a spatial database of santa cruz island...

Pages 229–244 in Damiani, C.C. and D.K. Garcelon (eds.). 2009. Proceedings of 229 the 7th California Islands Symposium. Institute for Wildlife Studies, Arcata, CA.

A SPATIAL DATABASE OF SANTA CRUZ ISLAND VEGETATION

BRIAN COHEN,1 COLEEN CORY,2 JOHN MENKE,3 AND ANNE HEPBURN3

1The Nature Conservancy, 402 W. Broadway, Suite 1350, San Diego, CA 92101; [email protected] Nature Conservancy, 3639 Harbor Blvd., Suite 201, Ventura, CA 93001

3Aerial Information Systems, Inc., 112 First St., Redlands, CA 92373

Abstract—In 2006, The Nature Conservancy (TNC) contracted with Aerial Information Systems (AIS) tocreate a spatial database of vegetation on Santa Cruz Island, a 243 km2 island jointly owned and managedby TNC and the National Park Service (NPS). AIS and TNC developed appropriate vegetation categoriesdown to the alliance level, based on plot and transect data provided by USGS-Biological ResourcesDiscipline, NPS, and TNC; The Manual of California Vegetation; and site visits conducted during thecourse of the project. A minimum mapping unit of 0.5 ha was established for the delineation of a visiblealliance or mapping unit. AIS used 1:12,000 natural color aerial photographs from 2005 for the manualdelineation of polygons. The delineation protocols established specific methods and criteria for eachalliance or mapping unit and documented them for future updates, creating the foundation for trackingchanges over time using sequential mapping techniques. The resulting vegetation database is discussed inrelation to the changes that have occurred on the island over the last 20 years, specifically the removal offeral ungulates on the island, and is compared to earlier vegetation mapping efforts. The specific attributesdeveloped for the database, particularly the cover measure and location of fennel, enable the exploration ofmore sophisticated and robust research questions.

INTRODUCTION

Mapping and tracking changes in vegetationpatterns are essential to the management and long-term protection of landscape-scale biodiversity andecological processes. The Nature Conservancy(TNC), which owns and manages 76% of SantaCruz Island (SCI), was particularly interested indocumenting changes in vegetation as a result offeral ungulate removal programs over the last 25years. To that end, TNC contracted AerialInformation Systems (AIS) in 2005 to create anisland-wide spatial vegetation database at a highspatial and categorical resolution and to documentthe methods sufficiently to allow for future updates.This paper describes the methodology used to createthis database, discusses the information collected onvegetation types and cover, and compares the newdatabase to earlier mapping efforts of the island.

Sheep, pigs, and cattle were introduced to SCIin the mid-1850s (Hochberg et al. 1980; Junak et al.1995), marking the beginning of severe ecologicaldegradation of the island (NPS 2002). All threeungulates ate native plant species, including somethat were unique to the island (Hochberg et al.

1980). After acquiring an interest in the island in1978, TNC began a program to eradicate sheep, andlater cattle and pigs, from their portion of the island.Beginning in the 1990s, the National Park Service(NPS) conducted a similar eradication of feralanimals from their portion. Over 46,000 sheep wereremoved from SCI between 1981 and 2001(Morrison 2008), and between 1988 and 1989nearly 2000 head of cattle were rounded up andshipped to the mainland (P. Schuyler, personalcommunication) Finally, over 5000 feral pigs wereeradicated from the island between 2005 and 2007(Morrison et al. 2007).

The removal of non-native grazers wase x p e c te d t o h a v e s ig n i f i c a n t e c o l o g i c a lconsequences , e spec ia l ly on vege ta t ion .Photomonitoring conducted between 1980 and2006 has already shown increases in native plantcover at several sites around the island (P. Schuyler,personal communication). To better understandthese changes, we embarked on a program todevelop a cost-effective, large-scale vegetationmonitoring methodology to provide informationsupporting long-term adaptive management of theisland’s resources. Previous vegetation mapping

230 COHEN ET AL.

efforts of the island (Minnich 1980; Jones et al.1993) provide a general overview of the vegetationcommunities. However, they do not have enoughaccuracy or precision to describe the island’s subtleand complex community compositions. Themethodologies employed in these mapping effortsare also not documented in enough detail tosufficiently replicate them, and therefore havelimited value for supporting current and futuremanagement decisions. In this paper, we describethe development of a vegetation database at highspatial resolution that will facilitate the monitoring,management, and recovery of distinct plantcommunities.

STUDY AREA

SCI is situated 55 km west of Los Angeles and40 km south of Santa Barbara, and is the largest ofthe California Channel Islands. The island trendseast to west, and has roughly 124 km of coastline.The 249 km2 island includes two mountain rangesflanking a central valley, a long east-west isthmus,and an eastern end fairly isolated by a high and steepridge. It sustains more than 625 plant species (Junaket al. 1995), 198 bird species (Jones et al. 1998), and12 species of native reptiles, amphibians, andterrestrial mammals (Laughrin 1980).

In 1978, a 90% interest in SCI was purchased byTNC, and outright ownership was gained in 1987.TNC transferred 8,500 acres (14% of the island) tothe National Park Service (NPS) in 2000, adding tothe 10% of the island that had been designated aNational Park in 1980. The island is managedcooperatively by both land owners.

METHODS

Based on the desired level of spatial andcategorical specificity, and the amount of fundingavailable, aerial photo interpretation was selected tocreate the database. Knapp (2005) described thefollowing reasons for selecting aerial photointerpretation versus remote sensing for thevegetation mapping of another Channel Island,Santa Catalina Island: 1) aerial photographyprovides higher resolution than satellite imagery; 2)satellite imagery requires extensive ancillary data

and additional labor to classify plant communities;and 3) some plant communities are too spectrallysimilar to be differentiated in satellite imagery.Knapp also suggested that mapping at very highresolution provided reduction of intra-polygonheterogeneity, as well as capturing of localizedvegetation change. IK Curtis Inc. (Burbank, CA)was contracted to acquire and print 156 1:12,000stereo pair, true color aerial photographs of theisland. The flights to acquire these images wereconducted in November 2005.

To generate the vegetat ion map, photointerpreters reviewed aerial photographs to identifyphoto signatures—unique combinations of color,texture, tone, and pattern associated with each landcover feature. By observing the context and extentof the photo signatures associated with specificvegetation types, the photo interpreter is able toidentify and delineate boundaries between plantcommunities, resulting in the creation of mappingunits or polygons with distinctive photo signatures.These photo signatures are then linked to vegetationattributes (e.g., type, cover, and the fennel mappingunit) observed in the field using a classificationsystem and photo interpretation key. In thefollowing sections, we describe the methods forfield data collection and development of theclassification system, photo interpretation, dataconversion of the final map layers, and accuracyassessment.

Field Data Collection and Classification SystemWe selected a floristic-level classification

system based on Sawyer and Keeler-Wolf’s (1995)and the newly revised alliance-based classificationsystem for California (Sawyer et al. 2009). This wasdetermined suitable for the management needs ofisland projects based on the system's ability tocapture rare plant assemblages, its ability to becombined into coarser levels of classification(Knapp 2005), and its consistency with the NationalVegetation Classification System (Grossman et al.1998; Jennings et al. 2003). This system is beingadopted by the Federal Geographic Data Committee(FGDC 2008) in the draft National VegetationClassification Standard (Version 2.0), and isbecoming more widespread in vegetation mappingprojects in California (D. Johnson, personalcommunication). We considered it to be importantto add to the developing statewide body of

SPATIAL DATABASE OF SANTA CRUZ ISLAND VEGETATION 231

knowledge of vegetation, while maintaining theability to compare with earlier vegetation maps andvegetation databases of other Channel Islands. It isimportant to note, however, that the classificationused for this project was derived from fieldsampling and analysis, and then expanded based onexpert opinion, and thus did not rest completelyupon the field-based classification process used inother recent studies. Therefore, although the shapeand size of each mapped polygon are likely toremain constant, their nomenclature may formallychange in the future if further field classification anddata analysis are done.

The classification system for SCI is arrangedhierarchically from class to formation, to mappingunit or alliance, and finally sub-alliance or potentialassociation, with class being the most inclusivecategory and sub-alliance being the least inclusive(Table 1). Primary sources of data used to determinethe floristic composition of each mapped polygonand develop the classification system were dataf rom a s e r i e s o f 362 r e l evé p lo t s , f i e ldreconnaissance, and A Flora of Santa Cruz Island(Junak et al. 1995).

Relevé Plots: The relevé plots are a series of 362locations spread non-systematically across theisland, developed and maintained by the USGS andthe NPS. At each point a visual survey is undertakento record a variety of aspects of the plot location,including the physical characteristics of slope andaspect, soil depth, and elevation; community type or

types, size, and distance to nearest adjacent stand;animal disturbance; substrate; species informationincluding coverage and phenology, stratum, andtree diameters; and associated species. Each plot is40 m by 10 m, with the long axis parallel to themajor landscape slope. The community types arederived from those detailed in A Flora of Santa CruzIsland (Junak et al. 1995).

F i e l d R e c o n n a i s sa n c e : T w o f i e l dreconnaissance trips were made to SCI to identifythe floristic composition of vegetation types to beused in the classification system. The first trip wasconducted in July 2006 to the west side and theisthmus (TNC side of the island), and the secondwas made in August 2006 to the east side of theisland (NPS side). Field reconnaissance wasperformed by AIS photo interpreters and the seniorecologist from California Department of Fish andGame’s Vegetation Classification and MappingProgram (Todd Keeler-Wolf), who served as areconnaissance advisor and exper t on theclassification of California vegetation types. TNCecologists also accompanied the reconnaissanceteam on field visits. The field reconnaissance visitsserved two major functions. First, they allowed thephoto interpreter to key the signature on the aerialphotos to the vegetation on the ground at each site.Second, they allowed the photo interpreter tobecome familiar with the flora, vegetationcommunities, and local ecology that occur in thestudy area. Understanding the relationship between

Table 1. Hierarchy of vegetation classification used to map Santa Cruz Island in 2006 compared to other established vegetationclassification hierarchies.

Sawyer et al. (2009)classification

U.S. National Vegetation Classification

AIS (2007) level of vegetation classification

Example (Code – Type)

Formation subclass Class Class 1000 – Forest

Formation Subclass

Division Group

Macrogroup Subgroup

Group Formation Formation 1200 - Temperate Needleleaf Evergreen

Forests

Alliance Alliance Mapping unit or alliance 1210 – Bishop Pine Alliance

Association Association Sub-alliance or potential association

1213 – Bishop Pine / Island Scrub Oak – Island

Manzanita

232 COHEN ET AL.

the vegetation units and the environmental contextin which they appear is useful in the interpretationprocess, and familiarity with regional differencesaids interpretation by establishing a context for aspecific area.

Prior to the field reconnaissance trip, each aerialphoto was reviewed under a stereoscope forrepresentative signatures of different vegetationtypes; geographic variables (% slope, aspect, shapeof the slope, elevation); and other abiotic variablesnoted on the photography. Field check sites andassociated notations were annotated directly onto aphoto field acetate overlay, thereby correlating thefield site to a specific location and photo signature.Multiple sites were chosen to provide alternatives ifone or more sites proved inaccessible. Additionalfield sites included areas encountered in transitbetween initially selected sites, areas of noteworthyor unusual significance, and other vegetation typesthe photo interpreter or field ecologist deemedimportant. Field routes were then planned toaccommodate a variety of factors including:maximizing the number of vegetation communitiesand elevation zones visited, responding to anyrecommendations of project staff, addressing timeconstraint considerations, and accessibility.

A total of 350 ground control points were takenin the field with a GPS unit. Distance and directiondata measured by laser range-finder and compass inthe field were then used to correct these locations.The environmental setting, dominant vegetation,and potential alliance were described for eachground control point. Color ground photos weretaken with a digital camera at selected locations, tobe compared later to the aerial photographs and thefield site notes. Using information derived from thefield reconnaissance and any existing field plot data,Keeler-Wolf developed a preliminary mappingclassification and photo interpretation signature keybased on the rules of the National VegetationClassification system and existing information fromother parts of California. Photo interpreters used AFlora of Santa Cruz Island (Junak et al. 1995) to aidin the development of the preliminary mappingclassification. In addition, the vegetation mapcreated by Richard Minnich in 1980 (Vegetation ofSanta Cruz Island) was used to help photointerpreters further correlate aerial photo signaturewith previously mapped vegetation stands, with theunderstanding that the 1980 mapping product was

created under a different set of criteria over 25 yearsago. This system was revised based on realisticconcerns of size and discernability of the differentvegetation types. Not all of the categories could bemapped from the aerial photography. Mapping ofthese categories therefore relied on plot data, fieldvisits, personal expertise, and other ancillaryinformation, including IKONOS multi-bandsatellite imagery from April 2005.

Preliminary Photo InterpretationPhoto interpretation (PI) is the process of

identifying polygons based on their photo signature.Photo interpretations for the SCI map were doneusing aerial photography at a scale of 1:12,000 (1” =1000 feet). Photo interpreters used 3X ocular lensesto enhance line detail to a scale of approximately1:3000. Viewing the final rectified linework overimagery at scales larger than 1:3000 may showspatial errors, which are beyond the resolution of theinput scale at which the interpretation was originallyperformed. Each aerial photo was prepared with a 9”x 9” frosted Mylar overlay for the photo signaturedelineations. Study area boundaries were draftedonto each photo overlay, defining the area within thephotograph to be interpreted. The study areaboundaries were edge matched to adjacent photos toensure complete coverage. Data from additionalcollateral sources (existing vegetation maps,supplemental imagery, soil data, plot data, etc.)were added to the overlay in order to document alllocations and information within the study area onan aerial photograph.

Using a mirror stereoscope, with a 3X ocularlens, photo signature units were delineated onto theoverlay. These initial photo delineations were basedon a number of signature characteristics includingcolor, tone, texture, relative height, and cover.Environmental factors such as elevation, slope, andaspec t were a l so cons idered in the photointerpretation decision-making process. Attributecodes were assigned to each polygon and annotatedonto the overlay. The vegetation polygons andcodes were edge matched to the adjoining photooverlays. Areas of land use were also mappedduring the mapping of the vegetation units. Inaddition to the criteria for defining each communityor alliance, the interpretation was conducted inaccordance with the prel iminary mappingclassification created as a result of the field


reconnaissance trips. Any questionable photosignatures encountered during this phase of themapping effort were sent to the TNC field ecologist(Coleen Cory). This required her to either use herexisting knowledge of the area or go out into thefield to the site in question to get an answer.

Attributes assigned to each polygon included avegetation classification type, a fennel category,and a cover rating. Specific management needsrelated to the concern over the status and trends ofproblematic pest plants required the inclusion of aprimary category for fennel (Foeniculum vulgare),along with an attribute modifier that described acategorical estimate of fennel cover for areas thatcontained fennel. If fennel was interpreted as thedominant species for a polygon, that polygon wasassigned the specific fennel category. Other areasthat contained fennel were assigned a value (#)corresponding to <5% (1), 5% to 10 (2), or > 10 (3).

Vegetative cover is a quantitative estimate ofplant cover derived from viewing the aerialphotography in stereo magnification. Each polygonhad a cover rating assigned to three differentcategories: conifer, hardwood, and shrub. The threecover estimates assist in describing the vegetationcharacteristics of each polygon. Photo interpretersused six categories to define vegetative cover: 1 =Greater than 60%; 2 = 40%–60%; 3 = 25%–40%; 4= 10%–25%; 5 = 2%–10 %; 9 = Not applicable.Photo interpreters can only quantify the vegetationthat is visible on the aerial photography andtherefore total vegetative cover may differ fromassessments done on the ground by field crews.Using aerial photography at scales smaller thanabout 1:12,000, photo interpreters generally cannotsee the amount of vegetation that is obscured by ahigher canopy, regardless of i ts l ife form.Understory vegetation that is not visible on theaerial photograph cannot be quantified whenassigning the total cover of vegetation for thatpolygon. Assigning cover values to the polygonsrequired AIS to develop a series of rules forassigning these values:

1) To determine vegetative cover, assignpercentages to the different life forms visible on theaerial photo, including non-vegetated areas. Thetotal percent cover of conifer trees, hardwood trees,shrubs, herbaceous, and non-vegetated must add upto 100%. The cover percentages are then convertedinto the six cover categories described above.

2) Do not code non-vegetated areas unless theymeet the 0.5 ha minimum mapping resolution andcan be mapped as a stand-alone polygon. Otherwise,it was assumed that all vegetation polygons containsome non-vegetated areas.

3) Consider the coverage pattern of the life formbefore assigning a cover code to the polygon.Estimating cover is more straightforward whenplants occupying the same strata are evenlydistributed throughout the polygon. However, whenpolygons contain populations of plants that areclumped or occur only in portions of the polygon,the area that is not occupied by plant cover isconsidered when determining total cover. To ensureconsistency, count the plants in polygons withclumped and unevenly distributed vegetation andthen compare them to similar sized polygons withan even distribution of plant cover.

4) Use vegetation stature and the scale of theaerial photography to determine the visibility ofindividual plants. With larger scale photography,trees and shrubs were usually visible as individuals.Grasses were rarely seen as individual plants,regardless of the scale of the photography.

5) In the case of trees and shrubs, the percentcover at a cover break was adjusted downward. Ifthe percent cover was at about 25%, the polygonwas assigned a cover category of sparse (10%–25%)instead of dispersed (25%–40%).

6) Dry grasses tend to be less dense than theyappear on the aerial photography. To moreaccurately depict the cover, the percent cover for drygrasses was adjusted downward. If the percent coverfell at the lower end of a cover class, the polygonwas assigned the next cover class down. Forexample, if the percent cover was 25%, the polygonwas assigned a cover category of sparse (10%–25%)instead of dispersed (25%–40%).

7) The date that the aerial photography is flownalso influences the cover assigned to vegetationtypes, especially for herbaceous dominatedvegetation types. Subsequent field verification andaccuracy assessments must take into considerationthe following factors that can cause apparentdiscrepancies between the cover evident on thephoto and those visible in the field:

a) Seasonality—The cover of most herbaceousplants is variable due to their annual growthcycle. Depending on the season the aerialphotography was taken, a mapped polygon

234 COHEN ET AL.

could show a different cover on the aerialphotographs than is observed during an on-sitevisit at a different time of the year. Anothereffect of seasonality is "leaf on/off" conditions.Photos of forest or woodland areas with "leafon" conditions obscure the understory. Photosof leaf off conditions would allow photointerpretation of the understory, but make itdifficult to identify the overstory species sincethere is no foliage present.b) Annual variability—The environmentalconditions at the time of the photography (wetvs. drought years, flooding, etc.) may affect thecover seen during the on-site field visits.The size of a minimum mapping unit was 0.5 ha

(5000 square m). This minimum mapping unitrepresented a compromise between the desire tohave as much spatial and categorical detail aspossible, and the reality of available funding.Exceptions to this minimum mapping unit weremade for unique vegetations types and features thatare often smaller than 0.5 ha in the landscape (Table2). Distinct vegetation polygons smaller than 0.5 hathat were surrounded by spatially larger vegetationtypes were incorporated into the surrounding

dominant vegetat ion type. The del ineatedvegetation boundaries represent the photointerpreter’s estimate at defining the modal breakpoint between two communities, whether at adetailed association level of mapping or at a moregeneral habitat level (AIS 2007).

AIS created detailed documentation thatdescribes how each category was delineated. Thesedescriptions include mapping descriptions,environmental setting, distribution, and photointerpretation signature—mapping characteristics.This documentation is too large to include in thispape r , bu t can be acqu i r ed f rom: h t t p : / /www.tnccalifornia.org/gis/.

Data Conversion Data automation was conducted using Mono

Digitizing Stereo Digitizing (MDSD) software.Control points were identified both on the IKONOSimagery and the aerial photography and were inputinto an ARC/INFO point coverage. The MDSDsoftware captured the vegetation linework,automatically georeferenced the data into real worldcoordinates using the control points generated in theprevious step, and converted the linework into an

Table 2. Vegetation features mapped at scale less than 0.5 ha.

Type # of polygons Min. polygon size (ha)

Max. polygon size (ha)

Avg. polygon size (ha)

Total area (ha)

Australian Saltbush Mapping Unit

52 0.13 30.34 2.16 112.56

Big Leaf Maple Alliance 4 0.16 0.61 0.44 1.77

Bracken Fern Alliance 3 0.11 3.44 1.41 4.24

Coastal Salt Pan Mapping Unit 4 0.25 0.65 0.42 1.66

Fremont Cottonwood – Black Cottonwood Superalliance

32 0.06 2.07 0.53 16.86

Giant Wildrye – Creeping Wildrye Superalliance

14 0.17 1.55 0.61 8.56

Harding grass 14 0.06 12.52 2.33 32.61

Ironwood Alliance 483 0.03 3.73 0.37 178.94

Saltgrass Alliance 39 0.05 14.07 1.52 59.25

Sea Blite San Miguel Island Locoweed

33 0.19 3.88 0.86 28.33

Silver Beachbur – Beach Sand – Verbena Alliance

23 0.07 3.02 0.89 20.45


ARC/INFO coverage. The coverage was checkedfor open polygons, data registration, and any spatialedge match problems between photos. Spatialrefinement was performed in ARCEDIT sessionsusing various user-defined tools. Lines depictingboundaries representing minimal ecotones (forexample—land use interface, water bodies, life-form interface) were refined.

The vegetation mapping type, conifer cover,hardwood cover , shrub cover , and fennelcomponent modifier codes were input for eachpolygon based on those created by the photointerpreter during the mapping phase. Automatedquality control measures that AIS created were runto check for code validity.

A hard copy edit plot of the converted spatialdata was produced and compared to the aerial photooverlays. Each plot was checked for cartographicquality of the arcs defining the polygon features andthe accuracy of the label assignments. Line and codecorrections were noted directly on the edit plot. Alledit plots were edge matched to verify line and codeaccuracy across the entire project area. Processorsconducted interactive ARCEDIT sessions to makethe necessary corrections to the coverages. The finalvegetation layer was examined by the senior photointerpreter. Final checks were conducted to test forinvalid codes, duplicate labels, missing or extralines, edge match problems, verification of theregistration of linework to the IKONOS baseimagery, and review of the distribution of speciesmapped within the study area.

Accuracy AssessmentUpon completion of the preliminary photo

interpretation, the senior photo interpreter reviewedeach photo for polygon delineation, PI signaturecode, cover codes, and fennel modifier accuracy.Each photo overlay was checked for completeness,consistency, and adherence to the mapping criteriaand guidelines established by AIS. A rigorousquality control/ field verification effort wasundertaken in order to assure consistencythroughout the mapping product and to ensure photosignatures were accurately depicting what wasnoted on the ground. This effort was accomplishedby creating hard-copy plots at a scale of 1:8400.These depicted the polygon data over the CIRimagery, with a 1000 m UTM grid draped over theentire map. All relevant data including the floristic

code, density, and other modifiers were labeled inthe polygon. Using a GPS along with a rangefinder,compass, and the hard copy plots, photo interpretersaccompanied the island ecologist, Coleen Cory, on a1-week effort to locate and check over 300 polygonswithin a ½ mile buffer from most of the travelableroads on the island. This represented slightly fewerthan 3% of the total polygons mapped. Once thiseffort was completed, photo interpreters madecorrections to polygons that were noted in the fieldverification effort. In addition, any incorrect trendsbetween what was mapped to the aerial photosignature and what was actually found on theground were ref ined and correct ions wereextrapolated onto the remainder of the map. Due tofinancial and time related constraints, AIS was notcontracted by TNC to perform a formal accuracyassessment on the final database.

RESULTS

The resulting database contains 10,652polygons ranging in size from 0.03 ha to 624.19 ha,with a mean of 2.33 ha. The final vegetation map canbe down loaded f rom TN C’s We b s i t e :www.tnccalifornia.org/conservation/sciveg.html.The classification system begins with five classes:Forest, Woodlands, Shrublands, Herbaceous, andLand Use/Sparsely or un-vegetated. Shrublands arethe dominant class, covering roughly 70% of theisland (see Appendix). Herbaceous vegetation is thesecond largest class, at 21.2%. Forest, consistingpredominantly of Bishop Pine forest, covers 3.8%.The Woodlands and Land Use classes make up theremainder of the island, covering 2.8% and 2.4%,respectively.

The five classes are further broken down into 17formations. These formations were further brokenout into mapping units or alliances, and then evenfurther into sub-alliances or potential associations,where possible (see Appendix). For example, theShrublands class contains four formations:Temperate Broadleaf Sclerophyll EvergreenShrublands (Chaparral); Temperate MicrophyllousEvergreen Shrublands; Temperate Xeric MixedDrought-Deciduous Shrublands; and TemporarilyFlooded Cold Season Deciduous Shrublands.Nested within the Temperate Broadleaf SclerophyllEvergreen Shrublands (Chaparral) formation is the

236 COHEN ET AL.

Island Scrub Oak alliance. That alliance comprises26.2% of the area mapped in its class (Shrublands),and 89.2% of the area mapped in its formation.Nested within the Island Scrub Oak alliance are sixsub-alliances, the largest of which is Island ScrubOak–Island Ceanothus, which accounts for 37% ofthe alliance, 33% of the formation, 9.7% of theclass, and 6.8% of the island. The 720.3 ha assignedto the Island Scrub Oak alliance could not be furtherclassified into one of the six sub-alliance andrepresents 15.8% of that group.

Cover Assigning one of five cover categories for

conifer, hardwood, and shrub cover to eachvegeta t ion po lygon y ie lds a mul t i tude ofcombinations between vegetation type and woodyspecies cover. There are 125 possible uniquedescriptors of woody species cover that could beassigned to each of the 10.652 delineated polygonsand 216 combinations if a zero value is one of thecover values.

Conifers, hardwoods, and/or shrubs occurred insufficient cover over nearly 90% of the island toallow a cover estimate to be assigned to at least oneof these woody plant groups (Table 3). Theremainder of the island did not have sufficientwoody cover to be assigned cover estimates forconifers, hardwoods, or shrubs. Over half of theisland was mapped as having a shrub cover <25%.The re-establishment of shrubs into grasslands orbarren areas post-grazing may account for these lowcover estimates. The cover measures for conifersmay also describe regeneration patterns. Over 70%of areas with conifers exhibit conifer cover <25%.

Cover values for hardwoods are even lower. Two-thirds of areas with hardwoods show cover <10%.

FennelPolygons dominated by fennel account for

roughly 1% of the island. Polygons that were givena fennel modifier, indicating some amount of fennelwas present, represent another 6% of the island.Nearly 40% of this latter category consists of fenneloccurring in the California Annual Grasslandsalliance. Another 20% consists of fennel occurringin the Coyote Brush alliance. In total, 3% of theisland has vegetation where fennel comprises >10%of the vegetation polygon (Table 4).

Table 3. Summary of cover estimates.

Cover category* Conifer (ha) % of island Hardwood (ha) % of island Shrub (ha) % of island

1 18.6 0.1% 244.9 1.0% 3398.4 13.6%

2 153.3 0.6% 301.6 1.2% 2101.1 8.4%

3 160.7 0.6% 222.0 0.9% 3109.4 12.5%

4 331.3 1.3% 193.1 0.8% 7158.1 28.7%

5 516.2 2.1% 1904.2 7.6% 6511.1 26.1%

Totals 1180.1 4.7% 2865.8 11.5% 22,278.1 89.5%

*Cover categories: 1 = >60%; 2 = 40%–60%; 3 = 25%–40%; 4 = 10%–25%; 5 = 2%–10%.

Table 4. Summary of area mapped with fennel modifier.

Fennel modifier* Ha % of island

4301 Fennel dominant 244.9 1.0%

3 – Severe** 489.1 2.0%

2 – Moderate 335.4 1.3%

1 – Minimal 679.7 2.7%

* 1 = Minimal: Generally less than 5% cover of fennel in the polygon. Photo interpreters may or may not be able to detect these small amounts, and ground based information is often necessary in assigning a fennel modifier of 1.

2 = Moderate: Approximately 5%–10% cover of fennel over most of the polygon. Polygons with this cover class are visible on the aerial photography.

3 = Severe: Over 10% cover of fennel is in the polygon. Fennel is often a co-dominant to other herbaceous vegetation.

** In this table Fennel modifier "severe" does not include polygons in the 4301 mapping unit.


Accuracy AssessmentWhile no formal accuracy assessment was

performed, the field verification process found afew labeling discrepancies that were subsequentlycorrected. Of the roughly 300 polygons checked foraccuracy, fewer than 10 polygons required changesto their attributes.

DISCUSSION

The spatial database created as a result of thismapping project represents the vegetationcondi t ions on SCI a t the end of 2005. Asmanagement of island resources continues, we willwant to track changes in vegetation that may resultfrom our management. This is best done byconducting an update to the spatial vegetationdatabase in the future. The decision rules described,implemented, and documented by AIS will allow usto reproduce methodology so that future iterationsof the database wil l be both spat ial ly andcategorically comparable. Furthermore, thedatabase structure will allow updates and futuremapping data to be seamlessly integrated with

existing data. In a similar fashion, if other CaliforniaChannel Islands were mapped using these sameprotocols, data could be easily compared acrossislands.

Minnich (1980) created a vegetation map forSCI using 1:22,000 scale color infrared aerialphotography dating from 1970. No a prioriclassification system was used. Vegetation wasinstead grouped by floristic and physiognomiccharacteristics and assigned to one of eightphysiognomic types: Grassland, Coastal SageScrub, Chaparral, Woodlands, Conifer Forest,Riparian, Woody Exotics, and Barren (Table 5).These types were further subdivided into one of 36vegetation sets based on the dominant species asdiscerned from the photos.

Jones et al. (1993) used 1:24,000 scale aerialphotography dating from 1985 to create an SCIspatial vegetation database based on a classificationscheme devised by Philbrick and Haller (1977).Island vegetation was assigned to 1 of 11 vegetationclasses based on tone, texture, and context ofvegetation observed in aerial photos (Table 6).

Table 5. Comparison of vegetation classification categoriesused in previous vegetation mapping of Santa Cruz Island.

Minnich 1980 Jones et al. 1993

Based on 1970 photos Based on 1985 photos

Physiognomic type Vegetation class

Grasses Grasses

Coastal Sage Scrub Coastal Sage Scrub

Chaparral Chaparral

Woodlands Island Oaks*

Oaks

Island Ironwoods

Conifer Forest Pines

Riparian Riparian

Woody Exotics Woody Exotics

Barren Barren

* Island Oaks refers only to Quercus tomentella. All other oaks are included in "Oaks" category.

Table 6. Changes in vegetation cover between 1985 and 2005on Santa Cruz Island.

Vegetation type(from Jones etal. 1993)

Hectares (1985)

Hectares (2005)

Grasses 16,578.8 5037.5

Coastal Sage Scrub

809.7 11,921.7

Chaparral 3585.8 5153.2

Oaks* 1031.0 666.2

Island Ironwoods 103.6 179.1

Pines 292.1 664.0

Riparian 463.8 373.5

Woody Exotics 27.1 33.6

Barren 1665.6 569.6

*The "Oaks" category from 1985 includes several species of oaks (but not island oaks) and thus represents more acreage than the 2005 data, which distinguishes among these oak species and assigns them to different and finer scale mapping units.

238 COHEN ET AL.

NPS (2004) created a vegetation database,using 2-ft digital aerial photography, that classifiedthe vegetation at the formation or sub-formationlevel where possible. These types included mediumtall grassland (35%), mainly deciduous shrubland(25.4%), mainly evergreen shrubland (22.6%),mainly evergreen woodland (9.5%), forb-dominated vegetation (5.4%), mainly evergreenforest (0.25%), mainly deciduous woodland (0.6%),human use (0.1%), and mainly deciduous forest (>0.1%).

Despite the differing methodology andvegetation groupings, we attempted to conduct acomparison between the 1985 (Jones et al. 1993)mapping data and our 2005 data. We constructed across-walk table that assigned each of our 2005vegetation categories to one of the more inclusivecategories from 1985. We assigned these categoriesbased on physiognomic characteristics, species

composition, and spatial distribution. Bare grounddecreased dramatically during the intervening 20years while at the same time all types of vegetationexpanded across the island (Fig. 1; Table 6). Theincrease in vegetative cover corresponds to the timeperiod during which more than 46,000 feral sheepand the remaining 2000 head of cattle were removedfrom the island.

Subsequent mapping can be used to trackadditional changes that are likely to occur as a resultof removing more than 5000 feral pigs from theisland between 2005 and 2007. Pigs demonstrated afood preference for certain plants, both native (oaksand bulb plants) and non-native fennel (NPS 2002).Their absence from the island may be reflected inshifting vegetation patterns that can be detectedthrough fine-scale vegetation mapping.

Fennel is a perennial herb native to theMediterranean basin. It was introduced to SCI

Figure 1. Vegetation change on Santa Cruz Island, 1985–2005. Maps depict generalized vegetation categories: bare ground andherbaceous vegetation, white; scrub and low stature vegetation, light gray; chaparral and medium canopy communities, dark gray;forest and woodland, black. (A) Vegetation prior to/during the eradication of feral sheep (adapted from Jones et al. 1993 andHowarth et al. 2005). (B) Vegetation of 2005. Island location in the state of California shown in inset.


sometime in the mid- to late 1800s. By 1887 it wasthoroughly established on the hillsides aroundPrisoners' Harbor (Junak et al. 1995). Sheep andcattle grazed fennel and spread seeds in their fecesand on their hooves. Pig wallows and rooting werenumerous in fennel areas (B. Cohen, C. Cory, and J.Menke, personal observation 2006), and pigs werealso likely vectors for seeds. It is interesting to notethat while sheep and cattle were present, fennel waskept low enough in stature to not be noticed orcategorized in previous vegetation maps (Minnich1980; Jones et al. 1993). With removal of cattle inthe mid-1990s, fennel became more conspicuous inthe landscape. The current database designates aseparate category for fennel-dominated areas (AIS2007). These fennel polygons were also assignedcover categories. Future vegetation mapping canthus track the extent and cover of fennel. Decreasingfennel cover can indicate recolonization by otherplants.

Conservation organizations and agencies spenda great deal of public and private money protectingand restoring elements of biodiversity. On SCI thishas included removal of feral non-native animalsand invasive weed species as well as supportingprojects to increase the numbers of native foxes,bald eagles, and rare plants. Tracking andmonitoring results and consequences of theseactions also require a significant investment of timeand money. For land managers of SCI, this type ofvegetation map and database represents a cost-effective way to monitor long-term changes across alarge landscape. Tradit ional ground-basedvegetation monitoring methods using plots,transects, relevés, etc., can be extremely costly interms of staff time. While such ground-based datamay be very detailed, they are limited in spatialscale, and often limited in scope due to rugged orinaccessible terrain. Unlike vegetation mappingbased on plot data that must then be extrapolated toareas not sampled, the methodology presented hereessentially samples the entire landscape andprovides relatively fine-scale information for anygiven point on the island. We believe that byemploying both fine-scale field methods and largerscale photo-based methods, we have developed ahighly sensitive and cost-effective means ofmonitoring changes to vegetation across alandscape within a broad range of scales.

ACKNOWLEDGMENTS

The authors would like to thank staff membersof Aerial Information Systems, Inc., TNC’s SantaCruz Island Project, the Channel Islands NationalPark Service, the USGS-Biological ResourcesDiscipline, and the California Department of Fishand Game for all of their assistance in making thisproject happen. We would also like to thank L.Lozier, R. Shaw, T. Keeler-Wolf, D. Garcelon, andS. Solis for their reviews and contributions to thismanuscript.

REFERENCES

Aerial Information Systems. 2007. Santa CruzIsland Photo Interpretation and MappingClassification Report. Aerial InformationSystems, Inc., Redlands, CA.

AIS. See Aerial Information Systems.Federal Geographic Data Committee. Vegetation

Subcommittee. 2008. National vegetationclassification standard, version 2. FGDCdocument number FGDC-STD-005-2008.Online document available through http://www.fgdc.gov.

FGDC. See Federal Geographic Data Committee.Grossman, D.H., D. Faber-Langendoen, A.W.

Weakley, M. Anderson, P. Bourgeron, R.Crawford, K. Goodin, S. Landaal, K. Metzler,K.D. Patterson, M. Pyne, M. Reid, and L.Sneddon. 1998. International Classification ofEco log i ca l Communi t i e s : Te r r e s t r i a lVegetation of the United States. Volume I: TheNational Vegetation Classification Standard.The Nature Conservancy. Arlington, VA, 126pp.

Hochberg, M., S. Junak, and R. Philbrick. 1980.Botanical Study of Santa Cruz Island for TheNature Conservancy. Santa Barbara BotanicGarden, Santa Barbara, CA, 90 pp.

Howarth, J., S. O'Connor, and L. Harrison. 2005.Vegetation of Santa Cruz Island Digitized from1985 Map by Steve Junak, Santa BarbaraBotan ica l Garden. Class projec t f romGeography 176c: GIS Design and Application.Spring 2005. University of California, SantaBarbara. CA.

240 COHEN ET AL.

Jennings, M., O. Loucks, D. Glenn-Lewin, R. Peet,D. Faber-Langendoen, D. Grossman, A.Damman, M. Barbour, R. Pfister, M. Walker,S. Talbot , J . Walker , G. Hartshorn, G.Waggoner, M. Abrams, A. Hill, D. Roberts,and and D. Tar t . 2003. Guidel ines forDescribing Associations and Alliances of theU.S. National Vegetation Classification. TheEcological Society of America, VegetationClassification Panel, Version 3.0 November2003. 100 pp. (+ Appendices).

Jones, J.A., S.A. Junak, and R.J. Paul. 1993.Progress in mapping vegetation on Santa CruzI s l and a nd a p r e l im in a ry ana ly s i s o frelationships with environmental factors.Pages 97–104. In: Hochberg, F.G. (ed.), ThirdCalifornia Is lands Symposium: RecentAdvances in Research on the CaliforniaIslands. Santa Barbara Museum of NaturalHistory, Santa Barbara, CA.

Jones, L., P. Collins, and R. Stefani. 1998. AChecklist of the Birds of Channel IslandsNational Park. 1st revised edition. SouthwestParks and Monuments Association, Tucson,AZ. 8 pp.

Junak, S., T. Ayers, R. Scott, D. Wilken, and D.Young. 1995. A Flora of Santa Cruz Island.Santa Barbara Botanic Garden and CaliforniaNative Plant Society, Santa Barbara andSacramento, CA, 408 pp.

Knapp, D.A. 2005. Vegetation communitymapping on Santa Catalina Island usingorthorectification and GIS. Pages 193–203. In:Garcelon, D.K., and C.A. Schwemm (eds.),Proceedings of the Sixth California IslandsSymposium. Institute for Wildlife Studies,Arcata, CA.

Laughrin, L.L. 1980. The Vertebrates of SantaCruz Island: Review, Current Status andManagement Recommendation. Reportprepared for The Nature Conservancy. SantaBarbara, CA, 59 pp.

Minnich, R. A. 1980. Vegetation of Santa CruzIsland and Santa Catalina Islands. Pages 123–137. In: Power, D.M. (ed.), The CaliforniaIslands: Proceedings of a MultidisciplinarySymposium. Santa Barbara Museum ofNatural History, Santa Barbara, CA.

Morrison, S.A. 2008. Reducing risk and enhancingefficiency in non-native vertebrate removalefforts on islands: a 25 year multi-taxare t rospect ive f rom Santa Cruz Is land,California. Pages 398–409. In: Witmer, G.W.,W.C. Pit t , and K.A. Fagerstone (eds.) ,Managing Vertebrate Invasive Species:Proceedings of an International Symposium.USDA/APHIS/WS, Na t iona l Wi ld l i f eResearch Center, Fort Collins, CO.

Morrison, S.A., N. Macdonald, K. Walker, L.Lozier, and M.R. Shaw. 2007. Facing thedilemma at eradication's end: uncertainty ofabsence and the Lazarus effect. Frontiers inEcology and the Environment 5:271–276.

National Park Service. 2002. Santa Cruz IslandPrimary Restoration Plan. Final EnvironmentalImpact Statement. Channel Islands NationalPark, U.S. Department of the Interior. Ventura,CA, 213 pp.

National Park Service. 2004. Santa Cruz IslandVegetation [computer file]. Ventura, CA.

NPS. See National Park Service.Philbrick, R.N., and R.J. Haller. 1977. The

southern California Islands. Pages 893–906.In : Barbour, M.G., and J. Major (eds.),Terrestrial Vegetation of California. JohnWiley and Sons, New York, NY.

Sawyer, J.O., and T. Keeler-Wolf. 1995. A Manualof California Vegetation. California NativePlant Society, Sacramento, CA, 471 pp.

Sawyer, J.O., T. Keeler-Wolf, and J. Evens. 2009.A Manual of California Vegetation, 2ndedition. California Native Plant Society,Sacramento, CA.


App

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242 COHEN ET AL.

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244 COHEN ET AL.

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es

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9420

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ock

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510.

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eds a

nd F

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39

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5%7.

0%96

00 –

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nted

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Lan

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al60

9.5

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App

endi

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ontin

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tribu

tion

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a spatial database of santa cruz island...

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