ORIGINAL PAPER

Remote detection of invasive plants: a review of spectral,textural and phenological approaches

Bethany A. Bradley

Received: 17 July 2013 / Accepted: 22 October 2013 / Published online: 31 October 2013

� Springer Science+Business Media Dordrecht 2013

Abstract Remote sensing image analysis is increas-

ingly being used as a tool for mapping invasive plant

species. Resulting distribution maps can be used to

target management of early infestations and to model

future invasion risk. Remote identification of invasive

plants based on differences in spectral signatures is the

most common approach, typically using hyperspectral

data. But several studies have found that textural and

phenological differences are also effective approaches

for identifying invasive plants. I review examples of

remote detection of invasive plants based on spectral,

textural and phenological analysis and highlight

circumstances where the different approaches are

likely to be most effective. I also review sources and

availability of remotely sensed data that could be

used for mapping and suggest field data collection

approaches that would support the analysis of remotely

sensed data. Remote mapping of biological invasions

remains a relatively specialized research topic, but the

distinct cover, morphology and/or seasonality of many

invaded versus native ecosystems suggests that more

species could be detected remotely. Remote sensing

can sometimes support early detection and rapid

response directly, however, accurately detecting small,

nascent populations is a challenge. However, even

maps of heavily infested areas can provide a valuable

tool for risk assessment by increasing knowledge about

temporal and spatial patterns and predictors of

invasion.

Keywords Aerial photograph �Hyperspectral �Invasive plant � Object-based classification �Phenology � Satellite remote sensing

Introduction

Spatial analysis of plant invasions is a research field that

continues to show incredible growth. Numerous studies

have used distribution maps of invasive plants to model

environmental correlates to invasion at landscape (see

for examples Vila and Ibanez 2011) and regional (see for

examples Bradley 2013) scales. Distribution maps and

associated risk models are critical for early detection and

rapid response (EDRR) to new invasives (Westbrooks

2004) and for support decision making for management

and control (Shaw 2005). Distribution maps have also

been used to assess or scale invasive plant impacts such

as altered fire frequency (Balch et al. 2013) and water

use (Zavaleta 2000).

However, before risk models, management efforts

or impact assessments can be undertaken, information

about current invasive plant distribution is needed. To

Electronic supplementary material The online version ofthis article (doi:10.1007/s10530-013-0578-9) contains supple-mentary material, which is available to authorized users.

B. A. Bradley (&)

Department of Environmental Conservation, University of

Massachusetts, Amherst, MA 01003, USA

e-mail: bbradley@eco.umass.edu

123

Biol Invasions (2014) 16:1411–1425

DOI 10.1007/s10530-013-0578-9

date, most studies documenting spatial patterns of

invasion and forecasting invasion risk have relied on

distribution data acquired from herbarium records

(e.g., GBIF 2013) or regional management records

(e.g., EDDMapS 2013). Unfortunately, occurrence

records tend to contain spatial bias in terms of their

collection locations (e.g., collected adjacent to roads),

which can strongly affect risk assessments (Wolma-

rans et al. 2010). Further, herbarium and EDRR

records oversample ‘rare’ locations with low invasive

plant abundance, which causes associated models to

overestimate invasion risk and potential impact (Mar-

vin et al. 2009; Bradley 2013).

One way to improve modeling of invasion risk as

well as document the current extents of plant invasion

is through comprehensive mapping. At landscape

scales, wall to wall mapping is rarely feasible using

field survey data alone. However, a number of studies

have shown that using remotely sensed imagery to map

invasive plants may be a viable option (Lass et al. 2005;

Underwood et al. 2007; Huang and Asner 2009; He

et al. 2011). Although invasive plant mapping based on

spectral differentiation is most common, a growing

number of studies are using textural and/or phenology-

based approaches to identify invaded landscapes (see

Table 1 for definitions). Here, I review examples of

remotely sensed mapping of invasive plants with an

emphasis on identifying circumstances when remote

detection could be a viable option. I further suggest

methods for field collection of invasive plant cover

data that could later be used for training or validation of

remotely sensed maps. Reviewed studies include a

Table 1 Definitions of common terms

Term Definition

Absorption Light energy that is not reflected off of

or transmitted through an object

Hyperspectral Imagery of the same region that

contains many (typically hundreds)

of spectral bands spanning visible,

near-infrared and often short

wavelength infrared

Multispectral Imagery of the same region that

contains multiple (typically 4–10)

spectral bands in visible, near-

infrared and often short wavelength

infrared

Near-infrared (NIR) Energy at slightly longer wavelengths

than VIS, typically referring to

wavelengths between 0.7 and 1 lm

where photosynthetic vegetation has

high reflectance. Plant reflectance

versus absorption in NIR

wavelengths is typically related to

water content

Phenology The seasonal reoccurrence of

biological events. Time series of

remotely sensed imagery, typically

using vegetation indices, can identify

phenological stages such as start of

season and end of season of

dominant photosynthetic vegetation

Pixel The smallest unit of measure of a

satellite or aerial image, typically

expressed in terms of the length of

one square side (e.g., a 30 m pixel is

900 m2)

Reflectance Light energy that is not absorbed by or

transmitted through an object.

Reflected light bounces off an object

and is typically measured by remote

sensors in the visible, near-infrared

and short wavelength infrared

wavelengths

Short wavelength

infrared (SWIR)

Energy at slightly longer wavelengths

than NIR, typically referring to

wavelengths between 1 and 2.5 lm.

Plant reflectance versus absorption in

SWIR wavelengths can be related to

water content, foliar N and other

plant compounds (e.g., lignin and

cellulose)

Spectral band Discrete wavelength regions sampled

by a sensor (e.g., the NIR band for

Landsat 5 integrates all reflectance

between 0.76 and 0.90 lm)

Spectrum Reflectance of a material across a

series of wavelengths

Table 1 continued

Term Definition

Texture Variation in reflectance between

neighboring pixels

Vegetation index A ratio of near-infrared to visible red

that highlights photosynthetic

vegetation. The most common is the

Normalized Difference Vegetation

Index [NDVI: (NIR - VIS)/

(NIR ? VIS)]

Visible (VIS) Wavelengths of visible (light) energy

between 0.4 and 0.7 lm. Plant

reflectance versus absorption in VIS

wavelengths is typically related to

chlorophyll content and

pigmentation

1412 B. A. Bradley

123

range of invaded ecosystems (see Supplemental

Table 1), but the mapping approaches are likely to be

more broadly applicable than the specific target

ecosystems.

Reflectance remote sensing

Sensor availability

There are a number of types of remote sensing imagery

currently available, acquired by both public and private

satellites as well as an array of airborne sources of

aerial photos and imagery (Table 2). With remotely

sensed data, there are tradeoffs between spatial extent

(size of the image), spatial resolution (pixel size),

spectral resolution (number and range of visible and

infra-red bands) and temporal resolution (frequency of

data acquisition). Larger spatial extents allow for more

extensive mapping of invasive plants and ultimately

provide more distribution data to inform spatial models

of invasion risk. However, spatial resolution is typi-

cally low, making only widespread and abundant

infestations potentially detectable. Finer spatial reso-

lution makes it more likely that individual species and

early infestations can be detected. However, spatial

extents and repeat temporal coverage is typically

limited. Higher spectral resolution creates opportuni-

ties for differentiating plant pigments and chemistry in

both visible and infra-red bands. As a result, hyper-

spectral sensors are most commonly used for invasive

plant detection (Huang and Asner 2009; He et al.

2011). But, these data typically have limited spatial and

temporal coverage and can be costly to acquire

(Table 2). There is no sensor that can achieve high

spatial, spectral and temporal coverage over a broad

spatial extent, so choice of remote sensing approach

will always be limited by tradeoffs along these axes.

At the highest spatial resolution end of available

imagery are aerial photos, airborne hyperspectral

imagery like AVIRIS and a number of satellite sensors

recently launched by private companies. Aerial photos

have low spectral resolution, typically acquiring only

grayscale (a single spectral band spanning all visible

light) or visible color (three spectral bands measuring

blue, green and red reflectance). High resolution satellite

sensors typically acquire four spectral bands with three

in the visible and one in the near-infrared. Examples

include IKONOS and Quickbird, while the recently

launched Worldview-2 has 8 spectral bands. Hyper-

spectral sensors have high spectral resolution, often

hundreds of spectral bands spanning visible and near-

infrared, with many also extending into short-wave

infrared wavelengths. All of the above sources of

imagery are either periodically acquired (e.g., aerial

photos) or acquisitions are tasked by end users, often at

substantial cost.

In the moderate range of spatial resolution are multi-

spectral sensors, typically with 4–10 spectral bands in the

visible, near infrared and short-wave infrared. Examples

include Landsat (30 m), ASTER (15–30 m) and SPOT

(20 m) with image swath widths of 60 km wide (for

ASTER and SPOT) to 185 km wide (for Landsat).

Landsat has a regular return interval of 16 days, and

Landsat TM (thematic mapper) satellites have been

active since the mid 1980s, creating a near continuous

record of imagery for 25 years within the US and many

other countries. In regions with low or moderate cloud

cover, hundreds of images could be available, creating

opportunities for both change detection and measure-

ments of phenology over multiple years. Moderate

resolution datasets are often free or low cost.

Coarser spatial resolution data are available from the

advanced very high resolution radiometer (AVHRR)

and moderate resolution imaging spectroradiometer

(MODIS), which have pixel resolutions between 250

and 1,000 m (Table 2). With a coarse pixel resolution

and wide swath width, both AVHRR and MODIS image

the entire Earth daily. These daily data are used to create

weekly or biweekly composites of surface reflectance

and vegetation metrics that minimize cloud cover (e.g.,

Gao et al. 2008). Time series have also been used to

extract vegetation phenology metrics (e.g., start of

season; http://phenology.cr.usgs.gov/) (Tan et al. 2010).

The AVHRR archive extends back to the 1980s, while

MODIS data were first acquired in 2000. Both datasets

are freely available.

Detecting land cover remotely

Species specific detection with remote sensing remains

relatively rare. More often, land cover maps are based

on plant functional type (e.g., shrubland), which are

then linked to dominant species based on knowledge of

regional ecosystems (e.g., sagebrush steppe shrubland).

Dominant land cover (based on plant functional type)

can be separated based on spectral signatures. For

Remote sensing of plant invasions 1413

123

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1414 B. A. Bradley

123

example, grasslands are spectrally distinct from forests

because grasslands are typically composed of a com-

bination of photosynthetic vegetation and soils, while

forests contain photosynthetic vegetation, woody veg-

etation, soils and dark shadows. Any given pixel will

contain a combination of all of these spectral features,

which are often sufficiently distinct from one another to

enable mapping (Fig. 1). Another way of identifying

plant functional types is based on seasonal phenology

(e.g., Loveland et al. 2000; Friedl et al. 2002). For

example, deciduous forests have a much more pro-

nounced seasonal change in photosynthetic activity

than do conifer forests, which can be used to differen-

tiate dominant land cover classes (e.g., Townsend and

Walsh 2001).

In order to detect an invasive plant species with

remote sensing, that species must have a unique

spectral, textural or phenological signal that could

distinguish it from surrounding native vegetation. The

invasive plant species must also achieve high percent

cover within the pixel relative to the spatial resolution

of the sensor (for example, to be detectable by Landsat,

the species would need to be widespread within 900 m2

pixels, whereas single invading trees might be detect-

able in 4 m2 pixels of aerial photos or high resolution

commercial imagery). How high of a percent cover is

needed for detection depends on how unique the species

is relative to the invaded ecosystem. Parker Williams

and Hunt (2002) were able to detect leafy spurge

(Euphorbia esula) at as low as 10 % cover, but another

study in a similar ecosystem showed that consistent

detection through time required at least 40 % cover of

E. esula (Glenn et al. 2005). For early detection and

rapid response (Westbrooks 2004) to invasions, higher

resolution imagery are therefore more appropriate,

whereas coarser resolution imagery might be more

useful for understanding landscape and regional inva-

sion risk at later stages of invasion.

Over time, successful invasive plants tend to invade

in high densities and often form near monotypic

stands. Additionally, invasive plants are often able to

exploit seasonally available resources that native

Fig. 1 Examples of pure (unmixed) spectral signatures from

hyperspectral and multispectral sensors. Vertical bars indicate

the approximate wavelengths measured by Landsat/MODIS

sensors, with the blue, green and red bars representing the

wavelengths of visible light. a Four spectrally distinct materials

are easily separated using hyperspectral data. b Four types of

photosynthetic vegetation have similar spectra, but may be

possible to separate using hyperspectral data. c Distinct

materials remain easy to identify with multi-spectral data.

d Photosynthetic vegetation types appear nearly identical with

multi-spectral data. (Color figure online)

Remote sensing of plant invasions 1415

123

species cannot (Herbold and Moyle 1986; Shea and

Chesson 2002), which might lead to unique pheno-

logical patterns (Wolkovich and Cleland 2011). Both

of these characteristics make it plausible that remotely

sensed imagery could successfully identify many more

invasive plants than studies have targeted to date.

Remote sensing of plant invasions

Spectral detection

Currently, the majority of studies aimed at mapping

invasive species remotely use spectral differentiation

of high spatial resolution imagery (see reviews by Lass

et al. 2005; Underwood et al. 2007; Huang and Asner

2009; He et al. 2011). This approach is also commonly

used for detecting invasive plants in agriculture

(reviewed by Thorp and Tian 2004; Mulla 2013 but

not discussed further here). A spectral distinction

implies that the target invasive species has one or more

unique light absorption or reflectance features relative

to native vegetation. Spectral differences are easiest to

identify with hyperspectral imagery (Fig. 1a, b) which

have hundreds of narrow spectral bands available to

identify unique reflectance or absorption features.

With numerous available spectral bands, hyperspec-

tral analyses can use spectral shape to differentiate

species or can target specific wavelengths or ratios of

two wavelengths that highlight differences in plant

pigmentation, water content or leaf chemistry. For

example, Underwood et al. (2003) used hyperspectral

imagery to identify iceplant (Carpobrotus edulis) in

coastal California, USA because the succulent inva-

sive species has higher leaf water content than native

coastal shrub vegetation. Asner et al. (2008) used

hyperspectral imagery to identify a number of invasive

plants in Hawaii based on unique leaf chemistry,

which alters spectral absorption features at specific

wavelengths.

Plant pigmentation is more commonly used to

identify invasive plants based on chlorophyll content

or unique coloration of leaves or flowers and typically

focuses on visible wavelengths. Both hyperspectral

(Lass et al. 2002; Hestir et al. 2008; Somers and Asner

2013) and multi-spectral (Fuller 2005; Schneider and

Fernando 2010) analyses have successfully identified

invasive plants based on unique leaf coloration or

cover. For example, tamarisk (Tamarix spp.) invasion

in southern California and the Colorado plateau is

spectrally distinct from surrounding upland vegetation

(Carter et al. 2009), but is likely to be confounded with

other riparian shrubs and trees (Hamada et al. 2007).

Multi-spectral differences are most likely to be

observed if the invasive plant is a different functional

type from the invaded ecosystem. For example, Wu

et al. (2006) used IKONOS data to identify the

invasive vine (Lygodium microphyllum), which over-

tops tree islands in the Florida everglades changing the

dominant spectral signature from a mixture of green

and woody vegetation to nearly homogeneous green

vegetation. Floating aquatic invasive plants have also

been identified remotely due to the strong spectral

distinction between photosynthetic vegetation and

water (which has low reflectance across all wave-

lengths, Fig. 1). Example targeted species include

water hyacinth [Eichhornia crassipes (Albright et al.

2004; Everitt and Yang 2007; Hestir et al. 2008;

Cavalli et al. 2009)], giant salvinia [Salvinia molesta

(Fletcher et al. 2010) and purple loosestrife (Lythrum

salicaria Laba et al. 2010)].

More promising than leaf pigmentation for spectral

differentiation is a focus on remote detection of

invasive plants’ flowers (Everitt et al. 1995; Parker

Williams and Hunt 2002; Glenn et al. 2005; Mullerova

et al. 2005; Andrew and Ustin 2006; Miao et al. 2006;

Andrew and Ustin 2008; Somodi et al. 2012; Mirik

et al. 2013). For example, leafy spurge (Euphorbia

esula) invading grass and shrublands of western North

America has characteristic yellow flowers that bloom

in the early summer. This distinct pigmentation

enables remote detection using both hyperspectral

data (Parker Williams and Hunt 2002; Glenn et al.

2005) as well as color aerial photos (Everitt et al.

1995). Similarly, aerial photos have been used to

identify invasive Acacia delbata in Chile based on its

distinct yellow flowering in winter images (Under-

wood et al. 2007). However, even species with distinct

flowering pigmentation can be misclassified if native

species are flowering at the same time. Perennial

pepperweed (Lepidium latifolium), detectable based

on its prolific white flowers, can be conflated with

other white flowering plants in more diverse invaded

ecosystems (Andrew and Ustin 2008).

Although not focused on invasive plants specifi-

cally, several studies have used spectral differences to

identify tree kills associated with forest pests or

pathogens (Bonneau et al. 1999; Wulder et al. 2006;

1416 B. A. Bradley

123

Meentemeyer et al. 2008). Strong spectral differ-

ences between green vegetation and senesced veg-

etation (Fig. 1a) enable detection and quantification

of dead tree cover, particularly in comparison to

imagery acquired prior to the outbreak. For exam-

ple, Bonneau et al. (1999) compared vegetation

indices derived from Landsat images in Connecticut,

USA from 1985 and 1995 to identify loss of

hemlock stands attributable to the hemlock woolly

adelgid (Adelges tsugae). Wulder et al. (2006)

review remote sensing studies documenting native

mountain pine beetle (Dendoctonus ponderosae)

damage, which is most apparent spectrally when

comparing late spring (before visible damage) to

late summer (after visible damage) imagery.

What circumstances create opportunities for spec-

trally based mapping of invasive plants? First, analysis

of reflectance spectra (used by all of the sensors in

Table 2) predominantly captures the layer that is

immediately below the sensor, so spectral differenti-

ation is limited to species in the vegetation canopy.

Forest understory species will rarely be detectable

based only on spectra unless they alter canopy

chemistry (e.g., Asner and Vitousek 2005). Active

sensors such as laser altimetry (LIDAR) could identify

changes to multiple forest canopies (Lefsky et al.

2002), but this approach is not considered further here.

Forest canopy invaders and invasive plants in single

story ecosystems (e.g., grassland, shrubland) are the

most likely to be detectable based on spectra. Second,

invasive plants with leaf chemistry, leaf or flower

pigmentation that is distinctly different from native

vegetation are the most likely to prove detectable. In

these cases, image selection or acquisition may need to

target specific time periods when invasives are spec-

trally distinct, such as flowering time.

Textural and object-based detection

Detection techniques based on unique spectral or

temporal qualities focus on analysis of the smallest unit

of measure, the pixel. In contrast, textural and object-

based detection identifies patterns within a neighbor-

hood of adjacent pixels. Textural analysis recognizes a

particular pattern and direction amongst groups of pixels

(Tuceryan and Jain 1998). To the human eye, textural

differentiation is relatively straightforward (imagine,

for example, differentiating between a grassland and a

planted cornfield—rows of corn are texturally obvious

to the eye). Object-based analysis is similar, but

typically focused on identifying a single object (tree,

building) from surrounding pixels (Blaschke 2010). In

object-based analysis, the target object must be larger

than the pixel size in order to be effectively identified.

Textural and object-based analysis of invasive plants

using machine learning is less common and sometimes

less accurate than visual classification.

Visual classification of imagery based on texture

or by identifying objects requires training of an

image analyst, but human interpretation can often

prove more accurate than computer algorithms (e.g.,

Pearlstine et al. 2005; Fig. 2). For example, cattail

(Typha spp.) invasions in the aquatic ecosystems in

Michigan, USA tend to grow in monoculture, which

makes the plant canopy appear visually homogeneous

relative to more diverse native communities (Boers

and Zedler 2008; Lishawa et al. 2013). This sort of

textural homogeneity may be common amongst plant

invaders that tend to grow in high density or near

monoculture. In Florida, the distinctive cylindrical

crown shape of Melaleuca quinquenervia enabled

McCormick (1999) to visually identify the invasive

tree from aerial photos. Even small species can be

detected visually given imagery with high enough

spatial resolution. Blumenthal et al. (2007) used

aerial photos with an incredible 2 mm pixel size to

visually identify the invasive forb Dalmatian toadflax

(Linaria dalmatica) in Wyoming, USA.

An alternative to visual interpretation of imagery

are approaches based on machine learning (Tuceryan

and Jain 1998; Blaschke 2010). This approach eval-

uates variance in reflectance within a multi-pixel

moving window to identify similar objects or textures.

Invasive trees are the most likely targets of object-

based classification because individuals are larger than

the image pixel size. This approach can be particularly

effective for identifying trees expanding into sur-

rounding shrubland or grassland. For example, auto-

mated identification of native pinyon-juniper and

Ponderosa pines has proven effective for measuring

range expansion into surrounding shrubland based on

historical aerial photos (Mast et al. 1997; Weisberg

et al. 2007). Identification of invasive trees in forests is

prone to higher classification error (Pearlstine et al.

2005; Fig. 2), but a number of studies have shown

good classification accuracy nonetheless (Pearlstine

et al. 2005; Tsai and Chou 2006; Xie et al. 2008; Gil

et al. 2013).

Remote sensing of plant invasions 1417

123

What circumstances create opportunities for tex-

ture- or object-based mapping of invasive plants? First,

the spatial resolution of the imagery is important. In the

above examples, the pixel size of the imagery was

much smaller than the invasive plant or aggregation of

plants, creating an opportunity to identify individuals

larger than a single pixel. The pixel size of aerial photos

and many high resolution satellite images is about 1 m

(Table 2), so this approach is most often employed for

identifying trees with canopies larger than 1 m2.

Second, in order for the invasive species to be

identifiable texturally, individuals or groups of indi-

viduals must have some unique shape, growth habit or

density relative to native species. Invasive plant

species that grow in monoculture (like the Typha

example (Boers and Zedler 2008; Lishawa et al. 2013)

may be potential targets, but textural analysis in these

cases is likely to capture high density invasions and

miss smaller, early infestations.

Phenological detection

Identifying an invasive species based on phenology

implies that the species has a different seasonal or

inter-annual growth pattern than native species. Inva-

sive plants with different phenologies have an advan-

tage in competition with native plant communities

(Willis et al. 2010; Wolkovich and Cleland 2011),

hence, distinct phenological patterns could provide

opportunities for remote detection. In order to assem-

ble the necessary time series, repeat image acquisition

is needed. This requirement precludes the use of most

aerial photos, hyperspectral and high spatial resolution

multi-spectral data (but, see Noujdina and Ustin 2008;

Somers and Asner 2013). Currently, the most viable

options for temporally repeating image acquisition are

multi-spectral sensors (Table 2), which range in

spatial resolution from 30 m to 1 km. Coarser reso-

lution reduces the likelihood of detecting small

Fig. 2 Textural analysis of Schinus terebinthifolius invasion in

south Florida. a The original false color (red has high

photosynthetic vegetation) imagery. The top image shows

invasive S. terebinthifolius and the bottom image shows native

S. palmetto. b Expert classification maps based on visual

interpretation coupled with substantial field data have high

classification accuracy, but are time consuming to create. c An

object based classification algorithm based on texture effec-

tively identifies invasive tree cover from native herbaceous

vegetation, but misclassifies S. palmetto as S. terebinthifolius.

Figure adapted from Pearlstine et al. 2005. Reprinted with

permission from the American Society for Photogrammetry and

Remote Sensing, Bethesda, MD, www.asprs.org. (Color figure

online)

1418 B. A. Bradley

123

populations, making a phenological approach most

appropriate for mapping dense patches and wide-

spread infestations (Bradley and Mustard 2005).

An invasive plant might be phenologically distinct

if it is an evergreen species invading a native

deciduous landscape, if it greens up earlier or stays

green longer than natives, or if it has a different inter-

annual signal such as higher inter-annual variability.

For example, Hoyos et al. (2010) used a Landsat time

series to map invasion of glossy privet (Ligustrum

lucidum) in Cordoba, Argentina, showing that the

invasive evergreen trees had a distinct phenology from

native deciduous forest. Similarly, Taylor et al. (2013)

speculate that winter imagery could be used to identify

evergreen Rhodedendron ponticum invasions in forest

understory. Lu et al. (2013) showed that the invasive

tree Leucaena leucocephala in Taiwan is phenologi-

cally distinct from native trees between wet and dry

periods. In the Colorado plateau, Evangelista et al.

(2009) showed that time series of Landsat images were

more effective than a single image for detecting the

evergreen tamarisk (Tamarix spp.). In Australia, Petty

et al. (2012) timed helicopter aerial surveys to capture

the dry season window when native grasses where

senescent, but invasive gamba grass (Andropogon

gayanus) was still green.

Even understory invasive plants can be detected if

they have phenologies distinct from overstory species.

Becker et al. (2013) used a timeseries of Landsat

images to demonstrate that understory buckthorn

(Frangula alnus; Rhamnus cathartica) is detectable

based on an extended green season relative to the

forest canopy. Kimothi et al. (2010) showed that

Lantana camara invasion is detectable in India after

canopy tree leaves have fallen. Several studies have

shown that bush honeysuckle (Lonicera maackii)

invasions in forest understory in the Midwest, USA

can be identified by targeting early spring and late fall

imagery—time periods when honeysuckle is green but

the tree canopy is not (Fig. 3) (Resasco et al. 2007;

Wilfong et al. 2009; Shouse et al. 2013). Understory

bamboo is also detectable due to its early spring green-

up prior to canopy leaf out in its native China (Tuanmu

et al. 2010), which suggests bamboo invasions could

also be phenologically distinct if they also green up

earlier than forest canopies in their non-native range.

Early spring green-up has also been used in semi-arid

ecosystems of the western US to map invasive cheat-

grass (Bromus tectorum) by identifying differences in

photosynthetic greenness captured in Landsat images

between early spring and early summer (Peterson 2005;

Singh and Glenn 2009; Clinton et al. 2010). In addition

to greening up early, B. tectorum also has high inter-

annual variability in cover and biomass in response to

periodic wet years (Fig. 4) and was widespread enough

to be detectable with both Landsat and AVHRR

(Bradley and Mustard 2005). Another desert invasive

grass, Lehmann lovegrass (Eragrostis lehmanniana)

was detectable in southern Arizona, USA based on inter-

annual variability recorded by MODIS (Huang and

Geiger 2008). Invasive plants often show the ability to

take advantage of periodically available resources

(Davis et al. 2000), so inter-annual variability may be

a promising avenue of research for invasive plant

detection and characterizing plant distributions.

What circumstances create opportunities for phe-

nology-based mapping of invasive plants? First,

because satellite image time series are available at a

coarser spatial resolution (e.g., Landsat, MODIS or

AVHRR), the target invasive species will only be

detectable once it has achieved high enough cover

(relative to pixel size) to influence the phenological

signal. ‘High enough’ cover is an unknown quantity

and will depend on the ecosystem. Small changes to

forest canopy due to selective logging have been

detected remotely (Koltunov et al. 2009), but cover of

invasives required to alter phenology needs to be

tested on a case by case basis. This type of mapping is

likely to be more useful at later stages of invasion

when the species is widespread and abundant. Land-

scape and regional maps of high density invasions

can then be used to identify related landscape and

Fig. 3 Longer growing season of invasive understory species

in forests provides opportunities for remote detection in the

early spring and late fall when trees are senescent

Remote sensing of plant invasions 1419

123

regional features (e.g., disturbance, soils, topography)

and model risk of invasion (e.g., Bradley and Mustard

2006). Second, phenology-based mapping is most

likely to be successful in cases where the invasive

plant is functionally different from native species and

where there is low spatial variability in phenology

(e.g., smooth topographic gradients). Non-native

evergreen plant invasions in deciduous forests are

one good example, as are annual invasions into

perennial ecosystems. The more different the life

cycle traits of an invasive plant relative to native

plants, the more likely time series or phenology-based

remote sensing methods could be used to detect it.

Change detection

One of the most exciting possibilities for invasive

plant mapping based on remote sensing is the ability to

go back in time to observe early and middle stages of

invasion. Although validation of historical imagery is

impossible for those lacking long-term data or time

machines, a consistent mapping approach on the same

or similar imagery should produce results that can be

reasonably compared to more current remote sensing

maps. For example, Gavier-Pizarro et al. (2012) used a

time series of Landsat images to map privet (L.

lucidum) invasion over 24 years in Cordoba, Argen-

tina and linked early expansion to propagule pressure

associated with urban areas. Bradley and Mustard

(2006) used extreme wet years to map cheatgrass (B.

tectorum) invasion in Nevada, USA over 29 years and

showed that expansion was strongly correlated to

distance to propagules associated with the earlier

invasion extents. Weisberg et al. (2007) used object-

based classification of historical aerial photos to

identify native pine expansion in Nevada, USA and

linked expansion to topographic conditions character-

izing more mesic environments. Andrew and Ustin

(2010) used a time series of hyperspectral imagery to

map invasion of perennial pepperweed (L. latifolium)

in California, USA and related invasion events to wet

spring climatic conditions. Boers and Zedler (2008)

used a time series of aerial photos to map texturally

distinct cattail (Typha) invasion in Wisconsin, show-

ing that invasion rates differed depending on flood

management. Finally, even if invasive species aren’t

detected directly, change detection from remote

sensing can still be used to understand invasion

dynamics. Mosher et al. (2009) used time series of

aerial photos in Massachusetts, USA to document

historical land use, which they then related to presence

and abundance of forest understory invaders.

Historical aerial photos in the US are consistently

available after the 1980s with the national aerial photo

programs (Table 2) and are often available for earlier

time periods. Landsat TM imagery date back to the

early 1980s and are available in consistent time steps

until present. Both historical time series of remotely

sensed data present opportunities for characterizing

habitat preferences and understanding the influence of

propagule pressure and dispersal dynamics through

time.

Linking remote mapping with field studies

As all field ecologists know, collecting field data takes

a considerable amount of planning and effort. Remo-

tely sensed classification of invasive plants relies on

field data to train and validate resulting maps. Unfor-

tunately, many existing field survey datasets are not

directly useable for remote sensing because the shape

and scale of the analysis is not appropriate or because

the data collected are not comparable to what the

sensor sees at the time the imagery is acquired.

Remote sensing imagery is pixel-based and

responds to cover of plant and other earth surface

materials. Field surveys that collect percent cover

Fig. 4 Following high-rainfall events in central Nevada in

1988, 1995 and 1998 (blue bars, measured from a local rain

gage), vegetated greenness (from the normalized difference

vegetation index, NDVI) of cheatgrass dominated areas (red

squares) was significantly higher than that of sagebrush

dominated areas (blue circles). This pattern of inter-annual

variability makes it possible to identify cheatgrass dominated

areas at landscape and regional scales. (Color figure online)

1420 B. A. Bradley

123

information within a defined (square shaped) area will

be most useful for comparison to imagery. The scale of

the defined area depends on the pixel resolution of the

planned remote sensing analysis. If high spatial

resolution imagery or aerial photography are used,

field sampling should be within 1–4 m2 areas compa-

rable to the pixel resolution with the GPS location of

each plot recorded. If Landsat is the target sensor, then

cover should be estimated within 900 m2. Sometimes,

multiple sensors with different spatial resolutions

could be used in analysis, in which case, a nested

design is most appropriate. For example, randomly

selected 1 m2 plots (the total number of samples

depends on the desired confidence interval for the

measurement) within a larger 900 m2 could estimate

cover at both an aerial photo and Landsat pixel

resolution (Fig. 5).

Percent cover measured for image classification

typically has slightly different goals than percent

cover measured for most field surveys. Because

photosynthetic vegetation is so similar spectrally

(Fig. 1b), identifying species may be less important

than measuring cover of green vegetation. For hyper-

spectral analysis, identifying common species will be

helpful for later spectral differentiation of those

species, but rare species will not contribute substan-

tially to the overall spectral signature and can be

considered as part of the background green vegetation

signal. Senescent and woody vegetation have dis-

tinctly different spectral signatures from photosyn-

thetic vegetation (compare, for example, dry grass in

Fig. 1a to green lawn grass in Fig. 1b) and their cover

should be categorized separately, even for the same

species. Lastly, bare ground and soil is an important

component of spectral and phenological signals so

cover of bare soil should be recorded. In heteroge-

neous landscapes, soil variability will influence spec-

tral reflectance signatures and may need to be

measured later in the lab or field in order to inform

spectral classification.

In cases where it is not feasible to collect percent

cover, collecting presence and absence rather than

presence alone is much more valuable for remote

sensing studies. Image classification (i.e. mapping)

typically focuses on minimizing the false negative

rate, measured with presence points, as well as

minimizing the false positive rate, measured with

absence points. Without both presence and absence, it

is difficult to take the first step of evaluating whether

spectral, textural or phenological differentiation is

feasible and it is impossible to measure overall map

accuracy. Collecting presence and absence of all

common species, including the target invasive plant,

would be most useful for training image classification

and for understanding the causes of errors in

classification.

A final important component to linking field and

remote measurements is that timing matters. This is

particularly true for classifications that rely on

phenology or on spectra from a specific time period

(e.g., flowering). Field collection at the same time as

image acquisition is ideal. But, barring that, field

collection during similar seasonal or growing condi-

tions will cause the least error.

Applications

Early detection and rapid response (EDRR) focuses on

identifying and eradicating early, nascent infestations

Fig. 5 Example of a nested field sampling scheme based on

ocular estimates. A MODIS-sized pixel (left, 250 9 250 m)

contains an array of nine Landsat-sized pixels (center,

30 9 30 m), which in turn contains an array of sixteen 1 m2

pixels which can be classified in the field into soil, woody

vegetation and green vegetation associated with the target

dominant species (right). (Color figure online)

Remote sensing of plant invasions 1421

123

as a strategy for controlling invasions (Westbrooks

2004). Although some remote sensing studies have

successfully identified low cover of invasive plants

(Parker Williams and Hunt 2002; Peterson 2005),

detection of more heavily invaded areas is much more

promising. Maps of heavily invaded areas may not be

useful for EDRR directly, but indirectly those maps

can be extremely valuable for modeling invasion risk

and understanding the invasion process to inform

management (Shaw 2005). Invasion ecologists should

not discard potential remote sensing tools because

they cannot detect early infestations.

Several studies have used remote sensing-derived

maps of heavily invaded areas to create invasion risk

models. For example, Bradley and Mustard (2006)

used a phenology-based classification of heavy

infestations of B. tectorum to correlate invasions

with landscape scale features, including disturbances

such as roads and powerlines as well as topography.

They used this information, in turn, to develop a risk

model for Nevada at 30 m spatial resolution iden-

tifying levels of invasion risk. Bradley (2009) used a

similarly derived regional map of B. tectorum to

correlate distribution with broader climatic condi-

tions and forecast potential shifts in abundance with

climate change. Andrew and Ustin (2009) used a

high-resolution, hyperspectral map of dense infesta-

tions L. latifolium to construct a habitat model based

on topography, soils and distance to existing inva-

sion patches. Petty et al. (2012) used high-resolution,

phenology-based aerial surveys to map large popu-

lations of A. gayanus and correlated abundance to

creeks and drainage lines. These results demon-

strated the need to control A. gayanus along drainage

corridors, an expansion of the previous focus on

transport corridors alone. Boers and Zedler (2008)

used the unique texture of monotypic stands of the

invasive Typha to map the aquatic invasive plant in

wetlands. By analyzing spread through time, they

showed that invasion rates were lower in areas

where natural fluctuations in wetland water levels

were allowed. Finally, Andrew and Ustin (2010)

used a time series of high-resolution hyperspectral

maps to measure dispersal of L. latifolium. Their

analysis showed inter-annual variability in spread

linked to wet spring climatic conditions, important

knowledge about a window of invasion risk for

future management and control. None of the above

case studies map early infestations, yet the information

they provide about invasion risk is clearly applicable

to EDRR efforts.

Remote characterization of invasive plants is an

underutilized tool for identifying invasions and

informing models of invasion risk. Given the large

number of problematic invasive plants and widespread

availability of imagery, it is likely that there are a

number of opportunities for remote detection of

invasives that have yet to be tested. If invasive species

are spectrally, texturally or phenologically unique then

collaborations between invasion ecologists and scien-

tists trained in remote sensing could prove fruitful.

Acknowledgments Thanks to S. Sesnie and E. Fleishman and

two anonymous reviewers for helpful comments and to L. Pearls-

tine for use of figures. D. Kocis compiled initial information on

data sources. This research was supported by the Department of

Defense through the Strategic Environmental Research and

Development Program (SERDP) grant number RC-1722 and by

Cooperative Agreement H8C07080001 between the National

Park Service and University of California, Davis.

References

Albright TP, Moorhouse T, McNabb T (2004) The rise and fall

of water hyacinth in Lake Victoria and the Kagera River

Basin, 1989–2001. J Aquat Plant Manag 42:73–84

Andrew ME, Ustin SL (2006) Spectral and physiological

uniqueness of perennial pepperweed (Lepidium latifolium).

Weed Sci 54(6):1051–1062

Andrew ME, Ustin SL (2008) The role of environmental context

in mapping invasive plants with hyperspectral image data.

Remote Sens Environ 112(12):4301–4317. doi:10.1016/j.

rse.2008.07.016

Andrew ME, Ustin SL (2009) Habitat suitability modelling of an

invasive plant with advanced remote sensing data. Divers

Distrib 15(4):627–640. doi:10.1111/j.1472-4642.2009.

00568.x

Andrew ME, Ustin SL (2010) The effects of temporally variable

dispersal and landscape structure on invasive species

spread. Ecol Appl 20(3):593–608

Asner GP, Vitousek PM (2005) Remote analysis of biological

invasion and biogeochemical change. Proc Natl Acad Sci

USA 102(12):4383–4386

Asner GP, Jones MO, Martin RE, Knapp DE, Hughes RF (2008)

Remote sensing of native and invasive species in Hawaiian

forests. Remote Sens Environ 112(5):1912–1926. doi:10.

1016/j.rse.2007.02.043

Balch JK, Bradley BA, D’Antonio CM, Gomez-Dans J (2013)

Introduced annual grass increases regional fire activity

across the arid western USA (1980–2009). Glob Change

Biol 19:173–183. doi:10.1111/gcb.12046

Becker RH, Zmijewski KA, Crail T (2013) Seeing the forest for

the invasives: mapping buckthorn in the Oak Openings.

Biol Invasions 15(2):315–326. doi:10.1007/s10530-012-

0288-8

1422 B. A. Bradley

123

Blaschke T (2010) Object based image analysis for remote

sensing. ISPRS J Photogramm Remote Sens 65(1):2–16.

doi:10.1016/j.isprsjprs.2009.06.004

Blumenthal D, Booth DT, Cox SE, Ferrier CE (2007) Large-

scale aerial images capture details of invasive plant pop-

ulations. Rangel Ecol Manag 60(5):523–528. doi:10.2111/

1551-5028(2007)60[523:laicdo]2.0.co;2

Boers AM, Zedler JB (2008) Stabilized water levels and Typha

invasiveness. Wetlands 28(3):676–685

Bonneau LR, Shields KS, Civco DL (1999) Using satellite

images to classify and analyze the health of hemlock for-

ests infested by the hemlock woolly adelgid. Biol Invasions

1(2–3):255–267

Bradley BA (2009) Regional analysis of impacts of climate

change on cheatgrass invasion shows potential risk and

opportunity. Glob Change Biol 15(1):196–208

Bradley BA (2013) Distribution models of invasive plants over-

estimate potential impact. Biol Invasions 15(7):1417–1429.

doi:10.1007/s10530-012-0380-0

Bradley BA, Mustard JF (2005) Identifying land cover vari-

ability distinct from land cover change: cheatgrass in the

Great Basin. Remote Sens Environ 94:204–213

Bradley BA, Mustard JF (2006) Characterizing the landscape

dynamics of an invasive plant and risk of invasion using

remote sensing. Ecol Appl 16(3):1132–1147

Carter GA, Lucas KL, Blossom GA, Lassitter CL, Holiday DM,

Mooneyhan DS, Fastring DR, Holcombe TR, Griffith JA

(2009) Remote sensing and mapping of tamarisk along the

Colorado river, USA: a comparative use of summer-

acquired Hyperion, Thematic Mapper and QuickBird data.

Remote Sens 1(3):318–329

Cavalli RM, Laneve G, Fusilli L, Pignatti S, Santini F (2009)

Remote sensing water observation for supporting Lake Vic-

toria weed management. J Environ Manag 90(7):2199–2211.

doi:10.1016/j.jenvman.2007.07.036

Clinton NE, Potter C, Crabtree B, Genovese V, Gross P, Gong P

(2010) Remote sensing-based time-series analysis of

cheatgrass (L.) phenology. J Environ Qual 39(3):955–963

Davis MA, Grime JP, Thompson K (2000) Fluctuating resources

in plant communities: a general theory of invasibility.

J Ecol 88(3):528–534

EDDMapS, Early Detection and Distribution Mapping System

(2013) The University of Georgia—Center for Invasive

Species and Ecosystem Health. Accessed 20 June 2013

Evangelista P, Stohlgren T, Morisette J, Kumar S (2009) Map-

ping invasive tamarisk (Tamarix): a comparison of single-

scene and time-series analyses of remotely sensed data.

Remote Sens 1(3):519–533

Everitt JH, Yang C (2007) Using Quickbird Satellite imagery to

distinguish two aquatic weeds in south texas. J Aquat Plant

Manag 45:25–31

Everitt JH, Anderson GL, Escobar DE, Davis MR, Spencer NR,

Andrascik RJ (1995) Use of remote sensing for detecting

and mapping leafy spurge (Euphorbia esula). Weed

Technol 9(3):599–609

Fletcher RS, Everitt JH, Elder HS (2010) Evaluating airborne

multispectral digital video to differentiate Giant Salvinia

from other features in Northeast Texas. Remote Sens

2(10):2413–2423

Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D,

Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper

A, Baccini A, Gao F, Schaaf C (2002) Global land cover

mapping from MODIS: algorithms and early results.

Remote Sens Environ 83(1–2):287–302

Fuller DO (2005) Remote detection of invasive Melaleuca trees

(Melaleuca quinquenervia) in South Florida with multi-

spectral IKONOS imagery. Int J Remote Sens

26(5):1057–1063. doi:10.1080/01430060512331314119

Gao F, Morisette JT, Wolfe RE, Ederer G, Pedelty J, Masuoka E,

Myneni R, Tan B, Nightingale J (2008) An algorithm to

produce temporally and spatially continuous MODIS-LAI

time series. Geosci Remote Sens Lett, IEEE 5(1):60–64

Gavier-Pizarro GI, Kuemmerle T, Hoyos LE, Stewart SI, Hu-

ebner CD, Keuler NS, Radeloff VC (2012) Monitoring the

invasion of an exotic tree (Ligustrum lucidum) from 1983

to 2006 with Landsat TM/ETM? satellite data and support

vector machines in Cordoba, Argentina. Remote Sens

Environ 122:134–145. doi:10.1016/j.rse.2011.09.023

GBIF (2013) Global biodiversity information facility (GBIF)

data portal. http://www.gbif.org/. Accessed Aug 2013

Gil A, Lobo A, Abadi M, Silva L, Calado H (2013) Mapping

invasive woody plants in Azores Protected Areas by using

very high-resolution multispectral imagery. Eur J Remote

Sens 46:289–304

Glenn NF, Mundt JT, Weber KT, Prather TS, Lass LW, Pett-

ingill J (2005) Hyperspectral data processing for repeat

detection of small infestations of leafy spurge. Remote

Sens Environ 95(3):399–412

Hamada Y, Stow DA, Coulter LL, Jafolla JC, Hendricks LW

(2007) Detecting Tamarisk species (Tamarix spp.) in

riparian habitats of Southern California using high spatial

resolution hyperspectral imagery. Remote Sens Environ

109(2):237–248

He KS, Rocchini D, Neteler M, Nagendra H (2011) Benefits of

hyperspectral remote sensing for tracking plant invasions.

Divers Distrib 17(3):381–392. doi:10.1111/j.1472-4642.

2011.00761.x

Herbold B, Moyle PB (1986) Introduced species and vacant

niches. Am Nat 128(5):751–760. doi:10.2307/2461954

Hestir EL, Khanna S, Andrew ME, Santos MJ, Viers JH,

Greenberg JA, Rajapakse SS, Ustin SL (2008) Identifica-

tion of invasive vegetation using hyperspectral remote

sensing in the California Delta ecosystem. Remote Sens

Environ 112(11):4034–4047

Hoyos LE, Gavier-Pizarro GI, Kuemmerle T, Bucher EH, Radeloff

VC, Tecco PA (2010) Invasion of glossy privet (Ligustrum

lucidum) and native forest loss in the Sierras Chicas of Cor-

doba, Argentina. Biol Invasions 12(9):3261–3275

Huang CY, Asner GP (2009) Applications of remote sensing to

alien invasive plant studies. Sensors 9(6):4869–4889.

doi:10.3390/s90604869

Huang CY, Geiger EL (2008) Climate anomalies provide

opportunities for large-scale mapping of non-native plant

abundance in desert grasslands. Divers Distrib 14(5):875–

884. doi:10.1111/j.1472-4642.2008.00500.x

Kimothi M, Anitha D, Vasistha H, Soni P, Chandola S (2010)

Remote sensing to map the invasive weed. Lantana camara

in forests. Trop Ecol 51(1):67–74

Koltunov A, Ustin SL, Asner GP, Fung I (2009) Selective log-

ging changes forest phenology in the Brazilian Amazon:

evidence from MODIS image time series analysis. Remote

Sens Environ 113(11):2431–2440

Remote sensing of plant invasions 1423

123

Laba M, Blair B, Downs R, Monger B, Philpot W, Smith S,

Sullivan P, Baveye PC (2010) Use of textural measure-

ments to map invasive wetland plants in the Hudson River

National Estuarine Research Reserve with IKONOS

satellite imagery. Remote Sens Environ 114(4):876–886.

doi:10.1016/j.rse.2009.12.002

Lass LW, Thill DC, Shafii B, Prather TS (2002) Detecting spotted

knapweed (Centaurea maculosa) with hyperspectral remote

sensing technology. Weed Technol 16(2):426–432

Lass LW, Prather TS, Glenn NF, Weber KT, Mundt JT, Pett-

ingill J (2005) A review of remote sensing of invasive

weeds and example of the early detection of spotted

knapweed (Centaurea maculosa) and babysbreath (Gyp-

sophila paniculata) with a hyperspectral sensor. Weed Sci

53(2):242–251. doi:10.1614/ws-04-044r2

Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) Lidar

remote sensing for ecosystem studies: lidar, an emerging

remote sensing technology that directly measures the three-

dimensional distribution of plant canopies, can accurately

estimate vegetation structural attributes and should be of

particular interest to forest, landscape, and global ecolo-

gists. Bioscience 52(1):19–30

Lishawa SC, Treering DJ, Vail LM, McKenna O, Grimm EC,

Tuchman NC (2013) Reconstructing plant invasions using

historical aerial imagery and pollen core analysis: typha in

the Laurentian Great Lakes. Divers Distrib 19(1):14–28.

doi:10.1111/j.1472-4642.2012.00929.x

Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L,

Merchant JW (2000) Development of a global land cover

characteristics database and IGBP DISCover from 1 km

AVHRR data. Int J Remote Sens 21(6–7):1303–1330

Lu M-L, Huang J-Y, Chung Y-L, Huang C-Y (2013) Modelling

the invasion of a Central American Mimosoid tree species

(Leucaena leucocephala) in a tropical coastal region of

Taiwan. Remote Sens Lett 4(5):485–493. doi:10.1080/

2150704x.2012.755274

Marvin DC, Bradley BA, Wilcove DS (2009) A novel, web-

based ecosystem mapping tool using expert opinion. Nat

Areas J 29(3):281–292

Mast JN, Veblen TT, Hodgson ME (1997) Tree invasion within

a pine/grassland ecotone: an approach with historic aerial

photography and GIS modeling. For Ecol Manag

93(3):181–194. doi:10.1016/S0378-1127(96)03954-0

McCormick CM (1999) Mapping exotic vegetation in the

Everglades from large-scale aerial photographs. Photo-

gramm Eng Remote Sens 65(2):179–184

Meentemeyer RK, Rank NE, Shoemaker DA, Oneal CB,

Wickland AC, Frangioso KM, Rizzo DM (2008) Impact of

sudden oak death on tree mortality in the Big Sur ecoregion

of California. Biol Invasions 10(8):1243–1255. doi:10.

1007/s10530-007-9199-5

Miao X, Gong P, Swope S, Pu RL, Carruthers R, Anderson GL,

Heaton JS, Tracy CR (2006) Estimation of yellow star-

thistle abundance through CASI-2 hyperspectral imagery

using linear spectral mixture models. Remote Sens Environ

101(3):329–341

Mirik M, Ansley RJ, Steddom K, Jones D, Rush C, Michels G,

Elliott N (2013) Remote distinction of a noxious weed

(Musk Thistle: Carduus nutans) using airborne hyper-

spectral imagery and the support vector machine classifier.

Remote Sens 5(2):612–630

Mosher ES, Silander JA, Latimer AM (2009) The role of land-

use history in major invasions by woody plant species in

the northeastern North American landscape. Biol Invasions

11(10):2317–2328. doi:10.1007/s10530-008-9418-8

Mulla DJ (2013) Twenty five years of remote sensing in precision

agriculture: key advances and remaining knowledge gaps.

Biosyst Eng 114(4):358–371. doi:10.1016/j.biosystem

seng.2012.08.009

Mullerova J, Pysek P, Jarosık V, Pergl J (2005) Aerial photo-

graphs as a tool for assessing the regional dynamics of the

invasive plant species Heracleum mantegazzianum. J Appl

Ecol 42(6):1042–1053

Noujdina NV, Ustin SL (2008) Mapping downy brome (Bromus

tectorum) using multidate AVIRIS data. Weed Sci

56:173–179

Parker Williams A, Hunt ER Jr (2002) Estimation of leafy spurge

cover from hyperspectral imagery using mixture tuned

matched filtering. Remote Sens Environ 82(2):446–456

Pearlstine L, Portier KM, Smith SE (2005) Textural discrimi-

nation of an invasive plant, Schinus terebinthifolius, from

low altitude aerial digital imagery. Photogramm Eng

Remote Sens 71(3):289–298

Peterson EB (2005) Estimating cover of an invasive grass

(Bromus tectorum) using tobit regression and phenology

derived from two dates of Landsat ETM plus data. Int J

Remote Sens 26(12):2491–2507

Petty AM, Setterfield SA, Ferdinands KB, Barrow P (2012)

Inferring habitat suitability and spread patterns from large-

scale distributions of an exotic invasive pasture grass in

north Australia. J Appl Ecol 49(3):742–752

Resasco J, Hale AN, Henry MC, Gorchov DL (2007) Detecting

an invasive shrub in a deciduous forest understory using

late-fall Landsat sensor imagery. Int J Remote Sens

28(16):3739–3745

Schneider LC, Fernando DN (2010) An untidy cover: invasion

of bracken fern in the shifting cultivation systems of

Southern Yucatan, Mexico. Biotropica 42(1):41–48.

doi:10.1111/j.1744-7429.2009.00569.x

Shaw DR (2005) Translation of remote sensing data into weed

management decisions. Weed Sci 53(2):264–273

Shea K, Chesson P (2002) Community ecology theory as a

framework for biological invasions. Trends Ecol Evol

17(4):170–176

Shouse M, Liang L, Fei S (2013) Identification of understory

invasive exotic plants with remote sensing in urban forests.

Int J Appl Earth Obs Geoinf 21:525–534. doi:10.1016/j.

jag.2012.07.010

Singh N, Glenn NF (2009) Multitemporal spectral analysis for

cheatgrass (Bromus tectorum) classification. Int J Remote

Sens 30(13):3441–3462

Somers B, Asner GP (2013) Multi-temporal hyperspectral

mixture analysis and feature selection for invasive species

mapping in rainforests. Remote Sens Environ 136:14–27.

doi:10.1016/j.rse.2013.04.006

Somodi I, Carni A, Ribeiro D, Podobnikar T (2012) Recognition

of the invasive species Robinia pseudacacia from com-

bined remote sensing and GIS sources. Biol Conserv

150(1):59–67. doi:10.1016/j.biocon.2012.02.014

Tan B, Morisette JT, Wolfe RE, Gao F, Ederer GA, Nightingale

J, Pedelty JA (2010) An enhanced TIMESAT algorithm for

estimating vegetation phenology metrics from MODIS

1424 B. A. Bradley

123

data. IEEE J Sel Top Appl Earth Observ Remote Sens

4(2):361–371. doi:10.1109/jstars.2010.2075916

Taylor SL, Hill RA, Edwards C (2013) Characterising invasive

non-native Rhododendron ponticum spectra signatures

with spectroradiometry in the laboratory and field: poten-

tial for remote mapping. ISPRS J Photogramm Remote

Sens 81:70–81. doi:10.1016/j.isprsjprs.2013.04.003

Thorp K, Tian L (2004) A review on remote sensing of weeds in

agriculture. Precis Agric 5(5):477–508

Townsend PA, Walsh SJ (2001) Remote sensing of forested wet-

lands: application of multitemporal and multispectral satellite

imagery to determine plant community composition and

structure in southeastern USA. Plant Ecol 157(2):129–149

Tsai F, Chou MJ (2006) Texture augmented analysis of high

resolution satellite imagery in detecting invasive plant

species. J Chin Inst Eng 29(4):581–592. doi:10.1080/

02533839.2006.9671155

Tuanmu M-N, Vina A, Bearer S, Xu W, Ouyang Z, Zhang H,

Liu J (2010) Mapping understory vegetation using phe-

nological characteristics derived from remotely sensed

data. Remote Sens Environ 114(8):1833–1844. doi:10.

1016/j.rse.2010.03.008

Tuceryan M, Jain AK (1998) Texture analysis. In: Chen CH, Pau

LF, Wang SPS (eds) The handbook of pattern recognition and

computer vision. World Scientific, Singapore, pp 207–248

Underwood E, Ustin S, DiPietro D (2003) Mapping nonnative

plants using hyperspectral imagery. Remote Sens Environ

86(2):150–161

Underwood E, Ustin S, Pauchard A, Maheu-Giroux M (2007)

Trends in invasive alien species. In: Strand H, Hoft R,

Strittholt J et al (eds) Sourcebook on remote sensing and

biodiversity indicators, vol CBD Technical Series No. 32.

Secretariat of the Convention on Biological Diversity,

Montreal

Vila M, Ibanez I (2011) Plant invasions in the landscape. Landsc

Ecol 26(4):461–472

Weisberg PJ, Lingua E, Pillai RB (2007) Spatial patterns of

pinyon-juniper woodland expansion in central Nevada.

Rangel Ecol Manag 60(2):115–124

Westbrooks RG (2004) New approaches for early detection and

rapid response to invasive plants in the United States 1.

Weed Technol 18(Sp 1):1468–1471

Wilfong BN, Gorchov DL, Henry MC (2009) Detecting an

invasive shrub in deciduous forest understories using

remote sensing. Weed Sci 57(5):512–520. doi:10.1614/ws-

09-012.1

Willis CG, Ruhfel BR, Primack RB, Miller-Rushing AJ, Losos

JB, Davis CC (2010) Favorable climate change response

explains non-native species’ success in thoreau’s woods.

PLoS One 5(1):e8878. doi:10.1371/journal.pone.0008878

Wolkovich EM, Cleland EE (2011) The phenology of plant

invasions: a community ecology perspective. Front Ecol

Environ 9(5):287–294. doi:10.1890/100033

Wolmarans R, Robertson MP, van Rensburg BJ (2010) Pre-

dicting invasive alien plant distributions: how geographical

bias in occurrence records influences model performance.

J Biogeogr 37(9):1797–1810. doi:10.1111/j.1365-2699.

2010.02325.x

Wu YG, Rutchey K, Wang NM, Godin J (2006) The spatial pattern

and dispersion of Lygodium microphyllum in the Everglades

wetland ecosystem. Biol Invasions 8(7):1483–1493. doi:10.

1007/s10530-005-5840-3

Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL

(2006) Surveying mountain pine beetle damage of forests:

a review of remote sensing opportunities. For Ecol Manag

221(1–3):27–41. doi:10.1016/j.foreco.2005.09.021

Xie Z, Roberts C, Johnson B (2008) Object-based target search

using remotely sensed data: a case study in detecting

invasive exotic Australian Pine in south Florida. ISPRS J

Photogramm Remote Sens 63(6):647–660

Zavaleta E (2000) The economic value of controlling an inva-

sive shrub. Ambio 29(8):462–467

Remote sensing of plant invasions 1425

123