Download Reviewed by: Vandana Vandanapu

Title: Mapping nonnative plants using hyper spectral imagery
Journal: Remote sensing of Environment, 2003, Vol. 86, 150-161
Authors: Emma Underwood, Susan Ustin, and Deanne DiPietro
Reviewed by: Vandana Vandanapu
Remote sensing Term Paper ES-5053 Fall 2004
Table of Contents
1. Abstract
-------------------------------------------------------------------3
2. Introduction -----------------------------------------------------------------4
3. Research Objectives ----------------------------------------------------- 6
4. Study site -------------------------------------------------------------------7
5. Methods -------------------------------------------------------------------- 9
6. Results -----------------------------------------------------------------------12
7. Discussion -------------------------------------------------------------------13
8. Conclusions -----------------------------------------------------------------14
9. Acknowledgements --------------------------------------------------------14
10. References ------------------------------------------------------------------15
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Abstract
Nonnative species are a current focus of interest of ecologists, biological conservationists
and natural resources managers due to their rapid spread, threat to biodiversity and
damage to ecosystems. Controlling and managing invasives requires new methods to map
and monitor their spread. Remote sensing can aid land manager’s efforts to control and
monitor invasive plants by providing detailed information on their location and extent of
spread. While digital multiband remote sensing and aerial photography have been
available for many years, newer detector technologies have made it possible to accurately
acquire a detailed laboratory-like spectrum of each pixel in an image from space. This
study used NASA’s Advanced Visible Infrared Imaging Spectrometer (AVIRIS), a 224band instrument with nominally 10 nm contiguous bands over the 400-2500 nm range for
mapping invasive species iceplant (Carpobrotus edulis) and jubata grass (Cortaderia
jubata) in Vandenberg Air Force Base, California. This research evaluated several data
processing (minimum noise fraction (MNF), continuum removal, and band ratio indices)
and classification methods and compared their success in determining the spatial extent
of these invasive plants. The MNF and band ratios techniques were able to accurately
identify new infestations into native habitats and Continuum Removal method is an
efficient way of characterizing presence / absence of iceplant.
Keywords: AVIRIS, mapping, nonnative species, iceplant, jubata grass.
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Introduction
The increasingly rapid redistribution of the world’s species by humans and
consequent detrimental invasion of ecosystems by alien species is one of the most serious
and challenging threats to the world’s biodiversity, human health, and economy ever
faced. An estimated 50,000 non-native species exist in the United States, and the invaders
among them cause major environmental damages and losses adding up to more than $138
billion per year (Pimentel et al., 1999). Exotic plants are invading approximately 700,000
hectares of wildlife habitat each year. After habitat loss due to land-use change,
biological invasion is one of the major contributors to local and global loss of
biodiversity, causing extinction through competition, predation, hybridization, and habitat
alteration (D’Antonio, 1997). The phenomenon is so wide-spread and has such farreaching effects that it may be considered a significant component of global change
(Vitousek et al., 1996).
It is now widely recognized that invasions by non-native plants present a
serious ecological threat to the already fragmented and greatly reduced native ecosystems
of California (Bossard et al., 2000; Barbour et al., 1993). The number of alien plant
species in California is estimated at 1,025, representing 17.5% of the flora (Rejmánek et
al., 1994). In response to the expanding ranges and increasing damage done by invasive
and noxious non-natives, control of invasive species has become a priority for
environmental management and an integral component of many habitat conservation
efforts.
Reducing the impacts to local ecosystems and biodiversity caused by alien
species and employing restoration and other remedy actions has become a trend in
conservation (Stein & Flack, 1996). To better understand the status and to support
researchers and decision makers to develop strategies and remedies for this threatening
problem, it is necessary to obtain accurate spatial information and the progression about
the invasions of alien species into native eco community. A key requirement for the
effective management of invasive plants is the ability to identify, map, and monitor
invasions. Hand-mapping in the field or from aerial photos are techniques commonly
used in eradication efforts, but these methods are labor intensive and limited. Hand
mapping from field observation requires access to the site from the ground, a prospect
that is not always practical, safe, or timely, especially on an active military base.
Unlike field- based investigations, remote sensing provides an timely and
economical approach for discriminating invasive plant species from local botanic
community, especially in a large - scale investigation. In contrast to field-based surveys,
imagery can be acquired for all habitats, over a much larger spatial area, and in a short
period of time. Consequently, researchers have sought to exploit unique phenological,
spectral, or structural characteristics of the nonnative species in the image to distinguish
them from the mosaic of species around them.
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Until relatively recently, aerial photographs and multispectral satellite
images are the primary sources of remote sensing applications to vegetation mapping and
have attained mixed success (for example, Lins et al., 1996; McCormick, 1999). In Aerial
photography techniques the problem species is usually mapped by taking advantage of a
characteristic such as flower or bract color that distinguishes it spectrally from
surrounding plants in a scene. Everitt and Deloach (1990) mapped Chinese tamarisk
using aerial photography when the plant was in its yellow phase just prior to leaf-drop in
the fall. However, the major disadvantage of this approach is that it relies on the
nonnative plant possessing visually detectable unique characteristics as well as requiring
extensive manual labor for processing.
In contrast, the use of digital multispectral imagery offers the opportunity
for automated image processing, access to recent historical data for time series analyses,
and large spatial coverage. The consideration of spatial resolution is also very important
in detection and mapping of individual species. It has been found that LANDSAT
Thematic Mapper and SPOT data, with ground resolution of 30 meters and 20 meters,
respectively, are not generally considered useful for mapping at the species level (Carson
et al., 1995) unless stands of the weed are both large enough to fill a pixel and strikingly
different from surrounding vegetation, as in the case of false broom weed mapped by
Anderson et al. (1993) in rangelands in south Texas. Because these types of data can
provide only limited spectral information, they may not be able to produce results with
high quality and confidence, especially when dealing with detection and mapping tasks
down to the species level (Chen et al., 2003).
Fortunately, the availability of hyper spectral imagery provides researchers
an opportunity to pursue more complex analysis. Hyper spectral imagery consists of tens
to hundreds of contiguous spectral bands therefore can provide more complete coverage
of spectral information about targets. Previous studies have demonstrated the possibility
to perform species - level vegetation classifications using hyper spectral data (Cochrance,
2000; Laba et al., 2003; Schmidt & Skidmore, 2003). AVIRIS (airborne visible/infrared
imaging spectrometer) is the principal hyper spectral instrument now in use. It is an
across-track scanner that collects radiance measurements in 224 contiguous bands
approximately 10nm wide (JPL, 2002). The range of the electromagnetic spectrum
sampled is approximately 400 – 2500nm, corresponding to the visible and near to shortwave infrared regions. With increased spatial and, more critically, fine spectral
resolution, offers an enhanced potential for mapping invasive species. Because of the
large number of wavebands (224), image processing is able to capitalize on both the
biochemical and the structural properties of the target invader.
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Research Objectives
The objectives of this research were:
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to map the spatial location, distribution and abundance of 2 nonnative
plants i.e., ice plant (Carpobrotus edulis) and jubata grass (Cortaderia
jubata) in VAFB, California.
to test the efficacy of AVIRIS imagery for nonnative plant mapping.
to compare three techniques for processing the imagery: minimum
noise fraction (MNF), continuum removal, and band ratio indices.
These three processes were compared for their ability to delineate the
spatial extent and the density of ice plant and jubata grass and also to
critically evaluate the relative ease of processing and repeatability of
each method.
The information collected through this effort will:
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increase the ability of resource managers to analyze and prioritize
invasive plant management needs, enhancing the time and costeffectiveness of management actions;
serve as a baseline for long-term monitoring, assist with the evaluation
of changes in alien plant populations over time and detecting new
infestations; and
serve as a critical tool for increasing public and political awareness and
education on invasive plant issues.
In addition, the data collected through this study will provide the basis for an invasive
plant management plan for Vandenberg Air Force Base, California.
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Study Site
The focal area for this study is Vandenberg Air force Base (VAFB) located along the
central coast of California (Fig 1). It is the third largest Air Force Base in the nation,
encompassing 39,800 ha, and for nearly half a century it has served as a launch and test
site for medium- to long-range ballistic missiles, as well as government and commercial
satellites. The vegetation is diverse and characteristic of central California coastal
habitats (Holland, 1986).
Fig. 1. Vandenberg Air Force Base.
Of the 836 vascular plants documented at VAFB, almost a quarter is invasive species. In
particular, C. edulis and C. jubata have successfully invaded two native community
types: coastal dune scrub community and maritime chaparral (Keil & Holland, 1998).
Hence the focus of this research is the encroachment of ice plant and jubata grass into
these native communities and specifically on the ability of AVIRIS to identify pixels of
different densities of these species.
Vegetation Descriptions
Ice plant (Carpobrotus edulis)
The California Exotic Pest Plant Council's (CalEPPC) List of Exotic Pest Plants of
Greatest Ecological Concern in California (1999) rated C.edulis as an A-1 species (The
Most Invasive Wild land Pest Plant: Widespread). Carpobrotus edulis (Fig 2) has been
invading native and non-native plant assemblages in California since its introduction in
the early 1900s.This succulent perennial was introduced from South Africa to United
States in the early 1900s for erosion control. The fleshy indehiscent fruits are widely
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Remote sensing Term Paper ES-5053 Fall 2004
dispersed by several native animals .As a result, Carpobrotus edulis has moved away
from areas where it was planted and is invading a variety of coastal plant communities
through out the pacific United States. D’Antonio & Mahall (1991) demonstrated that the
invasive perennial Carpobrotus edulis can directly compete with native coastal California
shrub species for soil resources. The species’ success is due to its tolerance of a range of
soil moisture and nutrient conditions, and utilizing a number of mammals for seed
dispersal (D’Antonio, 1993). Ecological impacts of ice plant include aggressive
competition with native species, such as Tidestrom’s lupine (Lupinus tidestromii),
destabilizing native dune communities and modifying soil pH (Moss, 1990). Economic
impacts stem from time and financial costs associated with both manual and mechanical
controls.
Fig 2: Ice plant (Carpobrotus edulis)
Jubata grass (Cortaderia jubata).
Another invasive plant with the potential to
significantly alter mediterranean-type ecosystems
in California is Cortaderia jubata (jubata grass).
C. jubata is a large perennial tussock grass native
to the Andean regions of Ecuador, Peru and Bolivia.
In the later half of the 19th century it was introduced
into California for use as a landscaping ornamental
(Lambrinos, 2000). It has subsequently escaped from
cultivation and expanded in coastal habitats along
the Pacific coast. Jubata grass poses a significant
threat to Mediterranean ecosystems because of its
prolific Wind dispersed seeds, tolerance of a broad
range of habitats, and its competitiveness for light,
moisture, and nutrients (Cowan, 1976).
Fig. 3. Jubata grass (Cortaderia jubata)
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Methods
1. Description of fieldwork and GPS data collection
The GIS database included topographic, vegetation, land use history and road layers.
Sampling was undertaken in five community types identified a priori: intact coastal dune
scrub, intact maritime chaparral, iceplant invaded scrub, iceplant invaded chaparral, and
chaparral invaded by jubata grass. Ecological data was collected at three scales: plot, site
and community, including % cover by species, canopy height, disturbance type and size,
and soil characteristics. GPS points (Trimble Pro-XRS, Trimble Navigation, Inc.) were
made in plot centers and polygons for iceplant, jubata grass, and intact community types.
Field measured reflectance spectra of the dominant native species, invasives, and soils
were acquired coincident with AVIRIS using a GER 2500 (Geophysical Research, Corp.)
over 400-2500 nm. Owing to almost continual coastal fog during the period around the
over flight, only 80 individual spectra of target species at VAFB were acquired. AVIRIS
data were acquired by the NOAATwin Otter aircraft on September 9, 2000 at 3810 m,
providing a nominal pixel resolution of 4.5 m.
5 community types
Coastal Scrub-iceplant
Intact Coastal Scrub
Chaparral-iceplant
Intact chaparral
Pampas-chaparral
Fieldwork at VAFB
2. Data processing techniques
AVIRIS data was calibrated to surface reflectance using the atmospheric correction
program, ACORN (Analytical Imaging & Geophysics). Image analyses were performed
using ENVI (Environment for Visualizing Images; Research Systems, Inc.). The AVIRIS
scenes were spectrally and spatially subset for georeferencing. Noisy bands were
removed and masks were created for vegetated areas (NDVI>0.2).
A supervised maximum likelihood classification was performed on the results of each of
the three processing techniques (MNF, continuum removal, and band ratio indices).
(a) Minimum Noise fraction classification
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A “Minimum Noise Fraction” (MNF) Transform is used to reduce the number of
spectral dimensions to be analyzed. The MNF transformation is a linear transformation
related to principal components that orders the data according to signal-to-noise-ratio
(Green et al., 1988). It can be used to determine the inherent dimensionality of the data,
to segregate noise in the data, and to reduce the computational requirements for
subsequent processing (Green et al., 1988; Boardman and Kruse, 1994). As a standard
image processing technique, the authors were interested in assessing how well it performs
for identifying the focal species.
(b) Continuum removal for water bands
The continuum removal technique isolates spectral features and standardizes
reflectance across the liquid water absorption features so that they may be intercompared
(Clark & Roush, 1984). In the continuum removal technique, a spectral absorption
feature is selected from pixel profiles and normalized by fitting a curve (or continuum)
across the profile as if to remove the "valley" created by the feature. The area of the curve
is then calculated and the depth of the absorption features compared (Research Systems,
1999). The values were then fed into an unsupervised classifier for grouping into classes.
In this study, a water absorption feature 15 around 985 nm (between the wavelengths
924–1061 nm) and 18 around 1184 nm (1108– 1254 nm) was used based on the
hypothesis that plant water content would be a distinguishing feature for Ice plant. Fig 4
shows a comparison of the reflectance spectra of pixels having two different densities of
ice plant compared to a pixel of intact coastal scrub. The graph shows two distinctive
absorptions for dense ice plant centered on 985 nm and 1184 nm wavelengths. The use of
the water absorption feature at 985 nm to map the ice plant resulted in the identification
relatively well than the 1184 nm feature due to the background atmospheric water vapor
absorption at 1240 nm.
Iceplant density (90-100%)
(Iceplant density (75-90%)
Intact coastal scrub
90-100%
Fig. 4. Reflectance spectra showing absorption in the water band.
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(c) Band ratio indices
Remote sensing analysts have found that a wealth of information can be extracted
from the spectral bands by multiplying or dividing the reflectance values of one band by
those of another (i.e., creating a band ratio). The resulting band is called an index.
Numerous vegetation indices have been developed and are routinely used by remote
sensing scientists. Perhaps the most common vegetation index is the Normalized
Difference Vegetation Index (NDVI) developed by Rouse et al. (1974). The NDVI is a
band-ratio index that utilizes the characteristics of the visible red band and the near
infrared band to provide a relative measure of green biomass and chlorophyll.
This study investigated the use of selected vegetation indices (NDVI, water
absorption, greenness, and pigment properties) to use in the classification, which
emphasize the biochemical and the biophysical properties of the vegetation contained in
physiologically important bands.
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Results
A visual comparison of the three classification results (Fig.5) shows that the iceplant is
clearly distinguished in all images with a similar spatial configuration: densest adjacent to
the coastline and tapering off eastwards. An essential component of any classification
routine is the assessment of the accuracy of the results obtained. One of the most
common ways of determining accuracy is to use a confusion matrix (Fuentes et al.,
2001). This approach uses pixels from the classified image and checks their labels against
a reference data source or “ground truth.” In this method, accuracy can be expressed in
three ways: (1) producer’s accuracy or omission errors; this refers to the probability (%)
that the correct class has been identified on the basis of the ground truth reference; (2)
user’s accuracy or commission errors; this is the probability that a pixel of a class has
been classified correctly based on the total number of pixels classified as that class; and
(3) overall accuracy or the total number of image pixels classified correctly. Another
measurement of map accuracy is the kappa coefficient, which is derived using the
elements of the error matrix. The kappa coefficient gives an estimate of overall accuracy
based on both omission and commission errors (Richards and Jia, 1999).
Fig 5: Results from mapping ice plant and jubata grass at VAFB.
The overall accuracy results showed that the supervised classification of the MNF results
performed best. [MNF=76.2% (kappa = 0.70), continuum removal classification = 54.9%
(kappa = 0.44) and band ratio technique =58.8% (kappa = 0.49)]. In contrast, the users’
accuracy of the MNF was the lowest, 83%, compared to 94% and 89% for the band ratios
and continuum removal, respectively. This implies that while the MNF is able to produce
a more accurate map of multiple vegetation classes, the continuum removal and the band
ratio techniques are better suited for detecting species with distinct characteristics, such
as iceplant, with its distinctive succulent characteristics.
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Discussion
A key requirement for the effective management of invasives is to be able to delineate
both the spatial extent and the severity of infestation, which are captured to different
degrees by the processing methods (MNF, Continuum removal and Band ratio indices).
The author evaluated these three approaches in terms of accuracy, logistics of processing,
and ease of interpretation, which are all necessary considerations for management.
A confusion matrix was performed using only ROIs collected from the intact scrub and
also areas in which iceplant has invaded the scrub and exists in different densities and
NOT of the entire image and other classes. Accuracy assessments were performed using
ROIs from the scrub class and also the different densities of iceplant. The high values for
MNF, Band ratios and Continuum removal not significantly different for scrub and each
density class. However, when comparing the accuracy of scrub and three classes of
iceplant density the MNF process worked best. This is expected as the MNF draws from
all available bands for the processing, whilst the band ratios and the continuum removal
are relying on a limited amount of information. However, in terms of classifying one of
the target species, ice plant, the continuum removal and the band ratio methods actually
performed better.
The author mentioned that the three processing techniques succeeded in capturing some
of the pertinent characteristics of iceplant and successfully classified it. For example
MNF worked extremely well identifying different densities of iceplant. This is because it
actively creates new bands using the most spectral information in the imagery. However,
it is difficult to interpret. Band ratio indices are intuitive as ratios emphasize important
ecophysiological information on land cover. Continuum removal works well for areas of
dense iceplant, but poorly for complex mosaics with other scrub species and is excellent
and reliable for classifying presence / absence of iceplant. Its greatest advantage is the
ease and efficiency of processing using a standard ENVI process. One disadvantage with
continuum removal is that the results were speckled and even when results of band ratios
was sieved and clumped it only improved the overall accuracy of the confusion matrix by
3%.
This research illustrated that AVIRIS imagery offers improved opportunities for mapping
invasive plants in a matrix of other vegetation types. The improved spectral resolution of
the AVIRIS imagery permits identification of vegetation characteristics that are not
possible using multispectral wavebands traditionally used in remotely sensed imagery.
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Conclusions
This study showed that the invasive plants iceplant and jubata grass in California’s
Mediterranean-type ecosystems can successfully be mapped using hyper spectral imagery
(AVIRIS data). The immediate benefit of this research has been to contribute to the
knowledge base of land managers at VAFB by providing improved information on the
spatial extent and the density of the iceplant and jubata grass, which will lead to better
protection of the native biodiversity. This research also described some encouraging
findings for applying hyper spectral imagery to mapping iceplant at Vandenberg Air
force base that can be repeated over time to detect change and that they can use to
monitor control efforts.
Acknowledgements
The authors acknowledged:
-John Brooks, Amparo Rifa, and George Scheer for helping to conduct fieldwork.
-George Scheer, Pablo Zarco-Tejada, and Karen Olmstead from CSTARS at U.C. Davis
for assistance in processing of the imagery.
-Teresa Magee at Dynamac for providing input advice on the project.
-Pablo Zarco-Tejada from CSTARS for helpful comments in reviewing the paper
-And Finally to the Strategic Environmental Research and Development Program and
NSF’s IGERT program for providing funding for their research.
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