long-term cli¡ retreat and erosion hotspots along the ...joe/fotogrammetria/partvonal.pdf · santa...

Long-term cli¡ retreat and erosion hotspots along the centralshores of the Monterey Bay National Marine Sanctuary

Laura J. Moore *, Gary B. GriggsEarth Science Department and Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA 95064, USA

Received 19 November 1999; accepted 16 July 2001

Abstract

Quantification of cliff retreat rates for the southern half of Santa Cruz County, CA, USA, located within theMonterey Bay National Marine Sanctuary, using the softcopy/geographic information system (GIS) methodologyresults in average cliff retreat rates of 7^15 cm/yr between 1953 and 1994. The coastal dunes at the southern end ofSanta Cruz County migrate seaward and landward through time and display net accretion between 1953 and 1994,which is partially due to development. In addition, three critically eroding segments of coastline with high averageerosion rates ranging from 20 to 63 cm/yr are identified as erosion `hotspots'. These locations include: Opal Cliffs,Depot Hill and Manresa. Although cliff retreat is episodic, spatially variable at the scale of meters, and the factorsaffecting cliff retreat vary along the Santa Cruz County coastline, there is a compensation between factors affectingretreat such that over the long-term the coastline maintains a relatively smooth configuration. The softcopy/GISmethodology significantly reduces errors inherent in the calculation of retreat rates in high-relief areas (e.g. erosion ratesgenerated in this study are generally correct to within 10 cm) by removing errors due to relief displacement. Althoughthe resulting root mean squared error for erosion rates is relatively small, simple projections of past erosion rates areinadequate to provide predictions of future cliff position. Improved predictions can be made for individual coastalsegments by using a mean erosion rate and the standard deviation as guides to future cliff behavior in combination withan understanding of processes acting along the coastal segments in question. This methodology can be applied on anyhigh-relief coast where retreat rates can be measured. ß 2002 Elsevier Science B.V. All rights reserved.

Keywords: Coastal geomorphology; Cli¡s; Erosion rates; Monterey Bay; Geologic hazards; Aerial photography; Digital photo-grammetry

1. Introduction

As coastal population continues to increase inthe USA, the con£ict between accelerating ocean-

front development and the inherent geological in-stability of the shoreline has become a dilemma ofincreasing magnitude. All coastal areas are dy-namic in nature with changes occurring overmany time scales. However, when this changemanifests itself as a landward movement of theshoreline occurring on a human time scale, quan-ti¢cation of erosion rates becomes important. Forexample, coastal erosion rates are used to deter-mine safe construction setbacks, to settle property

0025-3227 / 02 / $ ^ see front matter ß 2002 Elsevier Science B.V. All rights reserved.PII: S 0 0 2 5 - 3 2 2 7 ( 0 1 ) 0 0 2 7 1 - 7

* Corresponding author. Present address: U.S.G.S. Centerfor Coastal Geology, 600 4th Street South, St. Petersburg, FL33704, USA. Tel. : +1-727-803-8747 x3123; Fax: +1-727-803-2032.

E-mail address: [email protected] (L.J. Moore).

MARGO 3030 28-3-02 Cyaan Magenta Geel Zwart

Marine Geology 181 (2002) 265^283

www.elsevier.com/locate/margeo

disputes and to inform land-use changes. As inmany coastal regions throughout the world, nu-merous homes, roads and businesses along thecoastline of the Monterey Bay National MarineSanctuary (MBNMS) are threatened by coastalretreat. This study focuses on Santa Cruz County(Fig. 1) where consolidated mudstone and sand-stone/siltstone cli¡s fronted by narrow beaches,poorly consolidated blu¡s of Pleistocene sandfronted by wide beaches, and a short stretch ofactive coastal dunes characterize the coastline.

Strictly de¢ned, the shoreline is the boundarybetween land and sea. Taken literally this bound-ary is best represented by the high water line (seenas the boundary between wet and dry sand) that£uctuates signi¢cantly on short time scales (daysto years) due to the tidal cycle and seasonalchanges. When measuring shoreline erosion rates,however, the large, short-term £uctuations in thehigh water line, or wet/dry boundary, may renderit unreliable for identi¢cation of long-term trends.On the East and Gulf Coasts, alternative proxiesinclude the vegetation line and features such as aberm crest (e.g. Morton and McKenna, 1999;Overton et al., 1999). In contrast, along rockycoastlines such as the majority of the Californiacoast, cli¡s are not only the dominant feature, buttheir seaward edge dictates where developmentcan and cannot occur. For these reasons, thetop of the cli¡ edge is the most reasonable proxyfor `shoreline' position along the West Coast (andother high-relief coastlines) except in limited areasdominated by coastal sand dunes where the veg-etation line is used as a proxy.

Both terrestrial (gullying, landsliding, ground-water seepage, seismic shaking) and marine(waves) processes are responsible for coastal ero-sion, or cli¡ retreat, in California (Griggs andSavoy, 1985). The process of retreat is episodicwith the majority of erosion taking place duringthe simultaneous occurrence of high tide andstorm waves. Thus, while we tend to utilize aver-age annual erosion rates to describe the erosionrisk at a particular location, the failure of cli¡stypically takes place in large increments duringstorm conditions.

Many factors a¡ect the susceptibility of a cli¡to failure. These factors, including lithology, the

presence of structural weaknesses, degree of waveexposure, and the presence/width of protectivebeach, vary signi¢cantly over short distancesalong the coast making erosion not only episodic,but spatially variable. As a result of the episodicnature of coastal erosion, and because the shore-line is in£uenced by both marine and terrestrialprocesses operating over a range of time scales,erosion rates determined using di¡erent proxiesfor shoreline position should not be directly com-pared. The same caution applies when comparingerosion rates determined using sets of photo-graphs that span di¡erent time periods.

Coastal erosion rates are often determined bydividing the di¡erence between recent shorelineposition (as seen on a recent aerial photographor map) and past shoreline position (as seen ona historical aerial photograph or map) by the timeperiod between the two end-points. The longer thetime period between measurements, the more rep-resentative resulting erosion rates are of long-termtrends. There are also variations on the end-pointmethod that involve taking additional measure-ments between the two end-points with the hopeof producing rates more representative of long-term trends (e.g. linear regression method or jack-ni¢ng; Dolan et al., 1991).

Unfortunately, regardless of the number ofmeasurements taken, there are many displace-ments and distortions inherent to aerial photog-raphy and historical maps that can lead to signi¢-cant errors in calculated erosion rates. Theseerrors are discussed at length in Slama (1980)and summarized with respect to shoreline map-ping in Moore (2000). Numerous shoreline map-ping methods have been developed over the last27 years, each in an attempt to improve uponprevious methods by reducing the potential forerror (Moore, 2000). These techniques rangefrom simple measurements made directly fromuncorrected aerial photographs (Point Measure-ments ; Sta¡ord, 1971) to precise measurementsmade from computer recti¢ed aerial orthophoto-graphs in digital format (softcopy/geographic in-formation system (GIS) methodology; Moore etal., 1999).

Recent advances in photogrammetry and car-tography (Greve, 1996) have made methodolo-


L.J. Moore, G.B. Griggs / Marine Geology 181 (2002) 265^283266

gies, such as the softcopy/GIS methodology,which involves the production of orthophoto-graphs, available to the coastal scientist (e.g.Moore et al., 1999 and Overton et al., 1999). Soft-copy photogrammetry, also known as digital pho-togrammetry or as `softcopy', automates the ac-tions of the traditional analytical stereoplotterand allows for nearly complete removal of distor-tions and displacements inherent in aerial photo-graphs. The product of the error removal processis a corrected image called an orthophotograph(Greve, 1996). Perhaps the most valuable contri-bution of softcopy photogrammetry to the pro-cess of coastal erosion hazard mapping is its ca-pacity to remove displacements due to relief.Relief displacement ranges from less than 1 mto many meters depending on the amount of re-lief, the feature's distance from the center of thephotograph, and photo scale. For example, a lo-cation on a 10-m cli¡, 7 cm from the center of a1:20 000 scale photograph, would be displaced 4.6m from its true ground position (Anders andByrnes, 1991). Thus, the capability to remove re-lief displacement is especially important in regionswhere high-relief cli¡s or dunes are the dominantcoastal features.

Erosion rates for the high-relief coast of SantaCruz County, CA, USA, were determined usingthe softcopy/GIS methodology as part of the Fed-

eral Emergency Management Agency's (FEMA)program to assess the feasibility and economicsof adding oceanfront property prone to erosionto the federal £ood insurance program (Mooreet al., 1999). Using these erosion rates, three lo-cations in Santa Cruz County are identi¢ed asundergoing signi¢cantly greater than averagerates of retreat. Presentation and discussion ofthese erosion `hotspots' along with a discussionof the application of softcopy photogrammetryin their identi¢cation is the purpose of this paper.

1.1. Study area

The 67 km of shoreline in Santa Cruz County(Fig. 1), located south of San Francisco, CA,USA, in the MBNMS, supports a mixture ofurbanized cli¡ top and oceanfront developmentas well as agricultural and open-space uses. Thecentral and southern portions of Santa CruzCounty, located within the MBNMS, are the fo-cus of this investigation and are described below.

The cli¡s of central Santa Cruz County consistof sandstone and siltstone of the Pliocene (1.6^5.3Ma) Purisima Formation. The cli¡s are activelyretreating and signi¢cant damage to privatehomes, apartments, parks and public infrastruc-ture has occurred over the last two decades. Inthe southern portion of the county (Manresa

Fig. 1. Study area map of Santa Cruz County, CA, USA.


L.J. Moore, G.B. Griggs / Marine Geology 181 (2002) 265^283 267

and southward) the cli¡s are composed of thePleistocene dune deposits of the Aromas Forma-tion. Wide sandy beaches front these cli¡s suchthat erosion occurs primarily due to terrestrialprocesses and infrequent seismic shaking (Plantand Griggs, 1990). The active Pajaro Dunes, sub-ject to erosion during severe storms, front thesouthernmost coast of Santa Cruz County. Thereis considerable back beach development in thisarea and wave inundation is common.

2. Methods

As discussed above, the landwardmost edge ofthe blu¡ top or cli¡ top (Fig. 2) served as theprimary proxy for shoreline position in SantaCruz County. In areas characterized by low-lying,unconsolidated dune and beach deposits, such asthe Pajaro Dunes in the southern half of thecounty, the seaward edge of dune vegetationserved as a proxy for shoreline position.

2.1. The softcopy photogrammetric process

Photographs £own for the National Oceanicand Atmospheric Administration in 1994 at ascale of 1:24 000 provided the recent shorelineposition for this study. This £ight provided the

only existing continuous coverage within thetime frame required by FEMA. Aerial photo-graphs taken in 1953 at a scale of 1:12 000 werethe source of the historical shoreline position.

To generate shoreline erosion rates, softcopyphotogrammetry and GIS technology are com-bined in a series of steps called the softcopy/GISmethodology (Fig. 3). Softcopy photogrammetryautomates the actions of the traditional stereo-plotter and allows non-photogrammetrists,trained in the softcopy methodology, to produceorthorecti¢ed (undistorted) photographs, whichare essentially `photomaps'. The softcopy processbegins with conversion of all images to digitalformat by scanning at a resolution of 42 microns(600 dpi) using an Agfa Horizon Plus scanner.The resulting digital images (with pixel sizesof 1 m and 0.33 m for 1994 and 1953, respec-tively) are then imported to ERDAS ImagineProduction0. The next step is orthorecti¢cationof recent photographs using ERDAS ImagineProduction and the OrthoMAX0 add-on moduleby Vision International0.

Orthorecti¢cation, the removal of distortionsand relief displacements from aerial photographs,can be carried out for an entire block of photo-graphs (numerous sidelapping and overlappingstereo aerial photographs) at one time. Aerial tri-angulation begins the orthorecti¢cation process

Fig. 2. Ideal, sharp cli¡ edge for measurement of shoreline erosion rates, Depot Hill, Santa Cruz County.



and consists of two steps: interior orientation andexterior orientation (Slama, 1980).

Interior orientation establishes the geometry in-side the camera and requires that ¢ducial marks(marks in the corners or at the edges of eachphotograph) are digitized and camera system in-formation (coordinates of ¢ducial marks, focallength, and lens characteristics) be entered. Exte-rior orientation establishes the relationship of animage to the ground coordinate system by solvingfor exterior orientation parameters: latitude (x),longitude (y) and elevation (z) of the camera atthe time of exposure as well as the angles of ro-tation (roll, pitch and yaw) about these axes, re-spectively. Exterior orientation requires the inputof geographic coordinates for ground controlpoints and digitizing of both ground controlpoints and tie points (points common betweenoverlapping images but for which absolute posi-tion is unknown) on each photograph. The soft-ware ¢rst uses the ground control points and thespatial relationships between overlapping photo-

graphs to determine the relative and absolute po-sition of the tie points in geographic space. Then,the basic principle of photogrammetry, the Prin-ciple of Collinearity, is applied. This principlestates that in an undistorted photograph, aground control point, its corresponding point ona photograph, and the camera all lie on a straightline (Fig. 4). This relationship between the groundcontrol coordinates, the corresponding image co-ordinates, and the exterior orientation parametersis used to solve for the exterior orientation pa-rameters in a triangulation process called `bundleadjustment'.

Once a triangulation has been accepted as ad-equate, the program calculates transformationequations for each photograph that when appliedremove distortions and displacements from theimages. After equations have been generated, dig-ital stereo pairs are created for each overlap areain the photo block (100% of the study area hadthe necessary overlapping photo coverage). Eachstereo pair is then used to generate a three-dimen-sional depiction of the ground surface, called adigital elevation model (DEM). For the southernhalf of Santa Cruz County, this process resultedin the production of over 40 DEMs. Each DEMis then carefully edited (the incorporation of anaccurate DEM is required to remove relief dis-placement) by adding lines to delineate breaks inslope, by adding additional control points, and bycorrecting the elevation of points incorrectlyplaced by the automated DEM generation pro-

Fig. 3. Softcopy photogrammetry and GIS work£ow. Lightgray boxes indicate steps in the recti¢cation of recent pho-tography while dark gray boxes indicate steps in the geore-ferencing of historical photography.

Fig. 4. The basic principle of photogrammetry is the Princi-ple of Collinearity. This condition states that in an undis-torted photograph a point on the ground (A), its representa-tion on the photograph (A1) and the camera all lie on astraight line.



cess. Once editing is complete, the DEMs and thetriangulation equations are combined with thedigital images to generate orthophotographs forthe 1994 photos.

The historical images for this study are not or-thorecti¢ed because camera calibration reports,necessary for the orthorecti¢cation process, arenot available. Instead, the 1953 photographs arerecti¢ed to the 1994 orthophoto mosaic by select-ing a series of between six and nine well-distrib-uted, high-quality control points from the 1994photos for each historical image. From thesepoints a transformation is calculated and uponresampling, the historical image is georeferencedto the projection and coordinate system (UTM,WGS84 spheroid) of the 1994 photographs. Fi-nally, the recti¢cation process is veri¢ed by link-ing the georeferenced image with the orthophoto-graph and moving a linked cursor simultaneouslythroughout both images to ensure that commonfeatures are crossed by the cursor at the sametime.

2.2. Calculating shoreline erosion rates

Once orthorecti¢cation of recent photographsand recti¢cation of historical photographs arecomplete, shoreline vector coverages are createdfor both the recent and historical images by digi-tizing shoreline position. After digitizing, the vec-tor coverages are imported to ArcInfo0. Shoreli-negrid, an ArcInfo AML (a program written inthe ArcInfo Macro Language), then converts bothshoreline coverages to grids with a user-speci¢edspacing of 1 m (spacing selected to match the 1-mresolution of the 1994 photographs). In thesegrids, a value of `1' is assigned to a cell if theshoreline passes through it. The program thencalculates the shortest distance between cellswith a value of `1' in the recent grid and cellswith a value of `1' in the historical grid. Ratesare determined by dividing this distance by theduration between the historical and recent photo-graphs. Based on these rates and under the sim-plifying assumption that erosion will continue atthe same rate and in the same direction, this AMLcan also be used to project the position of theshoreline into the future. Potential problems in-

volved in using erosion rates in this manner willbe considered in Section 4.

2.3. Error analysis

There are numerous errors inherent in the cal-culation of erosion rates from aerial photographs.These errors result from the raw data, the recti¢-cation process and the measurement of shorelineposition. In this study, orthophotographs are gen-erated for the recent images (scale, 1:24 000) inorder to reduce raw data and recti¢cation errors(e.g. camera distortions, scale di¡erences withinphotos, and ground control errors (Anders andByrnes, 1991; Thieler and Danforth, 1994;Moore, 2000) to a minimum. These errors areincorporated into a root mean squared (RMS)error and reported by the orthorecti¢cation soft-ware. The resulting average total horizontal RMSof 1.6 m for the 1994 photographs is excellent.

At a scale of 1:12 000, with the shoreline lo-cated in the center of the images (both factorsreduce error), the historical photos are recti¢edusing ground control obtained from the recentimages. For this reason, and because we are in-terested only in relative o¡sets between the twosets of photographs, the error analysis only con-siders recti¢cation error for the historical photo-graphs. The average total horizontal RMS forrecti¢cation of the historical imagery is 2.0 m.

In addition to considering recti¢cation error forthe historical photographs, errors due to measure-ment of shoreline position are considered for bothsets of photographs. Shoreline position errors area result of the inability to measure shoreline posi-tion to sub-pixel resolution (pixel size = 1.0 m forrecent photos and 0.33 m for historical photos)and the potential for digitizing the incorrect pixel.To estimate these two errors we assume, with aprobability of 0.95, that the estimated shorelineposition lies within a circle about the true shore-line position. Since it is less likely that the shore-line will be incorrectly placed in diagonal pixelsthan orthogonal pixels, we use a circle that in-scribes the area within a three pixel by three pixelbox with the true shoreline position at its center.

To estimate the error in the shoreline position,we assume that each error (recti¢cation and



shoreline position) has a bivariate normal distri-bution. Under this model, the standard deviationof the recent shoreline position is 0.6 m and thestandard deviation (including recti¢cation error)of the historical shoreline position is 2.1, produc-ing a standard deviation (or RMS error) of esti-mated shoreline change around 2.2 m. With 41 yrbetween shorelines, this is a RMS error of 5 cm/yr. Thus, estimated annual erosion rates calcu-lated in this study based on one pair of pointsalong a line will generally be correct to within10 cm/yr (two standard deviations).

The error estimate of 10 cm/yr is small relativeto erosion rates calculated for erosion hotspots(32^63 cm/yr) but large relative to rates calculatedfor the majority of the Santa Cruz County coast-line (7^15 cm/yr). However, when averaging ero-sion rates across coastal segments, the standarddeviation of erosion rate estimates will likely beconsiderably smaller due to cancellation of errorsmaking mean erosion rates generally more reliablethan erosion rates calculated at a point. The exactdegree to which errors cancel, determined by fac-tors including the independence of observations

Fig. 5. (a) 1953 georeferenced image for Southern Manresa showing historical blu¡ position. (b) 1994 orthophotograph with bothhistorical and recent blu¡ positions shown. (c) Erosion rates, mean erosion rate and standard deviation for Southern Manresa(1953^1994). The standard deviation encompasses more than 68% of observations because the distribution is not perfectly nor-mal.



and the number of observations, is beyond thescope of this study.

3. Results

Erosion rates were generated for the majorityof the southern half of Santa Cruz County usingthe methodology described above. For each coast-al segment presented below (see Fig. 1 for loca-tions), the erosion rate at any point can be deter-mined by scaling the alongshore distance from thebeginning of the segment in the orthophotographand ¢nding the erosion rate reported at the samedistance on the corresponding erosion rate plot.

Because of natural breaks in the cli¡ line suchas gullies or stream valleys, the study area wasdivided into 30 sections for which erosion rateswere calculated individually. Despite the varietyof wave exposures, protective beach characteris-tics and the range of structural properties of thecli¡s along the coast of Santa Cruz County, mostof the cli¡s are retreating at rates averaging be-tween 7 and 15 cm/yr. This is in general agree-ment with an earlier analysis by Griggs and Savoy(1985).

Although most of the coastline is retreating atrelatively low rates (a representative segment isdescribed below), there are three locations where

erosion rates are signi¢cantly higher. These loca-tions, identi¢ed as erosion hotspots, have erosionrates of 30 cm/yr or greater and are discussedbelow. Mean retreat rates and standard deviationsfor each of the four segments discussed are sum-marized in Table 1. To account for local variabil-ity in retreat rates, a mean erosion rate is calcu-lated for each segment as:

x � 4�xi � li�lT

�1�

where xi = erosion rate, li = length of coastalong which erosion rate = xi, and lT =4li, (thetotal length of the coastal segment). The standarddeviation (Sx) is then given by:

Sx ��4li�xi3x�2=n31

q�2�

Fig. 6. Oblique photograph of the Opal Cli¡s area. Portions of the base of the cli¡ are armored with riprap.

Table 1Summary of mean erosion rates and standard deviations cal-culated for four coastal segments

Segment Mean Standard deviation(cm/yr) (cm/yr)

Opal Cli¡s 17 11Depot Hill 16 6Manresa 32 14South of Manresa 10 7



where n = lT, the number of erosion rate obser-vations along the segment.

Erosion rates generated for a coastal segmentsouth of Manresa are typical of erosion ratetrends throughout the majority of the county.This 600-m segment is characterized by 20^25-mcli¡s incised by gullies at the top and vegetated atthe base. The presence of gullies suggests thatterrestrial erosion is important in this location.A wide sandy beach protects the cli¡s fromwave attack and is likely responsible for prevent-ing high erosion rates along this segment. Com-parison of the historical georeferenced image andthe recent orthophotograph (Fig. 5a,b) for the

Manresa segment reveals up to 10 m of cli¡ re-treat over the 41-yr time period between photo-graphs. Erosion rates along this segment varyconsiderably over short distances, ranging from2 to 22 þ 10 cm/yr, with most values between 6and 12 þ 10 cm/yr (Fig. 5c). The mean retreatrate south of Manresa is 10 cm/yr with a standarddeviation of 7 cm/yr.

Three coastal segments within the southern halfof Santa Cruz County are identi¢ed as experienc-ing signi¢cantly high rates of cli¡ retreat. The ¢rstis Opal Cli¡s (Fig. 6). This 700-m segment is char-acterized by cli¡s, 12^15 m high, consisting ofpervasively jointed sandstone/siltstone capped by

Fig. 7. (a) 1953 georeferenced image of Opal Cli¡s showing historical cli¡ position. (b) 1994 orthophotograph with both histori-cal and recent cli¡ positions shown. (c) Erosion rates, mean erosion rate and standard deviation for Opal Cli¡s (1953^1994).



thick terrace deposits. The beach fronting the cli¡is narrow, and although portions of the base ofthe cli¡ have been protected with riprap, marineprocesses appear to dominate in this area. Thelack of a wide protective beach along this seg-ment, combined with structural weaknesses inthe cli¡, is likely responsible for the high erosionrates. Comparison of the georeferenced historicalimage and the recent orthophotograph reveals upto 19 m of cli¡ retreat along this segment between1953 and 1994 (Fig. 7a,b). Erosion rates generatedfor this length of coastline range from 0 to 46 þ 10cm/yr with a mean retreat rate of 17 cm/yr and astandard deviation of 11 cm/yr. Rates along thissegment are the highest in the county for a cli¡system dominated by marine erosional processes(Fig. 7c).

Depot Hill, located directly east of Capitola, isthe second erosion hotspot in Santa Cruz County.This 800-m segment consists of cli¡s, 25 m high,composed of the Purisima Formation and frontedby a narrow beach (Fig. 8). The cli¡ is not arm-ored along this segment, and the presence of threedistinct joint patterns makes large block fallscommon. Comparison of the 1953 cli¡ positionand 1994 cli¡ position (Fig. 9a,b) reveals a max-imum of 13 m of retreat yielding erosion rates upto 32 þ 10 cm/yr (Fig. 9c). The mean retreat rate is16 cm/yr with a standard deviation of 6 cm/yr.

These rates are the second highest in the countyfor a cli¡ system dominated by marine erosionalprocesses.

A third erosion hotspot is located at ManresaState Beach. This coastal segment consists ofblu¡s, 30^40 m high, of poorly consolidated Pleis-tocene dune deposits (Fig. 10). The blu¡s are sub-ject to gullying or rapid erosion when vegetationis removed or when the protective beach is eroded(Griggs and Savoy, 1985). This segment supportsa mixture of blu¡-face condominiums, blu¡-tophomes and agricultural uses. The large blu¡ faceseen from an oblique angle in Fig. 10 correspondsto the large blu¡ face seen in the georeferencedhistorical image and the recent aerial orthophoto-graph in Fig. 11a,b. The most signi¢cant cli¡ re-treat in the county occurs at the northern end ofthis blu¡ face where the blu¡ edge retreated up to25 m between 1953 and 1994. This corresponds toan erosion rate of up to 63 þ 10 cm/yr (Fig. 11c).Another signi¢cantly high rate of erosion, 40 þ 10cm/yr, was calculated at the south end of thisblu¡ where approximately 16 m was lost between1953 and 1994. At 32 cm/yr with a standard de-viation of 14 cm/yr, the mean retreat rate for theentire Manresa segment is nearly twice the meanrate at Opal Cli¡s and Depot Hill.

In their description of changes to MontereyBay beaches between 1982 and 1998, Dingler

Fig. 8. Oblique photograph of Depot Hill.



and Reiss (2002) report that Manresa State Beachexperienced extreme erosion during the 1982-83and 1997-98 El Nin¬o events. The Manresa sitesurveyed by Dingler and Reiss (2002) is 500 mnorth of the erosion hot spot identi¢ed here, yetbeach changes at that site are likely representativeof beach changes at the blu¡ erosion hotspotidenti¢ed in this study. At the beach survey loca-tion, even during the winter of 1982-83 whenbeach erosion was at a maximum, beach elevationat the base of the blu¡ was greater than 4 mabove mean sea level. This suggest that even dur-

ing winters of extreme coastal storms, the beachat Manresa is not narrow enough to providewaves with access to the base of the unconsoli-dated blu¡s. Unlike the two other erosion hotspots, the unconsolidated Manresa blu¡s failedsigni¢cantly during the 1989 Loma Prieta Earth-quake (Plant and Griggs, 1990). Thus, seismicshaking and slumping, not erosion by waves, ap-pear to be dominant erosion mechanisms alongthis segment and are largely responsible for pro-ducing the highest erosion rates in the county.

Although cli¡ lithology changes dramatically in

Fig. 9. (a) 1953 georeferenced image for Depot Hill showing historical cli¡ position. (b) 1994 orthophotograph with both histori-cal and recent cli¡ positions shown. (c) Erosion rates, mean erosion rate and standard deviation for Depot Hill (1953^1994). Thestandard deviation encompasses more than 68% of observations because the distribution is not perfectly normal.



the southern portion of Santa Cruz County, acorresponding change in erosion rates is not evi-dent. The lithi¢ed sandstone and siltstone of thePurisima Formation in the north should be moreresistant to erosive processes than the unconsoli-dated ancient sand dunes of the Aromas Forma-tion farther to the south. However, the Purisimacontains structural weaknesses and is fronted bynarrow beaches whereas the Aromas Formation isfronted by wide, protective beaches. Throughoutmost of the study area, these structural and mor-phological factors appear to o¡set lithological dif-ferences such that erosion rates in the north andsouth are similar.

4. Discussion

Although softcopy photogrammetry has beenexplored in the past as a way to measure sedimentyield from coastal cli¡s (Balson et al., 1996), theSanta Cruz County study is the ¢rst to apply soft-copy photogrammetry in the measurement ofshoreline erosion rates along a high-relief coast-line. As discussed earlier, the cli¡ or blu¡ edge istypically the most reliable feature for digitizingand measuring `shoreline' change along the Cali-fornia coastline, although in some low-relief areasthe edge of vegetation must be used.

When comparing shoreline positions over timeand calculating erosion rates, a cli¡ed coastlineposes challenges di¡erent from those of low-reliefcoastlines more typical of the East Coast where avegetation line is most commonly used as a proxyfor shoreline position. If all cli¡s along the Cal-ifornia coast were continuous, sharp-edged, anddevoid of vegetation, such as the cli¡ referred toearlier in Fig. 2, identifying the cli¡ edge andcalculating erosion rates in California using soft-copy photogrammetry would be straightforward.

However, since coastal erosion and cli¡ retreatoccur due to a complex interplay of processes,ideal cli¡s are uncommon. For example, althoughthe softcopy/GIS methodology allows for genera-tion of erosion rates along continuous segmentsof coastline, the cli¡ line in California is not con-tinuous, but rather interrupted by stream valleysand pocket beaches (Fig. 12a,b). Other factorsalso serve to `break' the cli¡ line, rendering itsposition unmappable. For example, trees and oth-er types of vegetation may obscure the cli¡ topwhen viewed in an aerial photograph and like-wise, the cli¡ edge cannot be accurately digitizedif it is rounded or stepped, thus lacking a clearlyde¢ned edge (Fig. 12c).

In addition to the di¤culties posed by naturalbreaks in the cli¡ line, shoreline morphology cana¡ect the process of orthophoto generation. The

Fig. 10. Oblique photograph of the Manresa area showing blu¡ failure from seismic shaking during the Loma Prieta earthquake.



presence of shore platforms (Fig. 12d) (low, ex-posed, features extending seaward from the baseof a cli¡), and stripped marine terraces (bedrockterraces from which more recent unconsolidateddeposits have been removed), may cause smearingof orthophotographs. This occurs due to disconti-nuities in the DEM in these regions and the factthat in areas of complex morphology, portions ofvertical faces visible from the ground are hiddenfrom the camera. Thus, when the aerial photo-graph is draped over the DEM to remove reliefdisplacement, existing pixels are stretched overareas of missing information resulting in a graysmear. In these few locations, the cli¡ edge could

not be digitized with certainty and erosion rateswere not calculated.

Shoreline armoring also poses a challenge whenmeasuring cli¡ retreat rates. Over 30% of SantaCruz County is armored. Unfortunately, the ex-tent of armoring, timing of emplacement, andtype of armoring vary such that it was virtuallyimpossible to calculate pre-emplacement andpost-emplacement rates within the scope of thisstudy. For this reason, rates generated for a sec-tion of armored coastline may include both pre-and post-emplacement time periods. On a site-by-site basis where the timing of emplacement isknown and where photographs bracketing the

Fig. 11. (a) 1953 georeferenced image for Manresa showing historical blu¡ position. (b) 1994 orthophotograph with both histori-cal and recent blu¡ positions shown. (c) Erosion rates, mean erosion rate and standard deviation for Manresa (1953^1994).



time periods pre- and post-emplacement are avail-able, it may be possible to investigate the e¡ec-tiveness and adjacent impact of armoring bystudying changes in erosion rates determined us-ing the softcopy/GIS methodology.

Despite the challenges described above, erosionrates for the period 1953^1994 were generated forapproximately 70% of the southern half of SantaCruz County. Cli¡ erosion is episodic and for this

reason, the actual amount of erosion at a partic-ular location over the course of any single year£uctuates considerably. Erosion rates generated inthis study are end-point rates, and thus representaverage rates for the period of time between his-torical and recent photographs. Along coastalsegments, these average rates are representativeof long-term trends, however, due to the localizedand spatially variable nature of cli¡ retreat, rates

Fig. 12. (a) Cli¡ line interrupted by seasonal stream valley. (b) Cli¡ line interrupted by pocket beaches. (c) Rounded cli¡ doesnot allow clear identi¢cation of cli¡ edge on an orthophotograph. (d) Shore platforms or stripped marine terraces may causesmearing of orthophotographs due to the complexity of the DEM in such an area. (e) Oblique photograph of development onthe Pajaro Dunes.



Fig. 12 (Continued).



for a speci¢c location could be di¡erent if calcu-lated over a longer, shorter, or di¡erent time in-terval.

The localized nature of erosion is con¢rmed bythe erosion rates generated in this study. For ex-ample, in Fig. 7c, erosion rates drop from over 35cm/yr at a distance of 290 m from the beginningof the segment to 12 cm/yr at a distance of 295 m.It is likely that this di¡erential erosion either ledto the formation or destruction of a small protru-sion in the coastline. Since over the long-term,coasts have a tendency toward smooth con¢gura-tions through preferential erosion of headlands, itis likely that erosion rates calculated over a futuretime interval would be the reverse of what thisstudy found, i.e. lower at 290 m and higher at295 m.

An additional illustration of the importance ofthe time interval over which erosion rates aremeasured can be drawn from the southernmostsection of Santa Cruz County where the shorelineis characterized by low sand dunes (Pajaro Dunes^ Fig. 12e). The edge of the vegetation line wasused as a proxy for shoreline position along thissegment, and the historical and recent imagesshown in Fig. 13 reveal that the 1994 shorelineis seaward of the 1953 shoreline. This indicatesthat the dunes have experienced net accretion dur-ing the time period of this study. In reality, they

have experienced multiple periods of erosion andaccretion over the last 60^85 years (Griggs andSavoy, 1985).

Prior to development, the Pajaro Dunes werecut away by winter storm waves and rebuilt bysummer wind and swell (Griggs and Savory,1985). However, since development, the potentialfor landward migration has been limited. Fig. 14reveals that these dunes have been active in thepast. The photo on the left was taken in 1931prior to development, whereas the photo on theright was taken post-development in 1976. In1931 the dunes were active and a washover fanwas present at the mouth of the river where con-dominiums are now located. Thus, despite the factthat negative erosion rates generated for this seg-ment (using the vegetation line as a proxy forshoreline position) might be interpreted to suggestthe dunes are accreting and safe for development,historical information and geologic insightstrongly suggest that these homes are seriouslythreatened by the potential for retreat duringstorms. The severe erosion that was documentedat this site during the 1983 El Nin¬o winter con-¢rms this conclusion (Griggs and Savoy, 1985).

In light of the di¤culties inherent in character-izing coastal cli¡ and dune retreat even whenmeasurement errors are signi¢cantly reduced us-ing the softcopy/GIS methodology, predicting fu-

Fig. 13. (a) 1953 georeferenced image of Pajaro Dunes showing historical vegetation line position. (b) 1994 orthophotograph withboth historical and recent vegetation line positions shown.



ture cli¡ position poses an even greater challenge.Coastal management and planning often requirethe projection of erosion rates in an attempt topredict `shoreline', or cli¡, position at a futuretime. This process is based on the assumptionthat erosion will continue at the same rate. Un-fortunately, due to the localized and episodic na-ture of cli¡ retreat and the geologically short timeperiod over which erosion rates can be measured,this assumption is likely to be invalid.

In some cases, for example, where cli¡ retreatrates are less variable alongshore (i.e. the standarddeviation of retreat rates is lower), projection ofrates into the future for short time periods up tothe length of time over which rates were generatedmay be acceptable. However, if the standard de-viation of erosion rates for a coastal segment ishigh or if a coastal segment tends to pass throughalternating periods of headland creation and de-struction, projection of rates will provide an in-

Fig. 15. Orthophotograph with 1994 cli¡ edge, 1953 cli¡ edge and projected 2054 cli¡ edge shown. Projected shoreline positionsshould be used with caution.

Fig. 14. Aerial photo comparison of Pajaro Dunes. The photo on the left was taken in 1931 and the photo on the right was tak-en in 1976. Although the photos are of di¡erent scale, the oval race track provides a reference between the two images. Notecoastal development in locations formerly occupied by active dunes and overwash fans in 1931.



correct estimate of future cli¡ position. For exam-ple, in Fig. 15, the 1953 cli¡ line consisted of ashort series of small-scale headlands and embay-ments. Between 1953 and 1994 the headlandseroded such that the 1994 coastline in this loca-tion displays a relatively smooth con¢guration.This process is likely to repeat in the future, butis not replicated by projection of past rates. Forexample, the highest historical erosion rates occurwhere the headlands were present in 1953 and forthis reason, projection of historical rates over thenext 60 years predicts that these areas will expe-rience the highest erosion rates in the future.However, headlands likely formed in these areasbecause the material composing the cli¡ is locallymore resistant than the material on either side.Thus, in contradiction to what is shown inFig. 15, it is more likely that new headlands willform in the same location as the old headlands,with high erosion rates for the next 60 yr occur-ring where the rates were lowest for the last 60 yr.Thus, despite our capability to measure past re-treat rates to within 10 cm/yr, projection of theserates still does not adequately predict future cli¡position.

Perhaps a better estimate of future cli¡ positionfor coastal segments with highly variable cli¡ re-treat can be achieved using the information pro-vided by both the mean retreat rate and the stan-dard deviation along individual coastal segments.For example, projection of the mean retreat rateand one standard deviation below and above themean provides a conservative band of possiblecli¡ positions for a coastal segment. Though thisalso produces an imperfect prediction, it does takeinto account the range of variability expectedbased on the previous behavior of the particularsegment in question.

In addition to digitizing and studying the cli¡edge in high relief coastal areas, other features,such as the base of the cli¡, also can be digitizedusing the softcopy/GIS methodology. Digitizingseveral geomorphic features and studying theirevolution through time in combination with theevolution of the cli¡ edge may provide a betterunderstanding of the complex processes involvedin coastal cli¡ retreat. Such an investigationwould be especially useful in hotspot areas where

change is relatively rapid. Finally, combining suchan understanding with projections based on meanerosion rates and standard deviations would pro-vide an even better prediction of future cli¡ be-havior.

5. Summary/conclusions

When calculating historical, time-averaged ratesof coastal retreat in high-relief areas, a method-ology such as the softcopy/GIS methodology,which involves the generation of at least one setof orthophotographs, should be used to signi¢-cantly reduce errors due to relief displacement.Erosion rates calculated at 1-m increments andstandard deviations for numerous segments alongthe northern MBNMS coastline using the soft-copy/GIS methodology illustrate the extreme spa-tial variability and thus the episodic nature of cli¡retreat.

Although the factors in£uencing cli¡ retreat,such as lithology and protective beach width,vary considerably along the coastline, historicalerosion rates for the majority of the cli¡s in theregion range from 7 to 15 cm/yr. This suggestscompensation between factors a¡ecting cli¡ re-treat such that over long periods of time the coastmaintains a relatively smooth con¢guration at aregional scale. Despite this apparently smoothlong-term con¢guration, three locations with sig-ni¢cantly higher historical average retreat ratessince 1953 are identi¢ed in Santa Cruz County.Two of these sites are dominated by marine pro-cesses and retreat historically at average rates ofup to 46 cm/yr, whereas the third is dominated byterrestrial processes and has retreated at rates ofup to 63 cm/yr.

Despite the removal of considerable errors andthe accuracy of cli¡ retreat measurements madeusing the softcopy/GIS methodology, a purelyphotogrammetric approach is not su¤cient toforecast future cli¡ position. Instead, characteriz-ing cli¡ behavior in segments using informationprovided by the mean erosion rate and the stan-dard deviation in combination with an under-standing of marine and terrestrial processes is nec-essary.



Once erosion hotspots have been identi¢ed,generation of erosion rates for numerous time pe-riods using the softcopy/GIS methodology, study-ing additional geomorphic features, and in-depth¢eld study may provide the better understandingof processes necessary to improve forecasting offuture cli¡ behavior.

Acknowledgements

Financial support for this project was providedby the FEMA (Grant # EMW-96-CA-0227). TheGIS and Imaging facility used to carry out theproject was largely funded by a grant from theNational Science Foundation Instrumentation andFacilities Program (Grant # EAR-9526765). Ob-lique photographs in this manuscript are courtesyof Gary Griggs. Many thanks to Cheryl Hapkeand Ben Benumof for their assistance with thisproject and to Gerry Weber for thought provokingcomments on an early draft of this manuscript.Special thanks to Bill Duffy of Northern Geo-mantics for his extensive work on the ArcInfoShorelinegrid program. This project benefitedgreatly from his tremendous generosity. Theauthors extend thanks to Steve Eittriem and twoanonymous reviewers whose useful and clevercomments greatly improved this manuscript.Finally, the authors are especially grateful toAndrew Solow of the Woods Hole OceanographicInstitution for his assistance with the erroranalysis.

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