This report was taken from the:
Final Report on ITD Contributions to the Healthy Forest Project
Prepared by
Institute for Technology Development
with input from
Science Systems and Applications, Inc.
Grant #NNS07AA21G
October 1, 2009
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Hyperspectral Data of Invasive Species and Sudden Oak Death
The Institute for Technology Development (ITD) has collected and analyzed hyperspectral data using
its patented hyperspectral sensor for demonstrating how the use of high resolution airborne
hyperspectral imagery could improve early detection of anomalies in forest conditions of surveyed
lands. Specifically, ITD engaged in two separate case studies. The first study investigated
hyperspectral imaging as a means to improve the pre-visual detection of Sudden Oak Death (SOD)
syndrome, while the second focused on the identification of invasive plant species that threaten
forests of the Eastern United States. The following two subsections are summarized from the
documents “Detection, Mapping, and Monitoring of Sudden Oak Death Using Hyperspectral
Imagery” (Copenhaver, et. al.) and “Identification of Invasive Plant Species in the Daniel Boone
National Forest Using Hyperspectral Imagery” (Copenhaver, et. al.2008). These compilations are
intended to be an initial draft of what could be a US Forest Service General Technical Report
publication.
Sudden Oak Death Detection Case Study
Sudden oak death (SOD) caused by the invasive non-native pathogen Phytophthora ramorum, is the
name given to an epidemic tree disease, first detected in 1995. This pathogen is an Oomycete – not a
fungus. It is in the water mold family and related to certain marine algae. It is lethal to a number of
oak species: coast live oak (Quercus agrifolia), California black oak (Q. kelloggii), and Shreve oak
(Q. parvula var. shrevei).and tanbark oak (Lithocarpus densiflorus) (McPherson et al., 2001). The
disease is currently known to exist in the coastal ranges in California and several locations in
southern Oregon (Kelly, 2001). Since first detected, over one million individual overstory trees of
tanoak, California black oak, and coastal live oak have died due to SOD infection (California Oak
Mortality Task Force, 2007a).
The symptoms of P. ramorum infection vary among plant species and symptoms are not necessarily
consistent within any given species. One characteristic of the infection in oak species is the sudden
simultaneous leaf death on a major stem or of the entire tree, thus, the term sudden oak death.
Infected trees may exhibit ‘bleeding‘ of sap from apparently intact bark or through cankers that
develop on the tree from near the soil level up to as high as 4 m (California Oak Mortality Task
Force, 2007b). Several other organisms are often associated with P. ramorum infection, including
ambrosia beetles (Monarthrum scutellare and M. dentiger) and the saprophytic fungus Hypoxylon
thuorsianum (Goheen et al., 2006). Black, dome-shaped fruiting bodies may appear near the P.
ramorum canker and on other portions of the bole of the tree (Garbelotto et al., 2001). At present, the
only definitive ways to diagnose a SOD infection in a tree are by culturing the pathogen or by
amplifying the DNA using polymerase chain reaction (PCR) techniques (Kelly, 2001).
Since it was first detected, SOD has had a significant economic impact. At a cost of more than $15
million, over 1.6 million plants were investigated by state agencies and USDA-APHIS for SOD. In
one Southern California nursery, over one million camellias valued at nearly $9 million, were
destroyed because of SOD infection (Alexander, 2005). California exports nearly half its nursery
production (i.e., plants and flowers) to markets in other states or countries, while Oregon exports an
estimated 90% if its production. Canada is a major market for that nursery stock and initially, Canada
shut its markets to most plant stock from the states of Oregon and California. Without reopened
market access, Oregon nurseries alone faced losses in sales to Canada of $15 to $20 million. Costs
also appear in the form of quarantines and regulations. Of the funds available to address the
epidemic, California has dispersed over $1 million dollars to SOD infected counties for the removal
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of trees that pose a threat to life, property, and/or public works (Cole, 2001). In Oregon, more than
$1.5 million has been spent in an attempt to eradicate the pathogen from forests.
There are many examples of the use of remotely sensed imagery to monitor overstory forest health
parameters. Although a majority of the work has focused on coniferous forests, remote sensing has
also been utilized to investigate the health and structure of hardwood forests (Boyer et al., 1998;
Everitt et al., 1999; Gong et al., 1999). High resolution, multispectral imagery has been used for the
identification of SOD in the coastal Western US forests (Kelly, 2001; Kelly, 2003; Kelly et al.,
2004). The seasonality of image acquisition is a critical consideration for accurate monitoring of this
disease. It appears that late spring through summer is the ideal time for data acquisition. The specific
months within this time frame may vary from year to year, but optimal imagery must be acquired
after complete leaf expansion in the oaks and before California buckeye (Aesculus californica)
begins its summer deciduous process. When buckeye begins to change color, it can appear very
similar to SOD-affected trees when viewed from a remote location. In some areas of California,
buckeye may begin changing in late June (Kelly, 2003).
Sudden Oak Death has several characteristics that make an integrated monitoring approach that
combines remote sensing with fieldwork ideal. Generally, as trees with the disease die, the entire
crown changes dramatically from healthy green to light brown over a relatively short period of time.
The change from green to light brown, however, may be more subtle with the canopy progressively
yellowing over a period from months to years. After canopy change has occurred, the brown leaves
can stay attached to the branches for months, and in areas where SOD is advanced, the affected trees
display spatial clumping, with diseased trees likely to be clumped together. This pattern can result in
dramatic spectral reflectance changes across broad areas (Kelly, 2001).
High spatial resolution remotely sensed data has provided mixed results in mapping and monitoring
SOD. Kelly (2003) observed that SOD has spectral reflectance shift across the visible to NIR range
as the disease progresses. They found, however, that automated spectral classification methods alone
do not provide high accuracies (i.e., 68.8%). None-the-less, classifications using the visible and NIR
bands combined with Tasseled Cap “greenness” and “wetness” components improved overall
accuracy to 81.7%. Further work (Kelly et al., 2004) evaluated supervised, unsupervised, and
“hybrid” classification methods for discriminating dead and dying tree crowns (as a result of SOD)
from the surrounding forest mosaic. They determined that unsupervised and supervised methods
provided overall classification accuracies of 66.1-78.8% and 73.5-87.9%, respectively. They
indicated that use of hybrid methods, which incorporated both unsupervised and supervised
techniques, resulted in overall accuracies of 93-95%.
The Western Wildland Environmental Threat Assessment Center (WWETAC) in Prineville, OR was
designated to coordinate U.S. Forest Service (USFS) efforts in the western U.S. to reduce the
likelihood of forest disturbances, mitigate the effects of disturbances, improve information access
through centralization, and improve tracking of forest risk and disturbance over space and time.
Another center, located in Asheville, NC has a similar directive, with a focus on threats to Eastern
forests. One high priority overarching goal of the threat assessment centers is the development of an
integrated, national Early Warning System (EWS) to identify, detect, and rapidly respond to forest
environmental threats. Although the operational requirements for this EWS are still evolving within
the USFS, it is envisioned to be a multi-source, multi-scale tool that provides broad area, high
frequency reconnaissance; detailed location-specific analysis using higher-resolution imagery and
ground data; and predictive modeling of potential and emerging forest threats.
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The overall goal of the ITD SOD case study was to evaluate the use of hyperspectral imagery for
improving early warning system capability as it relates to pre-visual detection of SOD syndrome.
Specific objectives are to: 1) collect representative ground data from forest species infected and not
infected with SOD; 2) perform two hyperspectral image acquisitions over the study area; 3) develop
a classification product that separates tanoak and other host species (when applicable) from other
forest species; 4) determine the degree of separability between SOD infected and non-infected
species and at what stage of development SOD may first be identified with hyperspectral imagery; 5)
identify spectral locations most useful for identifying SOD; and 6) classify and map the extent of
SOD in the study areas using multiple analysis techniques.
After the September 2006 working meeting in Portland, Oregon, a project was established to evaluate
the use of remotely sensed imagery for the detection and mapping of SOD. With the assistance of the
USFS, project collaborators were identified and two study sites selected: one in Humboldt County,
California; the other in Curry County, Oregon (Figure 1). Both areas had and continue to have
significant amounts of SOD. Both study areas contained numerous highly vulnerable tanoak trees
and experienced widespread yet variable occurrences of SOD.
Figure 1. Location of SOD Hyperspectral Case Study.
Hyperspectral imagery was acquired using ITD‘s RDACS-H4 hyperspectral sensor on two occasions
over both study areas and ground data were collected by the project collaborators and ITD for use in
all subsequent analyses. The data was converted to at-sensor radiance, was atmospherically
calibrated, and also georectified.
To address the project‘s objectives, a number of statistical and image processing techniques were
utilized to compute necessary cover type and SOD detection products. All analyses efforts focused
on techniques that may be integrated into the EWS, once it becomes operational. Because of the
nature of the work being performed, the conducted analysis relied on a tiered approach. First,
analysis was performed to separate the species of interest (i.e., tanoak) from other forest land cover.
Second, analysis was performed to identify SOD occurrence within the tanoak species cover type. In
addition to identifying the tanoak species cover, the hyperspectral data was also preliminarily
analyzed for its ability to identify and map the locations of other SOD host species such as California
Bay Laurel.
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To determine if statistical differences existed between the spectral reflectance data for infected and
non-infected trees and other native land cover, a mixed model (Littell et al., 1996) was utilized. The
mixed model analysis was used to characterize the spectral differences between forest land
cover/species types as well as the various symptoms expressed by SOD. For each combination of
land cover types and/or vegetation health, comparisons were made using the ESTIMATE statements
in the MIXED procedure in the SAS System Version 9.0 (SAS Institute, Cary, NC). The analysis was
performed on each image date as well as on the reflectance differences that were observed between
the two dates.
For the image classifications, most of the analysis was performed using the Iterative Self-Organizing
Data Analysis Technique (ISODATA) unsupervised classification algorithm (Tou and Gonzalez,
1974). The ISODATA algorithm was utilized to break the imagery into a large number of classes (at
least 60 initial cluster classes). After the initial cluster development, the resulting classes were
labeled and merged to identify tanoak and/or other species with SOD using statistical techniques and
ground data collected by the project team. If shadows were prevalent in the data, they were labeled as
such using unsupervised techniques. Accuracy assessments were performed using a portion of the
ground data that was set aside prior to the start of analysis. Because of the less diverse species
composition of the relevant forest types found in Oregon, a majority of the analyses was focused on
that study site. This was done with the hope that some of the spectral signatures developed from the
Oregon study site will be useful in identifying species of interest as well as SOD occurrence at the
California site.
Ground data points were collected by the project cooperators and by ITD staff during visits to the
study sites. The points that were collected not only recorded the locations of SOD, but also recorded
the locations of other species (e.g., alder, Douglas fir, madrone). At the California study site a total of
321 points were collected on the ground; while at the Oregon site, a total of 281 points were
collected. Additional ground points were obtained from the OakMapper online database of known
SOD infections in coastal California. From 2001 to 2007, nearly 1000 points of known SOD
infections (in tanoak only) were obtained and utilized in the analysis of the temporal MODIS datasets
provided by NASA SSC and SSAI.
The hyperspectral datasets provided excellent results for the identification and mapping of forest land
cover types, especially of the SOD host species. SOD detection, however, presented more of a
challenge and was only possible in the later, terminal stages of infection. The estimated classification
accuracies for the forest types for the study sites are summarized in Tables 1 and 2.
Table 1. Estimated forest land cover classification accuracies for the Oregon study site.
Class
Tanoak
Alder
Douglas fir
Clearing/burn area
Sparse vegetation
Overall
Accuracy
90
96
93
97
94
94
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Table 2. Estimated forest land cover classification accuracies for the California study site.
Class
California redwood
Tanoak
California bay laurel
White oak
Madrone
Coast live oak
Douglas fir
Overall
Accuracy
93
79
81
73
85
73
89
81
To identify SOD occurrence, the previously classified tanoak cover were segmented into additional
classes and then further processed to identify trees potentially infected with SOD. On several flight
lines of the Oregon study site, some of the tanoak had been treated with herbicide approximately five
weeks prior to image data collection. Thus, an attempt was also made to define a class that contained
the tanoaks that were treated with herbicide. Results from a portion of flight line OR-11 in Oregon
are shown in Figure 2. The classification shows the surrounding healthy vegetation as well as the
dead tanoak and the tanoak that are declining from the herbicide treatment. Accuracy assessment
results (Table 3) indicate very good accuracies for the mapping of the dead tanoak and the declining
tanoak. Overall accuracies were typically above 85% for the classifications identifying tanoak that
died as a result of SOD infection.
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Figure 2. Identification of dead tanoak (from SOD) and declining tanoak from a prior
application of herbicide to help control the spread of SOD.
Table 3. Estimated classification accuracies for detection of dead and declining tanoak in
Oregon.
Class
Advanced SOD
Declining tanoak
(herbicide treated)
Overall
Accuracy
90
83
81
For the California study site, it was expected that accuracies for the identification of SOD would be
lower than those observed in Oregon. The overall accuracies for the identification of SOD were
typically around 80%, agreeing well with the results Kelly et al. (2004) obtained from their study.
Figure 3 shows the results of an unsupervised ISODATA classification performed on flight line CA 9
(July 2007). The zoom area shown in the map indicates two instances where SOD was identified via
the ground data and in the imagery.
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Figure 3. Unsupervised classification products from flight line CA-9 in the California study
area.
In addition to the hyperspectral datasets, ASTER and MODIS satellite data were also obtained.
MODIS 250 meter vegetation phenology datasets for the June -July time frame of 2007 corresponded
to the collection period of the hyperspectral datasets. The coarser spatial resolution provided by
ASTER and the time-series MODIS products proved largely ineffective in discriminating between
healthy and SOD infected tanoak trees (accuracies ≤ 60%). The ASTER data at 15 to 30 meter spatial
resolution, however, was useful for mapping forest land cover types, especially at the Oregon study
site.
Despite the larger spatial extent covered by the satellite image datasets, the coarser spatial resolution
of the data resulted in significantly lower accuracies than those obtained using airborne hyperspectral
imagery. This is due in part to the small patch sizes of the SOD-infected forests. For this particular
application, the Multi-Temporal / Multi-Resolution (MT/MR) approach may prove difficult to
implement as the acreage of disturbance would need to be substantial for detection with moderate to
coarse spatial resolution satellite datasets. Alternative datasets with higher spatial resolution and
future sensors may increase the feasibility of this application. The classification methodology used in
this work developed by this effort has been described in detail in the report titled, “Detection,
Mapping, and Monitoring of Sudden Oak Death Using Hyperspectral Imagery” and should aid
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project collaborators in their ongoing efforts to mitigate the impact of SOD in coastal California and
Oregon (Copenhaver et al., 2008).
In summary, the RDACS-H4 hyperspectral datasets provided excellent results for the identification
and mapping of forest land cover types in the study areas, especially of those that are host species for
SOD. SOD detection, however, presented more of a challenge and was only possible in the later
stages of infection. From the two dates of imagery acquired (October 2006 and July 2007), the July
imagery proved to be the most useful in discriminating between SOD-infected and healthy tanoak
trees (accuracies ≥ 80%). Since the completion of this project, there have been new journal
publications on the use of high spatial resolution hyperspectral remote sensing for SOD detection,
including work by Pu et al., (2008a, 2008b). The latter publications also provide positive findings on
the feasibility of using airborne hyperspectral remote sensing for aiding SOD mitigation.
Case Study on Invasive Plant Species Detection
This subsection regards results of an ITD case study on the use of high spatial resolution airborne
hyperspectral image data for detecting targeted invasive plant species that occur in forests of the
eastern US. Invasive plant species cause serious ecological disturbances making monitoring and
mapping important. There are thousands of invasive plant species that occur in the Unites States many of which pose problems to native forests. Non-indigenous weeds are spreading and invading
approximately 700,000 hectares of US wildlife habitat per year (Babbitt, 1998). According to a
statement from U.S. Department of the Interior (1998, 2000), weeds have infested 100 million acres
in the U.S., spread at 14 percent per year – consuming 4,600 acres of Federal lands per day.
According to a research report in 2000, the annual damage caused by invasive plants is $34 billion
and at least 5000 introduced plant species have escaped and currently exist in natural U.S.
ecosystems (Pimentel, 2000). According to a 1998 conservative estimate count, there are 5000 alien
plant species, 350 of which experts say are serious and dangerous invaders. Farmers and ranchers
spend more than $5 billion just for controlling weeds and losses exceed $7 billion (Babbitt, 1998).
Traditional, field-based mapping and monitoring of the extent and density of invasive plant
infestation presents several challenges, including inaccessibility of areas for field sampling, extent
and density, and budget constraints on field sampling and monitoring (Jakubauskas, 2002). Early
discovery and mapping of invasive plants distribution can reduce cost, improve efficiency and reduce
time required in eradication efforts (Pimentel, 2000).
Several remote sensing technologies have shown potential for mapping invasive species occurrences
at small to broad regional scales with greater accuracy and precision than field based methods. Aerial
photographs have been effective in mapping a few weeds like leafy spurge (Everitt, 1996). However,
the major disadvantage of this approach is that it relies on the nonnative plant possessing visually
detectable unique characteristics at the observed time of year. Processing of the imagery via aerial
photo interpretation requires extensive manual labor (Everitt, 1996; Anderson, 1993) and only small
spatial areas can be collected (Underwood et al., 2003). Researchers have also tried using high
resolution multispectral digital imagery (Carson, 1995) to map invasions.
Hyperspectral remote sensing provides high-resolution spectral data and the potential for remote
discrimination between subtle differences in ground cover types. In some cases, hyperspectral data
from space, air, or in-situ has been used to identify occurrence of targeted invasive forest, shrub, and
herbaceous plant species. In these examples, advanced imaging techniques were often used to aid or
enable the detection process. For example, Underwood et al. (2003) evaluated three methods and
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hyperspectral imagery to detect invasive plants, namely, minimum noise fraction (MNF), continuum
removal, and band ratio indices for mapping iceplant and jubata grass. Validation with field sampled
data showed high mapping accuracy for all three methods.
Another example regards the use of hyperspectral imaging technology for the detection of kudzu. By
using the Haar discrete wavelet transform (DWT) to extract pertinent features from hyperspectral
signatures, a detection system was implemented and tested for practical use of kudzu detection (Li,
2001). The study used a hand held ASD spectroradiometer (Hatchell, 1999) to do spectral analysis
and reported 100% accuracy (overall) in detection of kudzu. Williams et al. (2002) used AVIRIS
airborne hyperspectral imagery to map leafy spurge using a mixture tuned matched filter (MTMF).
Overall performance of the MTMF for estimating the percent canopy cover of leafy spurge for all
sites was good (r2 = 0.69) with better performance in prairie areas (r2 = 0.79) and poorer performance
occurring on wooded sites (r2 = 0.57). These results demonstrated that in open canopies with leafy
spurge in the understory, the spectral signature is sufficiently distinct to be detectable.
Imagery from the Hyperion hyperspectral sensor on board the EO-1 spacecraft was used to estimate
and map the occurrence of Chinese tallow during senescence (Ramsey 2001). Using a sub-pixel
extraction model, five characteristic spectra that adequately represented the site-specific and also the
corresponding Hyperion imaged canopy reflectance spectra data sets were extracted. Ramsey (2001)
also employed Landsat ETM+ data to detect Chinese Tallow based on identification of subtle
spectral anomalies (Ramsey, 2001). Invasive species such as leafy spurge and spotted knapweed
randomly distributed in a non-uniform environment were mapped using hyperspectral imagery
obtained using the Probe-1 airborne sensor. An overall accuracy of 86% was obtained using the
Breiman Cutler classification method (Lawrence, 2006). In a project aimed towards early detection
of tamarisk, remotely sensed data products such as natural color, color-infrared, and hyperspectral
imagery were collected in the summer season. The results verified that such imagery could be used
for mapping tamarisk during the growing season but early detection would require sophisticated
analysis techniques (Laes and Maus, 2006). Additionally, extensive research has been carried out for
the detection of cogon grass using sophisticated analysis techniques such as wavelet transform
(Mathur, 2002), best-bases selection, decision-fusion (Venkataraman, 2005), and projection pursuit
(Lin, 2004; West, 2006).
Case study discussed in this subsection was done in collaboration with the Eastern Forest
Environmental Threat Assessment Center (EFETAC) in Asheville, NC. EFETAC was designated the
lead for developing and implementing a forest threat Early Warning System (EWS). EFETAC is
responsible for aiding U.S. Forest Service (USFS) efforts to identify, assess, and mitigate forest
threats in the eastern United States. In doing so, this group works to mitigate the effects and reduce
the likelihood of forest disturbances, improve information access through centralization, and improve
tracking of forest risk and disturbance over space and time. The Western Forest Threat center,
located in Prineville, OR has a similar directive, with a focus on threats to western forests. The
overarching goal of the threat assessment centers is the development of an integrated, national EWS
to identify, detect, and rapidly respond to forest environmental threats. Although the operational
requirements for the EWS are still evolving within the USFS, it is envisioned to be a multi-source,
multi-scale tool that provides broad area, high frequency reconnaissance; detailed location-specific
analysis using high-resolution imagery and ground data; and predictive modeling of potential and
emerging forest threats.
The overall goal of this invasive species study was to evaluate the use of airborne hyperspectral
imagery for improving early warning system capability as it relates to the identification and mapping
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invasive species in a forest ecosystem. Specific objectives were to: 1) collect representative ground
data from native and invasive species in the study area; 2) collect hyperspectral imagery over the
study area; 3) determine the degree of separability between species and identify spectral locations
most useful for species discrimination; 4) classify and map the extent of invasive species using image
classification techniques; and 5) evaluate the use of advanced analysis techniques (i.e., wavelet
analysis) for mapping invasive species. The study regarded a few targeted invasive plant species. The
study of all non-native invasive plants in the Eastern U.S. is beyond the scope of this project. Based
on discussions with the project collaborators, several species were identified that are particularly
harmful in Eastern US forests. This study focused on autumn olive, multiflora rose, ailanthus,
Chinese silvergrass, and mimosa.
The hyperspectral imagery was collected in portions of the Daniel Boone National Forest located in
Eastern Kentucky. This location was chosen by the EFETAC because of the large number of invasive
species present, especially in areas surrounding reclaimed mine lands, highways, and railroad rightof-ways. Ground points were collected using a differentially-corrected global positioning receiver
(DGPS) and a ruggedized handheld field computer on four different occasions. The ground data were
collected as points, lines, or polygons for every species identified and were used to provide training
as well as accuracy assessment points.
Remotely sensed imagery was collected using the ITD RDACS-H4 hyperspectral sensor. The
RDACS-H4 is an updated version of the sensor described by Mao (2000) and collects data from 294nm to 1173-nm at 6-nm bandwidths for a total of 150 bands. Imagery was collected at a spatial
resolution of one (1) meter. The image footprint from the sensor covers an area of 1600 × 5700
meters. Two data acquisitions were carried out: one took place 21 Oct. 2006 and the other on June
2007. Image acquisition dates were determined by Forest Service personnel in cooperation with ITD
and were timed to match phenological periods of optimal separability between invasive and native
species such as green-up, flowering or senescence.
During the October data collection mission, imagery was obtained over 17 flight lines and during the
June data collection mission, imagery was obtained over 7 flight lines. The number was reduced to
cover flight lines for which adequate ground data existed and also to provide time to acquire 0.5-m
resolution imagery over many of the study sites. Flight lines were spread throughout the locations
within the forest with known invasive weed problems (Figure 4). The data was converted to at-sensor
radiance, was atmospherically calibrated, and also georectified.
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Figure 4. Map of the June 2007 flight lines for the Daniel Boone National Forest.
Unsupervised classification categorizes the images into a specified number of spectral classes
according to image statistics and pixel values (Campbell, 2002). The ISODATA (Iterative SelfOrganizing Data Analysis Technique) method initially assigns pixels into spectral classes based on
statistics. The new centroids are found for each class and the pixels are then re-allocated to the
closest centroid if it has changed position. This process is repeated until the change threshold that is
determined by the analyst is met (Campbell, 2002). The change threshold was set at 95 percent
leaving only five percent of the pixels acceptable to be able to be changed for the classification. The
statistical settings are also determined by the analyst, for this dataset a standard deviation of 1.0 was
used and a minimum class distance of 3.0 was used. Smaller values for the class standard deviation
and class distance allow for less variance within each class. A total of 50 spectral classes were
utilized for the DBNF dataset.
Image data with spectral bands ranging from 400 to 819nm were utilized for all classifications.
Imagery was spectrally subset from the original 150 bands to 72 bands because of noise in the data
below 400 nm and spectral bleeding occurring in the near-infrared (NIR). To prepare the image for
use in the ISODATA unsupervised classification, all pixel values were multiplied by 1000. This was
done to provide a greater dynamic range for unsupervised classification with the ISODATA
algorithm.
This analysis utilized the information provided by the ground truth information collected for the
project. The points collected for each site were overlaid on the georeferenced and calibrated
hyperspectral datasets. The regions of interest (ROIs) were identified on the imagery as points,
buffered polygons, irregular polygons, and lines.
To perform the End Member analysis, a spectral library must first be built. This is done using the
Spectral library builder tool available in ENVI 4.2. The tool uses the previously identified ROIs to
build a spectrum unique to a particular species. The reliability of the spectrum is based on the
number of training points. For example, if 150 pixels are used to build a spectrum autumn olive, the
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spectrum would be more reliable than if a spectrum was built using only 15 pixels. Figure 5 shows
the spectra created from flight line 10. In this example, the image consisted of 72 bands and 15
different types of vegetation were identified from the ground points. The study area in flight line 10
consists primarily of a reclaimed strip mine and the invasive plant species of interest here are autumn
olive and bush honeysuckle.
Figure 5. Spectral library of End Member spectra of relevant native and invasive plant species.
After the spectral library has been constructed, it is then saved as an End Member collection file.
This is an ASCII file generated by ENVI and contains information about the unique set of spectra
that were collected. This file is then utilized in the classification process at a later stage.
The wavelet transform is a digital signal processing technique that can be used for scalar analysis of
hyperspectral reflectance curves. This signal processing tool is used in analysis to differentiate
invasive species from neighboring native species as well as to differentiate one invasive from
another.
The unsupervised ISODATA classification was performed on four different flight lines within the
Daniel Boone National Forest during the time period of June 2007. The reason these four flight lines
were chosen was because of the abundance of ground points that were collected within each flight
line. The DOQQ and information from the ground data collection missions in the area aided in
determining the appropriate land cover type each of the spectral classes represented.
The ISODATA unsupervised classification was utilized for the Daniel Boone National Forest dataset
because of the relatively diverse species matrix observed in the study area. The focus of this project
was identification of invasive species, which is why there was more ground truth points collected
within the invasive land cover types, while the native species land cover types were not as heavily
collected. This fact made supervised classification difficult because each land cover class needs
numerous points for it to be classified.
Of the different flight lines, Flight line 7 had a larger amount of ground truth points and this is
exhibited by the highest overall classification accuracy (85%) (Figure 6) (Table 4). Another reason
that flight line 7 might have higher classification accuracy is because the ground truth points were
collected within a larger land area (i.e., greater diversity). Most of the ground points within the flight
lines were all collected within a small area, whereas with flight line 7 the ground points collected
covered almost the entire length of the flight line. Also, when looking at the individual land cover
accuracies for flight line 7, the most accurate classification was the ailanthus, which had the most
pixels available to be classified. The number of pixels used for the accuracy assessment could also
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result in misleading classification accuracies for some of the land cover classes, particularly the
native species. When investigating the number of pixels that were utilized to evaluate the
classification, several species had less than 10 pixels. Thus, some bias (either positive or negative)
may exist when accuracies were computed.
Figure 6. Overview of the ISODATA classification from flight line 7 imagery acquired 12, June
2007. In this image, there are more roads and urban areas and more ground truth points that
were collected.
Table 4. Estimated accuracy of ISODATA classification of targeted invasive plant species –
computed from flight line 7.
Class
Ailanthus
Native
Overall Accuracy
Producer
Accuracy %
82.8
96.6
Consumer
Accuracy %
99.3
49.1
84.8
The End Member classification technique was applied to the hyperspectral imagery collected in the
Daniel Boone National Forest (DBNF) on 21 Oct. 2006 and 12 June 2007. The spectral library
developed from the ground data was used to classify the flight lines of interest for each collection.
The classification image in Figure 7 shows the different types of land cover identified in the flight
line 7 by the classification algorithm. An accuracy assessment was performed on each classification
and is shown in Table 5 for flight line 7.
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Figure 7. The overview of the End Member classification for flight line 7 (atmospherically
calibrated method) from 12 June 2007 imagery collection.
Table 5. Estimated classification accuracies of targeted invasive species from classification of
flight 7 data acquired 12 June 2007.
Class
Calibration Method
Ailanthus
Overall Accuracy
User Accuracy %
Emp. Line Atmospheric
97.8
96.5
Emp. Line
59
Producer Accuracy %
Emp. Line
Atmospheric
59
72
Atmospheric
56
The End Member classification was implemented on the imagery collected in the Daniel Boone
National Forest so that the spectral library developed using the ground data collected in one flight
line may be used for other flight lines with limited ground data. This made it possible for various
land covers to be collected during ground data collection. This method was very useful in
classification of flight lines where only a few ground points were collected, (i.e., flight line 17). The
spectral library for flight line 10 was used together with the spectral library created for flight line 17,
which made the objective of applying the End Member technique successful. Since the goal of the
project was to identify invasive species, individual class accuracies of the invasive species provide a
clearer picture of the effectiveness of this method of analysis. The overall accuracy could be
misleading since the distribution of ground points collected for all types of landcover in a particular
flight line was not evenly distributed. Ailanthus has been effectively identified in flight lines 7 and 17
with very good user accuracies. Autumn olive was best identified in flight line 10, especially for the
October data collection. Bush honeysuckle has not been identified effectively in flight line 10 mainly
due to the fact that there were very few training pixels. This was observed in the drastic
producer/user accuracy difference.
The wavelet analysis model was applied to flight lines 10 and 17 from the hyperspectral imagery
collected in October 2006 and flight lines 3, 7, 10 and 18 from the June 2007 imagery were analyzed
using the model that was developed for the analysis in this project. Both the Haar and db1 wavelets
15
were applied to the imagery. Analysis I consistently performed better than Analysis II. Hence, the
results from Analysis I are reported for June imagery analysis whereas results from both analyses are
shown for the October imagery.
The wavelet analysis model has been applied on data from both date imagery collections- October
2006 and June 2007. This model was applied to parts of the imagery neighboring areas from where
ground points were selected. The probability of finding invasive plants in a small area of the imagery
was explored here. These small areas were selected around regions that would be revisited during the
June ground data collection. Large areas of the imagery were not analyzed done due to time restraints
posed by the model execution for the entire imagery. The accuracy assessment of bush honeysuckle
was better using this method than any of the other analysis methods for the October imagery.
However, bush honeysuckle could not be considered for analysis for the June imagery since the
number of pixels available for analysis was too small to use for training or testing the classifier.
Autumn olive was identified with high accuracies in flight line 10 in October; however analysis of
June imagery yielded very poor results reconfirming the conclusion that autumn olive is better
discriminated at the end of growth season in fall than in spring. The frost that set in early spring of
2007 could have also affected the growth pattern thus leading to such poor accuracies. This was
isolated only to flight line 10 and not flight line 3 where autumn olive was identified with very high
accuracies. Wavelet analysis was successful in identifying Chinese silvergrass in flight lines 3 and 18
whereas End Member classification had failed. Ailanthus was identified in flight line 7 in the June
imagery with high accuracies whereas analysis of flight line 17 from the October imagery did not
yield extremely good results.
Hyperspectral imaging sensor technology has paved the way for the remote sensing community to
carry out detailed intensive research on target recognition applications. The purpose of this project
was to use hyperspectral imagery as an operational, diagnostic tool to analyze forest threats. To
achieve this objective, an end-to-end process that includes 1) four ground data collections, 2) two
hyperspectral imagery collections, 3) preprocessing of data, 4) analysis of processed imagery using
ISODATA, End Member, and Wavelet analysis techniques were carried out.
The unsupervised ISODATA classification technique was implemented on the hyperspectral dataset
since there were various species of vegetation present in the forest and limited ground truth points
collected for certain land cover types. Ailanthus (flight line 7) was classified accurately using this
analysis, but results for the other invasive species were not as accurate. It could be either that the
ISODATA classification technique was not successful for classification of the hyperspectral imagery
or that the number of pixels used for the accuracy assessment led to misleading classification
accuracy.
The End Member analysis technique was mainly implemented so that the spectral library created
using the ground data collected in one flight line may be used for other flight lines with limited
ground data. The limited number of ground points available for each class for both invasive and
native species led to the choice of this analysis technique. Hence, the objective of implementing was
achieved. The classification accuracies of invasive species such as autumn olive and ailanthus were
good in the flight lines of interest, but the classification of Chinese silvergrass and bush honeysuckle
were not impressive. A few reasons for this occurrence could be either 1) the spectral signature
created for the invasive species was not accurate; or 2) spectral separability of a given invasive
species from other invasive or native species nearby was not possible due to very similar spectra.
16
The Wavelet analysis technique was implemented to analyze minor variances in the spectra of the
invasive species and used features extracted from the minor variances to identify the presence of
invasive species. The wavelet analysis produced increased classification accuracies for all flight lines
except for flight line 10 (June collection). The classification accuracy for Chinese silvergrass
increased significantly in flight lines 3 as well as 18 from below 50% to over 75%. Bush honeysuckle
could not be classified using the wavelet analysis model due to the lack of training data.
The spectral-spatial resampling approach provided insight about the possibility of invasive plant
identification using lower resolution commercial imagery. The accuracies and the reliability of both
the wavelet and end member methods dropped significantly. Therefore, a multi-tier approach to
identify invasive species that have been identified in this project with high accuracies would
probably be a difficult task, at least given the techniques and data tried in this case study.
In summary, the feasibility of using airborne high spatial resolution hyperspectral imagery to detect
occurrences of targeted selective invasive plant species was demonstrated and validated for an area
of concern within the Daniel Boone National Forest. This is but one case study and more are
desirable to demonstrate and assess feasibility for additional invasive plant threats to native forests.
This potential has also been demonstrated previously for other invasive plants, though both its
research and operational use in regard to Federal, State, and private forest land surveys would require
additional investment of the USFS.
Public Dissemination of ITD Hyperspectral Research Findings
Efforts were also made to identify suitable conferences for public dissemination of the Healthy
Forest project‘s hyperspectral case studies. It was determined that the following conferences would
be highly relevant:
1) American Society for Photogrammetry and Remote Sensing Annual Conference (www.asprs.org)
2) Society of Photographic Instrumentation Engineers (SPIE) Conferences (spie.org/)
International Geoscience & Remote Sensing Symposium (IGARSS) (http://www.igarss08.org/)
3) USDA Forest Service Remote Sensing Conference (www.fs.fed.us/eng/rsac/)
Tree Care Industry Exposition (http://www.treecareindustry.org/public/meetings_tci_expo.htm)
4) Pacific Agriculture Show (www.agricultureshow.net/) – relevant to SOD only
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