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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.
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