Remote Sensing of Environment 135 (2013) 130–140

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Remote Sensing of Environment

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Shoreline changes interpreted from multi-temporal aerial photographsand high resolution satellite images: Wotje Atoll, Marshall Islands

Murray FordSchool of Environment, The University of Auckland, Private Bag 92019, Auckland, New Zealand

E-mail address: m.ford@auckland.ac.nz.

0034-4257/$ – see front matter © 2013 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.rse.2013.03.027

a b s t r a c t

a r t i c l e i n f o

Article history:Received 3 December 2012Received in revised form 12 February 2013Accepted 24 March 2013Available online 25 April 2013

Keywords:Shoreline changeSea level riseAtoll islands

Wotje Atoll is located at 9°25′N and 170°04′E within the Republic of the Marshall Islands in the central PacificOcean. As on other atolls, the islands perched along the rim of Wotje are low-lying and considered highlyvulnerable to the impacts of climate change. Awidely anticipated impact of continued sea level rise is the chron-ic erosion of island shorelines. Using a combination of aerial photographs and satellite imagery shorelinechanges are assessed over a 67-year period characterized by rising sea level. Results indicate that between1945 and 2010 shoreline accretion is more prevalent than erosion, with an average Net Shoreline Movement(NSM) of +1.74 m, indicating accretion. Shorelines were accretionary along the lagoon, ocean and channelfacing shorelines, as well as on elongate spits and small islands. A high-frequency assessment of shorelinechange on a subset of islands in the east of Wotje reveals that islands were stable, with a balance betweenshoreline accretion and erosion. Shorelines interpreted from high resolution satellite imagery captured between2004 and 2012 indicate that shorelines within this sample of islands are largely in an erosive state. Thepost-2004 shift toward erosion may be sea level rise induced, or part of an unresolved shoreline oscillation.This study demonstrates the critical need for improved shoreline change monitoring within atoll settings inorder to assess sea level rise impacts along island shorelines.

© 2013 Elsevier Inc. All rights reserved.

1. Introduction

Future trajectories of anthropogenic-driven climate changes raisequestions surrounding the long-term viability of low-lying atollislands as centers of human habitation (Barnett & Adger, 2003;Dickinson, 2009; Roy & Connell, 1991). Atoll islands are low-lyingaccumulations of reef-derived sediment found in the tropical andsub-tropical Atlantic, Pacific and Indian Oceans. On a geological timescale atoll islands are recent landforms, with radiocarbon dating indi-cating deposition during the late Holocene (Kench et al., 2005;Woodroffe & Morrison, 2001). Despite the relatively recent formationof atoll islands, they have been sites of human habitation sincethe first few centuries AD (Kayanne et al., 2011; Weisler, 2001;Weisler et al., 2012). Atoll islands provide the only habitable land inthe Marshall Islands, Tuvalu, Maldives and, with the exception of asingle high island, in Kiribati as well. Likewise, many other Pacificand Indian Ocean nations contain significant numbers of inhabitedatolls, which from a physical perspective are similarly vulnerable tosea level rise as those within atoll nations.

Climate projections indicate future variability in the intensity andfrequency of a range of oceanographic and meteorological hazards

rights reserved.

(Solomon et al., 2007). Globally, sea level has increased at approximate-ly 1.8 ± 0.3 mm/yr between 1950 and 2000 (Church et al., 2004), withconsiderable variations in the rates across the Pacific (Church et al.,2006). Current Intergovernmental Panel on Climate Change (IPCC) pro-jections indicate sea level will be 0.18–0.59 m above 1980–1999 levelsby the end of the 21st century (Solomon et al., 2007). However, there isa growing body of research which suggests sea level will rise at a ratebeyond IPCC projections (Pfeffer et al., 2008; Rahmstorf, 2007). Threeprimary impacts of sea level rise are expected to manifest on atollislands, notably: an increase in the frequency andmagnitude of inunda-tion events, increasing saltwater intrusion into groundwater resourcesand chronic coastal erosion (Mimura, 1999).

Despite the widespread perception that sea level rise will causechronic erosion of atoll islands there have been few successful attemptsto develop shoreline change monitoring programs on atoll islands(Kench & Harvey, 2003). Shoreline change assessments within atollsettings have typically been focused on urbanized atolls or thosewhich have undergone significant anthropogenic modification (Collenet al., 2009; Ford, 2011; Xue, 2001). Historically, shoreline change re-search has been driven by a need to calculate medium and long-termshoreline change rates which are used to establish coastal hazardzones and development setbacks (Crowell et al., 1991). As a result,methodologies have matured and shoreline change analysis is firmlyestablishedwithin both research andplanning/decision-making spheres.

131M. Ford / Remote Sensing of Environment 135 (2013) 130–140

In general, two approaches are used in assessing shoreline change. Firstly,field-based methods typically involving a range of topographic surveytechniques are used to establish two-dimensional, shore perpendicularprofiles which are surveyed repeatedly (Ruggiero et al., 2005; Thom &Hall, 1991). Secondly, analysis of a time series of shoreline positionsinterpreted remotely,most commonly fromaerial photographs and satel-lite images (Dolan et al., 1991; Fletcher et al., 2003; Thieler & Danforth,1994). Field-based surveys provide detailed and accurate cross-shoredescriptions of change and provide a high level of control over samplingfrequency (i.e. before and after storm events). However, the samplingfrequency and measurement precision of field-based measures oftencome at the cost of limited spatial coverage. In some cases (Fletcheret al., 2003), analyses of remotely sensed shorelines are coupled withfield-based data to better incorporate short-termvariabilitywithin shore-line change assessments.

Remote sensing-based approaches to mapping shoreline changeare widespread (Dolan et al., 1991; Fletcher et al., 2003; Thieler &Danforth, 1994). Shorelines are typically interpreted manually andvectorized for further analysis undertaken within GIS software pack-ages. Vector-based analysis easily allows for the incorporation of arange of secondary data sources in addition to aerial photographsincluding: cadastral survey maps, satellite imagery as well as topo-graphic survey data. The most widely applied analytical method in-volves calculating rates of change from a number of vector shorelinesderived from remote image sources collected through time. Such ana-lytical approaches to mapping shoreline change are common alongcontinental coasts. In contrast, similar approaches have rarely beenapplied on atoll islands, with research effort only recently evident.Few studies have utilized remotely sensed imagery to document recentshoreline changes on atoll islands (Ford, 2011; Rankey, 2011; Webb &Kench, 2010). Recent research (Ford, 2011; Kench & Brander, 2006;Rankey, 2011; Webb & Kench, 2010) has revealed atoll islands to behighly dynamic, with considerable variability in shoreline positiondriven by both physical and anthropogenic processes. These findings

14°N

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-6000 -3000 >-500Depth (m)

167165°0'E163°0'E161°0'E

Australia

HawaiiJapan

Fiji

Marshall Islands

Fig. 1. The Republic of the Marshall Islands in the central P

have questioned the existing paradigm that an increase in sea level isubiquitously accompanied by the landward displacement of thebeach and island erosion, and indicate a degree of complexity of atollshorelines not widely recognized.

The physical response of atoll island shorelines to sea level rise ispoorly understood and, when considered relative to the effort appliedto monitoring and modeling, sea level rise has received little researcheffort. Despite the widespread acknowledgment of atoll islandvulnerability, it is only recently that noticeable research attention hasbeen placed on documenting recent changes on atoll island shorelines(Ford, 2011; Webb & Kench, 2010). However, these early studies havebeen somewhat limited with respect to temporal resolution and spatialextent of analysis. This study presents an analysis of shoreline changeusing multiple sources of historic and modern imagery of Wotje Atollin the Marshall Islands over a 60+ year period associated with localsea level rise. Shorelines from up to eight time periods are analyzed,the most temporally rich assessment of atoll island shoreline changepresented to date.

2. Study site

The Republic of the Marshall Islands is comprised of 29 atolls and 5mid-ocean platform islands located along two largely parallel islandchains, the Ratak in the east and Ralik in the west (Fig. 1). The islandsextend from 4°34′ to 14°43′N and 160°48′ to 172°10′E. The populationof Marshall Islands recorded during the 2011 census was 53,158, withapproximately 74% residing on Majuro and Kwajalein atolls (EPPSO,2012). Wotje Atoll is located at 9°25′N 170°04′E and is elongated inshape with maximum north–south and east–west dimensions of~20 km and ~48 km respectively (Fig. 2). 77 islands are perched onthe atoll rim with a combined area of 9.15 km2 and 96 km of shorelinein 2006. In 2011 the population of Wotje Atoll was 859 (EPPSO, 2012).The majority of structures are on the island of Wotje in the east of theatoll, suggesting the bulk of the population is located on the largest

173°0'E171°0'E169°0'E°0'E

Wotje Atoll

Majuro Atoll

acific Ocean, showing Wotje Atoll at 9°25′N 170°04′E.

170°10'E170°0'E169°50'E

9°30'N

9°20'N

48 WotjeLagoon

1 2 3 4 56

78

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49505152535455

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5 km

47

65

66

6768

6970

71

Fig. 2. The islands of Wotje Atoll.

132 M. Ford / Remote Sensing of Environment 135 (2013) 130–140

island. Most islands show no sign of permanent habitation, althoughorganized coconut planting suggests a degree of temporary occupation.

2.1. Local sea level

Two tide gauges are currently operational in the Marshall Islands:at Uliga dock on Majuro atoll (~290 km SSE) and at the United StatesArmy base on Kwajalein Atoll (~270 km WSW). The Majuro tidegauge record is comprised of two separate records collected by theUniversity of Hawaii Sea Level Centre (UHSLC) from 1968 to 1999and by the Australian National Tidal Facility from 1993 to present.The Kwajalein record (Fig. 3) is near-continuous from 1946 to presentfrom which Becker et al. (2012) report a sea level rise rate of 2.2 ±0.3 mm/yr. Sea level within the Marshall Islands is highly sensitiveto the El Niño Southern Oscillation (ENSO), with low sea levels duringthe El Niño conditions and high during La Niña events (Chowdhury etal., 2007).

3. Data and methods

3.1. Image acquisition and preparation

Atolls are isolated landforms, mostly located in mid-ocean settingsand in many cases part of less-developed countries. As a result, thereis a general paucity of historic vertical aerial photos available for mostPacific atolls. However, due to strategic military role manyMicronesian

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1946 1951 1956 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011

Sea

leve

l (m

)

Year

Fig. 3. Monthly average sea level recorded at Kwajalein Atoll between June 1946 andJune 2012. Monthly averages are calculated from fast delivery daily average sea levelobtained from the University of Hawaii Sea Level Centre.

atolls played during World War II there are noteworthy collections ofvertical aerial photographs of many atolls from this period. For muchof the Marshall Islands low elevation vertical aerial photographs werecaptured at various stages of World War II. Three aerial photo surveyswere conducted in the Marshall Islands during the 1970s, althoughonly imagery from 1976 to 1978 surveys was available for Wotje. Noaerial photographs or high resolution imagery of Wotje was foundbetween 1978 and 2004.

Vertical aerial photographs from 1945, 1976 and 1978 were usedin this study along with high resolution satellite imagery from 2004,2005/2006, 2010 and 2012 (Table 1). The 1945 and 1978 aerialphotos cover most of the atoll, while 1976 aerial photographs coveronly Wotje Island and six adjacent islands. Due to the size andshape of Wotje Atoll it is not possible for high resolution satellitesensors to capture the entire atoll in a single pass. As a result, the2006 QB image is a mosaic of images captured at five differenttimes between 08/08/2005 and 05/13/2006. Similarly, three scenesfrom the 2010 WV2 mosaic are used, captured between 01/13/2010and 09/17/2010. The remaining satellite images used are singlepasses of Wotje Island and the eastern rim of the atoll. The spatial ex-tents of aerial photographs and satellite imagery used in this studyare outlined in Fig. 4.

In most cases, atoll islands have a paucity of anthropogenic featuressuitable for ground control points, rendering georeferencing of imagesproblematic. However, a range of natural features, such as lithifiedconglomerate deposits and beach rock can be used for control pointsin the absence of anthropogenic features. On Wotje Island a numberof pre-WWII features, including distinctive docks and sea walls areclearly identifiable in many images. Images were georeferenced inArcGIS using control points extracted from the 2006 image. The num-ber of control points used for each image ranged between 10 and 47.

Table 1Characteristics of aerial photographs and satellite imagery used in this study.

Date Image type Pixel size (m) Scale

03/29/1945 B/W aerial 1:500001/05/1976 B/W aerial 1:800009/04/1978–09/06/1978 Color aerial 1:20,00008/05/2004 IKONOS 0.808/08/2005–05/13/2006 QuickBird 0.601/13/2010–09/17/2010 WorldView-2 0.502/12/2010 GeoEye-1 0.504/29/2012 WorldView-2 0.5

08/08/200502/12/2006

05/13/2006

04/07/200604/12/2006

01/13/201008/15/201009/17/2010

03/29/194501/04/197609/05/1978*08/05/2004

2005/2006 QB mosaic

2010 WV2 mosaic

12/02/201004/29/2012

Date

Fig. 4. Spatial extent of imagery used to interpret shoreline positions between 1945 and 2012. Note: for most islands imagery covers the complete island with the exception of smallareas where cloud cover prevents shoreline interpretation.

133M. Ford / Remote Sensing of Environment 135 (2013) 130–140

Images were transformed using a 2nd order polynomial with RMSerrors ranging between 0.24 and 1.89 m.

3.2. Shoreline interpretation

Typically shoreline change studies have utilized a uniform verticallevel, such as low or high water level as a proxy shoreline (Anders &Byrnes, 1991; Dolan et al., 1980; Romine et al., 2009). Previous stud-ies of shoreline change on atoll islands have used the edge of vegeta-tion as a proxy for the seaward island boundary (Ford, 2011; Webb &Kench, 2010). This study adopts the edge of vegetation as a shorelineproxy primarily due to the relative ease of identification within allsources of imagery, the short-term stability of the feature and the im-portance of vegetation as a boundary for island residents. Vegetationlines were manually digitized by a single operator using ArcGIS 10.0.A consistent scale and series of procedures regarding the definition ofthe vegetation boundary were enforced to maintain consistency.

Due to the need to produce statistically defendable shorelinechange rates considerable attention has been given to understandingthe uncertainties associated with interpreting and digitizing remotelysensed imagery (Thieler & Danforth, 1994). Romine et al. (2009)identified seven potential sources of uncertainty in the position ofthe shoreline interpreted from aerial photographs and survey maps,five of which are appropriate for aerial photographs, being: pixelerror, rectification error, digitization error, seasonal error and tidalfluctuation error. Ford (2011) incorporated three of these sources of

Table 2Shoreline uncertainties on Wotje Atoll, Marshall Islands.

Image Pixel size(m)

Georeferencing(m)

Interpretation(m)

Total shoreline error(Te) (m)

1945 B/W aerial 0.5 0.24–0.98 2.17 2.24–2.431976 B/W aerial 0.5 1.89 1.42 2.421978 color aerial 0.8 0.48–1.63 2.34 2.52–2.962004 IKONOS 0.8 0.7 1.6 1.922006 QuickBird 0.6 0 1.21 1.352010WordView-2

0.5 0.79 1.21 1.53

2010 GeoEye-1 0.5 0.43 1.21 1.382012WorldView-2

0.5 0.87 1.21 1.57

uncertainty when calculating the positional uncertainty in edge ofvegetation on Majuro Atoll, being: rectification, pixel and digitizingerrors. Digitizing error was calculated as the standard deviation ofshoreline position from repeated digitization of the same section ofcoast by a single operator. This study adopts the same approach asFord (2011) with total shoreline error (Te) calculated as the rootsum of all shoreline position errors and ranged between 1.35 and2.96 m (Table 2).

The Digital Shoreline Analysis System (DSAS) (Thieler et al., 2009)is the predominant analytical tool used to analyze planform changesof shoreline position, and is run as an extension within ArcGIS. Toanalyze change, DSAS casts a number of transects perpendicularfrom a user-created baseline and records the position of the intersectbetween the transect and each shoreline (Fig. 5). Several statistics aregenerated automatically by DSAS, including the Shoreline ChangeEnvelope (SCE), Net Shoreline Movement (NSM) as well as regressionas rates calculated using the intercepts of shorelines and transects.Transects were cast at a 10 m interval along the baseline. Some smallerislands with complex shoreline shapes were considered inappropriatefor analysis and were excluded from analysis. A confidence interval of2σ (95.5%) was applied when calculating regression based shorelinechange rates.

4. Results

4.1. Atoll-wide Net Shoreline Movement 1945–2010

Themajority of islands onWotje Atoll have shorelines digitized fora minimum of four time periods, with near complete coverage from1945, 1978, 2005/2006 and 2010 (Fig. 4). Net Shoreline Movementwas calculated for atoll-scale analysis between the earliest and mostrecent complete shorelines. The commencement date for this analysisis consistent across the entire atoll (03/29/1945). However, the endpoint spans the acquisition dates of the constituent images compris-ing the 2010 WV2 mosaic. More recent 2010 and 2012 shorelineswere excluded from NSM analysis to ensure end points covered thegreatest proportion of the atoll over as short a timeframe as possible.Shorelines from 52 islands which had 1945 and 2010 shorelines wereutilized for this analysis.

Several islands show noteworthy movement of the shoreline, withareas of pronounced accretion and erosion evident (Fig. 6). However,

03/29/194501/04/197609/04/197808/05/200402/12/200601/13/201012/02/201004/29/2012

Baseline

Transects

Intersects

Shoreline Change Envelope

Net Shoreline Movement

Date

Image (c) DigitalGlobe, Inc. All Rights Reserved

Fig. 5. Example of key analytical outputs of the Digital Shoreline Analysis System (DSAS) employed to undertake change analysis of vector shoreline positions.

134 M. Ford / Remote Sensing of Environment 135 (2013) 130–140

despite some sections of shoreline being highly active, 70% of tran-sects had NSM values of ±5 m (Fig. 7A). The net result of the shore-line change over the 1945 to 2010 period was accretion, with anaverage NSM of 1.74 m. The End Point Rate (EPR) is calculated withinDSAS as the NSM divided by the time span between the earliestshoreline and most recent shoreline, providing an annualized rate ofchange with confidence intervals calculated as:

ECI ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiUncy Að Þ2 þ Uncy Bð Þ2

q

Date A−Date Bð1Þ

where

Uncy A the uncertainty value for shoreline A,Uncy B the uncertainty value for shoreline B,Date A the date of shoreline A,Date B the date of shoreline B.

EPR values range between −0.72 ± 0.043 m/yr and 0.62 ±0.044 m/yr (Fig. 7B). For shoreline change to be confidently detectedboth the upper and lower confidence intervals must be positive (accre-tion) or negative (erosion). Of the 6985 transects which intersectedboth 1945 and 2010 shorelines 35.55% were characterized by statisti-cally significant accretion and 15.42% by statistically significant erosion.Of the 52 islands analyzed 36 had positive average NSM values, withthe remaining 16 having negative NSM values (Table 3).

The net result of shoreline change on islands can be expressed interms of the change to island area. Changes in planform area havebeen used as a measure to summarize the net change to individualisland land mass (Ford, 2011; Webb & Kench, 2010). In heavily modi-fied settings, like the coastal areas of urbanized atolls, planform areais a powerful descriptor of island change due to the discrete, stepwisechanges to the shoreline as a result of coastal reclamation and shorelinearmoring. However, as a statistical measure, normalized changes ofplanform area are a function of island size and shape, making compar-isons of rates of change between islands difficult. Despite potentialshortcomings, island area is easily calculated, accurate and providesan aggregatemeasure complementary to NSM and EPRwhen describing

the net outcome of shoreline dynamics on islands. Intersecting cov-erage of both 1945 aerial photographs and 2010 WV2 exists for 52individual islands. Of these 52 islands, 3 islands had small breaks inthe shoreline due to cloud cover. Of the remaining 49 islands, 32islands increased in size over the 1945–2010 period,while the remaining17 islands decreased in planform area. The total planform area of the49 islands increased from 4.64 to 4.73 km2 over the study period(Table 3).

4.2. High frequency sampling 1945–2012

Islands along the eastern rim of Wotje Atoll have shorelines span-ning up to eight time periods (Fig. 4). The high temporal resolution ofsampling along the eastern aspect of the atoll allows for calculation ofstatistically robust measures of shoreline change. Shoreline changerates derived from linear regression of shoreline positions are widelyused in shoreline change studies (Crowell et al., 1993; Genz et al.,2007). DSAS calculates Linear Regression (LR) and Weighted LinearRegression rates (WLR) along each transect. The LR rate for each tran-sect is calculated by fitting the least-squared regression line to allshoreline intersects along the transect (Thieler et al., 2009). TheWLR rate places more emphasis on shorelines with lower positionaluncertainty values when determining the best fit line (Thieler et al.,2009).

WLR rates were calculated along 2527 transects which intersectboth the 1945 and 2012 shorelines and a minimum of four additionalshorelines (Fig. 8). WLR rates ranged between −0.65 and 0.48 m/yr,with an average rate of 0.00 m/yr. At 2σ of confidence 9.93% of tran-sects exhibited an accretionary trend, while 7.32% showed erosion.The average NSM along these transects was 0.01 m. Results indicatethat despite the high degree of variability documented along somestretches of shoreline; there was little net change to the shorelinesalong the subset of islands sampled in depth.

4.3. Recent shoreline changes 2004–2012

Since the launch of the IKONOSmission and the subsequent devel-opment of sub-meter sensors it is possible to remotely assess the

Fig. 6. Islands larger than one hectare and characterized by the greatest positive Net Shoreline Movement (NSM) (A&B) and the greatest negative NSM (C&D) between 1945 and2010. Note: the red and green lines represent the position of the 1945 and 2010 WV2 shorelines respectively.

135M. Ford / Remote Sensing of Environment 135 (2013) 130–140

short and medium-term shoreline dynamics on atolls. This studyutilized high-resolution imagery captured between 2004 and 2012,with several islands on the eastern rim captured five times overthis period. Shoreline change analysis was undertaken along 2140transects which intersected all five shorelines between 2004 and2012. The average NSM along the 2140 transects was −1.61 m. WLRrates ranged between −8.06 and 3.85 m/yr, with an average rate of−0.27 m/yr (Fig. 9). Results of WLR regression showed that between2004 and 2012 10.23% and 0.89% of transects indicated statisticallysignificant erosion and accretion at 2σ respectively. Seventeen of the18 islands with both 2004 and 2012 shorelines had a net loss of landarea over the period.

5. Discussion

5.1. Shoreline change on Wotje Atoll

This study presented the most temporally-rich assessment of atollisland shoreline changes undertaken to date. Imagery captured over a

67-year period reveals considerable spatial and temporal variabilityin the position of island shorelines. More shorelines have been shownto have accreted than eroded over a period of documented increaseof sea level. The observations of shoreline accretion documented inthis study are broadly consistent with recent assessments of shorelinechange on atolls (Webb & Kench, 2010). Importantly, the relativelyhigh frequency of sampling employed in this study provides uniqueand powerful insights into the behavior of atoll shorelines.

Atoll-wide NSM analysis revealed the resultant movement of theshoreline was accretion on 36 of the 52 islands (Table 3). A total of35.55% of transects exhibited statistically significant accretion com-pared to 15.42% showing erosion (Section 4.1). The net result of shore-line changes over this period was an average NSM of +1.74 m. As aresult, there was an increase of island landmass over this period. Simi-larly, the detailed sampling along the eastern rim of the atoll suggestedthat over the 1945–2012 period accretion outweighed erosion, but thedifferences were small (9.93% accretion vs. 7.32% erosion). Of note,analysis of recent (post 2004) satellite imagery indicated that therehas been a potential shift toward an erosive state on 17 out of 18 islands.

0

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.20

End Point Rate (m/yr)

Fre

quen

cy (

%)

Fre

quen

cy (

%)

Net Shoreline Movement (m)

>20

>.20

A

B

Fig. 7. Net Shoreline Movement (NSM) and End Point Rate (EPR) calculated along 6985transects which intersect both 1945 and 2010 WV2 shorelines.

Table 3Proportions of Wotje shorelines exhibiting erosion/accretion along 52 islands analyzedfrom 1945 to 2010 (Section 4.1). Note: three islands in 1945 have breaks in the shore-line as a result of cloud cover and as a consequence have no area calculated. See Fig. 3for location of islands.

IslandID

Transectsexhibiting stat.sig. erosion (%)

Transectsexhibiting stat.sig. accretion (%)

AverageNSM(m)

Area1945(ha)

Area 2010WV2(ha)

Areachange(ha)

2 17.53 25.77 0.34 16.92 16.99 0.075 18.66 27.36 0.81 51.0713 28.26 15.22 −0.77 1.66 1.63 −0.0314 25.88 14.12 −1.61 3.08 2.91 −0.1715 3.08 29.23 2.21 9.91 10.20 0.3016 25.71 15.24 −1.28 7.81 7.63 −0.1817 12.50 58.33 4.19 1.74 1.98 0.2418 18.18 57.14 6.90 4.50 5.13 0.6219 4.05 48.83 4.08 84.02 86.15 2.1420 31.78 30.84 −1.98 9.22 8.96 −0.2621 16.67 20.00 0.47 6.08 6.10 0.0222 13.95 23.26 −0.22 8.47 8.47 0.0024 2.78 12.04 0.27 6.53 6.55 0.0325 14.61 20.22 0.47 17.77 17.87 0.1026 11.11 35.19 1.52 2.03 2.11 0.0827 0.00 68.42 4.89 0.19 0.29 0.1028 21.95 28.05 0.06 2.57 2.57 0.0029 18.68 38.46 −0.33 4.01 3.96 −0.0530 22.22 9.66 −1.61 22.47 22.13 −0.3431 22.62 17.86 −0.17 5.44 5.43 −0.0132 14.29 21.43 0.94 1.39 1.42 0.0333 5.26 42.11 1.75 1.17 1.24 0.0736 23.76 12.87 −1.36 5.65 5.47 −0.1937 17.90 40.12 0.62 17.14 17.28 0.1438 20.59 25.49 0.58 7.90 7.98 0.0739 20.87 12.17 −1.68 10.27 10.03 −0.2340 19.35 12.90 −0.41 2.12 2.09 −0.0341 22.54 25.35 −0.17 3.61 3.60 −0.0242 20.51 32.05 0.07 2.68 2.70 0.0243 8.75 14.17 0.01 23.46 23.46 0.0044 16.00 33.00 −0.43 4.56 4.42 −0.1445 0.00 47.06 3.34 0.21 0.28 0.0748 24.34 41.20 3.06 243.5649 5.41 24.32 −0.14 0.82 0.81 −0.0150 15.63 23.44 0.07 3.35 3.33 −0.0251 4.55 40.91 2.82 10.94 11.30 0.3652 5.66 62.26 3.88 6.56 6.95 0.3958 0.00 51.85 5.72 0.41 0.64 0.2359 6.41 52.31 4.41 42.2861 1.36 73.92 4.85 49.97 52.12 2.1562 0.74 75.00 5.63 8.18 9.51 1.3363 39.47 17.11 −1.85 2.44 2.29 −0.1565 31.18 21.51 −0.48 3.52 3.44 −0.0766 22.75 34.92 1.83 16.62 17.01 0.3967 22.50 10.00 0.08 0.88 0.89 0.0168 3.77 26.42 2.72 2.11 2.28 0.1769 0.00 64.44 4.73 1.01 1.23 0.2370 35.15 35.15 5.60 17.41 18.36 0.9571 12.50 59.09 2.77 4.71 4.96 0.2572 26.90 32.75 0.33 16.70 16.76 0.0673 10.57 30.49 1.84 23.09 23.54 0.4574 0.00 41.38 2.31 0.63 0.71 0.07

136 M. Ford / Remote Sensing of Environment 135 (2013) 130–140

5.2. Modes of shoreline change

To further assess the spatial controls on shoreline change withinWotje the analysis of 1945 and 2010 shorelines and transects(Section 4.1) was divided into the following shoreline units: lagoon-facing, ocean-facing, spits and channel-facing shorelines on islands>5 ha and small islands b5 ha (Fig. 10). Due to the limited lengthof shorelines on small islands they were treated as a single shorelineunit and not further classified into sub-units. Shoreline changecharacteristics within each shoreline unit are summarized in Table 4.

5.2.1. Lagoon and oceanside shoreline changeAnalysis of shoreline change between 1945 and 2010 showed

statistically significant accretion was detected along 47.53% of lagoon-facing transects, while 11.52% of transects exhibited statistically signif-icant erosion (Table 4). Shoreline change values along lagoon shore-lines ranged between −18.89 m and 35.21 m with an average NSMalong all lagoon shoreline transects of 3.38 m. Ocean-facing transectswere predominantly accretionary, with 30.02% exhibiting accretionand 17.28% erosion. Webb and Kench (2010) report accretion along70% of the lagoon shoreline and 30% accretion along the ocean shore-line across a number of islands. Shoreline behavior on Wotje sharessome similarities with those presented by Webb and Kench (2010),with the dominance of accretion along the lagoon shoreline apparent.However, while Webb and Kench (2010) noted pronounced erosionalong the ocean-facing shoreline, accretion was the dominant modeof change on ocean-facing shorelines detected in this study.

5.2.2. Spit dynamicsElongate spits have been shown in previous studies to be highly

dynamic, as sites where both noteworthy erosion and accretionhave been documented (Ford, 2011; Rankey, 2011; Webb & Kench,2010). WithinWotje Atoll spits at the ends of islands tend to fluctuatein position considerably (Fig. 11). Statistically significant accretionwas detected along 34.22% of spit transects, while 24.90% of transects

exhibited statistically significant erosion. Shorelines within spit unitshave the largest SCE, averaging 6.25 m. However, the NSM of 0.81 mfor elongate spits was the lowest of all units on >5 ha islands. Thelarge SCE and relatively small NSM indicate that spit shorelines arehighly dynamic. Noteworthy shifts in shoreline position weredetected along spit shores, where overall accretion exceeds erosion,but by smaller margins than seen within other shoreline units.

The high degree of shoreline dynamism shown in this study, partic-ularly within the elongate spit units and in the 2004–2012 shorelines,suggests significant care needs to be taken when interpreting shorelinechange on dynamic sections of atoll islands. Temporally-rich shorelinedatasets for atolls are rare. As a result, it is not possible to establishwhether episodic shifts have driven shoreline change or the phase of

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Fig. 8. Shoreline change rates calculated using Weighted Liner Regression (WLR) of shoreline-transect intersects recorded over the 1945–2012 period between islands 25–49.

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38

31

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Fig. 9. Shoreline change rates calculated using Weighted Liner Regression (WLR) of shoreline-transect intersects recorded over the 2004–2012 period between islands 31–49.

137M. Ford / Remote Sensing of Environment 135 (2013) 130–140

OceanChannelSpitLagoon

Shoreline unit

Fig. 10. Example of shoreline units derived from geomorphic classification of island shorelines.

138 M. Ford / Remote Sensing of Environment 135 (2013) 130–140

potential shoreline oscillations from which historic shorelines are de-rived. This is particularly apparent if low sampling frequency recordswere to be used to assess change along spits, such as Wotje Island(Fig. 11A). The 1976–1978 and 2004–2006 images reveal periods ofpronounced shoreline position shifts along the northern spit at WotjeIsland (Fig. 11A), including periods of both erosion and accretion,over relatively short time periods.

5.2.3. Small islandsIslands smaller than five hectares were considered a distinct

shoreline unit. The smaller islands are often circular or elongate ren-dering the classification of the shoreline into the units used on largerislands problematic. Small islands had the lowest average NSM valueof any shoreline unit (0.74 m), with 32.23% transects exhibitingstatistically significant accretion and 16.54% exhibiting erosion.

5.2.4. Recent shoreline changesAnalysis of a subset of islands which have five shorelines derived

from imagery captured between 2004 and 2012 suggests a possibleerosion shift in recent years (Fig. 9). Erosion is the most prevalentmode of shoreline change over the 2004–2012 period, with 75.89%of transects have negative WLR rates. However, erosion can only beconfidently detected on 10.23% of transects. Erosion exceeds accre-tion within all shoreline units, and is particularly apparent within

Table 4Shoreline change 1945–2010 within classified shoreline units.

Shorelineunit

Erosion(%)

Accretion(%)

No significantchange (%)

AverageSCE (m)

AverageNSM (m)

NSM Std.Dev. (m)

Ocean 17.28 30.02 52.69 4.00 1.27 5.71Lagoon 11.52 47.53 40.96 5.38 3.38 6.74Channel 9.75 30.39 59.85 3.27 1.70 4.48Spit 24.90 34.22 40.88 6.25 0.81 9.41Smallislands

16.54 32.23 51.24 3.84 0.74 5.59

the spit unit where 26.24% of transects showed statistically significanterosion compared with 1.42% exhibiting accretion. The cumulativeshoreline movement between sample periods along transects whichintersect 1945–2012 shorelines is presented in Fig. 12. The 1976shoreline is excluded from the analysis due to the limited coverageof 1976 imagery. The cumulative displacement of the shoreline onthese 18 islands suggests a post-1945 period of accretion and stabilityhas been accompanied by a more recent period of erosion since 2004(Fig. 12).

Three possible explanations for the recent erosional shift areprovided, aspects of which have significant implications for the con-tinued remotely-sensed study of atoll island shoreline change. Firstly,the 2004–2012 erosional trends could be part of a transition towardisland erosion. A shift toward island erosion could be verified by con-tinued monitoring of a larger sample of islands in tandem withincreased sampling from 2004 to 2012 archival imagery. Secondly,the dynamics observed between 2004 and 2012 may be characteristicof the inherent natural shoreline dynamism, driven by episodic, sea-sonal or quasi-cyclical climatic processes such as ENSO, which drivessignificant sea level variability within the Marshall Islands. If shorelinechanges are reflective of a natural oscillation, then it suggests consider-able care is needed when interpreting multi-decadal change fromdatasets with low sampling frequencies.

Thirdly, results documenting recent erosion are a product of short-term records which can potentially favor the detection of erosion.Beach profile records often show the rapid loss of beach volumeassociated with the stripping of sand from the beach and subsequentdeposition within the nearshore (Thom & Hall, 1991). Relative to epi-sodic erosion the recovery of the beach is a gradual process wherethe beach goes through an accretionary phase as sediment is slowlyreturned to the beach from nearshore waters (Thom & Hall, 1991).Using the edge of vegetation as a shoreline proxy acts as a filter, withthe relative stability of vegetated areas less sensitive to episodicerosion than similarly exposed unconsolidated sand. This study hasdocumented relatively rapid seaward propagation of vegetation overrecent deposits (see Fig. 11 for example), suggesting that the difference

Fig. 11. Shoreline positions along spits showing a high degree of shoreline dynamism. Note: background image on panels A, C, D & E is the 2012 WV2 image, the background imageon panel B is the 2010 WV2 image.

139M. Ford / Remote Sensing of Environment 135 (2013) 130–140

between the rate at which vegetated shorelines accrete and the rate atwhich they may erode may be short relative to the time scale of theanalysis. However, despite the relative stability of the vegetationdampening the signal of episodic events the potential for erosion tobe more readily detected within short, as well as temporally-sparselonger term records of shoreline change is a potential source of bias.Quantifying this potential source of bias will become evident throughfurther collection of high-frequency multi-decadal shoreline records.

5.2.5. Implications for future island stabilityA potential outcome of continued sea level rise is the chronic

shoreline erosion, which may pose a serious threat to the habitabilityof atoll islands. However, the geomorphic response of atoll islandshorelines to increasing sea level is poorly understood and has notbeen widely documented. Discussions of atoll island vulnerabilityoften consider the islands as static, geomorphically inert deposits,which will inundate and erode as sea level rises (Dickinson, 2009;Yamano et al., 2007). However, Perry et al. (2011) question thenotion of SLR as the single control of island stability, suggesting theecological–geomorphological linkages within reef-island systems arecritical within sediment production and transport systems and there-fore island stability. Results of this study indicate that islands onWotje are geomorphically active under contemporary conditions,with periods of shoreline erosion and accretion seen over relativelyshort time periods. These results indicate that rather than being

00.20.40.60.8

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1945 1955 1965 1975 1985 1995 2005

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ulat

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e m

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Fig. 12. Cumulative displacement of shoreline positions within the east ofWotje between1945 and 2012. Note: only transects which intersected seven shorelines were consideredfor analysis.

geomorphically inert deposits, the islands are morphodynamically re-sponsive to a range of currently unresolved factors under contempo-rary conditions.

5.3. Outlook for remote sensing of atoll island erosion

The atolls of the Marshall Islands are sparsely distributed across anExclusive Economic Zone of over two million km2. With over 1000individual islands, the sheer size and scale makes remote monitoringthe only practical option to document shoreline changes on atollislands. Field-based monitoring is likely only feasible within theurbanized atolls, and even within these urban settings there has beena noteworthy lack of successful shoreline monitoring.

Shoreline change studies on atolls, including this study, have reliedon ad hoc acquisition of imagery with little evidence of a systematicapproach to image acquisition. The ad hoc nature of image acquisitionis largely a relic of the paucity of imagery available for most atolls. Withgrowing archives of high resolution imagery, and the proliferation ofsub-1 m resolution sensors, systematic monitoring is now possible.However, relative to sea level rise monitoring and modeling, thesystematic measurement of the shoreline change has received scantattention within atoll settings. Fortunately, any efforts to developremote shoreline change monitoring on atolls do not start with an ab-sence of data, as in the case of field-based methods. Many atolls haveimagery captured frequently dating back to the early stages of the21st century, with a significant number of atolls having rich archivesof imagery over a decade in length. Any organized attempt at remoteshoreline monitoring can utilize archival imagery and extend uponthis with structured sampling in the future. Likewise, high-frequencysatellite-derived datasets provide an opportunity to detect shorelineoscillations and short-term changes, providing higher degrees of confi-dence and interpretative power when assessing decadal scale shorelinechanges.

6. Conclusion

This study has provided the most temporally rich assessment ofshoreline change along atoll shores undertaken to date. Chronic ero-sion of atoll shorelines is considered a likely outcome of continuedand accelerating sea level rise. However, results have shown that island

140 M. Ford / Remote Sensing of Environment 135 (2013) 130–140

shorelines onWotje Atoll have largely accreted since 1945, a period co-inciding with increasing sea level. With the growing constellation ofhigh resolution satellite-based sensors the ability to resolve the com-plex shoreline dynamics has increased. Further research is needed tobetter resolve the signatures of episodic events such as storms, andlocal sea level rise driven result of quasi-cyclical processes such asENSO and longer-term global sea level rise.

Acknowledgements

Thank you to Tony Kimmet of the US Department of AgricultureNatural Resources Conversation Service for providing the 2006Quickbird and 2010 WV2 data. Thank you to the GeoEye foundationfor the 2004 IKONOS and 2010 GeoEye-1 data. Thank you the AleleMuseum in Majuro for access to the 1978 aerial photos. This workwas funded by a University of Auckland Faculty Research Develop-ment Fund award.

References

Anders, F. J., & Byrnes, M. R. (1991). Accuracy of shoreline change rates as determinedfrom maps and aerial photographs. Shore and Beach, 59, 17–26.

Barnett, J., & Adger, W. N. (2003). Climate dangers and atoll countries. Climatic Change,61, 321–337.

Becker, M., Meyssignac, B., Letetrel, C., Llovel, W., Cazenave, A., & Delcroix, T. (2012).Sea level variations at tropical Pacific islands since 1950. Global and PlanetaryChange, 80, 85–98.

Chowdhury, M. R., Chu, P. S., & Schroeder, T. (2007). ENSO and seasonal sea-levelvariability—A diagnostic discussion for the US-affiliated Pacific Islands. Theoreticaland Applied Climatology, 88, 213–224.

Church, J. A., White, N. J., Coleman, R., Lambeck, K., & Mitrovica, J. X. (2004). Estimatesof the regional distribution of sea level rise over the 1950–2000 period. Journal ofClimate, 17, 2609–2625.

Church, J. A., White, N. J., & Hunter, J. R. (2006). Sea-level rise at tropical Pacific andIndian Ocean islands. Global and Planetary Change, 53, 155–168.

Collen, J., Garton, D., & Gardner, J. (2009). Shoreline changes and sediment redistributionat Palmyra Atoll (Equatorial Pacific Ocean): 1874–present. Journal of Coastal Research,711–722.

Crowell, M., Leatherman, S. P., & Buckley, M. K. (1991). Historical shoreline change:Error analysis and mapping accuracy. Journal of Coastal Research, 839–852.

Crowell, M., Leatherman, S. P., & Buckley, M. K. (1993). Shoreline change rate analysis:Long term versus short term data. Shore and Beach, 61, 13–20.

Dickinson, W. R. (2009). Pacific atoll living: How long already and until when. GSAToday, 19, 4–10.

Dolan, R., Fenster, M. S., & Holme, S. J. (1991). Temporal analysis of shoreline recessionand accretion. Journal of Coastal Research, 723–744.

Dolan, R., Hayden, B. P., May, P., & May, S. (1980). The reliability of shoreline changemeasurements from aerial photographs. Shore and Beach, 48, 22–29.

Economic Policy, Planning and Statistics Office (EPPSO), Government of the Republic ofthe Marshall Islands (2012). The RMI 2011 census of population and housing summaryand highlights only.

Fletcher, C., Rooney, J., Barbee, M., Lim, S. C., & Richmond, B. (2003). Mapping shorelinechange using digital orthophotogrammetry on Maui, Hawaii. Journal of CoastalResearch, 106–124.

Ford, M. (2011). Shoreline changes on an urban atoll in the Central Pacific Ocean:Majuro Atoll, Marshall Islands. Journal of Coastal Research, 28, 11–22.

Genz, A. S., Fletcher, C. H., Dunn, R. A., Frazer, L. N., & Rooney, J. J. (2007). The predictiveaccuracy of shoreline change rate methods and alongshore beach variation onMaui, Hawaii. Journal of Coastal Research, 87–105.

Kayanne, H., Yasukochi, T., Yamaguchi, T., Yamano, H., & Yoneda, M. (2011). Rapidsettlement of Majuro Atoll, central Pacific, following its emergence at 2000 yearsCalBP. Geophysical Research Letters, 38 L20405.

Kench, P. S., & Brander, R. W. (2006). Response of reef island shorelines to seasonalclimate oscillations: South Maalhosmadulu atoll, Maldives. Journal of GeophysicalResearch, 111 F01001.

Kench, P., & Harvey, N. (2003). Monitoring coastal change in Pacific atoll nations: ThePacific Atoll Costal Monitoring and, Analysis Network (PACMAN).

Kench, P., McLean, R., & Nichol, S. (2005). New model of reef-island evolution:Maldives, Indian Ocean. Geology, 33, 145–148.

Mimura, N. (1999). Vulnerability of island countries in the South Pacific to sea level riseand climate change. Climate Research, 12, 137–143.

Perry, C. T., Kench, P. S., Smithers, S. G., Riegl, B., Yamano, H., & O'Leary, M. J. (2011).Implications of reef ecosystem change for the stability and maintenance of coralreef islands. Global Change Biology, 17, 3679–3696.

Pfeffer, W. T., Harper, J., & O'Neel, S. (2008). Kinematic constraints on glacier contribu-tions to 21st-century sea-level rise. Science, 321, 1340–1343.

Rahmstorf, S. (2007). A semi-empirical approach to projecting future sea-level rise.Science, 315, 368–370.

Rankey, E. C. (2011). Nature and stability of atoll island shorelines: Gilbert Island chain,Kiribati, equatorial Pacific. Sedimentology, 58, 1831–1859.

Romine, B. M., Fletcher, C. H., Frazer, L. N., Genz, A. S., Barbee, M. M., & Lim, S. C. (2009).Historical shoreline change, southeast Oahu, Hawaii; applying polynomial modelsto calculate shoreline change rates. Journal of Coastal Research, 1236–1253.

Roy, P., & Connell, J. (1991). Climatic change and the future of atoll states. Journal ofCoastal Research, 1057–1075.

Ruggiero, P., Kaminsky, G. M., Gelfenbaum, G., & Voigt, B. (2005). Seasonal tointerannual morphodynamics along a high-energy dissipative littoral cell. Journalof Coastal Research, 553–578.

Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., & Averyt, K. B., et al. (Eds.).(2007). The physical science basis. Contribution of working group I to the FourthAssessment. Report of the Intergovernmental Panel on Climate Change. Cambridge,United Kingdom and New York, NY, USA.

Thieler, E. R., & Danforth, W. W. (1994). Historical shoreline mapping (II): Applicationof the digital shoreline mapping and analysis systems (DSMS/DSAS) to shorelinechange mapping in Puerto Rico. Journal of Coastal Research, 600–620.

Thieler, E. R., Himmelstoss, E. A., Zichichi, J. L., & Ergul, A. (2009). The Digital ShorelineAnalysis System (DSAS) Version 4.0— An ArcGIS extension for calculating shorelinechange.

Thom, B., & Hall, W. (1991). Behaviour of beach profiles during accretion and erosiondominated periods. Earth Surface Processes and Landforms, 16, 113–127.

Webb, A. P., & Kench, P. S. (2010). The dynamic response of reef islands to sea-levelrise: Evidence from multi-decadal analysis of island change in the Central Pacific.Global and Planetary Change, 72, 234–246.

Weisler, M. I. (2001). Life on the edge: Prehistoric settlement and economy on UtrōkAtoll, northern Marshall Islands. Archaeology in Oceania, 109–133.

Weisler, M. I., Yamano, H., & Hua, Q. (2012). A multidisciplinary approach for datinghuman colonization of Pacific atolls. The Journal of Island and Coastal Archaeology,7, 102–125.

Woodroffe, C., & Morrison, R. (2001). Reef-island accretion and soil development onMakin, Kiribati, central Pacific. Catena, 44, 245–261.

Xue, C. (2001). Coastal erosion andmanagement of Majuro atoll, Marshall Islands. Journalof Coastal Research, 909–918.

Yamano, H., Kayanne, H., Yamaguchi, T., Kuwahara, Y., Yokoki, H., Shimazaki, H., et al.(2007). Atoll island vulnerability to flooding and inundation revealed by historicalreconstruction: Fongafale Islet, Funafuti Atoll, Tuvalu. Global and Planetary Change,57, 407–416.