the role of environmental context in mapping invasive plants with hyperspectral image data

Remote Sensing of Environment 112 (2008) 4301–4317

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Remote Sensing of Environment

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The role of environmental context in mapping invasive plants with hyperspectralimage data

Margaret E. Andrew ⁎, Susan L. UstinDepartment of Land, Air, & Water Resources, University of California Davis, Davis, CA 95616, United States

⁎ Corresponding author.E-mail address: [email protected] (M.E. Andre

0034-4257/$ – see front matter © 2008 Elsevier Inc. Aldoi:10.1016/j.rse.2008.07.016

a b s t r a c t
a r t i c l e i n f o
Article history:
Lepidium latifolium (perenn Received 30 May 2008Received in revised form 24 July 2008Accepted 26 July 2008
Keywords:HyMapHyperspectral imagingInvasive plant speciesLepidium latifoliumMixture tuned matched filtering (MTMF)Perennial pepperweedRemote sensingWeed mapping

ial pepperweed) is a noxious Eurasian weed invading riparian and wetland areasof the western US. Effective management of Lepidium requires detailed, accurate maps of its distribution, asmay be provided by remote sensing, to contain existing infestations and eradicate incipient populations. Wemapped Lepidium with 3 m spatial resolution, 128-band HyMap image data in three sites of California's SanFrancisco Bay/Sacramento–San Joaquin Delta Estuary (Rush Ranch in Suisun Marsh and the Greater JepsonPrairie Ecosystem and the Cosumnes River Preserve in the Delta). These sites are markedly different in termsof hydrology, salinity, species composition, and structural and landscape diversity. Aggregated classificationand regression tree models (CART), incorporating the results of mixture tuned matched filter (MTMF)analyses and spectral physiological indexes, were used to map Lepidium at the three sites. This approach wassufficiently flexible and robust to detect Lepidium with similar accuracies (~90%) at both Rush Ranch andJepson Prairie, but was unsuccessful at Cosumnes River Preserve. Comparisons of the behavior of the MTMFsand the CARTs between sites reveal the importance of environmental context in species mapping. RushRanch presents the simplest conditions for mapping Lepidium: it is the wettest and least diverse site andLepidium is spectrally distinct from co-occurring species. At Jepson Prairie, several co-occurring speciesclosely resemble Lepidium spectrally. Nevertheless, hyperspectral data provide sufficient spectral detail toresolve Lepidium even at this challenging site, which is facilitated by phenological separation from the matrixof annual grasses. At Cosumnes River Preserve, however, Lepidium is neither spectrally nor phenologicallydistinct, and consequently could not be mapped successfully. Evidence suggests that the success of a remotesensing analysis declines as site complexity increases (species, structural, and landscape diversity; spectralvariability; etc.), although this relationship is complex, indirect, and may be phenology-dependent.

© 2008 Elsevier Inc. All rights reserved.

1. Introduction

1.1. Remote sensing of invasive species

Invasive species are a form of “biological pollution” and a majorcomponent of anthropogenic global change (Ricciardi, 2007). Liketraditional pollutants, invasive species have large associated environ-mental, economic, and cultural costs (Mack et al., 2000). And, as withtraditional pollutants, the numbers of invasive species and of eco-systems impacted by them have increased over time (e.g.: Cohen &Carlton, 1998; Wilson et al., 2007). Developing accurate, spatiallyexplicit, fine-scale records of invasions is a high priority (Mack, 2005;Panetta & Lawes, 2005). It is essential to know where infestationsoccur in order to manage them and to understand their causes andtheir consequences. Currently available databases, typically county-level inventories, are too coarse for many applications, including effi-

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cient management. However, traditional ground-based surveyscannot comprehensively cover any but the smallest areas. Samplingmethods, while increasing survey efficiency, may yield biased recordsof distribution and metrics of abundance (Barnett et al., 2007; Rewet al., 2006).

Remote sensing technologies offer the capability to rapidly andsynoptically monitor large areas. The wealth of spectral informationprovided by hyperspectral sensors allows for the species-level detec-tion necessary to map invasive weeds (Clark et al., 2005; Underwoodet al., 2007). There exists a growing body of work on the hyperspectraldetection of invasive species (e.g., Asner & Vitousek, 2005; Chenget al., 2007; Hamada et al., 2007; Hestir et al., in press; Lass et al., 2005;Lawrence et al., 2006; Mundt et al., 2005; Noujdina & Ustin, 2008;Parker Williams & Hunt, 2002; Underwood et al., 2003, 2006).

Most weed mapping studies discuss their work in the context ofthe species characteristics that contribute to mapping success. Allplants are spectrally similar because they are composed of the samespectrally active materials: pigments, water, cellulose, etc. (Jacque-moud & Baret, 1990). The spectral uniqueness requisite for hyper-spectral detection most often occurs when invaders possess

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Fig. 1. (a) Photo of Lepidium latifolium (perennial pepperweed), highlighting the thick,white inflorescence. (b) A dense infestation of Lepidium in the Sacramento–San JoaquinRiver Delta. (c) Field spectra of flowering and fruiting phenologies of Lepidium, alongwith a typical reflectance spectrum of green vegetation for reference.

4302 M.E. Andrew, S.L. Ustin / Remote Sensing of Environment 112 (2008) 4301–4317

physiological traits or phenologies that are novel to the invadedecosystems. For example, remotemapping of foliar nitrogen andwaterallowed the detection of an invasive nitrogen fixer, Morella faya, andan invasive understory herb, Hedychium gardnerianum, respectively(Asner & Vitousek, 2005); quantification of the water absorptionfeature was sufficient to map the invasive succulent iceplant (Under-wood et al., 2003); pigment content of floral bracts enabled mappingof leafy spurge (Hunt et al., 2004, 2007); and phenological timingcontributed to the detection of tamarisk (Everitt & Deloach, 1990; Geet al., 2006) and cheatgrass (Bradley & Mustard, 2006; Noujdina &Ustin, 2008) via color changes and altered seasonality relative tonative species, respectively.

Very little attention has been paid, however, to the role of environ-mental context in determining mapping success. Most publishedstudies have been performed at only a single, small-scale site, pre-cluding any investigation into the effects that the specific ecologicalmatrix has on the remote detection of that species. Underwood et al.(2003) report variable success at detecting iceplant invading chaparralversus scrub communities, and attribute this to taller chaparralspecies overtopping the iceplant. Parker Williams and Hunt (2002)achieved different success modeling leafy spurge cover in differentplant communities, and relate this to the influence of moistureavailability andmicroclimate on phenology and cover and the effect oftree canopies on detection. Hestir et al. (in press) and Underwood et al.(2006) note that when mapping submerged aquatic vegetation at theregional scale, variation inwater quality and depth to theweed canopyinfluences detection. However, in all of these cases, the authors notethat detection differs between communities and locations, butprovide only anecdotal explanations of the ecological and biologicalbases behind those differences.

Although no communities are immune to invasion (Williamson,1996), habitats do vary in the degree to which they are susceptible toinvasion and in the relationships displayed between invasibility andbiotic and abiotic characteristics (e.g., Larson et al., 2001; Pino et al.,2006; Rouget & Richardson, 2003; Stohlgren et al., 1999, 2002; Vilàet al., 2007). It is important to understand how habitat also influencesdetectability so that inferences about habitat suitability are notconfounded by discrepancies in the mapping process. This concernis not unique to remote sensing but is relevant to all mapping andsurveying methods since on-the-ground detectability also varies withcontext.

Here we mapped Lepidium latifolium (perennial pepperweed) atthree sites in the San Francisco Bay/Sacramento–San Joaquin RiverDelta (henceforth: Delta) in California: Rush Ranch in Suisun Marsh,the Greater Jepson Prairie Ecosystem on the northwest side of theDelta, and Cosumnes River Preserve in the northeast Delta. Althoughgeographically close to each other, these sites are environmentallydifferent and present significantly different conditions for thedetection of Lepidium, highlighting the influence of site characteristics,specifically site complexity, on the detectability of this species.

1.2. Study organism

Lepidium is a noxious Eurasianweed invading the western US. It hasbeen present in California for much of the last century, but beganaggressively expanding its range in the 1980s (Howald, 2002). It is anherbaceous perennial that invades wetland and riparian areas and istolerant of a range of salinities. It has prolific seed production (Younget al., 1997) and spreads clonally via perennial roots and root fragments.Infestations form dense monocultures, displace native species (Blanket al., 2002; Young et al., 1998), and may alter biogeochemical cyclingand soil properties (Blank & Young, 1997, 2002). Very few managementstrategies are effective against Lepidium's belowground structures(Renz, 2002; Younget al.,1998). Several herbicides successfullyeradicatethis weed; however their use is often restricted due to toxicity concernsin the wetland habitats that Lepidium invades (Francis & Warwick,

2007). Sites where Lepidium has been eradicated are not readilyrecolonized by native species (Renz, 2002; Spenst, 2006).

Lepidium is now found in all but three California counties (Spenst,2006) andwas listedas a toppriority by24of 35weedmanagement areasin 2003, making it the fifthworst weed statewide (California InteragencyNoxiousWeedCoordinatingCommittee, 2003).Due to itsnearubiquitousdistribution in California, the dramatic impacts it has on ecosystems, andthe difficulty of controlling infestations, Lepidium is recognized as anoxious weed by both the California Invasive Plant Council and theCalifornia Department of Food and Agriculture. Frequent, high quality

http://dx.doi.org/doi:10.1016/j.rse.2008.01.022

4303M.E. Andrew, S.L. Ustin / Remote Sensing of Environment 112 (2008) 4301–4317

monitoring is essential to implement containment anderadicationeffortsand to evaluate their effectiveness (Panetta & Lawes, 2005).

Previous research has demonstrated that flowering and fruitingphenologies of Lepidium can be spectrally distinct from co-occurringspecies (Andrew & Ustin, 2006). Spectral uniqueness of Lepidium isconferred by its characteristic top-of-canopy panicle inflorescence ofwhite flowers, which obscures the sensor's view of the foliage andresults in bright, broad reflectance in the visible (Fig. 1). Here wepresent the first efforts to map this weed with hyperspectral imagedata.

2. Methods

2.1. Study sites

This research was conducted at the Rush Ranch Open SpacePreserve (122.02° W, 38.20° N), a complex of four preserves in theGreater Jepson Prairie Ecosystem (Eastern Wilcox Ranch, JepsonPrairie Preserve, Calhoun Cut Ecological Reserve, and Barker Slough;121.84° W, 38.26° N), and the Cosumnes River Preserve (121.40° W,38.28° N) (Fig. 2). Rush Ranch is located in the brackish, tidal SuisunMarsh (detailed salinity data were not available, but mean annualsalinity=5.6±4.3 g/kg; Uncles & Peterson, 1995) and provides habitat

Fig. 2. Locator map showing the locations of the three study sites. Preserves are outlined in blJoaquin River Delta are outlined in gray.

for several state and federally listed threatened and endangeredspecies. Vegetation communities are characterized by the wetlandspecies Schoenoplectus acutus and Schoenoplectus californicus (tule),Salicornia virginica (pickleweed), Distichlis spicata (saltgrass), Phrag-mites australis (common reed), and Typha angustifolia and Typha lati-folia (cattail) in the marsh and invasive annual Mediterranean grassesin the uplands. Lepidium was first observed at Rush Ranch in the late1990s and has been the subject of control efforts since 2001. Moreintegrated, widespread management of this weed is planned to startin 2008 (B. Wallace, Solano Land Trust, personal communication).

The Greater Jepson Prairie Ecosystem borders the Delta to thenorthwest. The area is characterized by vernal pools scatteredthroughout a matrix of invasive annual Mediterranean grasses andalso contains riparian and freshwater marsh habitat (salinity in theDelta is essentially zero; Uncles & Peterson, 1995). Vernal pools area formerly widespread component of California's Central Valley,often containing unique and endemic species, but are now anendangered landscape feature due to agriculture and development.Lepidium has been present in the Greater Jepson Prairie Ecosystemfor about 20 years and has been expanding since the 1990s. Lepidiumis a species of concern in this system and is undergoing grazing,mechanical, and chemical control (B. Wallace, Solano Land Trust,personal communication).

ack, image extents are shaded, and waterways of the San Francisco Bay/Sacramento–San


Cosumnes River Preserve, in the northeast Delta, is a mosaic ofagricultural and natural systems, including uplands, riparian forests,perennial lakes, and freshwater marshes. As at Jepson Prairie, salinityis essentially zero (Uncles & Peterson, 1995). The Cosumnes River hasbeen reconnected to its floodplain, making this riparian site unique inthe highly managed and strictly leveed Delta. Lepidium is a manage-ment priority at Cosumnes River Preserve.

2.2. Image data

HyMap imagerywas acquired for Rush Ranch (4flightlines; 5500 ha;26 June 2006), the Greater Jepson Prairie Ecosystem (3 flightlines;6300 ha; 22–23 June 2006), and the Cosumnes River Preserve

Fig. 3. Reflectance spectra of the dominant species at (a) Rush Ranch, (b) Jepson Prairie, and (c) C

(4 flightlines; 4000 ha; 28 June 2005) by the HyVista Corporation(http://www.hyvista.com/). All image data were collected in earlysummer, when spectrally unique phenologies of Lepidium are knownto occur (Andrew & Ustin, 2006). HyMap is an airborne imagingspectrometer that samples the wavelengths 450–2500 nm with 12815–20 nm bands (Cocks et al., 1998). Imagery was acquired from analtitude of 1.5 km, yielding a nominal 3 m ground instantaneous field ofview. Image data were atmospherically corrected by the vendor usingthe HyCorr program, which is based on the Atmospheric Removal(ATREM) radiative transfer algorithm (Gao et al., 1993) that uses theMODTRAN code resolved at 10 nmbands (USAF, http://www.kirtland.af.mil/library/factsheets/factsheet.asp?id=7915). Imagery was georefer-enced using an orthorectification algorithm developed by Analytical

osumnes River Preserve. These spectrawere used as training endmembers for theMTMFs.

http://www.hyvista.com/

http://www.kirtland.af.mil/library/factsheets/factsheet.asp?id=7915

http://www.kirtland.af.mil/library/factsheets/factsheet.asp?id=7915


Imaging and Geophysics, LLC (Boulder, CO). A minimum of 20 groundcontrol points with a total root mean square error b1.0 pixel wereselected for each flightline from 1 m digital orthophoto quarter-quads(USGS). USGS National Elevation Dataset DEMs were used for theorthorectification.

2.3. Ground reference data

Ground reference data (GRD) to train and validate classificationswere collected on 23 August 2006 at Rush Ranch and 29 August 2006at Jepson Prairie. Lepidium was fruiting and/or senescing at the timeof fieldwork; both of these phenologies are conspicuous to fieldcrews. Data were recorded for patches of Lepidium and co-occurringvegetation (especially Salicornia and Distichlis at Rush Ranch andCentaurea solstitialis (yellow starthistle) and Centaurea calcitrapa(purple starthistle) at Jepson Prairie). Patches were defined asdiscrete areas covered by a given species. GRD points were spatiallydispersed to avoid autocorrelation, no points were collected within20 m of a conspecific point and points were frequently much fartherfrom each other. Points were selected on the basis of known occur-rences and accessibility. Geographic coordinates were recordedwithin each patch (though not necessarily at the centroid) with aTrimble GeoXT handheld global positioning system unit. Patchcharacteristics including species identification, visual estimates ofpercent cover of dominant and co-occurring species, patch dimen-sions, and orientation were also recorded. Overhead and obliquefield photos were taken of each patch. A total of 79 points (48 Le-pidium) were collected at Rush Ranch and 132 (66 Lepidium) atJepson Prairie. For Cosumnes River Preserve, points were extractedfrom a comprehensive inventory of Lepidium collected over theperiod 2002 to 2006. Because this survey provided only presencedata, absence points of trees, agriculture, litter, and soil werephotointerpreted from the hyperspectral image. Sample size at thissite was 2230 (791 Lepidium). All points were screened relative tothe field photos and the georeferenced image data and suspectpoints were removed. Since the reference data for Cosumnes RiverPreserve differs from that for the other sites, the influence of GRDsource on classifications and site comparisons was assessed.Developing classifiers at Rush Ranch and Jepson Prairie on sets ofphotointerpreted points instead of field Lepidium absence recordshad negligible impact on classifier behavior and did not influence theconclusions of this research.

2.4. Image analysis

Flightline mosaics were created for each site. A minimum noisefraction (MNF) transformation was performed on various spectralsubsets of the mosaics. MNF is a statistical data reduction techniquethat performs a series of two principal components analyses to isolatenoise and reduce the dimensionality of a hyperspectral dataset (Greenet al., 1988) and is a necessary preprocessing step for mixture tunedmatched filtering (below). MNF bands that contained dramaticbrightness differences between flightlines in the mosaics (flightlineeffects) and those occurring after an 80% variance threshold werediscarded from further analyses. MNF outputs must be vetted forflightline effects because these may contribute spurious differencesbetweenmaterials of interest (i.e., when they are unevenly distributedover flightlines) and will reduce the generality of a classificationacross the image mosaic.

Mixture tuned matched filtering (MTMF) was performed on theMNF mosaics. MTMF is an advanced spectral unmixing algorithm thatdoes not require that all materials within a scene are known and haveidentified endmembers (Boardman et al., 1995). MTMF treats eachendmember independently and, at each pixel for each endmember,models the pixel as a mixture of the endmember and an undefinedbackground material. It outputs a matched filter (MF) score and an

infeasibility value for each endmember. The MF score, an estimate ofthe areal coverage of a pixel by the material of interest, is analogous tothe fraction value from simple SMA; the infeasibility is a measure ofhow likely a pixel is to contain the material of interest. Pixels are likelyto contain materials for which they receive high MF scores and lowinfeasibilities. MTMF has proven to be very powerful and is one of themost effective techniques for detecting specific materials that differsubtly from the background. MTMF has successfully mapped a varietyof sparse weed species (Glenn et al., 2005; Mundt et al., 2005; ParkerWilliams & Hunt, 2002).

MTMFs at each site were trained with endmembers of Lepidiumand of the other dominant land covers (Fig. 3) — Distichlis, Salicornia,C. solstitialis, water, and litter at Rush Ranch; C. calcitrapa, Typha, agri-culture, litter, water, and soil at Jepson Prairie; and trees, agriculture,litter, and soil at Cosumnes River Preserve. Lepidium at the CosumnesRiver Preserve exhibited high spectral and phenological variability;eight Lepidium endmembers were used to capture this variation. Thespectrumplotted in Fig. 3c is the average of all eight endmembers. At allsites, MTMFs were performed on the subset of MNF bands thatminimized flightline effects and that maximized the separation in MFscores between pixels with 0% cover of Lepidium and those dominatedby Lepidium. Although this resulted in different bands being used at thetwo sites, the same objective criteria were used to determine whichbands to omit (i.e., occurred after the cumulative 80% variation thresh-old; contained conspicuous flightline effects; or did not contribute todifferentiating Lepidium from co-occurring species).

Thirteen physiological indexes — SR, NDVI, mNDVI, SGR, PRI, RGratio, PI2, NDWI, WBI, NDNI, NDLI, CAI, and LI — were also calculatedfrom the reflectance mosaics at each site (Table 1). LI, Lepidium index,was developed to be sensitive to the uniformly bright reflectancedisplayed by Lepidium in the visible and was calculated as:

LI ¼ R630=R586 ð1Þ

where R630 and R586 are the reflectance at 630 nm and 586 nm. Thesebands were manually selected following inspection of many Lepidiumand green vegetation spectra. Vegetated pixels displaying a typicalpeak in the green have LI values b1, Lepidium-type pixels withrelatively uniform reflectance across the visible have LI≈1, and pixelsof soil and litter have LIN1. One additional feature, the area of thecellulose absorption at 2100 nm, was calculated for Jepson Prairiebecause preliminary analyses at this site confused Lepidiumwith litter,despite inclusion of the cellulose absorption index (CAI). This was nota problem at the other sites.

MTMF outputs (MF scores, infeasibilities, and the identity of theendmember with the maximum MF score) were combined with thesuite of physiological indexes in automated classification trees at eachsite (Breiman et al., 1998) using the See5 decision tree software(RuleQuest Research, St. Ives, New South Wales, Australia). Decisiontrees are powerful classification tools that can incorporate data of avariety of types and from multiple sources. Decision trees are non-parametric; they place no distributional assumptions on the data, andthus arewell suited for classeswithmulti-modally distributed spectralresponses. They have been found to outperform traditional remotesensing classifiers (Friedl & Brodley, 1997; Hansen et al., 1996). Treeswere developed on a training set of 50% of the GRD and a randomsample of pseudo-absence pixels extracted from each site. AtCosumnesRiver Preserve, the pseudo-absence samplewas constrainedto encompass only the range of NDVI (normalized difference vegeta-tion index) values typical of Lepidium pixels since separating Lepidiumfrom other sparse vegetation proved to be especially difficult at thissite. The size of the pseudo-absence sample influenced how liberallythe decision trees identified pixels as Lepidium. A training set of ~1500pseudo-absence points was found to produce the qualitatively mostappealing maps. Separate classifications were generated for each site.Classification trees were applied to the imagery and the results were

Table 1Physiological indexes calculated from reflectance data

Index Formula Citation

Pigment indexesSR, simple ratio R845

R665Tucker (1979)

NDVI, normalized differencevegetation index

R845−R665R845þR665

Tucker (1979)

mNDVI, modified NDVI R750−R705R750þR705

Fuentes et al. (2001)

SGR, summed green reflectanceP599

n¼500Rn Fuentes et al. (2001)

PRI, photochemicalreflectance index

R531−R570R531þR570

Gamon et al. (1992)

RG ratio, red/green ratio R600−699R500−599

Fuentes et al. (2001)

PI2, pigment index 2 R695R760

Zarco-Tejada (1998)

Water indexesNDWI, normalized differencewater index

R860−R1240R860þR1240

Gao (1996)

WBI, water band index R900R970

Peñuelas et al. (1997)

Foliar chemistry indexesNDNI, normalized differencenitrogen index

log R1680=R1510ð Þlog 1=R1680R1510ð Þ Serrano et al. (2002)

NDLI, normalized differencelignin index

log R1680=R1754ð Þlog 1=R1680R1754ð Þ Serrano et al. (2002)

CAI, cellulose absorption index 0.5⁎ (R2020+R2220)−R2100 Nagler et al. (2000)


sieved and clumped to remove spurious over-fitting to the trainingdata and to spatially generalize the classifications.

Because automated decision trees are unstable classifiers whoseresults depend on the specific points in the training sample, anensemble of 25 decision trees, as described above, was generated foreach site and aggregated using a majority rule to produce the finalmaps, an approach similar to bagging (bootstrapped aggregating).Aggregation has been shown to improve the performance of unstableclassifiers (Benediktsson et al., 1997; Benediktosson & Kanellopoulos,1999; Briem et al., 2002; Carreiras et al., 2006; Chan et al., 2001; Debeiret al., 2002; DeFries & Chan, 2000; Leverington &Moon, 2005; but seeChan et al., 2003). In comparisons of classifiers, decision tree baggingoftenperformswell, showing little difference fromoptimal, oftenmorecomplicated algorithms (Briem et al., 2002; DeFries & Chan, 2000).

Due to the small sample sizes available, it was decided to resamplethe full set of GRD for each tree for Rush Ranch and Jepson Prairie,rather than to bootstrap a designated training set. There were thus noindependent data for evaluating the final maps since any given pointwould have trained, on average, half of the individual trees. G-tests ofindependence (Sokal & Rohlf, 1995) between the error rates of thetraining and test sets were performed for each individual tree. In nocase were the accuracies significantly different following sieving andclumping, suggesting that the pooled set of GRD should providerelatively unbiased accuracy estimates. Independent training and testdata were used for Cosumnes River Preserve.

At Rush Ranch, an independent test set manually interpreted fromaerial photography acquired in May 2006 was used to further validatethe classification. This dataset revealed a gradient of Lepidium phe-nologies across the marsh, with a phenologically advanced class thatwas not represented in the GRD and omitted by the classifier,primarily located in the southern extreme of the marsh. Twenty-one“omitted” points were selected from the aerial photo to supplementthe field data, and a new ensemble of decision trees was generated.The ultimate Rush Ranch map was the union of these two aggregateclassifications. Accuracies from the photointerpreted set were verysimilar to those calculated with the GRD. No additional data wereavailable for the Greater Jepson Prairie Ecosystem.

Accuracy was assessed with producer's and user's accuracies, whichare measures of omission and commission errors, respectively, and thekappa statistic calculated relative to the Lepidium class (Rosenfield &Fitzpatrick-Lins, 1986). Kappa is a measure of agreement that accountsfor the rate of correct classifications occurring merely by chance.Interpretations of kappa values follow Monserud and Leemans (1992).

2.5. Site comparisons

MTMF performance and variable selection by the decision treeswere compared between the three sites and differences are discussedin terms of the spectral uniqueness of Lepidium at the sites. To supportthis interpretation, Jeffries–Matusita distances (Richards & Jia, 1999)were calculated between Lepidium and the common co-occurringspecies at each site. Although many studies of spectral uniqueness usepair-wise tests of significance at each band (e.g., t-tests, ANOVA, ornonparametric alternatives; Hunt et al., 2004; Peña-Barragán et al.,2006; Roberts et al., 2004; Schmidt & Skidmore, 2003) we chose to usethe multivariate Jeffries–Matusita distance measure because spectralclassification is a multivariate problem. Tests on a per-band basismay fail to characterize spectral differences between materials,such as those due to variation in shape. Spectra were extracted atthe GRD and from a set of points of P. australis, Schoenoplectus spp.,Typha spp., and Salix spp. (willows) in HyMap imagery of the Delta.These emergent species are major components of the marsh at RushRanch and the riparian sloughs of the Greater Jepson PrairieEcosystem and Cosumnes River Preserve, where Salix spp. are also aprominent riparian feature, but were not included in the GRD becausethey are not likely to be confused with Lepidium in the image data.Band-correlations were determined from the reflectance data andspectral distances were calculated on subsets of independentwavelengths (rb0.9). Since the field data of Cosumnes River Preserverecorded presences only, limited comparisons could be performed forthis site.

Site complexity was assessed with species, structural, spectral, andlandscape diversity measures to test the hypothesis that increasedcomplexity confounds species mapping. Regional and site-specificspecies richness were determined from the literature and preservewebsites. Biological diversity was also estimated from the hyperspec-tral data following the methods of Carlson et al. (2007):

Spectral Richness ¼ 0:44Rd530 þ 0:032Rd720 þ 0:25Rd1201þ 1:4Rd1523−4:2 ð2Þ

The terms are the range of the first derivative of reflectance at 530,720, 1201, and 1523 nm, which are wavelengths of key biochemicalabsorption features (Carlson et al., 2007). Since our datasets are verydifferent from those used to parameterize the relationship, we refer tothis index as spectral richness, rather than species richness, andinterpret it in relative terms. Spectral richness was calculated for 100samples (each a set of 100 randomly sampled pixels) per site.

Spectral variance of the three image sets was estimated as the sumof the eigenvalues from the MNF transforms. This was performed forthree subsets of pixels at each site: all pixels, all vegetated pixels, andLepidium pixels only. Lepidium pixels were those classified as such inthe Rush Ranch and Jepson Prairie imagery, and those included in theground inventory of Cosumnes River Preserve. Since this measure ofvariability does not provide any indication of correlations betweenbands, the dimensionality of the spectral data for each pixel subset ateach site was estimated as the number of MNF bands necessary tocontain 80% of the total variation.

Landscape heterogeneity at each site was quantified with land-scape-level metrics of patch size, shape, and evenness, calculatedusing Fragstats (http://www.umass.edu/landeco/research/fragstats/fragstats.html). Input patches were determined from unsupervisedIsodata classifications of all vegetated pixels. Patch shapewas assessed

http://www.umass.edu/landeco/research/fragstats/fragstats.html

http://www.umass.edu/landeco/research/fragstats/fragstats.html

Table 2Estimated importance of each variable to decision tree classifications of Lepidium atRush Ranch, Jepson Prairie, and Cosumnes River Preserve

Variablea Rush Ranch Jepson Cosumnes

MF Lepidium 55.2±38.1 4.0±6.3 0.5±0.7MF Lepidium2 na 1.5±3.5 0.4±0.6MF Lepidium3 na na 0.6±0.6MF Lepidium4 na na 1.9±3.6MF Lepidium5 na na 2.2±6.7MF Lepidium6 na na 0.5±0.6MF Lepidium7 na na 0.5±1.4MF Lepidium8 na na 3.7±9.1MF Distichlis 2.4±10.0 na naMF Salicornia 0.7±2.3 na naMF C. solstitialis 1.3±5.5 na naMF C. calcitrapa na 0.6±1.2 naMF Typha na 11.4±32.9 naMF agriculture na 1.8±2.9 1.0±0.9MF agriculture2 na na 0.7±0.7MF water 0.2±0.5 1.2±2.8 naMF litter 2.4±8.3 0.6±2.2 0.8±0.6MF litter2 na na 0.8±0.9MF soil na 0.6±2.0 0.6±0.5MF trees na na 0.8±1.0MF trees2 na na 1.8±2.5Inf Lepidium 1.0±3.0 3.4±17.0 0.4±0.6Inf Lepidium2 na 0.1±0.3 0.4±0.6Inf Lepidium3 na na 0.6±1.6Inf Lepidium4 na na 0.4±0.6Inf Lepidium5 na na 0.6±1.3Inf Lepidium6 na na 0.7±1.8Inf Lepidium7 na na 0.6±1.0Inf Lepidium8 na na 0.4±0.6Inf Distichlis 0.0±0.2 na naInf Salicornia 0.1±0.3 na naInf C. solstitialis 0.3±0.5 na naInf C. calcitrapa na 1.0±3.1 naInf Typha na 0.0±0.2 naInf agriculture na 0.1±0.3 0.4±0.5Inf agriculture2 na na 0.4±0.5Inf water 0.6±4.0 2.0±6.2 naInf litter 0.1±0.3 2.7±4.7 0.8±1.0Inf litter2 na na 0.6±0.6Inf soil na 3.1±8.2 0.4±0.9Inf trees na na 0.2±0.4Inf trees2 na na 0.5±0.9EM of max MF 0.9±0.6 1.4±3.9 1.0±0.0Class of max MF na na 1.3±2.2SR 10.7±20.6 3.6±8.9 1.4±2.9NDVI 10.5±31.0 2.2±6.3 15.8±11.1mNDVI 26.3±63.7 2.0±4.6 1.5±2.7SGR 3.6±11.4 9.3±14.9 3.0±4.2PRI 1.7±4.5 0.6±1.7 0.8±0.7RG ratio 0.5±1.4 0.3±1.0 0.8±1.0


as the ratio of patch perimeter to the perimeter of a circular patch ofthe same area, and indicates the degree of patch elongation andcomplexity. Evenness was calculated with Simpson's evenness index,which is a measure of how evenly the landscape area is distributedamong patch types. Elevation and vegetation height were derivedfrom LiDAR (Light Detection and Ranging) digital ground and surfacemodels of the sites.

3. Results

3.1. Rush Ranch

MNF transforms calculated using only the visible and near-infraredbands (450–1330 nm) optimized the ability of MTMFs to modelpercent cover of Lepidium and enhanced the separation of Lepidiumfrom co-occurring green vegetation in the GRD. Wavelengths in theshortwave-infrared (SWIR) provide important information aboutnonphotosynthetic vegetation: litter in the case of the herbaceousspecies studied here. Since Lepidium and the field-sampled specieswere all physiologically active at the time of the overflights,reflectance in the SWIR did not contribute to species separation bythe MTMFs. Information from the SWIR was still included in theclassifications, in several of the physiological indexes used by theCART (classification and regression tree models) to discriminate Le-pidium from background materials, such as litter, represented in thepseudo-absence sample.

MNF bands 1, 4, 6, 8, 9, 10, 13 and 14 were used at Rush Ranch.These collectively contained 42.5% of the scene's variation. Theomitted transformed bands either contained negligible variation(e.g., beyond band 16), exhibited conspicuous brightness differencesbetween flightlines in the image mosaic (e.g., bands 2, 3, and 16), orobscured the difference between Lepidium and co-occurring speciesby inflating within-species variation (e.g., bands 5, 7, 11, 12, and 15).MTMF modeled the presence/absence of Lepidiumwithin a pixel well,based on field comparisons. There was a strong positive relationshipbetween the percent cover of Lepidium estimated in the field and thematched filter score for Lepidium (MF Lepidium; R2=0.663; Fig. 4).This relationship was driven by the low MF scores of absence points.When considering only nonzero Lepidium points, the fit dropped toR2=0.221.

Individual classification trees used 7.2 variables in 16.8 nodes, onaverage (range: 4–11 variables and 5–28 nodes), to identify Lepidium.All variables were used by at least one tree, but they varied widely intheir importance to the classifications (Table 2). The MTMF outputswere the most important variables for Lepidium detection.

PI2 3.4±8.8 1.8±8.2 2.0±4.4NDWI 1.7±5.2 1.4±5.9 0.9±1.2WBI 0.7±3.0 0.8±2.6 0.8±1.0NDNI 4.7±13.9 1.9±5.4 0.6±1.1NDLI 2.6±8.6 1.8±4.5 1.0±1.3CAI 2.5±6.2 6.4±21.6 9.6±7.2LI 1.7±5.4 4.9±17.2 0.7±1.4Cellulose absorption na 2.1±4.5 na

Different endmembers were used at each site. Values presented are themean±standarddeviation importance values from the ensemble of classification trees. Importance isdescribed as the expected increase in error rate should a given variable be excluded.

a Abbreviations: MF=matched filter score for the specified endmember, Inf=infeasibilityfor the specified endmember. Indexes defined in Table 1.

Fig. 4. Regression of thematched filter score for the Lepidium endmember (MF Lepidium)against the percent cover of Lepidium, as estimated in the field, for Rush Ranch.

At Rush Ranch, most Lepidium occurs in a band along the marshmargin. This was reproduced well by the classifier and is reflected inthe resulting distribution maps (Fig. 5). However, pixels of Lepidiumwere also identified within the marsh proper; these detections wereconfirmed in the aerial photograph. This is significant because itindicates that Lepidium is invading areas that are unlikely to bedetected by traditional ground-based mapping.

Fig. 5. Map of the Lepidium infestation detected at Rush Ranch overlaid on a true-color image of the site.


The classifier detected 803,349 m2 of Lepidium in the Rush Ranchmosaic. Of these, 126,171 m2 were within the preserve (1.41% of thepreserve). Detection rate was independent of the density of the weed;both high- and low-cover patches of Lepidium were mapped withproducer's accuracy ~85%. The classifier did not confuse Lepidiumwithany co-occurringmarsh vegetation; user's accuracywas 100% (Table 3).The kappa coefficient for the Lepidium class was 0.79, indicating verygood agreement. These accuracy statistics are not strictly independentfrom the training set, however, the set of photointerpreted pointsyielded similar statistics (producer's accuracy=85.5%, user's accuracy=

86.2%, overall accuracy=89.7%, kappa=0.777). Commission errorstemmed largely from confusion with eucalyptus trees, a very minorcomponent of the landscape.

3.2. Jepson Prairie

As at Rush Ranch, SWIR bands hampered the ability of MTMF tomodel Lepidium presence and abundance in the GRD and wereexcluded from the MNF transform. Lepidium and the co-occurringgreen vegetation highlighted by the field campaignwere all beginning

Table 3Accuracy assessment of the Lepidium classification at Rush Ranch

Map Producer'saccuracy(%)

Lepidium Other Total

Field Sparse Lepidium 13 2 15 87Dense Lepidium 46 8 54 85All Lepidium 59 10 69 86Other 0 31 31Total 59 41 100

User's accuracy 100%Overall accuracy 90%Kappa 0.79

The dataset of all field points is presented since accuracies were very similar betweensets for all individual trees. (Note that the “all Lepidium” cells are the sum of dense(N50% cover) and sparse (≤50% cover) Lepidium, and are thus not included in columntotals to avoid double counting.)


to senesce at the time of image acquisition, and exhibited littlebetween-species variability in the SWIR. SWIR products were stillincluded in the CART inputs to support discrimination of Lepidiumfrom background materials not included in the field sample, butpresent in the pseudo-absence pixels.

MTMFs at Jepson Prairie used MNF bands 3, 6, 8, 9, 12, 16, and 17,which contained 25.9% of the scene's variation. As at Rush Ranch, theremaining bands displayed noticeable flightline effects (e.g., bands 1,2, 4, 5, 7, and 13) or did not contain information that contributed toLepidium detection (e.g., bands 10, 11, 14, 15, and those beyond 17,which contained negligible variation). MTMF was reasonably success-ful at modeling Lepidium abundance (Fig. 6a). At this site the rela-tionship between MF Lepidium and the percent cover of Lepidiumwasmuch stronger when considering only pixels with some Lepidium(R2=0.554) than when including zero Lepidium points (R2=0.368).This occurred because the MTMF could not consistently differentiatebetween Lepidium and other species at this site. For example, thepercent cover of C. calcitrapa was also well correlated to MF Lepidium(R2=0.510; Fig. 6b). Although the MTMF appeared to be detecting theamount of green vegetation present, it was also sensitive to featuresspecific to Lepidium, as is indicated by the fact that even though C.calcitrapa cover was equally well correlated with the MF score, C.calcitrapa (a purple-flowered species) pixels tended to have lower MFscores than Lepidium pixels (Fig. 6). The lack of any strong separationby MTMF between pixels containing Lepidium and other weeds at thissite prompted inclusion of the physiological indexes in an automateddecision tree classifier.

Fig. 6. Regression of the matched filter score for the Lepidium endmember (MF Lepidium)estimated in the field, for Jepson Prairie. In (a), the solid line is the regression relationshipwhecover in the regression. For (b), only pixels with nonzero abundances of C. calcitrapa were u

The automated decision trees identified an average of 8.72 (range:5–14) variables in 23.2 (range: 12–33) nodes as important for Lepidiumdetection (Table 2). Again, the MTMF outputs contributed the most tothe classification, but the difference in importance between MF scoresand spectral indexes was lower than at Rush Ranch (Table 2). Inter-estingly, the MF scores for non-target endmembers, especially Typha,contributed the most to Lepidium detection. Lepidium was detectedin riparian areas, farmyards, and other areas of human disturbance(Fig. 7).

An infestation of 91,980m2 of Lepidiumwas detected in the GreaterJepson Prairie Ecosystem, with an overall accuracy of 88%. Of these,24,435 m2 of Lepidium infested the four preserves (0.15% of thepreserve area). The classifier was most successful at mapping densepatches of Lepidium. Producer's accuracy of the dense Lepidium pixelswas 79% (c.f. 74% for all Lepidium pixels; Table 4) and kappa, whenconsidering only Lepidium dominated pixels, was 0.79, indicating verygood agreement (for comparison, κ=0.74 when considering all Lepi-dium densities). Commission rate was higher at Jepson Prairie; theuser's accuracy for Lepidium detection was 93%. As at Rush Ranch,there was confusion between Lepidium and rare trees, but here theclassifier was less able to differentiate the target species from otherweeds, especially C. calcitrapa.

3.3. Cosumnes River Preserve

At Cosumnes River Preserve, it was necessary to perform the MNFusing all bands and with variance calculated only with pixels con-taining sparse vegetation such as Lepidium to maximize the MTMF'sability to discriminate Lepidium from co-occurring vegetation.

MNF bands 1–3, 9, 10, and 12, which included 45.2% of the scene'svariation, were used to detect Lepidium at Cosumnes River Preserve.Bands beyond 38 contained negligible variation and significant flight-line effects were observed in bands 4 and 11. No remaining bandscontributed to Lepidium detection. MTMFs failed to model either Le-pidium abundance or presence/absence (Fig. 8). R2 of the best end-member (Lepidium6 in Table 2) were 0.184 and 0.000 when includingand excluding points with zero Lepidium, respectively.

Automated decision trees at Cosumnes River Preserve were verylarge, with an average of 140.0 decisions (range: 117–170) and 24.1 vari-ables (range: 11–34). NDVI and CAIwere themost important variables toLepidium detection (Table 2). The MTMF outputs had importance valuescomparable to or less than those for spectral indexes (Table 2).

Lepidium was mapped in two cover classes at Cosumnes RiverPreserve (Fig. 9): dense (pixels containing N50% cover by Lepidium) and

against (a) the percent cover of Lepidium and (b) the percent cover of C. calcitrapa, asn considering all field points. The dashed line includes only points with nonzero percentsed.

Fig. 7. Map of the Lepidium infestation detected at the Greater Jepson Prairie Ecosystem overlaid on a true-color image of the site.


sparse (pixels with ≤50% cover by Lepidium). The classifier detected2,879,568m2 of Lepidium at this site (283,086m2 and 2,596,482m2 forthe dense and sparse classes, respectively), which is 7.4% of the imagedarea. It is clear, both qualitatively and quantitatively, that the classifierfailed to accurately map Lepidium at this site. Overclassification wasespecially extreme: User's accuracy for the pooled Lepidium class was23%. The producer's accuracy over all covers of Lepidiumwas 64% andthe class conditional kappa was 0.316, which is indicative of pooragreement (Table 5; Fig. 9).

3.4. Spectral uniqueness

Jeffries–Matusita distances between Lepidium and common co-occurring species at the sites are given in Table 6. A Jeffries–Matusitadistance of two indicates complete separability (Richards & Jia, 1999).Reflectance bands were highly correlated. The values presented werecalculated using bands at 541.6, 673.6, 716.9, 844.9, and 1315.2 nm.Using different sets of independent band combinations did not changethe patterns observed. The species least separable from Lepidiumoccurred at Jepson Prairie. In general, when the same species werepresent at both sites, they were more distinct from Lepidium at RushRanch than at Jepson Prairie (Table 6). At Cosumnes River Preserve,

Table 4Accuracy assessment of the Lepidium classification at Jepson Prairie

Map Producer'saccuracy(%)

All Lepidium Other Total

Field Sparse Lepidium 6 4 10 60Dense Lepidium 22 6 28 79All Lepidium 28 10 38 74Other 2 63 65Total 30 73 103

User's accuracy 93%Overall accuracy 88%Kappa 0.74 (0.79 for the dense Lepidium class)

The dataset of all field points is presented since accuracies were very similar betweensets for all individual trees. (Note that the “all Lepidium” cells are the sum of dense(N50% cover) and sparse (≤50% cover) Lepidium, and are thus not included in columntotals to avoid double counting.)

Lepidium displayed separability values similar to but slightly betterthan those at Jepson Prairie (Table 6). These species-specific patternsof spectral similarity between the sites are also manifested image-wide, as revealed by mean spectral angle to a Lepidium endmemberand by the number of pixels classified as Lepidium by a conservativespectral angle threshold at each site (results not shown).

3.5. Site complexity

Species richness increases from Rush Ranch to Cosumnes RiverPreserve to Jepson Prairie. Published plant species richness of these sitesare 200, 230, and 400 species, respectively (www.solanolandtrust.org;www.cosumnes.org). In terms of tidal marsh vegetation, Suisun Marshcontains about 40 species while there are about 80 in the Delta (Atwateret al.,1979). Spectral richnesswas decoupled somewhat from total speciesrichness byphenology, and increased fromRushRanch to JepsonPrairie toCosumnes River Preserve (Table 7). For all pixel subsets, overall spectralvariation was highest at Jepson Prairie and lowest at Rush Ranch.Dimensionality of the spectral data, however, did not mirror this trend,

Fig. 8. Regression of the matched filter score for the best Lepidium endmember (MFLepidium) against the percent cover of Lepidium, as estimated in the field, for CosumnesRiver Preserve.

http://www.solanolandtrust.org

http://www.cosumnes.org

Fig. 9.Map of the Lepidium infestation detected at Cosumnes River Preserve overlaid on a true-color image of the site. Lepidiumwas mapped in two classes: dense Lepidium pixels arethose with N50% cover by this weed, and sparse Lepidium pixels are those containing ≤50% Lepidium.


instead being greatest at Rush Ranch and lowest at Jepson Prairie for boththe subset of all vegetated pixels and that of pixels containing Lepidium(Table 7).

Table 5Accuracy assessment of the Lepidium classification at Cosumnes River Preserve using indep

Map

Sparse Lepidium Dense Lepidium

Field Sparse Lepidium 139 28Dense Lepidium 31 55All Lepidium 170 83Pseudo-absence 588 249Total 758 332

User's accuracy 18.3% 16.6%Overall accuracy 93.6%Kappa 0.316 (0.156 for the dense Lepidium class)

Since field data is a presence only inventory of Lepidium, a random pseudo-absence sample w(N50% cover) and sparse (≤50% cover) Lepidium, and are thus not included in column totals

Landscape metrics of the three sites are given in Table 7. Notingespecially the standard deviations of these measures, it can be seenthat Cosumnes River Preserve is the most variable, while Jepson

endent test data

Producer'saccuracy (%)

All Lepidium Other Total

167 115 282 49.386 27 113 48.7253 142 395 64.1837 14,973 15,8101090 15,115 16,20523.2%

as used for accuracy assessment. (Note that the “all Lepidium” cells are the sum of denseto avoid double counting.)

Table 7Metrics of biological, spectral, and landscape heterogeneity at the three sites

Rush Ranch Jepson Prairie Cosumnes

Species richness 200 400 230Spectral richness 14.2±1.6 15.7±2.1 16.6±1.3MNFa variation — all 517 707 632MNFa variation — vegetated pixels 371 703 522MNFa variation — Lepidium pixels 225 812 276MNFa dimensionality — all (n bands) 41 44 31MNFa dimensionality — vegetatedpixels (n bands)

57 36 39

MNFa dimensionality — Lepidiumpixels (n bands)

71 30 64

Number of patches 412,591 138,746 207,616Edge density (m−1) 1553 323 1203Patch area (m2) 71±944 57±755 110±2463Shape 1.195±0.465 1.149±0.399 1.196±0.493Simpson's evenness 0.925 0.901 0.886Elevation (m) 2.49±4.57 2.93±1.40 4.66±1.46Vegetation height (cm) 20±85 29±172 162±426

Values presented are mean±standard deviation, where applicable.a MNF=minimum noise fraction.

Table 6Jeffries–Matusita distances to the dense Lepidium class

Species/cover type Rush Ranch Jepson Cosumnes

Distichlis 1.9553 – –

Salicornia 2.0000 – –

C. solstitialis 1.9697 1.4696 –

Litter 2.0000 1.9339 1.9997Typha 1.9780 1.7635 1.7809Schoenoplectus 1.9592 1.8113 1.8567Phragmites 1.9887 1.5169 1.8175C. calcitrapa – 0.9727 –

Salix – 1.5400 1.9222Other 1.5442 1.8689 –

Trees – – 1.7698Agriculture – – 1.3068Soil – – 2.0000


Prairie contains the least variation in patch characteristics. Othermetrics of patch shape and evenness yielded the same relationshipsbetween sites as those reported.

4. Discussion

4.1. MNF band usage

Evaluating the MNF bands that contributed to MTMFs to modelLepidium produced few generalizations. For all three sites, retainedbands seemed associatedwith overall brightness, brightness in discretespectral regions (VIS, NIR, SWIR), and the shape of reflectance in thevisible. The importance of brightness and shape in the visible is inkeeping with our understanding of the basis behind spectral unique-ness of this species. In addition, MNF bands that enhanced Lepidiumdetection at Jepson Prairie and Cosumnes River Preservewere sensitiveto the strength of the red edge, the depth of the water absorptionfeature at 980 nm, and to features distinguishing woody from herba-ceous vegetation. When inspecting the coefficients of the eigenvectorsassociated with each MNF band, reflectance at 586 nm was found tocontribute strongly toMNFbands sensitive to Lepidium at all three sites.Interestingly, this is one of the bands selected to construct LI (Eq. (1)).Reflectance at 630 nm, the other wavelength of this index, contributedto useful MNF bands at Cosumnes River Preserve.

4.2. Classifications

Lepidiumwas successfully mapped at both Rush Ranch and JepsonPrairie with hyperspectral image data, using a combined MTMF-physiological index-decision tree approach. Omission was the mostcommon error at both sites. This is unusual; remote sensing applica-tions to weed mapping have a tendency to over-classify the targetspecies (Lawrence et al., 2006). By far, the greatest error occurred atCosumnes River Preserve, where the classifier was unable todistinguish Lepidium from sparse vegetation. Omission and especiallycommission errors were high. Arguably, omission errors are moreserious for weed early detection programs than commission errorsbecause unidentified infestations will continue to serve as propagulesources, hampering control efforts.

Pixels with sparse cover were less reliably identified, especially atJepson Prairie. It is unfortunate that small, sparse patches of Lepidiumwere less detectable because isolated satellite populations contributedisproportionately to population growth (Renz, 2002). It has beensuggested that eradication programs should target small satellitepatches (Moody & Mack, 1988); no early detection system can beeffective unless it is able to identify them. Other researchers havedetected weeds at very low cover using MTMF and other algorithms.Glenn et al. (2005) identified leafy spurge at covers as low as 10%; Lasset al. (2002) could detect pixels with only 1% cover of spotted

knapweed; andMundt et al. (2005) reliablymapped hoary cress downto 30% cover. However, these species are generally more spectrallydistinct than Lepidium (as discussed below), which allows their detec-tion at lower abundances. One solution to detect small patches may beto increase the spatial resolution of image data, although this will notimprove detection of patcheswith sparse cover throughout.Moreover,finer spatial resolutions may actually reduce classification accuracy byincreasing within-class spectral variability (Underwood et al., 2007).

Mapping may be hampered by flightline effects, which were moresevere in the Jepson mosaic than at Rush Ranch. Strong artifactsrequired the exclusion of a number of MNF bandswhichmay have alsocontained Lepidium-specific features, potentially limiting Lepidiumdetection. However, this interpretation is not supported by theCosumnes River Preserve analysis, which was able to use a greateramount of variation than at the other two sites, yet was demonstrablyunsuccessful. Clearly, retaining a larger amount of variation unconta-minated by artifacts is desirable, yet does not guarantee ultimatesuccess. These considerations highlight one of the limitations ofMTMF. Although proven to be a useful tool for the detection ofinvasive weeds, especially those that differ only subtly from back-groundmaterials, it cannot be performed at larger scales because of itsdependence on the variance characteristics of the image data (throughthe MNF transform), and its concomitant sensitivity to flightlineeffects. MTMF works best when it can be performed on individualflightlines. The three- and four-flightline mosaics used by this studyare apparently approaching the size limits of this algorithm. Publishedstudies using MTMF have been confined to smaller areas (e.g., ParkerWilliams and Hunt (2002) and Mundt et al. (2005) analyzed twoscenes and one flightline, respectively. Glenn et al. (2005) used MTMFover three flightlines, however, these were noncontiguous so abruptflightline effects could not be assessed.) DiPietro et al. (2002) expe-rienced related problems when attempting to export an MNF-basedclassification derived from one flightline to an adjacent flightline.

An explanation for the variation in error rates is that Lepidium hasdifferential detectability at the three sites. Reduced spectral unique-ness of Lepidium relative to co-occurring vegetation and the back-ground is expected to reduce classification success. The hypothesisthat mapping success tracks the spectral uniqueness of the targetspecies at the three sites and that this is influenced by their relativecomplexities is discussed in the following sections.

4.3. Site comparisons — spectral uniqueness

The species composition of a site is dependent on species' niches,which are determined by complex biotic and abiotic interactions.

Fig. 10. Reflectance spectra of pure Lepidium and of a mixture of green vegetation andlitter. Note that the mixed spectrum was artificially generated assuming linear mixingwith a 50:50 mixture of the green vegetation spectrum from Fig. 1 and a senescent grassspectrum.


Different species have different environmental tolerances and compe-titive abilities. Wetland vegetation is under particularly tight controlsdue to flooding and salinity stress, which often require specific plantadaptations, and interspecific competition (Pennings & Callaway,1992). Rush Ranch, the Greater Jepson Prairie Ecosystem, and theCosumnes River Preserve present very different abiotic conditionsresulting in divergent plant communities and species compositions.This influences the remote detection of Lepidium because the specificspecies present may be more or less unique from Lepidium in reflec-tance space, both through the spectral characteristics of the co-occurring species and the effect of the abiotic environment on thespectral characteristics of the target species.

Lepidiumwas most spectrally distinct at Rush Ranch, as suggestedby visual inspection of the spectra (Fig. 3) and the Jeffries–Matusitadistances (Table 6), and this strong uniqueness is manifested in thebehavior of the classifier. Lepidium was highly separable from co-occurring species by its MF score, which responded primarily to thepresence or absence of Lepidium. As a result, CART models found MFLepidium to be the most important variable for Lepidium detection.This level of distinctness is expected given the characteristics of RushRanch where water is abundant, all marsh vegetation was intenselygreen, and Lepidium was flowering profusely at the time of imageacquisition. None of the co-occurring dominant species exhibit thepattern of reflectance across the visible that is characteristic of Lepi-dium (Fig. 3a), Jeffries–Matusita calculations found all co-occurringspecies to be nearly maximally separable from Lepidium (Table 6), andlarge architectural and background differences exist between Lepidiumand other marsh vegetation. Phragmites, Schoenoplectus, and Typha allpossess erectophile canopies (i.e., vertical leaf angle distribution)whileLepidium has a plagiophile canopy (i.e., leaves oriented at an anglebetween vertical and horizontal, data not shown). As expected forerectophile leaf angle distributions (Jacquemoud, 1993; Kimes, 1984;Verhoef, 1984), these species have much lower reflectance than Lepi-dium over all wavelengths. These plants may occur rooted in shallowwater, which also contributes to lower reflectance. The succulent Sa-licornia also has much lower reflectance than Lepidium due to its highfoliar water content, which produces much deeper water absorptionfeatures as well. C. solstitialis at Rush Ranch occurs in the uplands,unlike Lepidium which grows in the marsh and along the marsh/uplandmargin. Consequently, it is much drier than Lepidium and thesedifferences are evident between the spectra. C. solstitialis spectra haveaweaker red edge, more pronounced cellulose absorption feature, andhigher reflectance throughout the SWIR. Distichlis, a marsh grass, isspectrally the most similar to Lepidium at Rush Ranch, however itdisplays a reflectance peak in the green wavelengths typical of vege-tation that is clearly distinguished from Lepidium's uniform, brightreflectance throughout the visible. The genera tested are the dominantspecies in 90% (byarea) of themarsh andmarsh-marginhabitat at RushRanch (Vaghti & Keeler-Wolf, 2004).

The strong spectral uniqueness of Lepidium at Rush Ranch,particularly in visible wavelengths, suggests that Lepidium could bemapped with alternative image data, including aerial photography, atthis site. Lepidium has been successfully detected with visual aerialsurveys in similar environments in the eastern US (Orth et al., 2006),with manual photo-interpretation at a tidal wetland site in theSacramento–San Joaquin Delta (Kramer et al., 1995), and in this studyto generate an independent validation dataset, although none of thesestudies reported accuracies. However, aerial photography is animperfect solution for Lepidium monitoring. Not all patches could bedetected in the aerial photographs, including certain phenologies andespecially Lepidium in the Spring Branch Creek Watershed at RushRanch. This seasonal watercourse is separated from tidal activity by aberm and is much dryer than the rest of the marsh. Lepidium in SpringBranch Creek was more senescent and was not well representedspectrally by the majority of Rush Ranch Lepidium, which occurswithin the marsh itself. In fact, it much more closely resembled Le-

pidium at Jepson Prairie and it could not be identified in the aerialphotograph. However, it was resolved in the hyperspectral analysis.

At Jepson Prairie and Cosumnes River Preserve, Lepidium was notdistinct from co-occurring species (Fig. 3b, c, Table 6). Low spectralcontrast between the species present resulted in high MF scores forpixels dominated by other species, including C. calcitrapa at JepsonPrairie. As a result, MTMF outputs contributed less strongly to CARTmodels, and had importance values similar to or lower than physio-logical indexes. Larger CART models, with more decisions, were re-quired, especially at Cosumnes River Preserve, underscoring theincreasing difficulty of detecting Lepidium. Overall, importance scoreswere also lower at these two sites than at Rush Ranch, suggesting thatspectral differenceswere less clear-cut.Many vegetation types at thesesites display relatively flat, uniform reflectance throughout the visibleand are virtually indistinguishable by visual inspection. Architectu-rally, Lepidium is less unique than at Rush Ranch, and this may con-tribute to increased spectral similarity. For example, Lepidium andCentaurea spp. are sparse-canopy weedy species that bolt from abasal rosette to flower. The flowering stages, which are more conspi-cuous to remote observations and to ground crews, possess smallleaves and open canopies. Considerable signal is thus likely to becontributed from the background. Although Lepidium is spectrallyunique at the canopy level (Andrew & Ustin, 2006), that does notnecessarily entail uniqueness at the pixel level. Spectral mixing re-duces the uniqueness of Lepidium at these two sites. Green vegetationmixedwith soil or litter exhibits spectral characteristics similar to purestands of Lepidium (Fig. 10). At Jepson Prairie, actively growing speciesoccurred within a matrix of Mediterranean grasses, which weresenescent at the time of the overflight, resulting in substantial mixingof greenvegetationwith litter. This causedmanyother vegetatedpixelsto appear spectrally similar to Lepidium. Discrimination of forest spe-cies has been shown to be greater at the branch scale than at the standscale due to differences in mixing of components at these scales(Roberts et al., 2004) and the problem of spectral mixtures resemblingthe spectrum of another pure material has been reported for arid andsemiarid species previously (Dennison & Roberts, 2003; Okin et al.,2001; Price, 2004). Spectral variability of the sparse Lepidium class atCosumnesRiver Preservewas high due tomixingwith a diversity of co-occurring green vegetation, litter, and soil.

At Jepson Prairie, even pure Lepidium pixels were mixed with litterdue to advanced phenologies, as illustrated by the observed NDWI(normalized difference water index) and CAI values. For pixels with≥80% cover of Lepidium, the average NDWI, a measure of foliar watercontent, was 0.047 and 0.150 at Jepson Prairie and Rush Ranch,


respectively. The average CAI, which is sensitive to senescent plantmaterial, was 86.6 and −22.7 at Jepson Prairie and Rush Ranch,respectively. Strangely, Lepidium pixels at Cosumnes River Preservehad low mean values for both NDWI (0.002) and CAI (−11.3). Sene-scence served to reduce the spectral uniqueness of this species atJepson Prairie, even at 100% cover by Lepidium. Jeffries–Matusita dis-tances support the more advanced phenology of Lepidium at JepsonPrairie, where it is less distinct from both litter and the largelysenescent C. solstitialis than elsewhere (Table 6).

However, phenology did contribute to mapping success at JepsonPrairie, where detectability was due to phenological rather than spec-tral uniqueness: Lepidium was spectrally similar to many other vege-tated pixels; however, only 12.5% of pixels contained green vegetation(c.f., 68.6% of Cosumnes River Preserve). In contrast, phenology ham-pered detection at Cosumnes River Preserve. This site displays largevariability in management and hydrology, and consequently largephenological and spectral variability of Lepidium (Hestir et al., inpress). Phenology appears to be closely related tomoisture availability.Lepidium in the wet floodplain is delayed relative to that in the drygrasslands. This weed can thus be found in all possible phenologicalstates, including the undetectable vegetative and senescent stages,over the site. Muted phenological variation even exists within patchesand may be controlled by microtopography. High phenologicalvariation reduced the spectral uniqueness of the target species atthis site and contributed to the commission error of the high Lepidiumclass.

Phenological uniqueness, or lack thereof, also influenced the MTMFbehavior. This can be seen in the JepsonPrairie results,where the strongrelationship between MF Lepidium and either percent Lepidium orpercent C. calcitrapa (nonzero points only) suggests that the MTMF issensitive to the amount of green vegetation present in a pixel. This issupported by the NDVI, a measure of the “greenness” of those pixels.Both percent cover of Lepidium and percent cover of C. calcitrapa wereequally well correlated with NDVI (R2=0.57, data not shown), and thiscorrelation accounted for roughly the same amount of variation inNDVI as in the MF score for Lepidium. In contrast, at Rush Ranch andCosumnes River Preserve, where therewas no phenological separation,percent cover by Lepidium (when excluding zero points) was poorlycorrelated with both MF Lepidium and with NDVI (R2=0.29, R2=−0.12,respectively, for NDVI against Lepidium cover). That MF scores for theTypha endmember were more important to Jepson Prairie CARTs thanthe Lepidium MTMF outputs substantiates that the identification ofgreen vegetation was a major component of Lepidium detection there.

The phenological differences between Lepidium at Rush Ranch,Jepson Prairie, and the Cosumnes River Preserve suggest that thesesites differ in their optimal timing for Lepidium detection. Phenologyand the timing of image acquisition are important considerations ofspecies mapping. Two factors contribute to this: 1) particular pheno-logical stages may have more or less distinct spectral characteristics,such as flowering or pre-leaf drop phenologies with noticeablechanges in pigment content (e.g.: Everitt & Deloach, 1990; Ge et al.,2006; Hunt et al., 2004, 2007); and 2) species may have differentphenological schedules, allowing the discrimination of species withspectrally similar life forms that are present at different times (e.g.:Bradley & Mustard, 2006; Laba et al., 2005; Noujdina & Ustin, 2008).Both of these factors are involved in Lepidiummapping. At Rush Ranch,the late June image acquisition coincided with peak flowering, themost resolvable phenological stage. At Jepson Prairie, peak floweringoccurred earlier than the late June image acquisition, and Lepidiumwasbeginning to senesce. An earlier acquisition may improve detection.However, Lepidium has an unusual phenological timing at JepsonPrairie: It remains green far longer into the dry season than most co-occurring species. This enhanced discrimination in the image databecause pixels presenting a strong vegetation signal were more likelyto contain Lepidium. There may exist a trade-off in image timing forLepidium mapping at Jepson Prairie: Data acquired earlier may better

capture the most distinct phenological stage, but may also require itsdiscrimination from a greater number and variety of co-occurringspecies, as at Cosumnes River Preserve. Future research is necessaryto determine the optimal image timing to balance these effects.Cosumnes River Preserve illustrates that matching image acquisitionwith phenological timing is never simple. At this site, multiple imagedates are necessary to capture peak flowering of Lepidium in thedifferent communities.

4.4. Site complexity

Mapping success was inversely related to both the spectral andspecies richness of a site. Lepidium was most easily mapped at RushRanch, the site with the lowest spectral and species richnesses.Although Jepson Prairie has greater species richness than CosumnesRiver Preserve, for the purpose of this study, the reversewas effectivelytrue, and this is reflected in the spectral richness index. Much of thediversity at Jepson Prairie is restricted to specialized habitats such asvernal pools and playas, and nearly all of it was dormant at the time ofthe image acquisition. In terms of physiologically active vegetation inthe dry season, Cosumnes River Preserve exhibits the greatest diver-sity, and the remote detection of Lepidiumwas not possible at this site.Biodiversity may influence mapping success via a sampling effect: Asrichness increases, so too does the likelihood of co-occurring speciesresembling the target species. Success also decreased as the structuraldiversity of vegetation increased (the standard deviation of vegetationheight increases from Rush Ranch, to Jepson Prairie, to CosumnesRiver Preserve; Table 7). This cannot be interpreted as a direct effect oftrees: Lepidium was not confused with trees at Cosumnes River Pre-serve (Fig. 9), the site with the poorest mapping, and Lepidium ob-scured by tree canopies and shade was not considered. Rather, thissuggests that as sites become able to support a greater diversity of lifeforms, the ability to accurately detect a species is reduced.

The total amount of spectral variance over all three pixel subsetswas lowest at Rush Ranch, although dimensionality tended to be highhere. Higher dimensionality suggests fewer interband correlations,whichmayenhance species separability byprovidingmore usable axesof spectral differentiation. In contrast to the pattern of mapping suc-cess, spectral variationwas highest at Jepson Prairie, whichmay be dueto increased mixing with variable litter and soil backgrounds andincreased spurious variation, including flightline effects, relative to theother sites. Interestingly, all of the spectral variation in other vegetatedpixels is also present in Lepidium pixels, which corresponds with theobservation that Lepidium was one of the few green plants present inthe image data.

Landscapemetrics weremost variable at Cosumnes River Preserve,again indicating that increased complexity reduces mapping success.Although there is no direct logical relationship between heterogeneityat the landscape scale and pixel-level reflectance, patch characteristicsand landscape structure reflect past and current ecological processes.Landscape diversity may thus indicate habitat diversity, and, in conse-quence, species and spectral diversity, thereby influencing spectraluniqueness and mapping success. Landscape diversity was lowest atJepson Prairie, which is believed to be due to the image timing. Fewspecies were phenologically active in the image data, resulting in areduced variety of patch shapes and sizes.

This analysis shows that site complexity, both ecological andspectral, influences species mapping, which may operate via mechan-isms increasing both the probability of including co-occurring speciesthat are spectrally similar to the target species and the within-speciesvariability of the target. However, these relationships are complex,indirect, andmodified by, among other things, the phenology of targetand co-occurring vegetation. Further research over many more sites isnecessary to quantify the relationships between site complexity anderror rates of remote sensing analyses and to understand the mecha-nisms driving them.




4.5. Implications: detectability versus habitat suitability

Lepidium turned out to be most easily mapped at the site that isarguably least susceptible to its invasion. Brackish marshes such asRush Ranch are characterized by harsh edaphic conditions includingsalinity, flooding and the associated anoxia, that require specific plantadaptations (Mitsch & Gosselink, 2007), thus limiting invasibility.Although Lepidium is tolerant of salinity and flooding, it is limited to amore narrow range of conditions by these stressors and tends to occurwithin predictable zones (Spenst, 2006). In contrast, riparian areasfrequently exhibit high levels of invasion (e.g., DeFerrari & Naiman,1994; Larson et al., 2001; Stohlgren et al., 1998, 2002; Vilà et al., 2007).The freshwater conditions of the Delta are believed to be moreamenable to Lepidium growth and spread; however, the distributionof Lepidium in this system is relatively poorly known (Grossinger et al.,1998). Delta communities also experience more anthropogenic distur-bance than Suisun Marsh. Disturbance is often cited as a strongpromoter of invasions (e.g., Fox & Fox, 1986), and Lepidium tended tobe mapped in areas of anthropogenic disturbance at Jepson Prairie.

Given the relative lack of attention paid to Lepidium in the Deltaand its perceived high susceptibility to invasion by this weed,improved monitoring is required. Since the Delta presents spectraland environmental conditions similar to Jepson Prairie and encom-passes a vast 2000 km2, hyperspectral imaging is a must and thealgorithm applied here may be a viable strategy. Sensors less sensitiveto Lepidium in Delta conditions, such as aerial photography, will fail todetect infestations resulting in interpretations that may confoundinvasibility with detectability.

5. Conclusions

The environmental context in which a weed grows influences theability to map it with image data. Habitat characteristics can influencethe identity of co-occurring species, the phenologies of the target andco-occurring species, and the degree ofmixing and type of backgroundmaterial. All of these considerations influence the spectral uniquenessof the target species and, thus, mapping success, which seems to beinversely tied to the ecological and spectral complexity of a site.Despite differences in environmental context and its influence on thespectra of both Lepidium and the background, theMTMF-physiologicalindex-decision tree approach that we applied proved to be verypowerful and was flexible and robust enough to detect Lepidiumwithsimilar accuracies at both Rush Ranch and Jepson Prairie. However, it isnot suited for all environmental contexts and Lepidium mapping withremote sensing data may not be possible under all conditions.

Acknowledgements

This research was supported by the California Bay-Delta AuthorityAgreement No. U-04-SC-005. We are indebted to C. Rueda for devel-oping a program to apply See5 decision trees to image data. Theauthors wish to thank the Solano Land Trust (SLT) for initiating thisstudy and for access to their lands, and B. Wallace of SLT for assistancein the field. We also acknowledge the owners of the propertiesstudied: the Solano Land Trust (Rush Ranch, Jepson Prairie Preserve,Eastern Wilcox Ranch), the University of California Natural ReserveSystem (Jepson Prairie Preserve), and the California Department ofFish and Game (Calhoun Cut Ecological Reserve, Barker Slough). Wethank J. Viers for providing image and field data of the Cosumnes RiverPreserve, which was supported by the California Bay-Delta AuthorityEcological Restoration Program (ERP-01-NO1, ERP-02D-P66).

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