ORIGINAL PAPER

Seeing the forest for the invasives: mapping buckthornin the Oak Openings

Richard H. Becker • Kirk A. Zmijewski •

Todd Crail

Received: 15 November 2011 / Accepted: 7 July 2012

� Springer Science+Business Media B.V. 2012

Abstract Landsat TM and ETM? satellite images

from 2001 to 2011 were used to map the extent and

change of the invasive shrubs common and glossy

buckthorn (Frangula alnus and Rhamnus cathartica)

at Irwin Prairie State Nature Preserve (IPSNP), and

throughout Oak Openings, a 1,500 km2 region,

located in NW Ohio, USA and SE Michigan near

Lake Erie. In the Oak Openings, buckthorn directly

threatens native biodiversity and habitat health of this

globally rare ecosystem. Buckthorn that forms as

dense shrub thicket in the understory is often obscured

from satellite view by other canopy and is not

spectrally dissimilar enough to be characterized using

multispectral images. To address this, time series

tasseled cap greenness images of land surface areas

dominated by buckthorn thicket (which exhibit early

leaf-out, late senescence) was used to identify areas

covered by thicket with a heterogeneous background.

A time series of vegetation index values was calcu-

lated from 49 Landsat images and combined with

in-situ observations to define the land surface phenol-

ogy of buckthorn thicket and other vegetation types.

The phenological differences among land surfaces

dominated by distinct vegetation types in the Oak

Openings Region were used to map the extent of

buckthorn thicket using a supervised classification

method. Buckthorn thicket was identified in 0.43 % of

the classified pixels (940 ha) in the 2007–2011

imagery and in 0.31 % (690 ha) of the 2001–2006

images. A Kappa test of the 2007–2011 classification

yielded a value of 0.73 with 88 % overall accuracy of

presence or absence of thicket based on 60 samples

throughout the Oak Openings. The areal extent of

buckthorn thicket increased by 39 % (255 ha) in the

study area over the time period from 2001 to 2011.

Keywords Remote sensing � Buckthorn � Rhamnus

cathartica � Frangula alnus � Multi-temporal �Phenology � Invasive species � Landsat �Oak Openings

Introduction

Non-native invasive species can pose significant

threats to unique ecosystems, threatening native

species, ecosystem properties, and even regional

economies. In 2000 in the US, the estimated cost of

damages associated with management and control of

these invasive species was estimated to be at least

$137 billion per year (Pimentel et al. 2000). When

total value of biodiversity reduction, ecosystem ser-

vice loss, species extinctions and aesthetic decline are

included, the cost of damage increases drastically

(Nunes and van den Bergh 2001).

Understory vegetation has a significant role in

forest ecosystems. Changes in the structure and

R. H. Becker (&) � K. A. Zmijewski � T. Crail

Department of Environmental Sciences, University of

Toledo, 2801 Bancroft Ave, Toledo, OH 43606, USA

e-mail: richard.becker@utoledo.edu

123

Biol Invasions

DOI 10.1007/s10530-012-0288-8

species composition of understory plants, including

both native and invasive species, can affect the health

and biodiversity and change forest succession

through physical, chemical, and biological mecha-

nisms (Royo and Carson 2006). Alteration of the

normal species composition may lead to the forma-

tion of dense monoculture thickets of non-native

invasive shrubs due to a reduction in recruitment of

native species (Frappier et al. 2003; Fagan and Peart

2004). This can disrupt forest ecosystems as well as

ecosystems that may be adjacent to forests such as

wet prairies. Invasive plant species, such as common

buckthorn (Rhamnus cathartica L.), have the poten-

tial to alter ecosystems by changing carbon, nitrogen,

and pH of soils as well as soil moisture (Heneghan

et al. 2006).

Oak Openings and buckthorn invasion

The Oak Openings Region, located in NW Ohio and

extending into SE Michigan is a globally rare ecosys-

tem contained entirely in the Lake Erie watershed

(Fig. 1). This region provides a home to rare organ-

isms which depend on the extent and properties of the

ecosystem. For example, the purplish copper (Lycaena

helloides Boisduval) is an endangered species of

butterfly that depends on water smartweed (Polygo-

num amphibium L.) that grows only in wet prairies,

excellent examples of which are found in the Oak

Openings.

In the Oak Openings, invasive plant species directly

threaten biodiversity and habitat health by outcompet-

ing native plants and by forming dense monocultures.

Fig. 1 Location map showing the Oak Openings Region. Outlined area marked ‘A’ shows the location of Fig. 5

R. H. Becker et al.

123

Other invasive shrubs which form similar thickets in the

region include honeysuckles (Lonicera sp.) and autumn

olive (Elaeagnus umbellata). At locations throughout

the Oak Openings, buckthorn has emerged as a great

threat to the native plant communities such as: oak

woodland, mesic tallgrass prairie, twig rush prairie and

oak flatwood wetlands. The native plant communities

are adapted to low nutrients and periodic fires;

invasions are successful when this regime is disrupted

either through suppression of fires or an influx of

nutrients (Abella et al. 2001; Zouhar et al. 2008).

Recent studies suggest that areas with higher biodiver-

sity such as the Oak Openings, more frequently

experience invasions than do lower diversity areas

(Stohlgren et al. 2003). This region is covered by a

variety of heterogeneous land uses, including suburban,

agricultural, forested, prairie, roadway, with Toledo

Express airport also located in this area. Within the

Ohio Oak Openings Region there are multiple large

metroparks and nature preserves including Oak Open-

ings Metropark, Secor Metropark, Wildwood Preserve

Metropark, Irwin Prairie State Nature Preserve (IPS-

NP), and the Kitty Todd Nature Preserve (shown in

Fig. 5). All of these areas are actively managed for

invasive species, of which glossy and common buck-

thorn are included.

Common and glossy buckthorn (Rhamnus cathar-

tica and Frangula alnus) are woody shrubs of

European origin. They form dense thickets, and have

been used to form hedgerows. Both species of

buckthorn were introduced in the early to mid 1800s,

but did not reach Ohio until the 1920s (Possessky et al.

2000; Torrey 1824; Knight et al. 2007). However,

plant surveys in the Oak Openings listed both buck-

thorn species to be ‘‘rare’’ or ‘‘infrequent’’ as recently

as 1978 (Easterly 1979; Moseley 1928). Contempora-

neous introduction of invasive birds such as the

European starling also contributed to the spread of

these plants throughout the continent (Howell and

Blackwell 1977).

Buckthorn has high fecundity and germination rates

upwards of 85 % with as many as 90 % of the berries

falling directly under the parent tree (Frappier et al.

2003; Knight et al. 2007). Frappier et al. (2003) found

dispersal in excess of six meters per year without the

assistance of birds. The berries can survive inundation

for multiple weeks and also can germinate after

digestion by birds such as the European starling and

American robin (Knight et al. 2007; Skinner et al.

2005; Lafleur et al. 2007). Some of the berries also

float in surface water, further contributing to expan-

sion of the plant extent (Howell and Blackwell 1977).

All of these factors contribute to high rates of

spreading after the initial invasion has been

established.

Buckthorn also may facilitate the invasion and

spread of other noxious invasive species. The Asian

soybean aphid (Aphis glycines) has emerged in the

past decade as a top agricultural pest threatening

soybean crops by providing a host for overwintering

(Venette and Ragsdale 2004; Voegtlin et al. 2005).

Invasive earthworms also pose a significant problem

by removing forest floor leaf litter and other organic

material (buckthorn does not germinate well in thick

organic layers) and in turn enriching the soil with

nitrogen which buckthorn may utilize more readily

than native plants (Hale et al. 2006; Heimpel et al.

2010). Buckthorn thickets provide shade on the forest

floor and large amounts of high quality leaf litter

which earthworms preferentially consume providing a

positive feedback loop for both invasive species

(Madritch and Lindroth 2009; Heneghan et al. 2007).

When woody shrub plants (such as buckthorn) exceed

approximately 30 % cover in woodlands, they reduce

the species richness and suppress the growth of tree

seedlings (Woods 1993). Glossy buckthorn also alters

the properties of the soil, increasing the soil nitrogen,

carbon, pH and soil moisture.

Past methods of identifying invasives

from satellite

Previous methods of identifying non-native plant

species using remote sensing have included using

moderate spatial and spectral resolution images such

as Landsat TM over very short temporal windows

associated with specific phenological differences

(Resasco et al. 2007); a combination of vegetation

response to rainfall and early season phenology over a

short time window(Bradley and Mustard 2006); or

using phenology metrics such as accumulated degree

days (De Beurs and Henebry 2005), minimum relative

humidity (Brown and de Beurs 2008), length of

season, or leaf-out day of year (Tuanmu et al. 2010). In

addition, high spatial resolution images such as color

infrared aerial photography and IKONOS or Quick-

bird imagery (Mullerova et al. 2005) have been used.

High temporal resolution low spatial resolution

Mapping buckthorn in the Oak Openings

123

remote sensing such as a AVHRR and MODIS has

been used to examine variations in the phenology over

large spatial extents with changing climate conditions

(e.g. Justice et al. 1985; Morisette et al. 2009); and

hyperspectral remote sensing to identify variations in

spectral absorption between plant species (e.g. Under-

wood et al. 2003). Other methods have focused on

single image acquisitions, and examining how sensi-

tive a variety of different vegetation indices are to the

vegetation type to discriminate between vegetation

types (Kandwal et al. 2009). Many of these methods

assess the variation of an invasive against a homoge-

neous background canopy or vegetation cover. Unfor-

tunately, a homogeneous background is not present in

the Oak Openings. As a whole, these methods either

sacrifice temporal resolution for spatial resolution or

sacrifice spatial resolution for temporal resolution.

Recently, Landsat images have become available

en masse at no cost, so it is no longer necessary to try to

discriminate phenology using moderate resolution

(30 9 30 m) images, while limiting analyses to only

2–4 scenes in a year. By taking advantage of a high

number of scenes in a multi-year image stack, we can

more fully utilize the variations in phenology to

discriminate among land covers and identify species

of interest.

Phenology

The phenology of buckthorn species is different from

other vegetation species in the study area. Buckthorn

leafs out earlier and remains green longer than a

majority of the tree canopy (shown in Fig. 2). The

NDVI and tasseled cap greenness of other land covers

either remains fairly constant throughout the year

(pine plantation, water, urban), starts later and ends

earlier (oak savanna, oak blueberry forest), or is not as

high (wet prairie, grasslands). Agricultural fields

exhibit land surface phenologies related to time of

planting and crop type. As a result, pixels dominated

by buckthorn species exhibit a distinct land surface

phenology which can be used to discriminate these

pixels from those with other land covers utilizing only

a time series of vegetation indices throughout the

course of a year.

Theoretical approach

In the Oak Openings Region, buckthorn appears in the

understory of forested areas, as a dense monoculture

encroaching over prairie, and along edges of disturbed

areas, such as agricultural drainage ditches adjoining

fields and pasture lands. Any method to identify these

thickets must be able to identify the plants both nested

in the understory and when they appear in dense

monoculture environments. In addition, the high

heterogeneity of the landscape relative to the scale

of lower resolution sensors means that methods

utilizing lower spatial resolution sensors (e.g. Justice

et al. 1985; Reed et al. 1994), such as MODIS, MERIS

and AVHRR, are not sufficient for identifying these

thickets. Buckthorn thickets may occur on the spatial

scale ranging from 50 m across up to several hectares

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

100 150 200 250 300 350

Tass

eled

-Cap

Gre

nn

ess

Ind

ex

Day of Year

Land Surface Phenologies

Buckthorn Thicket Wet Prairie Oak Savanna Oak Forest

Fig. 2 Land surface

phenology derived from

Landsat TC-G images from

2007 to 2011, showing high

early season greenness

index of buckthorn thicket.

Dashed lines represent 1

standard deviation of TC-G

in that land surface type

R. H. Becker et al.

123

in extent. Thickets as small as 50–100 m across may

be identified using Landsat resolution.

The spectral characteristics of buckthorn are not

sufficiently different from oak species (Quercus

spp.) for them to be uniquely discriminated by

spectral means using an instrument with moderate

spectral resolution, such as Landsat TM/ETM?,

SPOT or ASTER. In addition, high spectral resolu-

tion imagery is spatially and temporally limited, and

expensive.

Previous techniques using moderate resolution data

which either used specific senescence dates (Resasco

et al. 2007), or a regression between phenology at

multiple dates (Peterson 2008) were found not to work

in this environment. In order to discriminate between a

the wide variety of heterogeneous land cover types

found in the Oak Opening Region, a full-year phenol-

ogy was found in this study to be far more effective

than simply using two dates. The different land covers

which exhibit distinct satellite-based land surface

phenologies for the area include: buckthorn thicket,

twig rush wet prairie, pine plantation, maintained

(irrigated) golf course, un-irrigated pasture, farm fields

(corn, soy, alfalfa), and non-vegetated cover including

blacktop and water (sample land surface phenology

curves for pixels representing these land cover types

are shown in Fig. 2).

In the lower Great Lakes region, cloud cover can be

a significant issue in the acquisition of satellite

imagery. This cloud cover, combined with the low

temporal repeat of Landsat (14 days), means that a

single year of data can rarely be used to reveal a

complete phenological cycle of any of the plant

communities. To overcome this shortcoming of the

annual data set, we made use of a multi-year data set,

atmospherically corrected, spatially georeferenced,

and arranged by day of year to both define different

phenology types, and to classify image pixels. Because

the image data are acquired at discrete intervals in time,

the phenologic curve is interpolated between measure-

ments to give a continuous annual phenology. This

interpolated continuous curve can be resampled to

coincide with a different time series covering a

different set of years, and acquired on different days

of those years. Once the time series of vegetation index

images is created, these can be temporally resampled to

match the new image data stack, and then classified

using a supervised classification method based on the

phenology curve.

Methods

Satellite image processing

To generate the Landsat based land surface phenology,

and classify areas for presence/absence of buckthorn

thicket, the following steps were conducted.

Forty-nine images were acquired from the USGS

Eros Data Center. Images from Landsat 5 TM and

Landsat 7 ETM? were acquired covering path 20 and

row 31 (Table 1). Scenes were selected to minimize

cloud cover over the area. To eliminate inter-annual

variation due to drought, data was not selected for the

image stacks during years in which the monthly

historical Palmer Drought Severity Index (PDSI) for

the study area showed moderate to severe drought

conditions (PDSI below -2.0) for more than two

Table 1 Landsat image acquisition dates used in both the

2001–2006 and 2007–2011 stacks

2001–2006 Sensor type 2007–2011 Sensor type

14-Mar-01 ETM? 7 4-Mar-09 ETM? 7

22-Mar-01 TM 5 18-Apr-08 ETM? 7

13-Apr-03 TM 5 24-Apr-07 TM 5

18-Apr-05 TM 5 5-May-11 TM 5

29-Apr-03 TM 5 10-May-10 TM 5

1-May-01 ETM? 7 18-May-07 ETM? 7

4-May-05 ETM? 7 20-May-08 ETM? 7

23-May-06 TM 5 28-May-08 TM 5

16-Jun-03 TM 5 6-Jun-11 TM 5

24-Jun-06 TM 5 11-Jun-07 TM 5

7-Jul-02 ETM? 7 24-Jun-09 ETM? 7

15-Jul-02 TM 5 7-Jul-08 ETM? 7

23-Jul-05 TM 5 15-Jul-08 TM 5

31-Jul-05 ETM? 7 16-Aug-08 TM 5

8-Aug-05 TM 5 1-Sep-08 TM 5

21-Aug-01 ETM? 7 15-Sep-10 TM 5

1-Sep-02 TM 5 25-Sep-08 ETM? 7

6-Sep-04 TM 5 1-Oct-10 TM 5

17-Sep-02 TM 5 9-Oct-10 ETM? 7

30-Sep-04 ETM? 7 17-Oct-10 ETM? 7

8-Oct-01 ETM? 7 19-Oct-08 TM 5

24-Oct-01 ETM? 7 2-Nov-10 ETM? 7

12-Nov-05 TM 5 7-Nov-09 TM 5

20-Nov-05 ETM? 7 1-Dec-09 ETM? 7

1-Dec-03 ETM? 7

The sensor used to acquire the image is also listed

Mapping buckthorn in the Oak Openings

123

consecutive spring and summer months (Palmer

1965).

Images were atmospherically corrected using the

FLAASH atmospheric correction package imple-

mented in the ENVI software (Cooley et al. 2002).

Visibility at the Toledo Express airport was obtained for

the hour closest to scene acquisition for model input

(Roche 2011). Toledo Express is located at the center of

the scene of interest. Orbital parameters, scene center

acquisition date/time, latitude and longitude of scene

center were acquired from the image headers.

Images were co-registered and resampled to a

common base image. In order to allow for the highest

quality vegetation index generation, nearest neighbor

resampling was used to preserve the original spectra.

The co-registered, atmospherically corrected land

surface reflectances were used to calculate Tasseled

Cap Greenness (TC-G) transformation (Crist and

Kauth 1986) using the formula:

G ¼ �0:2728TM1� 0:2174TM2� 0:5508TM3

þ 0:7221TM4þ 0:0733TM5� 0:1648TM7

Enhanced Vegetation Index (EVI) and Normalized

Difference Vegetation Index (NDVI) were also cal-

culated for the same scenes based on the same co-

registered atmospherically corrected images.

Locations with known-type land surface coverage

were identified in the field, including buckthorn

thicket, turf grass, evergreen forest, open water,

asphalt, oak forest, wet prairie, and oak savanna. An

area of interest, with a minimum of 15 pixels and a

maximum of 300 pixels, was defined for each of

these land cover types. TC-G, EVI and NDVI images

spanning 2007–2011 were stacked in an image stack

indexed by day of year, generating a land-surface

phenology curve for each pixel location throughout

the entire region. For each of the land cover types, a

time series of each vegetation index was extracted

using the z-profile tool in ENVI based on the area of

interest chosen for each of the designated type land

surface coverages.

An image stack for 2007–2011 was generated for

each vegetation index, indexed by day of year

within the image stack. The parallelepiped method

of image classification was employed on each time

series stack and locations with buckthorn thicket

were identified using training sets that were obtained

from field sites that were identified as buckthorn

thicket. In addition, the maximum likelihood clas-

sification method was used, using phenology of

buckthorn thicket, turf grass, evergreen forest, open

water, asphalt, oak forest, wet prairie, and oak

savanna, as the land cover classification types. The

accuracy assessment used for each image classifica-

tion based on field validation data was also assessed

and is discussed in Sect. 3.3.

Image stacks covering the periods 2001–2006, and

2007–2011 were generated. Based on the accuracy

assessment results, the TC-G was selected as the best

vegetation index, and the parallelepiped classification

method as producing the most accurate results as

determined by the kappa test. The land surface

phenology curves were resampled to the day of year

indicies that were used in each image stack. The TC-

G image stacks were classified using the resampled

phenology curves with the parallelepiped classifica-

tion method.

Field characterization of vegetation

To provide a basis for validation of the remotely sensed

classification of buckthorn species thicket, the follow-

ing field measurements were conducted at IPSNP and

Bumpus Pond, a property owned by Toledo Area

Metroparks. Both of these properties have extensive

buckthorn thickets and are actively managed to remove

buckthorn and restore native wet prairie.

Areas of buckthorn thicket were sampled for stem

density, species composition, and canopy closure to

determine the definition and variability of thickets at a

set of ten P-Plot locations (Fig. 4). Nested vegetation

sampling has been widely used as a means to provide

an accurate representation of both plant species

richness and diversity at multiple spatial scales from

1 to 1,000 m2 plot sizes (Stohlgren et al. 1995). Pixel

nested plot (P-Plot) sampling utilizes a similar sam-

pling style as a modified-Whittaker plot but at a scale

that is similar to the nominal ground resolution of

sensors such as Landsat TM and ETM? (Kalkhan

et al. 2007). Due to the impenetrable nature of

buckthorn thickets, a 30 9 30 m P-Plot (a hybrid of

a pixel nested plot and a modified Whittaker plot) was

used. Figure 3 shows the layout of the plot. Because

graminoids and non-woody herbaceous species in this

R. H. Becker et al.

123

study area make a minimal contribution to overall

reflectance values at the 30 m Landsat scale, their

presence was noted in each sub-plot, but only woody

plants were considered during sampling. Ten

1 m x 1 m plots and two 3 m 9 3 m (A and B) were

sampled for stem density, species composition, and

canopy closure. All species within the 10 m 9

10 m sub-plot (C) were noted for presence. The larger

30 m 9 30 m plot was surveyed for species that were

not found in any of the smaller plots. A spherical den-

sitometer was used to determine canopy coverage

(Lemmon 1956). Each subplot was averaged to

determine species composition, density, and canopy

coverage for the entire 30 m 9 30 m plot. Based on

these ten P-plot locations, distributed over a 6.5 km2

area in a variety of buckthorn densities, a character-

ization of low, medium, and high buckthorn coverage

was determined and used for a rapid assessment of

larger numbers of locations.

In addition, 50 sample locations throughout the Oak

Openings in a 35 km2 area were selected for method

validation. Based on the measurement of buckthorn

density at the ten P-plot locations, a measure of ‘‘low’’,

‘‘medium’’ or ‘‘high’’ was assigned to samples at 50

other selected sample locations (Table 2).

Accuracy assessment

The image classification results and field measure-

ments were compared to determine the accuracy of

products derived using the above method. The

classifications of buckthorn derived from each of

the vegetation (TC-G, EVI, NDVI) index stacks

using each classification method on the 2007–2011

image stacks were checked against the results of

field measurements. Measurements at the ten P-plot

sites and the 50 other field validation sites were

used to assess the accuracy. Kappa values were

calculated for each image interpretation method

(Congalton 1991) for the presence or absence of

buckthorn thicket at locations throughout the

images (Table 3).

Fig. 3 Configuration of P-Plot canopy coverage sampling

scheme used to determine buckthorn thicket density of locations

in Fig. 4

Table 2 Ten 30 9 30 m

P-plots used in the study

(marked as stars on Fig. 4)

showing the percentage of

buckthorn plants classified

as overstory, the percentage

of buckthorn plants

compared to overall species

composition, total canopy

coverage (late May 2011),

and density classification

(None-High) based on the

number of buckthorn plants

per area

The latitude and longitude

of the plots is also listed

Plot

ID

Buckthorn

percentages of

overstory species

composition

Buckthorn overall

percentage of

species

composition

Canopy

coverage

Buckthorn

density

Latitude Longitude

A 23 44 92 LOW 41.6563 -83.7809

B 97 92 93 HIGH 41.6527 -83.7753

C 96 76 93 HIGH 41.6356 -83.7919

D 60 86 94 MED 41.6363 -83.7903

E 94 91 90 HIGH 41.6359 -83.7912

F 23 74 83 LOW 41.6529 -83.7696

G 0 0 87 NONE 41.6577 -83.7713

H 78 79 86 MED 41.6568 -83.7784

I 90 96 95 HIGH 41.6522 -83.7765

J 86 87 88 HIGH 41.6577 -83.7807

Mapping buckthorn in the Oak Openings

123

Results and discussion

Time series images of vegetation indexes from 2007 to

2011 were used to produce presence absence maps of

buckthorn species in the Oak Openings Region. With a

kappa test value of 0.73, the TC-G image stack

processed with the parallelepiped classification

method was found to have the highest accuracy of

buckthorn species thicket identification at 88 %

(Figs. 4, 5). The phenological differences are suffi-

cient to successfully discriminate buckthorn species

dominated thicket from various heterogeneous back-

grounds including wet prairie, pine plantation, main-

tained (irrigated) golf course, non-irrigated pasture,

farm fields (corn, soy, alfalfa), and non-vegetated

cover including blacktop and water.

Thickets of buckthorn species were identified

throughout the entire region—present in 0.43 %

(945 ha) of the land cover in the Oak Openings

Region in 2007–2011 (Fig. 5). Larger areal extents of

thicket were identified in clusters surrounded by or

adjacent to agricultural drainage ditches. These

ditches were observed to deliver high sediment,

nutrient rich waters to areas adjoining them, such as

Fig. 4 Map of buckthorn

thicket classified from 2007

to 2011 Landsat TC-G

images in the areas near

IPSNP and Bumpus Pond.

Stars mark the location of

P-plots used in the study.

Locations marked with x’s

show confirmation samples

Table 3 Confusion matrix showing accuracy assessment of

buckthorn thicket classification of the 2007–2011 Landsat

images

Class Buckthorn No buckthorn Row total

Buckthorn 15 4 19

No buckthorn 3 38 41

Column total 18 42 60

Overall accuracy 88.33 %

Kappa hat 0.7266

Buckthorn No buckthorn

Ommission error 16.67 9.52

Commission error 21.05 7.32

R. H. Becker et al.

123

the IPSNP, during spring thaw and flooding poten-

tially contributing to the success of the buckthorn

species.

Using the two time steps of 2001–2006, and

2007–2011, we were able to document the increase

in the extent of buckthorn thicket. This change in land

cover showed a consistent increase throughout the Oak

Openings Region from 0.31 % (690 ha) to 0.43 %

(945 ha) of pixels in the study area.

Examination of the time series images at IPSNP

shows that results from these images agree with

documented management practices at IPSNP. For

example, in 2002–2003, a 11.5-ha management unit in

the south-central portion of the nature preserve

received multiple treatment methods to remove buck-

thorn (Moser et al. 2005). In Fig. 6, area ‘C’,

buckthorn is absent in both the 2001–2006 and

2007–2011 image stack (Fig. 6, area ‘C’). An addi-

tional 10 ha area in the north central portion of the

preserve was remediated by removal using a hydro-ax

in 2007, and this area shows buckthorn present in the

2001–2006 image stack, but not in the 2007–2011

image stack (Fig. 6, area ‘A’) A final area was

remediated in the fall of 2010, and this area shows a

presence of buckthorn in an image stack from 2007 to

2009, but absent in a stack from 2007 to 2011 (Fig. 6,

area ‘B’).

This method is of land surface phenology extraction

from multi-year Landsat data, and classification of

species type is applicable for other areas or species

where a distinct phenological difference is notable.

The technique could prove particularly useful in other

areas where a species of interest present in understory

leafs out earlier than the overstory canopy.

Fig. 5 Regional

distribution map of Landsat

derived, non-native

buckthorn thicket locations

in the Oak Openings. Large

regions of buckthorn thicket

are linked to areas with

significant agricultural

drainage ditches

Mapping buckthorn in the Oak Openings

123

Acknowledgments This research was funded by the through

the Ohio Lake Erie Commission (OLEC) and the Lake Erie

Protection Fund (LEPF) Small Grant #416-11. The LEPF is

supported by the citizens of Ohio through their purchase of the

Lake Erie License Plate. We thank both Toledo Area

Metroparks and Irwin Prairie State Nature Preserve for

allowing us access to their property to conduct this research,

and Jonathon Sanders for field assistance. This is contribution

#2012-10 from the Lake Erie Research Center.

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Landsat derived buckthorn

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photograph. Area A was

remediated in 2006–2007,

area B was remediated in

2010–2011, and area C was

remediated in 2002–2003

before a majority of image

acquisitions occurred

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