. Human activities, sediment discharge, and sea level data was collected from some previous studies [30,37,38]. Note that the annual estuarine islands change (i.e., first order difference of annual land area of estuarine islands) was used for

Water 2018, 10, 171 11 of 17

4.3. Influencing Factors Analysis The results of the influencing factors analysis conducted on human activities, sediment discharge, and sea level are presented in Figure 13. In this study, the human activity mainly indicates the land reclamation in coastal zones around Hengsha Island and Changxing Island, which can directly reshape the coastline position of estuarine islands. Human activities, sediment discharge, and sea level data wasWater collected 2018, 10, x from FOR PEER some REVIEW previous studies [30,37,38]. Note that the annual estuarine islands11 change of 16 (i.e., first order difference of annual land area of estuarine islands) was used for analysis. Similar Water 2018, 10, x FOR PEER REVIEW 11 of 16 operationsanalysis. Similar were conducted operations to were sediment conducted discharge to sediment and sea leveldischarge data. and Our sea results level suggest data. Our that results human suggest that human activity is significantly correlated with the temporal change of coastline (R2 = activityanalysis. is significantlySimilar operations correlated were withconducted the temporal to sediment change discharge of coastline and sea (R2 level= 0.41, data.p-value Our results < 0.001). 0.41, p-value < 0.001). During the past 30 years, land expansion caused by human activities is 85.72 Duringsuggest the that past human 30 years, activity land expansionis significantly caused corre by humanlated with activities the temporal is 85.72 kmchange2, accounting of coastline for 87.73%(R2 = km2, accounting for 87.73% of total land expansion of 97.71 km2. No significant relationship between of0.41, total p land-value expansion < 0.001). of During 97.71 km the2 .past No significant30 years, land relationship expansion between caused annual by human sediment activities discharge is 85.72 and annual sediment discharge and annual estuarine islands change was found (R2 = 0.047, p-value = 0.28). annualkm2, accounting estuarine islands for 87.73% change of total was foundland expansion (R2 = 0.047, of 97.71p-value km =2. 0.28).No significant Also, no significantrelationship relationship between Also, no significant relationship between annual sea level and annual estuarine islands change was betweenannual annualsediment sea discharge level and and annual annual estuarine estuarine islands islands change change was was observed found ( (RR22 == 0.047, 0.011, p-valuep-value = 0.28). = 0.59 ). observed (R2 = 0.011, p-value = 0.59). The results suggest that no significant relationship between Also, no significant relationship between annual sea level and annual estuarine islands change was Thesediment results suggestdischarge that and no estuarine significant islands relationship change, se betweena level, sedimentand estuarine discharge islands and change estuarine was found islands observed (R2 = 0.011, p-value = 0.59). The results suggest that no significant relationship between change,at an annual sea level, scale and during estuarine 1987–2016. islands change was found at an annual scale during 1987–2016. sediment discharge and estuarine islands change, sea level, and estuarine islands change was found at an annual scale during 1987–2016.

Figure 13. Influencing factors analysis. (a) Annual human activities; (b) Annual sediment discharge; Figure 13. Influencing factors analysis. (a) Annual human activities; (b) Annual sediment discharge; (c) Annual sea level. (c)Figure Annual 13. sea Infl level.uencing factors analysis. (a) Annual human activities; (b) Annual sediment discharge; To(c) Annualfurther seauncover level. the impact of sediment discharge and sea level on estuarine islands change, theTo same further analysis uncover was also the conduc impactted of sedimentafter removing discharge the data and with sea dramatic level on estuarinechanges (i.e., islands 1993, change,2007, To further uncover the impact of sediment discharge and sea level on estuarine islands change, theand same 2008). analysis No significant was also conductedrelationship after between removing annual the datasediment with discharge dramatic changesand annual (i.e., estuarine 1993, 2007, the same analysis was also conducted after removing the data with dramatic changes (i.e., 1993, 2007, andislands 2008). change No significant was found relationship (R2 = 0.045, between p-valueannual = 0.76) sediment (Figure discharge 14a). andBy contrast, annual estuarine a significant islands and 2008). No significant relationship between annual sediment discharge and annual estuarine changerelationship was found between (R2 = annual 0.045, p sea-value level = and 0.76) annual (Figure estuarine 14a). By contrast,islands change a significant was observed relationship (R2 =between 0.114, islands change was found (R2 = 0.045, p-value = 0.76) (Figure 14a). By contrast, a significant annualp-value sea < level0.01), and which annual indicates estuarine a significant islands changenegative was relationship observed (Figure (R2 = 0.114, 14b).p After-value removing < 0.01), which the relationship between annual sea level and annual estuarine islands change was observed (R2 = 0.114, indicatesdata with a significantdramatic changes, negative a significant relationship increase (Figure of R-square14b). After between removing annual the sea data level with and dramaticannual p-value < 0.01), which indicates a significant negative relationship (Figure 14b). After removing the changes,estuarine a significantislands was increase found. ofHowever, R-square no between significant annual relationship sea level between and annual the estuarinesediment islandsdischarge was data with dramatic changes, a significant increase of R-square between annual sea level and annual and estuarine islands change was observed. found.estuarine However, islands no was significant found. However, relationship no significant between therelationship sediment between discharge the and sediment estuarine discharge islands changeand estuarine was observed. islands change was observed.

Figure 14. Influencing factors analysis after removing the data with dramatic changes. (a) Annual sediment discharge; (b) Annual sea level. Figure 14. Influencing factors analysis after removing the data with dramatic changes. (a) Annual Figure 14. Influencing factors analysis after removing the data with dramatic changes. (a) Annual sediment discharge; (b) Annual sea level. 5. Discussionssediment discharge; (b) Annual sea level.

5.1.5. Discussions Comparison of Results from Landsat TM/ETM+ Data

5.1. ComparisonTo test whether of Results the fromresults Landsat are sensitive TM/ETM+ to Datathe selection of Landsat imagery, the annual land areas of Changxing Island and Hengsha Island were calculated from the TM data, and corresponding To test whether the results are sensitive to the selection of Landsat imagery, the annual land ETM+ data were obtained and compared. The time series during the 2000–2010 period with available areas of Changxing Island and Hengsha Island were calculated from the TM data, and corresponding Landsat TM and ETM+ data was used for comparison (red box in Figure 15). Two time series of the ETM+ data were obtained and compared. The time series during the 2000–2010 period with available annual land area of Changxing Island (blue lines in Figure 15) exhibit good agreement (R2 = 0.95, p- Landsat TM and ETM+ data was used for comparison (red box in Figure 15). Two time series of the annual land area of Changxing Island (blue lines in Figure 15) exhibit good agreement (R2 = 0.95, p-

Water 2018, 10, 171 12 of 17

5. Discussions

5.1. Comparison of Results from Landsat TM/ETM+ Data Water 2018, 10, x FOR PEER REVIEW 12 of 16 To test whether the results are sensitive to the selection of Landsat imagery, the annual land areas valueof Changxing < 0.01). IslandSimilarly, and two Hengsha time Islandseries wereof the calculated annual land from area the TMof Hengsha data, and Island corresponding (yellow lines ETM+ in Figuredata were 15) exhibit obtained good and agreement compared. (R The2 = 0.92, time p-value series during< 0.01). theAs shown 2000–2010 in the period inset of with Figure available 15, the Landsat annual TMland and area ETM+ of the dataentirety was of used Changxing for comparison Island and (red Hengsha box in Figure Island 15 also). Two shows time goodseries agreement of the annual (R2 = 0.99, land 2 parea-value of Changxing< 0.01). In general, Island good (blue ag linesreement in Figure is achieved 15) exhibit between good the agreement annual variation (R = 0.95, of thep-value land

Figure 15. Different time series of the annual land area of Changxing Island and Hengsha Island Figure 15. Different time series of the annual land area of Changxing Island and Hengsha Island derived from the TM and ETM+ data. The time series in the red box was used for the analysis. The derived from the TM and ETM+ data. The time series in the red box was used for the analysis. The inset inset figure shows the relationship between land areas derived from the TM and ETM+ data during figure shows the relationship between land areas derived from the TM and ETM+ data during the 2 the2000–2010 2000–2010 period, period, which which displays displays good good agreement agreement (R2 =(R 0.99, = 0.99,p-value p-value < 0.001). < 0.001).

5.2. Influencing Factors of Estuarine Islands Change In the future, accurate tidal levels during the Landsat observations can be collected to investigate the associationAccording betweento previous tidal analysis level bias in Section and island 4.3, expansion area. Furthermore, of estuarine Landsat-8 islands during Operational the past Land 30 yearsImager was (OLI) significantly data can also (R2 be = used0.41, for p-value estuarine < 0.001) islands linked mapping, with andthe Landsathuman TM,activities ETM+, (i.e., and land OLI reclamation).images can be Based compared on previous together. studies, land reclamation in Changxing Island and Hengsha Island resulted in a great land expansion of 85.72 km2, accounting for 87.73% of total land expansion of 97.71 km5.2.2 Influencingfrom 1987 Factorsto 2016. of Additionally, Estuarine Islands high-resolution Change images from Google Earth also confirmed the sharpAccording changes in to land previous area analysisthat occurred in Section in 2 0074.3, and expansion 2008, which of estuarine exhibi islandsted an agreement during the with past previous30 years wasstudies significantly [35]. Thus, (weR2 can= 0.41, concludep-value that < hu 0.001)man linkedactivities with contributed the human to the activities great expansion (i.e., land ofreclamation). Changxing BasedIsland on and previous Hengsha studies, Island land in the reclamation past 30 years. in Changxing In the future, Island human and Hengsha activities Island can continuouslyresulted in a greatresult land in the expansion expansion of 85.72of two km estuarine2, accounting islands for according 87.73% of totalto the land Shanghai’s expansion new of strategic97.71 km 2developmentfrom 1987 to plans 2016. [3]. Additionally, high-resolution images from Google Earth also confirmed the sharpApart changes from human in land activities, area that natural occurred factors in 2007 such and as sediment 2008, which discharge exhibited and ansea agreement level were withalso previouspotential studiesfactors [of35 ].estuarine Thus, we islands can conclude change. that After human removing activities the contributeddata with dramatic to the great changes expansion (i.e., 1993,of Changxing 2007, and Island 2008), and we Hengsha explored Island the associatio in the pastn between 30 years. sediment In the future, discharge, human sea activities level, and can estuarine islands change. No significant relationship was found between estuarine islands change continuously result in the expansion of two estuarine islands according to the Shanghai’s new strategic and sediment discharge (R2 = 0.045, p-value = 0.76). This means that the sediment discharge is not a development plans [3]. dominant factor in the expansion of estuarine islands. Observations have confirmed a declining trend in sediment discharge during the past decades [30]. Li’s study suggest that deposition occurred in the Yangtze River Estuary in the inshore area from a depth of 6.4 m after the construction of the TGD [39]. Significant erosion in the area below that water depth was observed until 2013. They concluded that the impoundment of TGD mainly controlled the evolution of topography around the Yangtze River Estuary. The observed nearshore deposition is mainly caused by coastal land reclamations. In

Water 2018, 10, 171 13 of 17

Apart from human activities, natural factors such as sediment discharge and sea level were also potential factors of estuarine islands change. After removing the data with dramatic changes (i.e., 1993, 2007, and 2008), we explored the association between sediment discharge, sea level, and estuarine islands change. No significant relationship was found between estuarine islands change and sediment discharge (R2 = 0.045, p-value = 0.76). This means that the sediment discharge is not a dominant factor in the expansion of estuarine islands. Observations have confirmed a declining trend in sediment discharge during the past decades [30]. Li’s study suggest that deposition occurred in the Yangtze River Estuary in the inshore area from a depth of 6.4 m after the construction of the TGD [39]. Significant erosion in the area below that water depth was observed until 2013. They concluded that the impoundment of TGD mainly controlled the evolution of topography around the Yangtze River Estuary. The observed nearshore deposition is mainly caused by coastal land reclamations. In reality, the Yangtze River Submerged Delta will undergo continuous erosion under sediment starvation induced by TGD and the construction of some other hydropower projects in the next decades. Additionally, a significant negative relationship was observed between estuarine islands and sea level (R2 = 0.114, p-value < 0.01). The results show that the sea level variability can partly explain the estuarine islands change on an annual scale. Hence, we can conclude the human activities is the most important driving factor to temporal change of estuarine islands on an annual scale, which is consistent with the observations of Chen’s study [40]. They found that the reclamation area increased with the decrease of barren mudflat and around Shanghai. Besides, sea level can partly explain the change of estuarine islands. Furthermore, the significant negative relationship between sediment discharge and estuarine islands change suggests that the estuarine islands may face greater risk of erosion from future sea level rise. The Yangtze River Submerged Delta will undergo continuous erosion under sediment reduction and sea level rise. In the future, the tidal flat in Shanghai will increase slowly and even decrease without protection. The coastline without levees will also advance to the ocean slowly and even retreat to the land. Consequently, it will retard the expansion of estuarine islands and even bring about the shrinkage of estuarine islands. Although human activities can significantly result in the expansion of estuarine islands, sediment discharge reduction and sea level rise are two potential factors associated with the erosion of estuarine islands. Hence, siltation promotion is needed to protect the wetland and land resource in coastal zones in the future. In this study, we focused on the impact of sediment discharge and human activities on the Yangtze River Delta. However, some other estuarine processes, such as longshore currents, tidal currents, and wave currents, can also have potential influences on the estuarine islands change [41]. Liu et al. found that much sediment from the Yangtze River was transported southward by the longshore along the inner shelf [42]. The tidal current on the flat is mainly back and forth along the river and gyratory on the seaward side of the estuarine islands. The flow velocity on the intertidal flat gradually decreased when the elevation increased. The river discharge can enhance ebb flows and change current asymmetry, especially on the lower flat in neap , although hydrodynamics over the study area is tide-dominated [43]. The ebb tide, together with the longshore current, can carry about half of the sediment from the Yangtze River [44]. The wave current is another essential factor for the sedimentation in intertidal flat [43]. In subtidal area of the Yangtze River Estuary, wave climate was found dominated by waves generated by local winds. Such wind-induced wave current can result in short-term erosion-accretion processes because of the monsoon climate. In the future, these influencing factors can be quantified and the association between the longshore currents, tidal currents, wave currents, the estuarine islands change can be explored. Note that although two driving factors of estuarine islands change have been identified, a sharp land expansion in 1994 remains unclear. From Figure5, we can observe that some tidal channels changed from water to land in 1994 and some of them gradually became inundated by ocean in the next few years. Water 2018, 10, 171 14 of 17

5.3. Further Considerations A Landsat time series was used to map the estuarine islands and continuously track the temporal change of land area at an annual scale. A dataset of the yearly estuarine island maps that were produced in this study can be used to examine the impact of human activities on the evolution of estuarine islands. Owing to the limitation of spatial-temporal resolution of Landsat imagery, some spatial-temporal variabilities were undetectable. For the coastline of estuarine islands with a tiny change, a sub-pixel mapping technique may be needed to solve the spectral mixture problem of boundary pixels between the land and the ocean in estuarine islands mapping [45,46]. The monthly variations of the estuarine island areas can be captured by fusing multi-temporal MODIS (moderate-resolution imaging spectroradiometer) and Landsat data [47]. This dataset can support a better understanding of the seasonal variability of estuarine islands. Moreover, these data can be used to explore the response of estuarine islands to the occurrence of natural disasters (such as typhoons and tsunamis) around their coastal zones. Apart from the area, some other metrics, such as shape and centroid, can be used to better characterize the morphological change of estuarine islands [48]. According to Shanghai’s new strategic development plans [3], in the future, land reclamation will continue to be implemented in the Hengsha East Shoal region (planned to be completed by 2019). There is still a vast expanse of that can be used for reclamation at the east side (approximately 370 km2) of Hengsha Island. This means that reclamation will keep occurring on Hengsha Island for a certain period in the future. Hence, continuous observations of the estuarine islands are important to track their temporal changes and assess the influence of continued human activities on the coastal environment. Changxing Island and Hengsha Island are two typical estuarine islands modified by intensive human activities. In many other coastal regions with high population densities, such satellite data and the methodology of this study can also be used to monitor the temporal changes in the estuarine islands or the coastline.

6. Conclusions In this paper, the annual-scale temporal changes of two estuarine islands, which are located in Shanghai near the Yangtze River Estuary, were obtained from Landsat (TM and ETM+) data for the period of 1987–2016 based on Google Earth Engine and MATLAB. The water index and threshold segmentation methods were the main techniques used to map the estuarine islands. The temporal changes of the land area of Changxing Island and Hengsha Island were presented in a manner that comprehensively describes the variations of the two islands. The main conclusions are as follows:

1. The time series of Landsat (TM and ETM+) imagery can be used for mapping estuarine islands and tracking their temporal changes on an annual scale. The mapping results derived from the TM data and ETM+ data are in good agreement, which supports the stability of the method. These data and the described method can also be used to continuously track the temporal change of other ground features (e.g., the river, lake, and coastline features). 2. The two estuarine islands in Shanghai experienced a net increase of their land area from 183.2 km2 to 280.9 km2 with a change rate of 3.3 km2 per year during the period of 1987–2016, and they also exhibited a certain consistency in their temporal variations. During the sub-period from 1993 to 1998, both Changxing Island and Hengsha Island exhibited a significant land shrinkage according the Landsat observations. 3. Sharp increases in land area were observed in 2007 and 2008 for Hengsha Island and Changxing Island, respectively. According to the high-resolution images from Google Earth, human activity (i.e., the Hengsha east shoal reclamation projects and the Qingcaosha Reservoir project) plays an essential role in the rapid expansion of the estuarine islands. Compared with Landsat images, high-resolution images can provide more details from a spatial perspective. 4. From 1987 to 2016, human activity was the most important driving factor in the temporal change of estuarine islands on an annual scale (R2 = 0.41, p-value < 0.001). Human activities in Changxing Water 2018, 10, 171 15 of 17

Island and Hengsha Island resulted in a great land expansion of 85.72 km2, accounting for 87.73% of the total land expansion of 97.71 km2. Sea level can also partly explain the change of estuarine islands (R2 = 0.114, p-value < 0.01). Meanwhile, sediment discharge is not a significant driving factor in the estuarine islands’ change (R2 = 0.045, p-value = 0.76).

Acknowledgments: This work was supported by the National Natural Science Foundation of China (No: 41474001) and the Surveying and Mapping Basic Research Program of National Administration of Surveying, Mapping and Geoinformation (No: 13-01-05). The authors also greatly appreciate the editors and anonymous reviewers for their efforts and time spent on this paper. Author Contributions: Nan Xu and Dongzhen Jia conceived and designed the experiments; Nan Xu performed the experiments and analyzed the data; Nan Xu and Dongzhen Jia wrote the manuscript; Lei Ding and Yan Wu revised the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Yang, S.L.; Milliman, J.D.; Li, P.; Xu, K. 50,000 dams later: Erosion of the Yangtze River and its delta. Glob. Planet. Chang. 2011, 75, 14–20. [CrossRef] 2. Chen, X.; Zong, Y. along the Changjiang deltaic shoreline, China: History and prospective. Estuar. . Shelf Sci. 1998, 46, 733–742. [CrossRef] 3. Ruan, W.; Gong, H.; Lou, F. Discussion on the new space of strategic development and function of Hengsha, Shanghai. Eng. Sci. 2014, 12, 52–58. 4. Huang, B.; Ouyang, Z.; Zheng, H.; Zhang, H.; Wang, X. Construction of an eco-island: A case study of , China. Ocean Coast. Manag. 2008, 51, 575–588. [CrossRef] 5. Gao, A.; Yang, S.L.; Li, G.; Li, P.; Chen, S.L. Long-term morphological evolution of a tidal island as affected by natural factors and human activities, the Yangtze Estuary. J. Coast. Res. 2010, 26, 123–131. [CrossRef] 6. Tian, B.; Wu, W.; Yang, Z.; Zhou, Y. Drivers, trends, and potential impacts of long-term coastal reclamation in China from 1985 to 2010. Estuar. Coast. Shelf Sci. 2016, 170, 83–90. [CrossRef] 7. Ma, Z.; Melville, D.S.; Liu, J.; Chen, Y.; Yang, H.; Ren, W.; Zhang, Z.; Piersma, T.; Li, B. Rethinking China’s new great wall. Science 2014, 346, 912–914. [CrossRef][PubMed] 8. Cao, W.; Wong, M.H. Current status of coastal zone issues and management in China: A review. Environ. Int. 2007, 33, 985–992. [CrossRef][PubMed] 9. Murray, N.J.; Clemens, R.S.; Phinn, S.R.; Possingham, H.P.; Fuller, R.A. Tracking the rapid loss of tidal wetlands in the . Front. Ecol. Environ. 2014, 12, 267–272. [CrossRef] 10. Zhang, C.; Zheng, J.; Dong, X.; Cao, K.; Zhang, J. Morphodynamic response of Xiaomiaohong tidal channel to a coastal reclamation project in Jiangsu Coast, China. International Coastal Symposium (Plymouth, England). J. Coast. Res. 2005, 65, 630–635. [CrossRef] 11. Chen, L.; Ren, C.; Zhang, B.; Li, L.; Wang, Z.; Song, K. Spatiotemporal dynamics of coastal wetlands and reclamation in the Yangtze Estuary during past 50 years (1960s–2015). Chin. Geogr. Sci. 2017, 1–14. [CrossRef] 12. Dai, Z.; Liu, J.T.; Wen, W. Morphological evolution of the south passage in the Changjiang (Yangtze River) estuary, China. Quat. Int. 2015, 380–381, 314–326. [CrossRef] 13. Zhang, T.; Yang, X.; Hu, S.; Su, F. Extraction of coastline in aquaculture coast from multispectral remote sensing images: Object-based region growing integrating edge detection. Remote Sens. 2013, 5, 4470–4487. [CrossRef] 14. Liu, Y.; Wang, X.; Ling, F.; Xu, S.; Wang, C. Analysis of coastline extraction from landsat-8 OLI imagery. Water. 2017, 9, 816. [CrossRef] 15. Fugura, A.A.; Billa, L.; Pradhan, B. Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image. Estuar. Coast. Shelf Sci. 2011, 95, 395–400. [CrossRef] 16. Sekovski, I.; Stecchi, F.; Mancini, F.; Rio, L.D. Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery. Int. J. Remote Sens. 2014, 35, 3556–3578. [CrossRef] 17. Maglione, P.; Parente, C.; Vallario, A. Coastline extraction using high resolution worldview-2 satellite imagery. Eur. J. Remote Sens. 2014, 47, 685–699. [CrossRef] Water 2018, 10, 171 16 of 17

18. Almonacid-Caballer, J.; Sánchez-García, E.; Pardo-Pascual, J.E.; Balaguer-Beser, A.A.; Palomar-Vázquez, J. Evaluation of annual mean shoreline position deduced from landsat imagery as a mid-term coastal evolution indicator. Mar. Geol. 2016, 372, 79–88. [CrossRef] 19. Ghosh, M.K.; Kumar, L.; Roy, C. Monitoring the coastline change of hatiya island in bangladesh using remote sensing techniques. ISPRS J. Photogramm. Remote Sens. 2015, 101, 137–144. [CrossRef] 20. Rahman, A.F.; Dragoni, D.; El-Masri, B. Response of the sundarbans coastline to sea level rise and decreased sediment flow: A remote sensing assessment. Remote Sens. Environ. 2011, 115, 3121–3128. [CrossRef] 21. Ford, M. Shoreline changes interpreted from multi-temporal aerial photographs and high resolution satellite images: Wotje , Marshall Islands. Remote Sens. Environ. 2013, 135, 130–140. [CrossRef] 22. Yu, K.; Hu, C.; Muller-Karger, F.; Lu, D.; Soto, I. Shoreline changes in west-central Florida between 1987 and 2008 from Landsat observations. Int. J. Remote Sens. 2011, 32, 8299–8313. [CrossRef] 23. Zhu, Z.; Woodcock, C.E.; Olofsson, P. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ. 2012, 122, 75–91. [CrossRef] 24. Boak, E.H.; Turner, I.L. Shoreline Definition and Detection: A Review. J. Coast. Res. 2005, 21, 688–703. [CrossRef] 25. Sagar, S.; Roberts, D.; Bala, B.; Lymburner, L. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. Remote Sens. Environ. 2017, 195, 153–169. [CrossRef] 26. Gens, R. Remote sensing of coastlines: Detection, extraction and monitoring. Int. J. Remote Sens. 2010, 31, 1819–1836. [CrossRef] 27. Shelestov, A.; Lavreniuk, M.; Kussul, N.; Novikov, A.; Skakun, S. Exploring google earth engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Front. Earth Sci. 2017, 17, 1–10. [CrossRef] 28. Dai, Z.; Liu, J.T.; Wei, W.; Chen, J. Detection of the three gorges dam influence on the Changjiang (Yangtze River) submerged delta. Sci. Rep. 2014, 4, 6600. [CrossRef][PubMed] 29. Wu, S.; Cheng, H.; Xu, Y.J.; Li, J.; Zheng, S. Decadal changes in bathymetry of the Yangtze River estuary: Human impacts and potential saltwater intrusion. Estuar. Coast. Shelf Sci. 2016, 182, 158–169. [CrossRef] 30. Yang, H.F.; Yang, S.L.; Xu, K.H.; Wu, H.; Shi, B.W.; Zhu, Q.; Zhang, W.X.; Yang, Z. Erosion potential of the Yangtze Delta under sediment starvation and climate change. Sci. Rep. 2017, 7, 10535. [CrossRef][PubMed] 31. Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [CrossRef] 32. Sunder, S.; Ramsankaran, R.; Ramakrishnan, B. Inter-comparison of remote sensing sensing-based shoreline mapping techniques at different coastal stretches of India. Environ. Monit. Assess. 2017, 189, 290. [CrossRef] [PubMed] 33. Rokni, K.; Ahmad, A.; Selamat, A.; Hazini, S. Water feature extraction and change detection using multitemporal landsat imagery. Remote Sens. 2014, 6, 4173–4189. [CrossRef] 34. Wang, X. Change in area of Ebinur Lake during the 1998–2005 period. Int. J. Remote Sens. 2007, 28, 5523–5533. 35. Ling, F.; Li, W. Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. J. Appl. Remote Sens. 2012, 6, 3609. 36. Du, Z.; Li, W.; Zhou, D.; Tian, L.; Ling, F.; Wang, H.; Gui, Y.; Sun, B. Analysis of landsat-8 OLI imagery for land surface water mapping. Remote Sens. Lett. 2014, 5, 672–681. [CrossRef] 37. Gong, R.; Cui, C. Investigation and analysis of beach reclamation in three islands of Chongming. Shanghai Water 2011, 27, 20–21. (In Chinese) 38. Jin, X.; He, Y.; Kirumba, G.; Hassan, Y.; Li, J. Phosphorus fractions and phosphate sorption-release characteristics of the sediment in the Yangtze River estuary reservoir. Ecol. Eng. 2013, 55, 62–66. [CrossRef] 39. Li, B.; Yan, X.X.; He, Z.F.; Chen, Y.; Zhang, J.H. Impacts of the three gorges dam on the bathymetric evolution of the Yangtze River estuary. Chin. Sci. Bull. 2015, 60, 1735–1744. (In Chinese) 40. Ying, C.; Dong, J.; Xiao, X.; Min, Z.; Bo, T.; Zhou, Y.; Li, B.; Ma, Z. Land claim and loss of tidal flats in the Yangtze Estuary. Sci. Rep. 2016, 6, 24018. 41. Wei, T.; Chen, Z.; Duan, L.; Gu, J.; Saito, Y.; Zhang, W.; Wang, Y.; Kanni, Y. Sedimentation rates in relation to sedimentary processes of the Yangtze Estuary, China. Estuar. Coast. Shelf Sci. 2007, 71, 37–46. [CrossRef] Water 2018, 10, 171 17 of 17

42. Liu, J.P.; Li, A.C.; Xu, K.H.; Velozzi, D.M.; Yang, Z.S.; Milliman, J.D.; DeMaster, D.J. Sedimentary features of the Yangtze River-derived along-shelf clinoform deposit in the . Cont. Shelf Res. 2006, 26, 2141–2156. [CrossRef] 43. Yang, S.; Ding, P.; Zhu, J.; Zhao, Q.; Mao, Z. Tidal flat morphodynamic processes of the Yangtze Estuary and their engineering implications. China Ocean Eng. 2000, 14, 307–320. 44. Yang, S.L.; Belkin, I.M.; Belkina, A.I.; Zhao, Q.Y.; Zhu, J.; Ding, P.X. Delta response to decline in sediment supply from the Yangtze River: Evidence of the recent four decades and expectations for the next half-century. Estuar. Coast. Shelf Sci. 2003, 57, 689–699. [CrossRef] 45. Niroumand-Jadidi, M.; Vitti, A. Reconstruction of river boundaries at sub-pixel resolution: Estimation and spatial allocation of water fractions. ISPRS Int. J. -Inf. 2017, 6, 383. [CrossRef] 46. Foody, G.M.; Muslim, A.M.; Atkinson, P.M. Super-resolution mapping of the waterline from remotely sensed data. Int. J. Remote Sens. 2005, 26, 5381–5392. [CrossRef] 47. Dao, P.D.; Liou, Y.A.; Chou, C.W. Detection of Flood Inundation Regions with Landsat/MODIS Synthetic Data. In Proceedings of the International Symposium on Remote Sensing 2015, Tainan, Taiwan, 22–24 April 2015. 48. Gangloff, A.; Verney, R.; Doxaran, D.; Ody, A.; Estournel, C. Investigating Rhône ( of Lions, France) dynamics using metrics analysis from the MERIS 300m Ocean Color archive (2002–2012). Cont. Shelf Res. 2017, 144, 98–111. [CrossRef]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).