Mapping Russian Olive
Using Remote Sensing to map an Invasive Tree
September 2006
RSAC-0087-RPT1
1
United States
Department of
Agriculture
Forest
Service
Remote Sensing
Applications Center
Abstract
Hamilton, R.; Megown, K.; Lachowski, H.; Campbell, R. 2006. Mapping Russian olive: using remote sensing to map an
invasive tree. RSAC-0087-RPT1. Salt Lake City, UT: U.S. Department of Agriculture Forest Service, Remote Sensing
Application Center. 7 p.
With funding from the Remote Sensing Steering Committee, a pilot project was initiated to develop a cost-effective method
for mapping Russian olive (Elaeagnus angustifolia L.), an invasive tree species, from scanned large-scale aerial photographs. A
study area was established along a riparian zone within a semiarid region of the Fishlake National Forest, located in central
Utah. Two scales of natural color aerial photographs (1:4,000 and 1:12,000) were evaluated as part of the project. Feature
Analyst, an extension for ArcGIS and several image processing software packages, was used to map the invasive tree. Overall,
Feature Analyst located Russian olive (RO) throughout the imagery with a relatively high degree of accuracy. For the map
derived from 1:4,000-scale photographs, the software correctly located the tree in 85 percent of all 4-by-4 meter transect cells
where Russian olive was actually present. However, smaller trees were sometimes missed and the size of trees and groups of
trees were frequently underestimated. The map derived from 1:4,000-scale photographs was only slightly more accurate than
the map derived from 1:12,000-scale photographs, suggesting that the smaller scale photography may be adequate for mapping
Russian olive.
Key Words
Russian olive, invasive species, remote sensing, Feature Analyst, large scale photos, accuracy assessment
Authors
Randy Hamilton is an Entomologist and Remote Sensing Specialist working at the Remote Sensing Applications Center
and employed by RedCastle Resources.
Kevin Megown is a Senior Project Leader and Biometrician working at the Remote Sensing Applications Center and employed
by RedCastle Resources.
Henry Lachowski is Program Leader for the Integration of Remote Sensing Program at the Remote Sensing Applications
Center in Salt Lake City, Utah.
Robert B. Campbell is an Ecologist working at the Fishlake National Forest in the Intermountain Region in Richfield, Utah.
ii
Table of Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
iii
iv
Introduction
Invasive plants (weeds) have infested
hundreds of millions of acres of forest,
rangeland, and grassland throughout the
United States and pose a serious threat
to ecosystem health and function,
biodiversity, and endangered species
(USDA Forest Service 2004). Annually,
invasive plants invade an estimated
700,000 hectares of U.S. wildlife
habitat (Babbitt 1998).
The best defenses against invasive weeds
are prevention, early detection, and
eradication. However, once populations
become well established, management
objectives shift from eradication to
containment—establishing a perimeter
around the infestation, eliminating
small outlying patches, and gradually
reducing the perimeter of the primary
infestation. At this stage, mapping
established populations becomes critical.
An accurate map provides baseline
information and becomes a strategic
planning tool for future management
efforts. Combating invasive weeds
without accurate baseline maps has been
likened to fighting wildfires without
knowledge of their locations (Dewey
1995). Unfortunately, the cost of
traditional field-based mapping can, in
some cases, be prohibitive.
Russian olive (Elaeagnus angustifolia L.),
a thorny shrub or tree with origins in
Southeastern Europe and Western Asia,
was intentionally introduced and planted
in the United States for windbreaks,
erosion control, wildlife habitat, and
other horticultural purposes (figure 1)
(Katz and Shafroth 2003). The tree is
particularly well adapted to semiarid
and saline environments. Early in the
20th century, Russian olive escaped
cultivation and spread, particularly into
moist riparian environments in arid or
semiarid regions of the western United
States (Stannard and others 2002).
Although the invasive nature of Russian
olive was known years ago, public and
private agencies continued to promote
planting it for various purposes as
recently as the 1990s, and it is still
commercially available (Katz and
Shafroth 2003).
Figure 1—Russian olive, an invasive tree, invades riparian environments in semiarid
regions of the western United States. Photo courtesy of J. Scott Peterson, USDA NRCS,
www.forestryimages.org
Within the Fishlake National Forest
(FLNF), located in central Utah,
Russian olive invaded and became wellestablished in riparian zones of the more
arid regions of the Forest. Because this
tree was largely ignored until recently,
when Sevier County designated it as a
noxious weed, FLNF had not mapped
the location and extent of the invasion.
The Fishlake National Forest is
proactive and has an aggressive weed
management program. Nevertheless,
budgetary constraints severely limit the
scope of their weed management
activities. The need to map Russian
olive placed additional strain on the
Forest’s limited budget.
In 2005, the USDA Forest Service
Remote Sensing Steering Committee
awarded funding for a proposal submitted
by the Fishlake National Forest to, in
part, develop a cost-effective remote
sensing alternative to field surveys for
mapping Russian olive.
Remote sensing has been used with
varying degrees of success to map weed
infestations (Hunt and others 2003;
1 | RSAC-0087-RPT1
Casady and others 2005; Lass and
others 2005; Maheu-Girouxa and de
Blois 2005). Biological and
phenological characteristics that
distinguish a weed from its
surroundings, as viewed from above, are
critical for successful mapping using
remote sensing. Russian olive on the
Fishlake National Forest was considered
a good candidate for mapping because
of the distinctive silver-gray color of its
leaves, petioles, and current-year
branchlets (figure 2) (Katz and Shafroth
2003). In addition, Russian olive trees
are large compared to other invasive
weeds and occur in dense stands. On
the Fishlake National Forest, these trees
are generally confined to sparsely
vegetated areas along riparian corridors,
which further increases the ease of
mapping them using remote sensing.
Specific objectives of this study were to
evaluate and compare the utility of two
scales of scanned natural color aerial
photography for mapping Russian olive
infestations within a pilot study area on
the Fishlake National Forest.
process of noting a few features that
were correctly identified and others that
were incorrectly identified; digitizing
additional samples when features were
missed in the classification; and finally,
reprocessing the image. This iterative
process continues until a satisfactory
result is achieved. Although the output
from Feature Analyst can be remarkably
good, some manual editing may be
required to produce the final map.
Figure 2—The distinct silver/gray coloration of Russian olive foliage can distinguish
it from its surroundings, making it a good target for mapping using remote sensing.
Photo courtesy of Paul Wray, Iowa State University, www.forestryimages.org.
Methods
A pilot study area was established
within the Fishlake National Forest
along a six-mile segment of Salina
Creek, just east of Richfield, Utah
(figure 3). Natural color aerial
photographs were acquired at 1:4,000
and 1:12,000 scales over the area on 11
September 2005 using a Zeiss RMK-A
camera equipped with an 80mm lens.
Negatives were subsequently scanned at
14 microns yielding 5.6 and 16.8
centimeter (0.18 and 0.55 foot) pixels
for the respective scales. The digital
images were then orthorectified using
the Leica Photogrammetry Suite.
For this study, Feature Analyst 4.0, an
extension for ArcGIS, ERDAS Imagine,
and other image processing software
packages, was used to map Russian
olive. The image classification method
implemented in Feature Analyst is
known as feature extraction. Unlike
traditional image classification, feature
extraction uses both spectral and spatial
properties of an image to identify the
feature of interest (tree crowns in our
case). Feature Analyst “learns” to
recognize features of interest through an
iterative “training” process. First, the
user digitizes a few sample features.
Using the spectral and spatial
information contained in the samples,
Feature Analyst attempts to identify
similar features in the imagery. The user
then proceeds through an iterative
Feature Analyst provides the user with
several parameters to fine-tune the
classifier to achieve optimal results.
First, the user specifies a kernel or input
representation, which approximates the
size and shape of the feature of interest.
To accommodate the circular shape of
the Russian olive tree crowns, we used a
circle input representation with a sevenpixel diameter for the 1:4,000-scale
photography and a five-pixel diameter
for the 1:12,000-scale photography
(figure 4).
Because Feature Analyst currently
cannot process data sets larger than 2
GB, the imagery was cropped,
eliminating some areas unsuitable for
Russian olive establishment, then subset
into sections slightly less than 2 GB in
size. To increase processing speed, a
resampling factor was set, which
Figure 3—The study area, a six-mile section of Salina Creek, is located east of
Richfield, Utah within the Fishlake National Forest. Natural color 1:4,000-scale
scanned and orthorectified aerial photographs of the study area are shown overlaid
on a shaded relief of the area.
2 | RSAC-0087-RPT1
Figure 4—A circle input representation,
7 pixels in diameter, was used to
accommodate the circular shape of
Russian olive tree crowns in the 1:4,000scale imagery.
effectively allows the software to
resample the imagery to a larger pixel
size, thereby reducing the amount of
data that is processed. For this study,
resampling factors of 4 and 2 were set
respectively for the 1:4,000 and
1:12,000-scale data. Default settings
were accepted for all other parameters.
The processing time was further reduced
by creating a polygon mask to eliminate
other regions of the imagery not suited
for Russian olive establishment.
imagery, no field visits were made prior
to classifying the imagery. However, we
visited the site as part of an accuracy
assessment. To assess the accuracy of the
map, the study area was sampled using a
transect design. First, the study area was
divided into 1-kilometer segments. Line
transect (100-meters in length by 4meters wide, subdivided in 4-meter
segments) were randomly located on the
imagery within the riparian zone of
every other 1-kilometer segment (figure
5). Transects were not allowed to cross
the stream due to high water. Because
of this constraint and because the
riparian zone was relatively narrow
(usually less than 100-meters on either
side of the stream), transects generally
paralleled the stream. The orientations
of transects were fixed at some whole
number multiple of 22.5 degrees from
north. For each transect, a field
datasheet with transect overlaid on the
imagery was printed on a color laser
printer.
In July 2006, transects were located on
the ground using a combination of GPS
(global positioning system) units, transect
locator maps, and the individual field
datasheets. We walked the length of
each transect. When Russian olive was
encountered, the tree was outlined
directly on the transect datasheet. Back
at the office, outlines drawn on the
transect datasheets were manually
digitized over the imagery in ArcGIS.
The digital transects were intersected
with the digitized Russian olive tree
outlines and with the Feature Analyst
classification of Russian olive (figure 5).
The percent composition of digitized
and classified Russian olive was
computed for each 4-by-4 meter
transect cell. These data were then
categorized using three different scales
(for comparison). (A) the Daubenmire
cover class scale, frequently used for
collecting ecological data (0, <1, 1–5,
5–25, 25–50, 50–75, 75–95, and 95–
100 percent); (B) 10-percent intervals
or classes (0, 0–10, 10–20, 20–30, 40–
50, 50–60, 60–70, 70–80, 80–90, and
90–100 percent); and (C) present/
absent intervals (classes). Contingency
tables comparing the Feature Analyst
classification to the field observations
were created for the three different data
categorizations for each scale of imagery
as well as for the unedited and manually
edited maps.
The time required to train Feature
Analyst can be reduced by training first
on a small representative subset of the
imagery, then applying the resulting
model to the rest of the imagery. We
evaluated this approach in several areas,
but the results were generally
unsatisfactory. Therefore, Feature
Analyst was trained using an entire 2
GB section of imagery and retrained for
each additional section.
After Russian olive was mapped by
Feature Analyst, the maps for the two
scales of imagery were manually edited
to eliminate obvious errors in the
classification.
Because of the high spatial resolution of
the imagery and the relative ease of
recognizing Russian olive on the
Figure 5—A 100-meter accuracy assessment transect overlaid on 1:4,000-scale
imagery. Transects were intersected with the Feature Analyst classification of Russian
olive (blue) and the digitized polygons of Russian olive identified in the field (yellow).
3 | RSAC-0087-RPT1
Producer’s, user’s, and overall accuracy
were computed from each contingency
table as well as the kappa statistic
(Lillesand and Kiefer 1994). To
accommodate measurement errors
resulting from sketching and digitizing
the field data, the accuracy statistics
were also computed using a 10-percent
fuzzy tolerance applied to the 10percent interval categorization. In other
words, we assumed that the true
percentage of Russian olive in a cell
could deviate from its computed
percent-RO interval by as much as 10
percent, or one 10 percent interval
above or below the computed interval.
For example, if the percent Russian
olive computed from digitized field data
was in the 80–90 percent interval, then
we assumed that the true value could
fall within the 70–80, 80–90, or 90–
100 percent intervals.
things as Russian olive. These errors of
commission are addressed by the user’s
accuracy. A high user’s accuracy, for
example, tells us that almost everything
mapped as Russian olive is Russian olive
and that the analyst mislabeled very few
other things as Russian olive.
The kappa statistic measures the extent
to which the accuracies are due to true
agreement between the map and field
verification data versus chance agreement.
Kappa typically ranges between 0 and 1.
A high kappa value indicates that the
classification is much better than could
be achieved by chance alone.
Producer’s and user’s accuracies are
measures of accuracy computed for each
map class (e.g. each percent-RO class)
in a classification, while overall accuracy
is a summary statistic of the accuracies
of all classes combined. Fundamentally,
producer’s accuracy tells us how well the
analyst mapped Russian olive actually
found in the transects. For example, a
high producer’s accuracy indicates that
the analyst’s map correctly identified
most Russian olive trees or, in other
words, very few Russian olive trees were
omitted or missed by the analyst’s map.
However, producer’s accuracy does not
tell us whether the analyst committed
errors by incorrectly labeling other
Results and
Discussion
The final output from Feature Analyst
consisted of sets of polygons outlining
Russian olive tree crowns or clumps of
trees (figure 6). Depending on the
specific data categorization and whether
or not the map was edited, overall
accuracies ranged from 70–91 percent
for the 1:4,000-scale imagery and 67–
88 percent for the 1:12,000-scale
imagery, with respective kappa statistics
ranging from 0.49–0.81 and 0.39–0.73
(table 1). In general, the accuracy of the
map derived from 1:4,000-scale imagery
Figure 6—Map of Russian olive (blue outlines) created from 1:4,000-scale scanned
aerial photographs using Feature Analyst.
Table 1—Overall accuracies and kappa statistics for the edited and unedited Russian olive classifications derived from 1:4,000
and 1:12,000-scale imagery for each percent-RO categorization, as well as the fuzzy assessment of the 10-percent interval
categorization.
1:4,000 Edited
Categorization
Overall
(Percent)
Kappa
1:4,000 Unedited
Overall
(Percent)
Kappa
1:12,000 Edited
Overall
(Percent)
Kappa
1:12,000 Unedited
Overall
(Percent)
Kappa
(A) Daubenmire classes
75
0.55
73
0.53
71
0.44
68
0.42
(B) 10 percent intervals*
74 (41)
0.52 (0.33)
70 (40)
0.49 (0.32)
69 (29)
0.42 (0.19)
67 (28)
0.39 (0.18)
(B) 10 percent intervals*
(fuzzy tolerance)
86 (71)
0.85 (0.60)
82 (71)
0.81 (0.61)
80 (54)
0.79 (0.37)
77 (54)
0.76 (0.37)
(C) Present/ absent
91
0.81
88
0.75
88
0.73
86
0.69
* Overall accuracies and kappa statistics are presented both with and without (in parentheses) the 0-percent or absent class. The absent class had a
disproportionately high
4 | RSAC-0087-RPT1
was better than that of the map derived
from the 1:12,000-scale imagery.
Nevertheless, the increase in accuracy
was modest, suggesting that 1:12,000scale photography may be adequate for
mapping Russian olive in many cases.
Except for intervals (classes) containing
either no Russian olive or a very high
percentage of Russian olive, producer’s
and user’s accuracies were quite low,
ranging from only 3–46 percent and
4–48 percent respectively for the
Daubenmire and 10-percent (nonfuzzy) categorizations (tables 2 and 3).
In many cases, it appeared that the low
accuracies for intermediate values of
percent-RO were due to variability in
how the field data were digitized and
in how Feature Analyst drew the
polygons. Because of this, we imposed
a 10-percent fuzzy tolerance to the 10percent interval categorization
(categorization B) accuracy assessment
data.
In addition to imposing the fuzzy
tolerance, we also eliminated the 0percent or “absent” category from the
fuzzy tolerance contingency table. This
category had an exceptionally high
number of samples (n=889) compared
to the other categories (n<105), which
confounded the accuracies (particularly
the overall accuracies) and kappa
statistics. The influence of the
disproportionate number of samples on
overall accuracy and the kappa statistic
is illustrated in table 1, where these
statistics are presented for the 10percent categorizations both with and
without the absent category. Applying
the fuzzy tolerance and removing the
“absent” category increased the
producer’s and user’s accuracies of the
percent-RO intervals substantially,
(compare tables 3 and 4).
The overall ability of Feature Analyst to
locate Russian olive was assessed by
collapsing the contingency tables to two
classes—present and absent. For both
scales of imagery, overall accuracies were
in excess of 85 percent (table 1). User’s
Table 2—User’s and producer’s accuracies for Daubenmire percent-RO intervals (categorization A) for edited and non-edited RO
classifications derived from 1:4,000 and 1:12,000-scale imagery.
1:4,000 Edited
1:4,000 Unedited
1:12,000 Edited
1:12,000 Unedited
Percent-RO
Intervals
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
Absent
92%
95%
92%
90%
87%
96%
88%
91%
<1
5%
5%
11%
14%
6%
10%
6%
10%
1–4.9
13%
19%
11%
16%
12%
8%
10%
8%
5–24.9
35%
35%
34%
41%
27%
27%
25%
29%
25–49.9
40%
36%
36%
36%
28%
21%
24%
22%
50–74.9
45%
43%
42%
46%
16%
11%
19%
14%
75–94.9
43%
41%
48%
43%
29%
29%
27%
27%
95–100
85%
57%
85%
66%
76%
45%
76%
45%
Table 3—User’s and producer’s accuracies for 10 percent-RO intervals (categorization B) for edited and non-edited RO classifications
derived from 1:4,000 and 1:12,000-scale imagery.
1:4,000 Edited
1:4,000 Unedited
1:12,000 Edited
1:12,000 Unedited
Percent-RO
Intervals
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
Absent
92%
95%
92%
90%
87%
96%
88%
91%
0.01–9.9
40%
42%
33%
40%
32%
29%
29%
29%
10–19.9
21%
23%
20%
27%
17%
23%
14%
23%
20–29.9
19%
19%
19%
19%
14%
10%
12%
13%
30–39.9
16%
16%
15%
16%
7%
5%
6%
5%
40–49.9
18%
17%
16%
19%
11%
8%
10%
8%
50–59.9
29%
27%
21%
23%
5%
4%
4%
4%
60–69.9
25%
24%
23%
24%
7%
3%
6%
3%
70–79.9
19%
15%
18%
15%
10%
11%
10%
11%
80–89.9
30%
29%
30%
29%
9%
9%
6%
6%
90–100
87%
65%
82%
67%
73%
47%
73%
47%
5 | RSAC-0087-RPT1
Table 4—User’s and producer’s accuracies computed with a 10 percent fuzzy tolerance for 10 percent-RO intervals (categorization
B) with the absent category removed for edited and non-edited RO classifications derived from 1:4,000 and 1:12,000-scale imagery.
1:4,000 Edited
1:4,000 Unedited
1:12,000 Edited
1:12,000 Unedited
Percent-RO
Intervals
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
0.01–9.9
71%
89%
78%
85%
53%
79%
54%
77%
10–19.9
68%
82%
73%
80%
51%
71%
51%
72%
20–29.9
60%
72%
65%
72%
46%
55%
44%
57%
30–39.9
53%
57%
52%
57%
39%
41%
36%
42%
40–49.9
54%
56%
51%
55%
33%
33%
33%
31%
50–59.9
52%
55%
48%
55%
30%
29%
31%
29%
60–69.9
59%
54%
55%
59%
32%
25%
34%
25%
70–79.9
69%
57%
68%
62%
55%
35%
55%
35%
80–89.9
90%
73%
88%
76%
75%
59%
75%
58%
90–100
96%
78%
96%
80%
85%
65%
85%
64%
Table 5—User’s and producer’s accuracies for a present/absent categorization (categorization C) for edited and non-edited RO
classifications derived from 1:4,000 and 1:12,000-scale imagery.
1:4,000 Edited
1:4,000 Unedited
1:12,000 Edited
1:12,000 Unedited
Percent-RO
Intervals
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
User’s
Producer’s
Present
91%
85%
82%
86%
90%
74%
83%
77%
Absent
92%
95%
92%
90%
87%
96%
88%
91%
and producer’s accuracies ranged
between 74 and 96 percent, depending
on the scale of the imagery and whether
the map was edited (table 5). For the
edited map derived from the 1:4,000scale imagery, Feature Analyst correctly
identified Russian olive in 85 percent of
transect cells where it occurred, while
91 percent of cells where Feature
Analyst mapped Russian olive contained
Russian olive.
Overall, Feature Analyst located Russian
olive throughout the imagery with a
relatively high degree of accuracy.
However, some trees (especially smaller
trees) were occasionally not identified
by Feature Analyst. Also, the polygons
created by Feature Analyst often
underestimated the actual area occupied
by the particular tree or group of trees.
In other words, errors of omission were
usually greater than errors of
commission. This is illustrated by the
fact that within transects, the total area
of Russian olive observed in the field
was 15 or 25 percent higher than the
edited area mapped by Feature analyst
from the 1:4,000 and 1:12,000-scale
photography respectively.
Costs
The expenses involved with mapping an
invasive plant using remote sensing can
vary widely depending on the specific
objectives, imagery requirements, vendor
availability, location, availability of an
in-house analyst, the analyst’s level of
experience, and a variety of other factors.
In addition, the per-acre cost of aerial
photography is scale-dependent and
decreases with increasing acreage. Our
pilot study area was very small, yielding
a high cost per acre. Because of the
many variables that can affect the cost,
the economics and feasibility of any
proposed weed mapping project should
be carefully evaluated before selecting a
specific mapping method. In some cases,
remote sensing may prove to be the best
and most economical approach, while in
6 | RSAC-0087-RPT1
other cases it may not.
For reference, the two scales of scanned
photography for this project were
acquired at a cost of approximately
$5,100. Acquiring a single scale of
photography over a larger area would
decrease the relative cost (e.g., cost per
acre) of acquisition. It is estimated that
three full-time person weeks would be
required to processes the scanned
imagery, with an additional 1–2 person
weeks to complete an accuracy
assessment. At a cost of $300 per day,
processing would cost $4,500, with an
additional $3,000 for an accuracy
assessment. Using these estimates, the
total cost, including imagery,
processing, and accuracy assessment is
estimated at $12,600. The Fishlake
National Forest estimated that doing a
ground survey to map this same region
of Salina creek would cost between
$12,000 and $14,000.
Compared to field surveys, one benefit
of mapping Russian olive from aerial
photography is that the image
processing and analysis can be done
during winter months. This frees field
crews for other projects during summer
months and allows the bulk of the work
to be done during the winter when
personnel are less busy.
Conclusions
Russian olive within riparian areas of
semiarid regions of the Fishlake
National Forest proved to be a good
target for mapping using large-scale,
scanned, natural color aerial
photography. In particular, the silver/
gray foliage of Russian olive coupled
with its large size and clumped
distribution distinguished the tree from
its surroundings. Also, its preference for
moist riparian areas allowed us to
simplify the image processing by
excluding non-riparian areas from
further analysis.
Feature Analyst proved to be an
effective tool for mapping Russian olive.
Overall, the software was able to locate
Russian olive with a high degree of
accuracy; however, smaller trees were
sometimes missed and the size of trees
and groups of trees was frequently
underestimated. Training the classifier
on a subset of the imagery and applying
the model to the rest of the imagery in a
batch processing mode did not produce
reliable results. Therefore, the classifier
had to be retrained for each section of
imagery, which is a time-consuming
process.
References
Babbitt, B. 1998. Statement by Secretary of the
Interior on invasive alien species. Proceedings,
National Weed Symposium, BLM Weed Page. April
8-10.
Casady, G.M.; Hanley, R.S.; Seelan, S.K. 2005.
Detection of leafy spurge (Euphorbia esula) using
multidate high-resolution satellite imagery. Weed
Technology 19:462-467.
For additional information, contact:
Henry Lachowski
Remote Sensing Applications Center
2222 West 2300 South
Salt Lake City, UT 84119
phone: 801-975-3750
e-mail: hlachowski@fs.fed.us.
This publication can be downloaded from the
RSAC Web sites: http://fsweb.rsac.fs.fed.us
and http://www.fs.fed.us/eng/rsac
Dewey, S.A.; Jenkins, M.J.; Tonioli, R.C. 1995.
Wildfire suppression - a paradigm for noxious weed
management. Weed Technology 9:621-627.
Hunt, E.R. Jr.; Everitt, J.H.; Ritchie, J.C.; Moran, M.
S.; Booth, D.T.; Anderson, G.L.; Clark, P.E.;
Seyfried, M.S. 2003. Applications and research
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Photogrammetric Engineering and Remote Sensing
69:675-693.
Katz, G.L.; Shafroth, P.B. 2003. Biology, ecology
and management of Elaeagnus angustifolia L.
(Russian olive) in western North America. Wetlands
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Lass, L.W.; Prather, T.S.; Glenn, N.F.; Weber, K.T.;
Mundt, J.T.; Pettingill, J. 2005. A review of remote
sensing of invasive weeds and example of the
early detection of spotted knapweed (Centaurea
maculosa) and babysbreath (Gypsophila paniculata)
with a hyperspectral sensor. Weed Science
53:242-251.
Lillesand, T.M.; Kiefer, R.W. 1994. Remote
Sensing and Image Interpretation, 3rd ed. John
Wiley and Sons, Inc. New York, NY. 750 pp.
Maheu-Girouxa, M.; de Blois, S. 2005. Mapping the
invasive species Phragmites australis in linear
wetland corridors. Aquatic Biology 83:310-320.
Stannard, M.; Ogle, D.; Holzworth, L.; Scianna J.;
Sunleaf, E. 2002. History, biology, ecology,
suppression and revegetation of Russian-olive sites
(Elaeagnus angustifolia L.). Plant Materials
Technical Note No. 47. Boise, ID. U.S. Department
of Agriculture, Natural Resources Conservation
Service, 14 p.
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2004. National Strategy and Implementation Plan
for Invasive Species Management. Rep. FS-805.
Washington, DC: U.S. Department of Agriculture,
Forest Service. 17 p.
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