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Grasslands Discriminant Analysis Using
Landsat TM Single and Multitemporal Data
Xulin Guo, Kevin P. Price, and James Stiles
Abstract
Grassland management practices influence many bio- and
geophysical processes. The ability to discriminate among
different land-use practices is critical to an improved understanding of agro-ecosystem dynamics in the tallgrass prairies
of the Central Great Plains. The overall objective of this study
was to assess the spectral separability of three land-use practices on warm-season (C4 dominated) and cool-season (C3
dominated) grasslands using data obtained from multitemporal Landsat Thematic Mapper (TM) imagery. Results showed
that cool- and warm-season grasslands could be discriminated
with a high level of accuracy (91.5 percent). When grasslands
were categorized by three common management practices
(Conservation Reserve Program [CRP], grazing and haying),
they could be discriminated with a moderately high level of
accuracy (70.4 percent). Grassland management practices
within warm- and cool-season grasslands (six types) were
spectrally discriminated with a moderate level of accuracy
(67.6 percent overall). The use of a three-date Landsat TM
image dataset spanning the spring-summer-fall seasons significantly improved classification accuracy over the use of a
single-date TM approach.
Introduction
Prairies of the central U.S. have been highly fragmented by
conversion of these lands to croplands and non-native grasslands (Sims, 1988). It is estimated that only 1 percent of all
native prairies still exist in the plains of North America
(Risser, 1988). Prairie species composition and biological
function are differentially altered by fragmentation and various land-use practices (Collins and Steinauer, 1998). Examples of prairie land-use practices include grazing by livestock,
haying, burning, and re-vegetation activities. The alteration of
prairie biophysical properties also influences surface hydrology, plant and animal diversity, biogeochemical fluxes, as
well as future land-use practices.
Although the biological and ecological responses of
prairies to fragmentation and land use continue to be investigated, the impacts of changing land-use practices on the
present agro-ecosystems of the Central Great Plains are far
X. Guo was with the Kansas Applied Remote Sensing
Program, University of Kansas, 2335 Irving Hill Road,
Lawrence, KS 66045; he is currently with the Department of
Geography, University of Saskatchewan, 9 Campus Drive,
Saskatoon, Saskatchewan, Canada S7N 5A5
([email protected]).
K.P. Price is with Kansas Applied Remote Sensing Program,
University of Kansas, 2335 Irving Hill Road, Lawrence,
KS 66045 ([email protected]).
J. Stiles is with the Radar Systems and Remote Sensing Lab,
University of Kansas, 2291 Irving Hill Dr, Lawrence, KS 66044
([email protected]).
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
from clear. In the prairie environments of Kansas, for example, it is not economically feasible to map the land use of
prairies using conventional field mapping and aerial photography interpretation techniques. Depending upon the time of
year of acquisition, the interpretation of land-use practices
(i.e., haying versus grazing of grasslands) using aerial photography is often not possible at an acceptable accuracy level.
For this reason, there has been considerable interest in developing cost-effective and repeatable methods for mapping
prairie and non-native grasslands and their associated land
use. From previous work (Price et al., 1992; Egbert et al.,
1995; Egbert et al., 1997) it has been discovered that many
prairie and non-native grassland types and land-use practices
are spectrally distinguishable, especially when measurements
are obtained at optimal times over the growing season.
In the past, research on the spectral differentiation and
characterization of tallgrass prairies was limited primarily to
studies of the 1987 NASA-sponsored First ISLSCP (International
Satellite Land Surface Climatology Program) Field Experiment
(FIFE) project conducted on the Konza Prairie near Manhattan,
Kansas (Sellers et al., 1990). During the FIFE investigation,
ground-level radiometer and satellite remotely sensed measurements were used to distinguish between bare soil, senescent vegetation, and green vegetation (Asrar et al., 1989); measure the effects of mowing and fertilization on tallgrass
productivity and spectral reflectance (Dyer et al., 1991; Turner
et al., 1992); examine radiation flux (Irons et al., 1988;
Dubayah et al., 1990); assess biophysical properties of tallgrass vegetation (Weiser et al., 1986); and study relationships
between canopy light interception and leaf area (Asrar et al.,
1986). As part of the FIFE project, Briggs and Nellis (1991) also
studied seasonal variation in prairie texture as measured by
the Systeme Pour L’Oberservation de la Terre (SPOT) satellite
to identify management differences among grazed and burned
prairie ecosystems.
Since the FIFE project, Briggs et al. (1997) used remotely
sensed data combined with abiotic factors to explore the spatial and temporal patterns of vegetation within the Flint Hills
of Kansas and Oklahoma. Frank et al. (1994) found a strong
relationship between forage dry matter accumulation and several vegetation indices. Lauver and Whistler (1993) developed
a hierarchical classification method to identify high quality
and low quality native grasslands using single-date Landsat
Thematic Mapper (TM) imagery. In a study conducted by
Goodin and Henebry (1997) at the Konza Prairie, C3C4 abundances were successfully estimated using temporal trajectories of vegetation indices.
Photogrammetric Engineering & Remote Sensing
Vol. 69, No. 11, November 2003, pp. 1255–1262.
0099-1112/03/6911–1255/$3.00/0
© 2003 American Society for Photogrammetry
and Remote Sensing
November 2003
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Objective
The overall objective of this study was to assess the spectral
separability of three management practices on warm- and
cool-season grasslands using data obtained from multitemporal Landsat TM imagery. From previous studies in Kansas
(Egbert et al., 1995; Price et al., 1997), we have learned that
multitemporal Landsat TM imagery can be used with a high
degree of accuracy (better than 95 percent) to discriminate between grassland and non-grassland areas. For this reason, this
research has focused on assessing the spectral discrimination
among typical grassland management practices. We had five
questions with respect to spectral separability among grassland treatments: (1) with what degree of accuracy can warmseason grasslands be discriminated from cool-season grasslands; (2) with what degree of accuracy can land-use practices
(Conservation Reserve Program [CRP], grazed, and hayed), irrespective of the seasonality of the grasslands, be spectrally
discriminated; (3) is a multitemporal classification approach
superior to a single-date approach for discriminating among
grassland management practices; (4) when a single-date
approach was used, what period of the growing season is best
for discriminating grassland management practices; and
(5) with what degree of accuracy can grassland management
practices be identified when seasonality and management
practices are jointly evaluated?
Study Area
The study area is Douglas County, Kansas (Figure 1), which
has a mid-continental temperate climate. The area receives an
average of 900 mm of precipitation per year with 70 percent
falling during the growing season (April through September).
The average annual temperature is 13°C with a mean low
monthly temperature of 2°C in January and a high of 26°C in
July. The average growing season (frost-free period) is 185
days (USDA, 1977).
Prior to early European settlement in the area, native
grasses were the dominant vegetation types with respect to
biomass production. In 1990, the native and non-native grasslands covered 41 percent of the county, which has a total area
of 122,766 ha (Whistler et al., 1995). The remaining land-cover
types in the county in their respective order of dominance include croplands, forestlands, and urban areas. The grasses in
the county can be classified as either native (warm-season,
dominated by C4 plants, or prairie) or non-native (cool-season,
dominated by C3 plants, or introduced, generally speaking).
(Hereafter, native grasses will be referred to as “warm-season”
and non-native grasses as “cool-season” grasses.) The domi-
nant warm-season grasses include big bluestem (Andropogon
gerardii Vitman.), little bluestem (Andropogon scoparius
Michx.), Indiangrass (Sorghastrum avenaceur Michx. Nash.),
and switchgrass (Panicum virgatum L.). Based on findings at
the Kansas Ecological Reserve, which is a few kilometers north
of the study area, the species composition of the native prairie
is approximately 75 percent forb (broadleaf herbs) and 25 percent grasses, but the grasses produce approximately 70 percent
of the biomass (Price et al., 1992; Dunham and Price, 1996).
The dominant cool-season grasses are smooth brome (Bromus
inermis Leyss.), tall fescue (Festuca arundinacia Schreb.),
Kentucky bluegrass (Poa pratensis L.), and orchard grass
(Dactylis glomerata L.). The cool-season grasses become most
photosynthetically active in the early spring and later fall periods when temperatures are cooler. Warm-season grasses have
adapted to the hot summer temperatures of the area and are
most photosynthetically active during the warmer summer
months (Weaver, 1954).
In this study, we stratified the grasslands into warm- and
cool-season types. We then categorized these two types by
land management practices which include haying, grazing,
and grasslands being managed under the U.S. Department of
Agriculture (USDA) CRP for a total of six land-use types.
The haying activity in the study area involves the cutting
of grass to let it hay or dry off while lying in the fields. The
haying of cool-season grasslands in Douglas County normally
takes place in mid- to late June. The haying of warm-season
grasses normally takes place in mid-July to mid-August,
but some individuals hay their grasslands even as late as
September. Cattle and some horses are the predominant
livestock grazers that are placed in grass pastures in Douglas
County. Cattle are normally placed in the pastures sometime
in May, and removed by mid- to late July. In years when late
summer soil moisture is sufficient to support a strong resurgence of cool-season grasses, livestock may be moved back
onto pastures in late summer.
The CRP was instituted under the 1985 U.S. Food Security
Act, and has as its primary objective, the reduction of soil erosion in areas where crops are grown on highly erodible soils.
In the study area, most CRP lands were reseeded to a native
grass mixture that includes big bluestem, little bluestem,
switchgrass, and Indiangrass. To a much lesser degree, some
CRP lands were planted to pure stands of smooth brome,
which is a common pasture grass that is planted throughout
the study area. Under normal circumstances, burning and
mowing for weed controls are the only land management
practices allowed on CRP lands.
Figure 1. Map of the State of Kansas with shaded area in the eastern central part of the state
showing the location of Douglas County where the study area is located.
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Methods
Field Data Collection
During late spring and summer of 1997, 73 field sites were
randomly selected throughout the grassland areas of Douglas
County. The field-sampling period was designed to coincide
with the July satellite overpass as closely as possible (approximately two weeks before and after the overflight date). At each
field site, a 90- by 90-m study plot was located in the center of
each field site. All field measurements were taken within the
study plot. Unfortunately, four sites were recently hayed and
no biophysical measurements could be collected. Estimates of
forb and grass cover (percent of area covered) were made for
69 sites using a line intercept technique described by Warren
and Olsen (1964). For this technique, two 50-m transects were
positioned at 90-degree angles running from corner to corner
within the study plot. Fifty point samples were taken at 1-m
intervals along both transects for a total of 100 samples. The
cover type based on grass, forb, and bareground was recorded
using a cross right above each point. Other biophysical parameters, including biomass and species richness, were also
measured at each plot. The detailed description of biophysical
measurement and analysis were discussed in another paper
(Guo et al., 2000). The geographic location of each study plot
was recorded using a Garmin Survey II Global Positioning
System (GPS) receiver to an accuracy of better than 15 meters.
Image Processing and Statistical Analysis
Three nearly cloud-free Landsat 5 TM images (path 27, row 33)
of the study area were acquired. The coverage dates of these
images were 15 May, 02 July, and 04 September 1997. The
thermal band was eliminated from each image for this study.
The digital numbers were converted to radiance and then reflectance using the method described by Markham and Barker
(1986). The reflectance values were adjusted for atmospheric
scatter using the Improved Dark Object Subtraction technique
described by Chavez (1988). The images were georegistered to
the Universal Transverse Mercator (UTM) projection. The
geometric transformation equation was computed using 38
ground control points that produced a final RMS error of better
than 0.35 pixels (better than 10.5 m). The spectral values for
each pixel were interpolated using a nearest-neighbor resampling approach, and the data were output at a 30- by 30-m
pixel size. Three images were stacked together resulting in an
18-band dataset (six bands for each date).
The geographic locations for the 73 study plots were overlaid onto the 18-band dataset using ERDAS IMAGINE v8.3.
The locations of the plots in the image were also guided by
visual cues of field boundaries as well as surrounding features
such as roads, streams, and buildings. At each location, the reflectance values of the nine closest pixels (3- by 3-pixel area)
were used to estimate the spectral mean and variance associated with each plot. The averaged red and near-infrared reflectance values of the nine pixels were also used to calculate
the Normalized Difference Vegetation Index (NDVI) for each
plot. The NDVI was calculated using the standard equation:
[(TM4 TM3)(TM4 TM3)], where TM4 is the reflectance in TM
band 4, and TM3 is the reflectance in TM band 3. The coefficient of variation (CV) for reflectance values was calculated by
dividing the standard deviation by the mean and multiplying
by 100. Due to some isolated cumulus clouds in the May and
September images, the vegetation reflectance characteristics
for one site in May and one in September could not be
derived. Therefore, some of the analyses were performed
using data from only 71 sites.
All the statistical analyses were performed using the
SPSS v7.0 software package. Multiple Analysis of Variance
(MANOVA) tests were used to determine whether spectrally significant differences existed among different grassland classes.
Canonical Discriminant Analysis was used to test whether the
cool- and warm-season grasslands could be discriminated and
whether the different management treatments could be discriminated, and for the testing of discrimination with the
image dates. The calculation of users and producers accuracies from the error matrix was presented in a table for the six
grassland types. Discriminant analysis is used for classifying
subjects into groups on the basis of a battery of measurement,
so it was selected for this study. In discriminant analysis, the
major differences among groups are revealed through the use
of uncorrelated linear combinations of original variables, i.e.,
the discriminant functions, which can be used to determine
which variables contribute most to the function according to
the standardized coefficients and variable correlations. We
tested classification accuracy using a Jack-Knife Cross Validation (JCV) approach (Olden and Jackson, 2000). This approach
was implemented by withholding the spectral data for one
site, and building the discriminant functions using the data
from the remaining 70 sites. The users and producers classification accuracies were evaluated using the methods described
by Congalton and Green (1998). Measurements of producer
error indicate the chances of underestimating (errors of omission) a particular cover class, while measurements of user
error indicate the chance of overestimating (errors of commission) a particular class. The Khat statistic is used to estimate (kappa), which is a “measure of the difference between the observed agreement between two maps (as reported by the diagonal entries in the error matrix) and the agreement that might
be attained solely by chance matching of the two maps”
(Campbell, 1996).
Results and Discussion
Biophysical and Spectral Characteristics
Summary statistics for the field data show that grass and forb
cover among the six treatments varied from a high of 87.9 percent for warm-season hayed to a low of 72.3 percent for coolseason CRP (Table 1). The total plant cover averaged 84.5 percent for the warm-season, and 74.3 percent for the cool-season
sites. The mean NDVI was 0.67 for both warm-season and coolseason sites. It is interesting to note that the two highest NDVI
TABLE 1. MEAN PERCENT COVER AND MEAN NDVI VALUES CALCULATED FROM THE REPLICATE SAMPLE PLOTS FOR THE SIX GRASSLAND TYPES
Vegetation Cover (%)
NDVI
Land-Use Types
(Number of replicates)
Grass
Forb
Grass Forb
May
July
September
Mean NDVI
Warm-Season Grazed (13)
Warm-Season Hayed (12)
Warm-Season CRP (12)
Cool-Season Grazed (12)
Cool-Season Hayed (9)
Cool-Season CRP (11)
Mean
55.5
64.9
70.7
55.4
64.8
65.2
62.8
26.8
23.0
12.5
20.4
9.9
7.1
16.6
82.3
87.9
83.2
75.8
74.8
72.3
79.4
0.60
0.59
0.39
0.69
0.72
0.61
0.60
0.81
0.82
0.72
0.77
0.61
0.63
0.73
0.72
0.68
0.67
0.74
0.67
0.62
0.68
0.71
0.70
0.59
0.73
0.67
0.62
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TABLE 2. VALUES OF MANOVA TEST FOR GRASSLAND TYPES, MANAGEMENT
PRACTICES, AND THE SIX GRASSLAND TYPES FOR 18 BANDS. NUMBERS IN BOLD
SHOW THAT THE SPECTRAL DIFFERENCE IS SIGNIFICANT AT THE 0.05 LEVEL
Date
Bands
Grasses
Practices
Six Types
May
Band 1
Band 2
Band 3
Band 4
Band 5
Band 7
Band 1
Band 2
Band 3
Band 4
Band 5
Band 7
Band 1
Band 2
Band 3
Band 4
Band 5
Band 7
0.002
0.006
0.000
0.000
0.000
0.000
0.126
0.578
0.011
0.000
0.016
0.117
0.428
0.322
0.950
0.430
0.000
0.078
0.000
0.000
0.000
0.007
0.002
0.001
0.039
0.107
0.031
0.000
0.594
0.060
0.466
0.089
0.195
0.000
0.368
0.737
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.056
0.000
0.000
0.011
0.009
0.747
0.323
0.535
0.000
0.001
0.506
July
September
Figure 2. Spectral reflectance with standard error bars for
the six TM bands of three TM image acquisition dates within
the study area categorized by cool- and warm-season
grassland types.
values were calculated for the cool- and warm-season grazed
sites, respectively. It is also worth noting that the highest NDVI
values were not associated with the sites with the highest
total plant cover, but rather with those that had the highest
percentage of forb cover (Table 1) because broad leaf plants
tend to have a higher near-infrared (NIR) reflectance than do
grasses (Norman et al., 1985).
Figure 2 shows the multitemporal spectral patterns for
the cool- and warm-season grasses at our field sites. This
graph illustrates that in May and July these two grassland
types are most spectrally distinct in TM bands 4, 5, and 7 (NIR
and the two middle-infrared (MIR) bands). These grassland
types are nearly spectrally identical in September; however,
there is some spectral separability in bands 5 and 7. The May
response curve for the cool-season grasses also shows lower
visible and higher NIR reflectance than the warm-season
grasses, and the opposite is true in July, which indicates the
cool-season grasses were more photosynthetically active than
were the warm-season grasses in May, but less active in July.
The TM bands 5 and 7, which are strongly influenced by plant
moisture, indicate greater vegetation moisture on the coolseason sites in May, and greater vegetation moisture on the
warm-season sites in July. MANOVA test result confirmed the
visual comparison; the spectral difference between cool- and
warm-season grasslands was significant on band 5 in all of
these three images, and it was significant for all bands in May
and red and NIR bands in July (Table 2).
Figure 3 shows spectral differences by treatments for CRP,
grazed, and hayed sites. This graph shows spectral differences
among treatments with the greatest spectral separability, again
in the NIR and MIR regions of the spectrum. In Table 2, the
MANOVA test shows that all bands in May were good for separating management practices, plus NIR bands in July and
September. However, the Tukey’s Post-Hoc test (Sokal and
Rohlf, 1995) showed no significant differences existing between grazed and hayed sites for most bands except for NIR in
July. The differences between CRP and grazed sites as well as
between CRP and hayed sites for most bands were significant.
Figure 3 also shows that the CRP sites were consistently higher
in the visible, and lower in the NIR, meaning that there was
less green plant material on the site as compared to the
grazed and hayed sites. CRP vegetation moisture condition
was lower in May and approximately the same as the other
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November 2003
two treatments in July and September. Interestingly, the reflectance patterns showed more photosynthetically active
vegetation in grazed sites than in the other two treatments.
This conclusion is corroborated by our field measurements
(Table 1). One long-term effect of grazing of Douglas County
grasslands is a change in plant species composition. Fields
that were grazed heavily the previous year will have less
standing dead plant material the following year. The standing
dead material can cover the live vegetation, which results in
spectral response curves that indicate less vegetation on a site.
In our study area, grazing frequently resulted in the increase
of less palatable forb species that usually absorb more visible
and reflect more NIR energy. Here we see that grazing has altered plant species composition, which in turn resulted in
less standing dead plant material, and more broadleaf plants.
Classical clipping studies have shown that simulated grazing
increases total forage production by promoting tillering. Many
wheat growers in the Northern Great Plains also have their
wheat fields grazed to promote tillering and increase yields.
Figure 3. Spectral reflectance with standard error bars for
the six TM bands of three TM image acquisition dates within
the study area categorized by CRP, grazing, and haying—
three common land-management practices.
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Figure 4. Spectral reflectance with standard error bars for the six TM bands of
three TM image acquisition dates within the study area categorized by the six
grassland types.
The spectral response pattern associated with hayed sites
was usually positioned on the spectral response graphs between the CRP and grazed spectral response curves. In May,
the hayed and grazed sites were the most spectrally similar.
This may be explained in that the sites were not hayed until
later in the year, and thus grazing has had little impact on the
sites at this time. In July, the CRP and hayed sites were spectrally similar, and by September all three treatments were
spectrally distinct only in the NIR band.
Spectral response patterns of six grassland types (Figure 4) show the spectral differences for CRP, grazing, and
haying management practices on cool- and warm-season
grasslands. These patterns indicated that NIR bands from
images of May, July, and September were the most effective
bands for separating grassland types. There were considerable
differences between treatments in bands 5 and 7 as well as
band 4 in May and July. MANOVA test showed that all bands in
May, all bands in July but band 2, and bands 4 and 5 in
September had significant differences for the six grassland
types (Table 2). Tukey’s Post Hoc test revealed that warmseason CRP type was the one with significant differences
from other types.
Figure 5 shows the CV plotted by image date, TM bands,
and the three grassland management treatments. There were
considerable variations between all bands except band 4 in
July and moderate variations in all bands in May and September. This suggested that bands 4 and 5 were the best for discrimination because they showed the least amount of variation and the greatest spectral difference.
The CV values for CRP and grazed treatments were similar
over the three image dates, with CRP values generally a little
higher than grazed. The CV values for the hayed treatment
however were generally lower than the other two treatments
in May, but in July the hayed CV values were at least twice as
high as those in CRP and grazed. By September, the hayed CV
values decreased to about the same as the numbers in the
other two treatments. The higher variation for hayed sites in
July can be explained by the various conditions of the hayed
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
field at the time of the July satellite overflight. Fields that
were hayed only a few days before the overflight would be
mostly devoid of vegetation, and exposed soils would be very
dry, because climatological records showed no measurable
precipitation at least one week prior to the satellite overflight
date. Sites that had not been hayed at the time of the overflight would have more moisture stored in the live plant tissue, and the shaded soils would be less dry than sites that had
already been hayed.
The high spectral variation of the hayed sites in July suggests that satellite spectral measurements were sensitive to
Figure 5. The coefficients of variation of spectral reflectance for three land-management practices show the
hayed treatment in July to be much more spectrally
variable in bands 1, 3, and 7 as compared to May and
September.
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TABLE 3. A LIST OF ACCURACY ESTIMATES DERIVED USING THE JCV APPROACHES SHOWING BOTH CORRECTLY CLASSIFIED SITES NUMBERS AND THE ASSOCIATED
PERCENTAGE. THIS TABLE ILLUSTRATES THAT A MULTITEMPORAL CLASSIFICATION APPROACH IS SUPERIOR TO A SINGLE-DATE APPROACH
Image Dates
May
July
Sept
May-Jul
May-Sept
Jul-Sept
May-Jul-Sept
Classification
Accuracy (%)
45.8
57.5
54.2
56.9
62.0
63.9
67.6
within-treatment variation and that inferences about the status of hayed treatments were possible. The problem, however,
is that due to the high variation within the hayed treatment,
these sites were also the most difficult to discriminate from
the other land-use treatments.
Discriminant Analysis and Classification Accuracy
Due to seasonal time constraints associated with collecting
the field data, we were only able to visit 73 sites, or approximately 12 sites per grassland management practice. With this
small number of sample sites per treatment, we faced the difficulty of having an adequate sample size for developing our
classification algorithms. For this reason, a subset of the data
could not be used to validate classification accuracy. We
addressed this problem by using the JCV approach described
previously. According to a study conducted by Olden and
Jackson (2002), the JCV method was generally unbiased
compared to resubstitution and data-splitting techniques.
We first tested the ability to spectrally distinguish between cool- and warm-season grasslands. We developed our
classification algorithm using all three dates of Landsat imagery and discriminant analysis. Results from the JCV approach showed that the Canonical Discriminant Functions
were able to separate cool- from warm-season grasslands at
91.5 percent accuracy.
Next, we tested the ability to spectrally discriminate
among the three grassland management practices (CRP, grazed,
and hayed). Again, three dates of imagery and discriminant
analysis were used to develop our classification algorithms.
The JCV approach results indicated that we could discriminate
among the three types with 70.4 percent accuracy.
Table 3 lists classification accuracy results based on
various date combinations of the Landsat imagery. From these
results, it is clear that a multitemporal classification approach
improved spectral discrimination among the six grassland
management practices. The JCV results also showed that a twodate classification approach was 6.4 to 18.1 percent more accurate than a single-data approach, and a three-date approach
was 3.7 to 5.6 percent more accurate than a two-date approach.
When a single-date approach was used, July was found to be
the best date for discriminating the six treatments.
Table 4 lists the JCV classification accuracy results by six
types. The classification algorithm was developed using all
three Landsat images. The Canonical Discriminant Functions
were successful in identifying 48 out of 71 sites for a total of
67.6 percent overall accuracy. KAPPA analysis yielded a Khat
statistic of 61.1 percent (Congalton, 1991). The greatest class
confusion occurred between warm-season hayed, and warmseason grazed, and between cool-season grazed and warmseason grazed. CRP sites were most accurately classified with
user and producer accuracies ranging from 75 to 100 percent.
By investigating the relative contribution of the original bands
to the canonical functions, it confirmed the result from the
MANOVA test. The three significant contributors to the canonical function one were bands 4 and 5 in May and band 5 in
September. The two significant original bands contributing to
function two were band 4 in both July and September.
There are many factors that affect our ability to spectrally
distinguish among grassland management practices. For example, grazing intensity, timing, duration, and frequency all
affect spectral characteristics of grassland. The timing of haying is also a factor that increases spectral variability within
the hayed treatment. For example, site 21 is a cool-season
grazed site that was misclassified as warm-season grazed,
probably because it was mowed, which rejuvenated the coolseason grasses, causing them to appear spectrally similar to
TABLE 4. AN ERROR MATRIX SHOWING THE CLASSIFICATION RESULTS BY SIX GRASSLAND TYPES WITH BOTH CORRECTLY CLASSIFIED SITE
NUMBERS AND PERCENT OF SITES
Predicted Group Membership
Grassland
Types
CC
CG
CH
WC
WG
WH
TOTAL
Producer’s
Accuracy
(omission)
CC
CG
CH
WC
WG
WH
Total
10
90.9%
0
0
0
0
0
0
0
0
0
0
10
100%
0
0
8
66.7%
1
8.3%
0
0
3
23.1%
1
8.3%
13
61.5%
1
9.1%
0
0
10
83.3%
0
0
0
0
0
0
11
90.9%
0
0
0
0
0
0
9
81.8%
1
7.7
2
16.7%
12
75%
0
0
4
33.3%
1
8.3%
1
9.1%
5
38.5%
3
25.0%
14
35.7%
0
0
0
0
0
0
1
9.1%
4
30.8%
6
50%
11
54.5%
11
100
12
100
12
100
11
100
13
100
12
100
48/71
User’s Accuracy
(commission)
90.9%
66.7%
83.3%
81.8%
38.5%
50.0%
67.6%
Note: CC Cool/CRP; CG Cool/Grazed; CH Cool/Hayed; WC Warm/CRP; WG Warm/Grazed; WH Warm/Hayed.
1260
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02-001.qxd 10/8/03 12:19 PM Page 1261
the native grass sites in July. Site 42 is a warm-season CRP site,
which was reseeded with warm-season grasses three years
prior to our study. We believe this site was misclassified as
warm-season hayed because the newly established grasses
shared similar biophysical properties with hayed lands. Site
67 is a warm-season hayed site that was not hayed until sometime in August. This site was misclassified as a warm-season
grazed site. Site 65 is a warm-season hayed site that was
mowed only a few days prior to the 02 July satellite overflight.
This site was misclassified as a warm-season CRP site. Grasses
on warm-season CRP sites typically start their most active
growth in early July. Standing dead litter that is left over from
the previous year often heavily covers them. Therefore, a
newly hayed site with more exposed soil could have spectral
characteristics similar to many warm-season CRP sites, because bare soil and dry plant litter are spectrally similar. After
removing the anomalies in the data set as described above
and rerunning the discriminant analysis, the JCV discriminating accuracy for six grassland types was improved to
71.6 percent.
Conclusions
In our study, cool- and warm-season grasslands are seasonally
spectrally distinct and can therefore be discriminated with a
high level of accuracy (91.5 percent). Three common grassland management practices (CRP, grazing, and haying) can also
be spectrally discriminated with a moderate high level of accuracy (70.4 percent) at our site. Grassland management practices within cool- and warm-season grasslands can be spectrally discriminated at a moderate accuracy level (67.6 percent
overall). Accuracy levels varied depending upon the grassland
treatment. CRP sites were most easily discriminated, and
hayed sites were most frequently misclassified. The use of a
three-date Landsat image dataset significantly improved classification accuracy over use of a single-date approach. When
single-date classification results were compared, July was
found to be the best for discriminating grassland types. However, the results of the study are site-specific and resolutiondependent. They may not work elsewhere. Studies at different
sampling resolutions may give very different results.
Acknowledgments
This study was supported through the Kansas NASA EPSCoR
Project and the Kansas Applied Remote Sensing (KARS) Program and Department of Geography, University of Kansas.
Additional support was provided by the NASA Earth Science
Enterprise, Earth Science Applications Research Program
(ESARP). This work was being conducted in cooperation with
the Department of Geography at Kansas State University and
the Earth Science Department at Emporia State University.
Authors thank the anonymous reviewers for their valuable
comments and suggestions.
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