Efficient Location and Extraction of the Iceberg Calved Areas of The

remote sensing

Article Eﬃcient Location and Extraction of the Iceberg Calved Areas of the Antarctic Ice Shelves

Mengzhen Qi 1,2,3 , Yan Liu 1,2,3, Yijing Lin 1,3, Fengming Hui 2,3,4, Teng Li 1,2,3 and Xiao Cheng 2,3,4,* 1 State Key Laboratory of Remote Sensing Science, and College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; [email protected] (M.Q.); [email protected] (Y.L.); [email protected] (Y.L.); [email protected] (T.L.) 2 Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China; [email protected] 3 University Corporation for Polar Research, Beijing 100875, China 4 School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China * Correspondence: [email protected]

Received: 14 July 2020; Accepted: 16 August 2020; Published: 18 August 2020

Abstract: Continuous, rapid, and precise monitoring of calving events contributes to an in-depth understanding of calving mechanisms, which have the potential to cause significant mass loss from the Antarctic ice sheet. The difficulties in the precise monitoring of iceberg calving lie with the coexistence of ice shelf advances and calving. The manual location of iceberg calving is time-consuming and painstaking, while achieving precise extraction has mostly relied on the surface textural characteristics of the ice shelves and the quality of the images. Here, we propose a new and efficient method of separating the expansion and calving processes of ice shelves. We visualized the extension process by simulating a new coastline, based on the ice velocity, and detected the calved area using the simulated coastline and single-temporal post-calving images. We extensively tested the validity of this method by extracting four annual calving datasets (from August 2015 to August 2019) from the Sentinel-1 synthetic aperture radar mosaic of the Antarctic coastline. A total of 2032 annual Antarctic calving events were detected, with areas ranging from 0.05 km2 to 6141.0 km2, occurring on almost every Antarctic ice shelf. The extraction accuracy of the calved area depends on the positioning accuracy of the simulated coastline and the spatial resolution of the images. The positioning error of the simulated coastline is less than one pixel, and the determined minimum valid extraction area is 0.05 km2, when based on 75 m resolution images. Our method effectively avoids repetition and omission errors during the calved area extraction process. Furthermore, its efficiency is not affected by the surface textural characteristics of the calving fronts and the various changes in the frontal edge velocity, which makes it fully applicable to the rapid and accurate extraction of different calving types.

Keywords: Antarctica; ice shelves; iceberg calving; coastline; ice velocity; remote sensing

1. Introduction The Antarctic ice shelf system is an important area connecting the ice sheets and the ocean. Its stability is closely related to the Antarctic mass balance. Iceberg calving, the shedding of ice from glaciers or ice shelf frontal edges, is the main process contributing to the dynamic mass loss from the ice sheet to the ocean. Iceberg calving accounts for half of the net mass loss of the Antarctic ice shelves [1–3]. Although iceberg calving plays an important role in the Antarctic ice shelf mass balance, the majority of the current ice sheet and ocean climate models do not represent the calving process in a physically realistic manner. Understanding this process is critical for accurately predicting the future impact of climate change on ice sheets.

Remote Sens. 2020, 12, 2658; doi:10.3390/rs12162658 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 2658 2 of 15

Comprehensive and detailed observations of iceberg calving of different sizes help to accurately assess the mass loss and explore the calving mechanisms. The overall range of multiscale, large sample observations makes it possible to investigate this issue using statistical tools. For example, based on calving observations for Svalbard, Alaska, Greenland, and Antarctica, Åström et al. [4] found that calving events ranging from 1 m3 to 1 1012 m3 follow a uniform statistical pattern. They revealed × that the calving termini of the ice sheets and glaciers behave as self-organized critical systems. Medrzycka et al. [5] suggested the necessity of studying calving events of different magnitudes due to the variations in the physical mechanisms that triggered the phenomenon. Fine-scale monitoring of calving events is of great significance in improving the existing ice sheet models [6,7]. Currently, extensive and detailed iceberg calving observations are time-consuming and laborious. This is mainly because of the massive volume of high-resolution remotely sensed data and the lack of efficient extraction methods. This has led to a paucity of continuous monitoring of calving events at the continental scale. The most apparent manifestation of iceberg calving is the change in the coastline of the ice shelf’s frontal edge. There are many methods to extract the coastline changes, before and after calving, which mainly fall into the following three categories: the manual extraction of coastlines in different phases [8,9], automatic extraction [10–12], and feature matching between different phase images to extract the advance and retreat of the coastline [13–15]. The first two methods measure the area changes of ice shelves as a whole, while the third one successfully separates the advance and retreat of the ice shelves from the total coastline change. However, observing iceberg calving is much more complicated than monitoring changes in the coastline. This is because the expansion of the ice shelves coexists with the calving events, but the changes in the coastline can only reflect the overall advance or retreat of the coastline. One of the main objectives of the detailed monitoring of iceberg calving is to extract the location, shape, area, and mass of each single calving event. The National Ice Center (NIC) regularly releases iceberg locations throughout the year using multisource, high-resolution, remotely sensed data. This dataset is helpful to identify the locations of calving events, but the dataset includes only large icebergs. A number of large calving events are ice shelf collapses resulting from densely distributed rifts. A single collapse may produce numerous tiny icebergs, for example, the collapse and breaking up of the Larsen B Ice Shelf in March 2002 [16]. As for the extraction of a single calving event, Liu et al. [1] compiled a comprehensive inventory of annual Antarctic calving events larger than 1 km2 from 2005 to 2011. They used a feature matching algorithm to match the images before and after calving events with the vectors of the ice shelf frontal edge and other distinct textural features. In some specific regions, they distinguished the calved area with the assistance of the GLAS (Geoscience Laser Altimeter System) elevation dataset and ice velocity. This is the first time that independent calving events have been separated from the overall advance–retreat of the coastline. However, this method requires repeated comparisons between the frontal edges in two temporal images to check whether calving has occurred, which is time-consuming and laborious. Moreover, the image matching method for high-precision monitoring is quite demanding in terms of the image’s quality and texture coherence before and after calving. In this study, we propose a new method of extracting the calved area. First, we developed a simulated coastline of another phase by adding the displacement, based on the ice velocity, to the position of the original coastline. Then, we compared the simulated coastline with the frontal edges in the corresponding remotely sensed images. Theoretically, the simulated coastline overlaps with the ice shelf’s frontal edge where iceberg calving has not occurred. Thus, the calved area is the closure zone between the real and simulated coastlines. We validated the feasibility of this method by extracting the entire Antarctic calved area for four annual periods, from August 2015 to August 2019. Its advantages are as follows. In our method, calving events can be identified using single-phase images, in which the calved area is easy to distinguish. The extraction process has a high theoretical accuracy and strong adaptability, which also reduces the rate of repetition and omission in the detection. In addition, the differences in the frontal ice velocity and the image textural characteristics do not affect the extraction results. Remote Sens. 2020, 12, 2658 3 of 15

2. Data Satellite Imagery. The Sentinel-1 EW L1 ground range detected (GRD) products, which repeatedly covered the Antarctic coastline in August each year from 2015 to 2019, were used in our study. The data were downloaded from the European Space Agency (ESA) website (https://www.esa.int/). The Sentinel-1 constellation consists of two polar-orbiting satellites, each of which carries a dual-polarized C-band SAR (synthetic aperture radar) sensor with an operating frequency of 5.405 GHz. The revisit cycle of this binary system is about 6 days, but it can be shorter in higher latitudes. The extra-wide swath (EW) mode, which is one of the four operating modes, is mainly suitable for large, converging areas such as the polar regions. It has an imaging swath of 400 km and an orientation resolution of 40 m. The GRD products released by the core ground station record the signals of the backward scattering factor of the incident microwave radiation. They can perfectly reflect the geometry and electromagnetic properties of the objects being measured [17]. Therefore, we can distinguish between ice shelves and fast ice using the images, which is the basis for discovering calving events. Grounding Line. The grounding line defines the extent of the ice shelves, setting it apart from the land ice. We used the MEaSUREs (Making Earth System Data Records for Use in Research Environments) grounding line dataset version 2, downloaded from the National Snow and Ice Data Center (NSDIC) website (https://nsidc.org/data/nsidc-0709/versions/2). The MEaSUREs project is one of the NASA’s Earth Science Programs dedicating to advancing Earth remote sensing. This dataset was produced by extracting the grounding points from multiple satellites between 7 February 1992 and 31 December 2015. It provides comprehensive, high-resolution Antarctic grounding line datasets, with an accuracy of 25 m to 250 m, measured using D-InSAR (Differential Synthetic Aperture Radar Interferometry) technology [2,18,19]. Ice Velocity. We used the MEaSUREs Antarctica Ice Velocity Map Version 2 to obtain the spatial distribution of the ice velocity values. It is a raster dataset with a 450 m 450 m spatial resolution × and a 20-year (1/1996–12/2016) temporal coverage that is extracted using Interferometric Synthetic Aperture Radar (InSAR). MEaSUREs Antarctica Ice Velocity Map records the velocity value of each grid point in the X- and Y-directions with a well-corrected error of 17 m a 1. It can be downloaded · − from the NSDIC website (https://nsidc.org/data/NSIDC-0484/versions/2)[18,20,21].

3. Methods Our methodology consisted of three parts: data preprocessing, iceberg calving extraction, and accuracy assessment. The data preprocessing included SAR image processing, ice velocity area extension, and benchmark ice shelf digitalization. The iceberg calving extraction included simulating the front edge of the diﬀerent phases and detecting calving events from the simulated coastline and the corresponding images.

3.1. Data Preprocessing SAR image processing. We processed the Sentinel-1 EW L1 GRD products using the Sentinel Application Platform (SNAP), developed by Brockmann Consult, SkyWatch and C-S and released by the the European Space Agency. After the calibration and terrain correction, we chose the Antarctica polar stereographic projections and resampled to a 75 m resolution. Then, we used ENVI (Environment for Visualizing Images), developed by Exelis Visual Information Solutions in the US, to mosaic the preprocessed images for each year, during which images with a date adjacent to 1 August would be put on the upper level to restrict the bias in imaging time. Figure1 shows the SAR image processing results. Ice velocity area extension. Glaciers are in constant motion. Therefore, the ice velocity product may not cover the newly advanced ice shelf’s frontal edge. To solve this problem, we extended the area of the original raster dataset using the Moore-neighbor algorithm. Our methodology can be divided into three steps. First, identify the boundary between the empty and non-empty values of the ice velocity raster image. Next, assign values equal to the average of eight neighboring raster Remote Sens. 2020, 12, 2658 4 of 15 cells to the empty center cells in a 3 3 window along the boundary. Then, iterate the first two steps × 100 times to obtain the expanded ice velocity raster dataset. As for accuracy validation, the original ice velocity area was retreated inward by 2 km and then, it was iterated outward 50 times in the same way. We marked this result as a verification value. We calculated the average extension error by comparing theRemote extended Sens. 2020 value, 12, x FOR to the PEER verification REVIEW value. The result shows that the error is about 8 m a 1. 4 of 15 · −

Figure 1.1. Annual Sentinel-1 synthetic aperture radar (SAR) (SAR) mosaic of the Antarctic coastline in early August fromfrom 20152015 toto 2019.2019.

Benchmark ice shelf digitalization. We automatically extracted and manually modified the Ice velocity area extension. Glaciers are in constant motion. Therefore, the ice velocity product coastline vector for August 2015 as the original coastline benchmark, using an updated version of the may not cover the newly advanced ice shelf’s frontal edge. To solve this problem, we extended the Canny edge detection algorithm. The accuracy was about 60 m [10]. Then, we checked the topological area of the original raster dataset using the Moore-neighbor algorithm. Our methodology can be relationship between the grounding line vectors and the coastline vectors. To make sure the grounding divided into three steps. First, identify the boundary between the empty and non-empty values of line and the corresponding coastline were enclosed within closed polygons, we checked to make sure the ice velocity raster image. Next, assign values equal to the average of eight neighboring raster cells theto the coastline empty wascenter continuous cells in a and3 × 3 each window grounding along linethe intersectedboundary. Then, with the iterate coastline. the first We two transformed steps 100 thetimes area to enclosedobtain the by expanded the ice shelf ice frontal velocity coastlines raster da andtaset. the groundingAs for accuracy lines into validation, polygons the with original a unique ice ID.velocity Then, area we obtainedwas retreated the accurate inward iceby shelf2 km outlinesand then, for it Augustwas iterated 2015, outward which were 50 times used asin thethe inputsame dataway. forWe the marked following this steps.result as a verification value. We calculated the average extension error by −1 3.2.comparing Iceberg Calvingthe extended Extraction value to the verification value. The result shows that the error is about 8 m∙a . Benchmark ice shelf digitalization. We automatically extracted and manually modified the coastlineVelocity-based vector for August ice shelf 2015 front as the edge original simulation. coastlineWe benchmark, converted the using benchmark an updated ice shelfversion polygon of the verticesCanny edge into pointsdetection and algorithm. recorded theirThe accuracy order. Then, was theabout following 60 m [10]. operations Then, we were checked carried the outtopological for each point.relationship First, webetween judged whetherthe grounding the point line was vectors on the groundingand the coastline line or not. vectors. Our study To make focused sure on the short-termgrounding front-edgeline and the changes, corresponding so we could coastline assume were no enclosed changehad within taken closed place polygons, in the position we checked of the groundingto make sure line. the Next, coastline based was on thecontinuous velocity and of each each coastline grounding point, line we intersected identified with its movement the coastline. and newWe transformed location. After the joining area enclosed the points by to the the ice line, shelf a new frontal coastline coastlines was derived, and the namelygrounding the simulatedlines into coastline.polygons with The schematica unique ID. of thisThen, process we obtained is shown the in accurate Figure2. ice shelf outlines for August 2015, which wereConsidering used as the input that the data points for the in following the fast flow steps. regions may move more than one pixel in a month, we calculated the ice movement of each month and iterated for 12 times to obtain the annual ice movement,3.2. Iceberg Calving which Extraction means that the displacement of a coastline point in the fast flow region may be Velocity-based ice shelf front edge simulation. We converted the benchmark ice shelf polygon vertices into points and recorded their order. Then, the following operations were carried out for each point. First, we judged whether the point was on the grounding line or not. Our study focused on the short-term front-edge changes, so we could assume no change had taken place in the position of the grounding line. Next, based on the velocity of each coastline point, we identified its movement and new location. After joining the points to the line, a new coastline was derived, namely the simulated coastline. The schematic of this process is shown in Figure 2. Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 15

Considering that the points in the fast flow regions may move more than one pixel in a month, Remotewe calculated Sens. 2020, 12the, 2658 ice movement of each month and iterated for 12 times to obtain the annual5 of ice 15 movement, which means that the displacement of a coastline point in the fast flow region may be determined by several different velocities, to improve the simulation accuracy. After traversing all of determinedthe points, we by severalsimulated diﬀ theerent theoretical velocities, location to improve of the the coastline’s simulation feature accuracy. points After for traversing the following all of theyear. points, we simulated the theoretical location of the coastline’s feature points for the following year.

Figure 2. Schematic of the velocity-based ice sh shelfelf front-edge simulation. In panel ( a), the blue line is the original coastline for the previous years, and the green points on the the blue blue line line are are its its feature feature points. points. The yellow points are the moving result of the green points based on the ice velocity. They represent the feature points of the next year’s coastline. coastline. We We connected connected the yellow yellow points points sequentially sequentially to generate the next year’syear’s simulatedsimulated coastlinecoastline (the(the redred line). line). Panel Panel ( b(b)) shows shows a a no-calved no-calved scene. scene. Panel Panel (c ()c shows) shows a calveda calved scene, scene, in whichin which the the calving calving events events can becan detected be detected using using the simulated the simulated coastline coastline and the and image. the image. Calved area extraction. An independent annual calving event is defined as its location not being spatiallyCalved adjacent area extraction. to other calving An independent events in the annual same calving year. That event is, allis defined of the annual as its location calving not polygons being duringspatially a adjacent specific yearto other are calving not overlapped events in or the adjacent same year. in the That projection is, all of space.the annual The calving following polygons year’s simulatedduring a specific ice shelf year polygons are not can overlapped be obtained or adjacent by sequentially in the projection connecting space. the moved The following feature pointsyear’s withsimulated the grounding ice shelf polygons line points can (the be yellowobtained line by in sequentially Figure3a). Byconnecting comparing the the moved simulated feature ice points shelf polygonwith the togrounding its contemporaneous line points (the images yellow and byline correcting in Figure the 3a). frontal By comparing edge of the the ice simulated shelf, so that ice itshelf fits intopolygon the actualto its contemporaneous coastline position, images we can obtainand by the correcti exactng ice the shelf frontal polygon edge for of thethe followingice shelf, so year that (the it redfits into line the in Figure actual3 b).coastline By subtracting position, thewe can corrected obtain ice the shelf exact polygons ice shelf frompolygon the theoreticalfor the following simulated year polygons,(the red line we in obtained Figure calved-area3b). By subtracting polygons the during corrected that yearice shelf (blue polygons translucent from area the in theoretical Figure3c), meanwhile,simulated polygons, we also obtainedwe obtained their calved-area shape and polygons area properties during under that year polar (blue projection. translucent By pushing area in theFigure new, 3c), actual, meanwhile, coastline we backward also obtained to the their previous shape year’sand area ice properties shelf, we foundunder thepolar fracture projection. line and By apushing similar the blue new, translucent actual, coastline region overlap, backward between to the theprevious polygon’s year’s outline ice shelf, and we the found fracture the linefracture (the blackline and line a insimilar Figure blue3d). translucent We checked region the topological overlap, between relationships the polygon’s of all of outline the extracted and the regions fracture in theline same(the black year line for Antarctica,in Figure 3d). with We the checked criterion the that topological polygons relationship do not intersects of all with of the each extracted other. After regions the correction,in the same we year obtained for Antarctica, vectors for with each the calved criterion area duringthat polygons the year. do While not acquiringintersect with the calved each other. areas, theAfter coastlines the correction, were updatedwe obtained year vectors by year. for The each updated calved coastlinesarea during can the be year. regarded While asacquiring benchmark the coastlinescalved areas, for the nextcoastlines phase were of the updated calving extraction.year by year. The updated coastlines can be regarded as benchmark coastlines for the next phase of the calving extraction. Remote Sens. 2020, 12, 2658 6 of 15 Remote Sens. 2020, 12, x FOR PEER REVIEW 6 of 15

FigureFigure 3.3.Schematic Schematic of of the the iceberg iceberg calving calving extraction. extraction. (a,d )(a displays) and (d the) displays satellite imagesthe satellite in August images 2017, in asAugust well as 2017, (b,d as) for well images as (b) in and August (d) for 2018. images Blue in lines August in (a ,2018.d) represent Blue lines the in actual (a) and coastline (d) represent in August the 2017.actual Yellow coastline lines in August in (a,c) indicate2017. Yellow the simulated lines in (a coastline) and (c) indicate in August the 2018. simulated Red linescoastline in (b in,c) August stands for2018. the Red actual lines coastline in (b) and in August (c) stands 2018. for Blue the areaactual S1 coastline shows the in extractionAugust 2018. result Blue through area S1 our shows method, the andextraction S2 through result the through previous our extraction method, methodand S2 bythrough feature the tracking. previous extraction method by feature tracking. 3.3. Accuracy Assessment 3.3. AccuracyPositional Assessment accuracy of the simulated coastline. The samples from the ice shelves’ frontal edges only advanced,Positional butaccuracy those of without the simulated calving events coastline. were The chosen samples to assess from the the accuracy ice shelves’ of the frontal simulated edges coastline.only advanced, The speciﬁc but those method without used calving is as follows. events were First, chosen calculate to theassess distance the accuracy from the of moved the simulated feature pointscoastline. on The the specific simulated method coastlines used is to as the follows. automatically First, calculate extracted, the distan and manuallyce from the corrected, moved feature pixel boundariespoints on the of thesimulated ice shelves coastlines and the to sea the in theautoma images.tically Then, extracted, determine and the manually directions. corrected, If the feature pixel pointsboundaries fall within of the theice sea,shelves the positionaland the sea error in the is theimages. distance Then, value determine above. the If the directions. feature pointsIf the feature fall on thepoints ice shelf,fall within the positional the sea, the error positional is the negative error is valuethe distance of the distance. value above. If the feature points fall on the iceExtraction shelf, the accuracy positional of error the calved is the negative area. The value samples of the of distance. the calved areas with distinct fracture linesExtraction on the images accuracy were selectedof the calved to assess area. the The observation samples of and the extraction calved areas accuracy. with distinct For the fracture calving lines on the images were selected to assess the observation and extraction accuracy. For the calving extraction from the same spatial resolution images, we compared the results obtained using our new Remote Sens. 2020, 12, 2658 7 of 15

Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 15 extraction from the same spatial resolution images, we compared the results obtained using our new method with with those those obtained obtained using using the the previous previous me methodthod of image of image matching matching and andfeature feature tracking. tracking. The Theareas areas in our in ourresults results (e.g., (e.g., the blue the blue translucent translucent region region in Figure in Figure 3c)3 arec) are recorded recorded as asS1, S1, and and the the areas areas in inthe the control control group group (Figure (Figure 3d)3 d)are are denoted denoted as asS2. S2. We We define define S2 as S2 the as thetrue true value value of the of thecalved calved area area and 2 andthe difference the difference between between S1 and S1 S2 and (m S22) (mas the) as area the extraction area extraction error. error. As the As scale the of scale each of calving each calving event eventvaries, varies, we use we the use error-equivalent the error-equivalent perimeter perimeter width width (m) to (m) describe to describe the accuracy. the accuracy. It is It calculated is calculated by bydividing dividing the thearea area extraction extraction error error by byits itsperimeter. perimeter. Assessment ofof thethe minimumminimum eeffectiveffective extractionextraction areaarea.. We definedefine thethe minimumminimum eeffectiveffective extraction area as a value equal to thethe squaresquare ofof thethe productproduct ofof thethe comprehensivecomprehensive error-equivalenterror-equivalent pixel (2√22 times times the the ratio ratio of of the the positional positional error to the spatial resolution of the images) and the image spatial resolution.

4. Results and Analysis 4. Results and Analysis We randomly selected 30 sample boxes of 5 km 5 km (the locations are shown as red boxes We randomly selected 30 sample boxes of 5 km ×× 5 km (the locations are shown as red boxes in inFigure Figure 4)4 from) from the the ice ice shelves’ shelves’ frontal frontal edges edges where where only only advancement and no calvingcalving occurredoccurred toto validate the positionalpositional accuracy of thethe simulatedsimulated coastline.coastline. We also chosechose 30 calved-areacalved-area samplessamples (blue(blue boxes in in Figure Figure 4)4) with with distinct distinct fracture fracture line liness to to assess assess the the extraction extraction accuracy. accuracy. Furthermore, Furthermore, we weextensively extensively tested tested the the validity validity and and efficiency eﬃciency of of th thisis method method by by extracting extracting four four interannual interannual calving datasets from the Sentinel-1 SARSAR mosaicmosaic ofof thethe AntarcticAntarctic coastlinecoastline inin AugustAugust fromfrom 20152015 toto 2019.2019.

Figure 4.4. SpatialSpatial distribution distribution of theof the samples samples and testand areastest overlainareas overlain on the Moderate-resolutionon the Moderate-resolution Imaging SpectroradiometerImaging Spectroradiometer (MODIS) based(MODIS) Mosaic based of AntarcticaMosaic of (MOA)Antarctica (adapted (MOA) from (adapted [22]). Redfrom boxes [22]). show Red theboxes samples show usedthe samples to verify used the positionalto verify the accuracy position ofal the accuracy simulated of the coastlines, simulated the coastlines, blue boxes the indicate blue theboxes samples indicate used the to samples assess theused extraction to assess accuracy,the extraction and the accuracy, black boxes and the represent black boxes the locations represent of the six typicallocations regions. of six typical regions.

Remote Sens. 2020, 12, 2658 8 of 15 Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 15

4.1. Positional Accuracy of the Simulated Coastline The details of each sample are shown inin FigureFigure5 5..

Figure 5. SamplesSamples used to validate validate the positional accuracy of the simulated coastline. The purple lines are the benchmark coastlines of the ice shelf’s frontal edge in 2015. Th Thee blue points and yellow points are feature points before and after moving based on the ice velocity, respectively.respectively. The red lines are the simulated coastlines in 2016 generated byby sequentiallysequentially connecting the yellowyellow points.points. The sample locations are shownshown inin FigureFigure4 4..

We measuredmeasured the positionalpositional errors of all of thethe featurefeature pointspoints (752(752 inin total)total) inin 3030 samples.samples. As is shown in Figure6 6a,a, thethe errorserrors generallygenerally exhibitexhibit aa normalnormal distributiondistribution withwith aa standardstandard deviationdeviation ofof 74.6 m and a mean value of 27.9 m.m. To some extent,extent, there is a systematicsystematic error. This is because we regarded thethe distancedistance fromfrom thethe featurefeature points points to to the the edge edge of of the the raster raster as as the the positional positional error, error, when when in fact,in fact, the the edge edge pixels pixels are are usually usually mixed mixed pixels pixels of iceof ice and and water. water. RemoteRemote Sens. Sens.2020 2020, 12, ,12 2658, x FOR PEER REVIEW 9 of9 of 15 15

Figure 6. Error distribution histogram. The red and black vertical dashed lines represent the mean errorFigure and 6. the Error standard distribution deviation histogram. of error frequency The red and distribution, black vertical respectively. dashed lines (a) The represent positional the error mean anderror (b) and the extractionthe standard error deviation in the form of erro ofr error-equivalent frequency distributi perimeteron, respectively. widths. (a) The positional error and (b) the extraction error in the form of error-equivalent perimeter widths. 4.2. Extraction Accuracy of the Calved Area 4.2. Extraction Accuracy of the Calved Area The extraction accuracy depends on the positional accuracy of the simulated coastline and the spatialThe resolution extraction of the accuracy images. depends In 30 samples on the of positional calved areas, accuracy the error-equivalent of the simulated perimeter coastline widths and the ofspatial each calving resolution event of rangedthe images. from In 9.230 samples m to 10.9 ofm, calved with areas, a standard the error-equivalent deviation of 5.0 perimeter m (Figure widths6b). − Byof synthesizing each calving theevent accuracy ranged analysis from −9.2 above, m to we10.9 conclude m, with a that standard based deviation on the 75 of m 5.0 resolution m (Figure SAR 6b). images,By synthesizing lengths greater the accuracy than three anal pixels,ysis above, both we along conclude and perpendicular that based on to the the 75 ice m flow resolution direction, SAR areimages, valid. Thatlengths is, thegreater minimum than three effective pixels, extraction both along area and in thisperpendicular study is about to the 0.05 ice km flow2. direction, are valid. That is, the minimum effective extraction area in this study is about 0.05 km2. 4.3. Extraction Results 4.3.In Extraction the time Results interval we studied, 2032 annual Antarctic calving events were detected with areas 2 2 rangingIn fromthe time 0.05 interval km to we 6141.0 studied, km 2032(Table annual1). WeAntarctic found calving that as events the calving were detected scale decreases, with areas itsranging frequency from increases 0.05 km exponentially,2 to 6141.0 km2 which (Table means 1). We that found the that monitoring as the calving workload scale also decreases, increases its 2 exponentially.frequency increases Among them,exponentially, there were which 1209 calvingmeans eventsthat the smaller monitoring than 1 kmworkload, with aalso total increases area of 2 483.7exponentially. km and an Among average them, annual there area were ratio 1209 of 4.1%. calving In particular, events smaller in 2017–2018, than 1 km the2 total, with calved a total area area of of the483.7 smallest km2 and scale an accounts average for annual the highest area ratio proportion of 4.1%. (8.9%),In particular, compared in 2017–2018, with the same the total scales calved in other area years.of the Therefore, smallest scale smaller-scale accounts calving for the eventshighest cannot proportion be ignored (8.9%), in compared the accurate with estimation the same of scales mass in loss,other which years. highlights Therefore, the smaller-scale importance of calving precisely events monitoring cannot be iceberg ignored calving. in the accurate estimation of mass loss, which highlights the importance of precisely monitoring iceberg calving. Table 1. Statistics on iceberg calving at different scales from August 2015 to August 2019.

Year Table 1. Statistics< 1on km iceberg2 1–10 calving km 2at differe10–100nt scales km2 from100–1000 August km 20152 to> August1000 km 2019.2 Total YearFrequency 322<1 km2 1621–10 km2 10–100 34 km2 100–1000 9 km2 >10001 km2 Total 528 2015–2016 AreaFrequency 134.0 322 515.4 162 1116.8 34 3058.3 9 893.9 1 528 5718.4 2015–2016Area ratioArea 2.3%134.0 9.0%515.4 19.5%1116.8 53.5%3058.3 893.9 15.6% 5718.4 - FrequencyArea ratio 210 2.3% 167 9.0% 19.5%50 53.5% 6 15.6% 1 - 434 2016–2017 AreaFrequency 91.2 210 563.0 167 1478.2 50 1077.9 6 6141.0 1 434 9351.4 2016–2017Area ratioArea 1.0%91.2 6.0%563.0 15.8% 1478.2 11.5% 1077.9 6141.0 65.7% 9351.4 - FrequencyArea ratio 361 1.0% 145 6.0% 15.8%21 11.5% 2 65.7% 0 - 529 2017–2018 AreaFrequency 135.4 361 460.1 145 516.8 21 409.5 2 0 - 529 1521.7 2017–2018Area ratioArea 8.9%135.4 30.2%460.1 34.0% 516.8 26.9% 409.5 - - 1521.7 - FrequencyArea ratio 316 8.9% 198 30.2% 34.0%22 26.9% 5 - 0 - 541 2018–2019 AreaFrequency 123.1 316 610.1 198 478.3 22 1717.9 5 0 - 541 2929.5 2018–2019Area ratioArea 4.2%123.1 20.8%610.1 16.3% 478.3 58.6% 1717.9 - - 2929.5 - Area ratio 4.2% 20.8% 16.3% 58.6% - - Remote Sens. 2020, 12, 2658 10 of 15 Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 15

Figure7 shows the spatial distribution of the di ﬀerent scale calving events. Tiny (<1 km2) and small Figure 7 shows the spatial distribution of the different scale calving events. Tiny (<1 km2) and scale (1–10 km2) calving events occurred on almost every Antarctic ice shelf. The former appeared most small scale (1–10 km2) calving events occurred on almost every Antarctic ice shelf. The former frequently in western Antarctica and the Queen Maud Land region in eastern Antarctica. The latter was appeared most frequently in western Antarctica and the Queen Maud Land region in eastern common in western Antarctica, but in the Queen Maud Land region in eastern Antarctica, it occurred Antarctica. The latter was common in western Antarctica, but in the Queen Maud Land region in less often. Medium-scale (10–100 km2) calving events occurred frequently in eastern Antarctica eastern Antarctica, it occurred less often. Medium-scale (10–100 km2) calving events occurred between the Amery Ice Shelf and the Ross Ice Shelf, and in western Antarctica between the Ross Ice frequently in eastern Antarctica between the Amery Ice Shelf and the Ross Ice Shelf, and in western Shelf and the Antarctic Peninsula. Large-scale (10–100 km2) and extra-large (>100 km2) calving events Antarctica between the Ross Ice Shelf and the Antarctic Peninsula. Large-scale (10–100 km2) and occurred sporadically. extra-large (>100 km2) calving events occurred sporadically.

FigureFigure 7. 7.Spatial Spatial distributiondistribution ofof icebergiceberg calvingcalving atat didifffferenterent scalesscales fromfrom AugustAugust 20152015 toto AugustAugust 2019.2019. 2 2 2 (a(a)) Tiny-scaled Tiny-scaled (< (<11 km km);2 ();b )(b Small-scaled) Small-scaled (1–10 (1–10 km km); (c2)); Medium-scaled (c) Medium-scaled (10–100 (10–100 km ); (kmd) Large-scaled2); (d) Large- 2 2 (100–1000scaled (100–1000 km ); (e km) extra-large2); (e) extra-large scaled ( >scaled1000 km(>1000). km2). 5. Discussion 5. Discussion As little research has been conducted on the precise extraction of independent calving events, As little research has been conducted on the precise extraction of independent calving events, some exploration has been done in our study to improve the extraction permanence at the full Antarctic some exploration has been done in our study to improve the extraction permanence at the full scale. In this section, the accuracy, efficiency, and comparisons with traditional methods in typical Antarctic scale. In this section, the accuracy, efficiency, and comparisons with traditional methods in regions are discussed. typical regions are discussed. The extraction accuracy depends on the spatial resolution of the images and the accuracy of the The extraction accuracy depends on the spatial resolution of the images and the accuracy of the input coastline. Considering that August is the Austral winter, during which the calving frequency is input coastline. Considering that August is the Austral winter, during which the calving frequency lower than in other months [4], we used early August from one year to the next year as the extraction is lower than in other months [4], we used early August from one year to the next year as the cycle. For the image time selection, priority was given to images from early August, and the missing extraction cycle. For the image time selection, priority was given to images from early August, and parts were filled in with other nearby times. To some extent, this operation can miss the occurrence of the missing parts were filled in with other nearby times. To some extent, this operation can miss the calving events within the tiny time interval covered by the images, which may cause the misjudgment occurrence of calving events within the tiny time interval covered by the images, which may cause of the year. In terms of the imagery source selection, August is the polar night in Antarctica, during the misjudgment of the year. In terms of the imagery source selection, August is the polar night in which optical sensors do not work well; therefore, SAR images covering the Antarctic coastline with Antarctica, during which optical sensors do not work well; therefore, SAR images covering the a shorter revisit period were selected. As for the input coastline, we chose one for which both the Antarctic coastline with a shorter revisit period were selected. As for the input coastline, we chose accuracy and the temporal coverage fit our needs from a number of Antarctic coastline products. one for which both the accuracy and the temporal coverage fit our needs from a number of Antarctic We also performed some manual corrections to make it more applicable as an input for this method. coastline products. We also performed some manual corrections to make it more applicable as an input for this method. Remote Sens. 2020, 12, 2658 11 of 15 Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 15

InIn termsterms of of the the extraction extraction effi efficiency,ciency, combined combined with with the simulatedthe simulated coastline, coastline, this method this method can detect can didetectfferent different types of types calving of calving using only using single-phase only single-phase images images of the post-calving of the post-calving coastline, coastline, which greatly which expeditesgreatly expedites the extraction the extraction process. process. We determined We determined that in only that 3–5in only h, we 3–5 can h, obtainwe can the obtain overall the calvingoverall eventscalving for events the Antarctic for the Antarctic ice shelves ice inshelves one year in one based year on based 75 m on resolution 75 m resolution image data. image data. Then,Then, wewe furtherfurther illustrated thethe advantages ofof ourour method using six typical regions (black boxesboxes inin FigureFigure4 4)) with with di differentfferent calvingcalving characteristicscharacteristics asas examples.examples.

5.1.5.1. Detection of Infrequent Tabular CalvingCalving EventsEvents InfrequentInfrequent tabular tabular calving calving is mostlyis mostly dominated dominated by stresses by stresses propagating propagating along preexisting along preexisting crevasses, andcrevasses, it has longerand it has recurrence longer recurrence intervals in intervals the same in spatialthe same neighborhood spatial neighborhood [1]. We can [1]. see We the can obvious see the fractureobvious linefracture and onlyline and one only or two one tabular or two icebergstabular icebergs in the pre-calving in the pre-calving and post-calving and post-calving images. images. TheThe Ross,Ross, Ronne-Filchner, Ronne-Filchner, and and Amery Amery ice shelves,ice shelve thes, Larsen the Larsen C Ice Shelf C Ice on theShelf Antarctic on the Peninsula,Antarctic andPeninsula, the large and ice the shelves large ice in theshelves Queen in the Maud Queen Land Maud region Land are region relatively are relatively stable overall, stable with overall, slow with ice velocities.slow ice velocities. The expansion The expansion of rifts on of the rifts surface on the triggers surface fracturingtriggers fracturing and calving. and calving. ForFor example,example, inin thethe testtest areas onon thethe FimbulFimbul IceIce ShelfShelf andand thethe KingKing BaudoinBaudoin IceIce Shelf, signiﬁcantsignificant rift-openingrift-opening disintegrationdisintegration only only occurred occurred in 2015–2016in 2015–2016 (Figure (Figure8). The 8). shapeThe shape and location and location of the calvedof the areascalved are areas visible are visible on the on pre- the and pre- post-disintegration and post-disintegration images. images. The occurrence The occurrence of such of such infrequent infrequent and crevasse-distinctand crevasse-distinct iceberg iceberg calving calving events events can be can accurately be accurately monitored monitored using both using our both proposed our proposed method andmethod previous and previous methods methods [1]. [1].

FigureFigure 8.8. Extraction results of the infr infrequentequent “tabular” “tabular” calving. calving. (a (a)–(–cc)) forfor testtest areaarea inin thethe FimbulFimbul iceice shelf,shelf, andand ((dd–)–(f)f for) for the the King King Baudoin Baudoin ice ice shelf, shelf, respectively. respectively. Locations Locations are are shown shown in in Figure Figure4. 4.

5.2.5.2. Detection of Frequent DisintegrationDisintegration CalvingCalving EventsEvents FrequentFrequent disintegration calving calving is is mainly mainly associated associated with with the the basal basal and/or and/ orsurface surface melting melting of ice of iceshelves, shelves, and and it has it hasshorter shorter recurrence recurrence intervals intervals in th ine thesame same spatial spatial neighborhood neighborhood [2]. Most [2]. Most of the of pre- the disintegration images have a high density of crevasses. After disintegration, numerous small, Remote Sens. 2020, 12, 2658 12 of 15

Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 15 pre-disintegration images have a high density of crevasses. After disintegration, numerous small, fragmented icebergs are present. These These icebergs icebergs rema remainin drifting drifting near near the the calving calving front front for for some some time, time, which causes didifficultiesﬃculties forfor traditionaltraditional calved-areacalved-area extractionextraction byby imageimage matching.matching. Our coastline simulation monitoring method is is not subject to the textural characteristics of the images before and after the disintegration. Therefore, Therefore, it it is is more more advantageous advantageous for for extracting extracting this this type type of of iceberg iceberg calving. calving. Figure 99 showsshows thethe extractionextraction resultsresults forfor fourfour consecutive consecutive years years within within the the test test areas. areas.

Figure 9. ExtractionExtraction results results of of the the frequent frequent “disinte “disintegration”gration” calving. Test Test area area located in ( (aa)) the the Totten Totten ice shelf; ((bb)) thethe PinePine IslandIsland ice ice shelf; shelf; ( c()c the) the Getz Getz ice ice shelf, shelf, and and (d )(d the) the Thwaites Thwaites ice ice shelf. shelf. Locations Locations are areshown shown in Figure in Figure4. 4.

The frontal edge of the Totten IceIce ShelfShelf isis inin rapidrapid advance–retreat.advance–retreat. During 2015–2019, calving events occurredoccurred sequentiallysequentially atat various various locations locations along along its its coastline coastline in ain clockwise a clockwise direction direction in the in testthe testarea area (Figure (Figure9a). Although 9a). Although fracture fracture lines are lines evident are in evident the images in beforethe images and after before the disintegration,and after the disintegration,the large diﬀerence the large in the difference velocity directionin the velocity at the frontaldirection edge at the causes frontal errors edge in thecauses feature errors tracking. in the feature tracking. In contrast, our method incorporates the velocity variables into the frontal edge simulation, so the detection results are not affected by the differences in the frontal ice velocities. The frontal edge of the Pine Island Ice Shelf is also in rapid advance–retreat. Large disintegrations (>100 km2) occurred in this test area during 2015–2016, 2017–2018, and 2018–2019. Remote Sens. 2020, 12, 2658 13 of 15

In contrast, our method incorporates the velocity variables into the frontal edge simulation, so the detection results are not affected by the differences in the frontal ice velocities. The frontal edge of the Pine Island Ice Shelf is also in rapid advance–retreat. Large disintegrations (>100 km2) occurred in this test area during 2015–2016, 2017–2018, and 2018–2019. However, in 2016–2017, we extracted only six small disintegrations of <15 km2 (Figure9b). This demonstrates that small, sporadic calving that occurs after large disintegrations can be detected using our proposed method. The test area on the Getz Ice Shelf is the smallest (i.e., only 1 km 1 km). The total areas × of disintegration during 2015–2019 were 114.7 km2, 11.4 km2, 0.7 km2, and 2.5 km2 (Figure9c). Such small-scale iceberg calving events are often missed. Furthermore, there are no obvious textures in the images, which makes it impossible to detect the calved areas through feature tracking. Using our method, the disintegration region can be finely extracted, regardless of the image texture characteristics. The Thwaites Ice Shelf is fast-flowing, with typical frequent disintegration calving events occurring in each of the four years (Figure9d). Numerous crevasses and fragmented icebergs characterize the images of these calving areas, but detection using a simulated coastline and a single post-calving image is still applicable and accurate.

6. Conclusions The difficulties in the precise monitoring of individual calving events lie in the coexistence of advance and retreat of the ice shelf’s frontal edge. Calved areas cannot be directly captured by only detecting changes in the coastlines. Traditional extraction methods, which can be time-consuming and laborious, rely heavily on the textural characteristics and the quality of the images. To deal with these difficulties and deficiencies, we developed a new method using the ice velocity and remotely sensed imagery for the rapid, accurate, and precise extraction of iceberg calving. We separated the changes in the ice shelf’s frontal edge into two processes: expansion and calving. The expansion can be visualized as a simulated coastline created by adding the ice velocity to the pre-calving coastline. Then, we detected the calved area using the simulated coastline and the post-calving images. While acquiring the calved areas, the coastlines were updated year by year. The updated coastlines can be regarded as benchmark coastlines for the next phase of the calving extraction. This effectively avoids omission and repetition errors in a long sequence. The accuracy of this method depends on the ice velocity error and the spatial resolution of the images. Using the latest ice velocity data and 75 m resolution images, the positional accuracy of the simulated coastlines is 74.6 m, which is less than one pixel. As both the positional error and the manual extraction error ± are not systematic biased, the extraction accuracy is equivalent to only 5.0 m on the perimeter, which is almost negligible. Based on the positioning accuracy, we conclude that the minimum effective extraction area of iceberg calving based on 75 m resolution SAR data is 0.05 km2. We extensively tested the validity and efficiency of our proposed method by extracting annual calving events at each scale between August 2015 and August 2019, using the Sentinel-1 SAR mosaic of the Antarctic coastline. Compared with the method of Liu et al. [1], the simulation of the advance of the ice shelves’ frontal edge effectively avoids the omission of small calving events, caused by repeatedly comparing the changes in two temporal-phase images. It also saves a lot of time. In addition, we can accurately acquire the calved areas by comparing only the simulated ice shelf’s frontal edge with the post-calving edge in the images, which avoids the time-consuming extraction task of matching features from dual images and relieves the requirement for image quality and textural features. In the four-year interval we studied, 2032 annual calving events were detected in Antarctica, of which 1209 were smaller than 1 km2, with a total area of 483.7 km2 and a maximum annual area ratio of 8.9%. Therefore, small-scale calving events cannot be ignored in the accurate estimation of mass loss, and the precise monitoring of iceberg calving is meaningful. Furthermore, we determined the extraction effect of six typical test areas with different calving characteristics. The results demonstrate that this new method can effectively achieve iceberg calving extraction in various situations, including but not Remote Sens. 2020, 12, 2658 14 of 15 limited to, areas with numerous crevasses, broken iceberg patches, widely varying frontal ice velocities, and little apparent surface texture. In conclusion, this calving extraction method has a simple implementation, a wide applicability, a fine extraction accuracy, and a high efficiency. This method has been automated for coastline extension based on ice velocity. The manual extraction process can be automated in the future. Nevertheless, the manual extraction efficiency of this method is already very high, unless the automatic extraction is sufficiently accurate to eliminate the post-processing process of visual inspection, it may not have a superior efficiency. The idea of separating the advance and retreat of ice shelves, used in this method, can be used for the automatic extraction of coastlines in long time series. Compared with the traditional automatic coastline extraction method, the manual post-processing of coastline splicing is eliminated, and the interference of the automatically identified sea ice in the optical images is effectively reduced. We are now using this method to complete the precise extraction of a longer sequence of iceberg calving inventory.

Author Contributions: Conceptualization, Y.L. (Yan Liu) and M.Q.; methodology, Y.L. (Yan Liu), M.Q. and Y.L. (Yijing Lin); validation, M.Q.; formal analysis, M.Q.; data curation, Y.L. (Yan Liu) and M.Q.; writing—original draft preparation, M.Q.; writing—review and editing, Y.L. (Yan Liu), X.C., F.H. and T.L.; visualization, M.Q.; supervision, Y.L. (Yan Liu), X.C.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National key research and development Program of China (Grant No. 2018YFA0605403), National Natural Science Foundation of China (Grant No. 41925027), and Qian Xuesen Lab.—DFH Sat. Co. Joint Research and Development Fund. Acknowledgments: We greatly thank ESA for providing the Sentinel-1A (https://www.esa.int/) imagery, and we truly appreciate the National Snow and Ice Data Center (https://nsidc.org/data) for providing the ice velocity and grounding line products. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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