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Effects of Temporal Variability in Ground Data Collection on
Classification Accuracy
Greg A. Hocha; Jack F. Cully Jr.b
a
Division of Biology, Kansas State University, Manhattan, KS, U.S.A. b USGS - Biological Resources
Division, Kansas Cooperative Fish and Wildlife Research Unit, Division of Biology, Kansas State
University, Manhattan, KS, U.S.A.
To cite this Article Hoch, Greg A. and Cully Jr., Jack F.(1999) 'Effects of Temporal Variability in Ground Data Collection
on Classification Accuracy', Geocarto International, 14: 4, 7 — 14
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Effects of Temporal Variability in Ground Data Collection on
Classification Accuracy
Greg A. Hoch
Division of Biology, Kansas State University
Manhattan KS 66506, U.S.A.
Jack F. Cully, Jr.
USGS – Biological Resources Division
Kansas Cooperative Fish and Wildlife Research Unit
Division of Biology, Kansas State University
Manhattan KS 66506, U.S.A.
Downloaded By: [Kansas State University] At: 16:09 27 May 2011
Abstract
This research tested whether the timing of ground data collection can significantly impact the accuracy of land
cover classification. Ft. Riley Military Reservation, Kansas, USA was used to test this hypothesis. The U.S. Army’s
Land Condition Trend Analysis (LCTA) data annually collected at military bases was used to ground truth
disturbance patterns. Ground data collected over an entire growing season and data collected one year after the
imagery had a kappa statistic of 0.33. When using ground data from only within two weeks of image acquisition the
kappa statistic improved to 0.55. Potential sources of this discrepancy are identified. These data demonstrate that
there can be significant amounts of land cover change within a narrow time window on military reservations. To
accurately conduct land cover classification at military reservations, ground data need to be collected in as
narrow a window of time as possible and be closely synchronized with the date of the satellite imagery.
Introduction
The Department of Defense (DoD) is one of the largest
land stewards in the United States overseeing 10.4 million
ha of land. The DoD’s primary mission is to keep an
adequate defense force in readiness. To this end it must
maintain the quality of it’s training installations. Severe
degradation of it’s lands can lead to erosion hindering the
ability of the military to continue training on these lands.
The military also has a mission to preserve habitat and
protect threatened and endangered species (Diersing et al.
1992). Military training can have severe consequences on
both vegetation and animal abundance and distribution by
destroying individual plants or entire plant communities
(Severinghaus et al. 1979), destroying burrow systems,
compacting soils, and disrupting soil surfaces allowing
invasion of weedy species and erosion (Wilson 1988). The
prevention of severe land degradation and erosion is a top
priority for the military since training sites which are made
unusable by these processes ultimately affects military
preparedness.
The DoD oversees several installations within the Central
Plains region of the United States. Remote sensing should
provide an economical way of monitoring these installations
Geocarto International, Vol. 14, No. 4, December 1999
Published by Geocarto International Centre, G.P.O. Box 4122, Hong Kong.
through time. The military currently collects vegetation
and disturbance data along transects across many of it’s
installations as part of it’s Land Condition Trend Analysis
(LCTA) program (Tazik et al. 1992). The stated goals of
this program include describing plant communities,
documenting disturbance, estimating soil erosion potential,
and determining allowable use estimates for training
activities (Diersing et al. 1992). Remote sensing should
allow land managers to extrapolate the results of these plot
and transect level studies to an entire installation. With
repeat coverage every 16 days the Landsat series of satellites
allows land managers to make repeated surveys of an
installation throughout the growing season. More
importantly, the extensive archive of Landsat data dating
back to 1974 (Green and Sussman 1990) allows managers
to study year to year differences over the past two decades.
Satellite imagery could help identify the spatial extent
and patterns of disturbance, determine how these patterns
change through time, and allow land managers to identify
the frequency of disturbance to specific areas. These data
may be important for monitoring lands that may harbor
populations of threatened or endangered species. When
satellite derived data are overlaid with soil and slope
coverages, managers will be able to identify areas most
7
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susceptible to erosion which impacts both habitat and
sustainability of training activities.
The Flint Hills region of eastern Kansas has a rich
history of remote sensing work. However few grassland
classification studies have been done in this area. Lauver
and Whistler (1993) were able to discriminate between
grazed and ungrazed prairie with a 76-82% accuracy. Glenn
et al. (1994) were able to differentiate between dry, low
productivity uplands and moist, high productivity lowlands.
Briggs and Nellis (1989) predicted aboveground biomass at
the watershed level using NDVI at Konza Prairie Research
Natural Area (KPRNA). All classifications to date have
been based primarily on differences in herbaceous plant
biomass. Initial results of a study currently being conducted
on Ft. Riley show that undisturbed areas have seven times
the biomass of disturbed areas (Rubenstein 1998). Thus, Ft.
Riley should he amenable to landcover classification.
The objectives of this research are to 1) test whether data
that are annually collected at military installations can he
integrated with remote sensing imagery to accurately map
the extent of the disturbance, and 2) determine how or if the
timing of ground truth data collection affects the accuracy
level of a classification.
Flint Hills (Herbel and Anderson 1959). Fires may be
ignited during any month of the year by exploding ordnance,
expecially in the Impact Zone (Fig. 1).
Methods
Image Processing
Landsat Thematic Mapper (TM) imagery (path 28, row
33) for 30 July 1993 was used in this analysis. All image
processing and analysis were performed using ERDAS
Imagine v8.2 (ERDAS 1994). Bands 3, 4, 5, and 7 (red,
NIR, midIR, and midIR) were selected based on the results
of an Optimal Index Factor which was determined in a
previous study in Kansas (Price and Nellis 1994). The
image was georeferenced to UTM coordinates using 6
ground control points and a nearest-neighbor resampling
approach. Root mean square error was below 0.35. The
perimeter of the study areas was clipped from the
Study Area
Ft. Riley Military Reservation lies within the Flint Hills
region of eastern Kansas, the last expanse of tallgrass
prairie. The Flint Hills region is approximately 70 km wide
and stretches from near the Kansas-Nebraska border south
into Oklahoma, an area of roughly 50,000 km2 (Reichman
1987). Dominant prairie grasses include big bluestem
(Andropogon gerardii), Indiangrass (Sorgastrum nutans),
little bluestem (Schizachyrium scoparium), and switchgrass
(Panicum virgatum). All of these grasses are C4 perennial
species. The shallow, rocky soils and steep topography
deterred the row crop agriculture that is now present over
most of the historic range of the tallgrass prairie.
Ft. Riley is located in the northwest corner of the Flint
Hills region (39º15’N, 96º45’W). Ft. Riley was established
in 1853, expanded in 1943 and 1965, and currently
encompasses 40,470 ha and includes portions of Riley and
Geary Counties, KS (Fig 1). The landscape across the
undeveloped part of the reservation is primarily upland
prairie with woody or riparian corridors along the streams.
Some areas, mostly on the western, flatter portions of Ft.
Riley were planted to row crops or brome grasses before the
land was purchased by Ft. Riley. Some of these former
croplands are still planted as wildlife foodplots or as
firebreaks around the installation’s perimeter while others
are being replanted to native grass species.
Ft. Riley is used primarily for the training of mechanized
infantry units. In addition to its military role, the area has
other land uses which produce a variety of land covers.
Large areas are leased to local ranchers for haying in midto late-summer. Some areas are burned in the spring with
prescribed fires, a common management practice in the
8
Figure 1
Map of Ft. Riley Army Reservation, near Manhattan KS at the
northern end of the Flint Hills region. The Impact Zone and
MPRC are off limites at all times. The Danger Fan was closed
during field sampling. (Provided courtesy of H. Michaels and
J. Wiens)
16
14
# of LCTA transects
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surrounding area.
This study was designed specifically to analyze
grasslands. Grasslands cover approximately 91% of Ft.
Riley and are the land cover most impacted by military
training. The road network (bare ground) and riparian
corridors (woody vegetation) could potentially bias the
statistical results of these analyses. For this reason, an
unsupervised classification, ISODATA (Iterative SelfOrganizing Data Analysis Technique), (ERDAS 1994) was
used to create masks for these two cover types. The forest
mask was verified using LCTA (see later in methods) data.
The mask for the road network was confirmed using maps
of the area.
To classify the two land cover types, disturbed grasslands
and undisturbed grasslands, the four band dataset was
submitted to an ISODATA clustering algorithm to generate
10 initial classes. The raw band and classified image were
simultaneously examined and each of the 10 classes was
visually assigned to either the disturbed or undisturbed land
cover class.
Field Data Collection The U.S. Army’s Land
Condition Trend Analysis (LCTA) program was designed
to provide a standardized method for ecological data
collection, analysis, and reporting for all Army installations
by using vascular plant and wildlife inventories at permanent
field plots. One hundred twenty-nine permanent field
transects were established at Ft. Riley in 1989. The transects
are 100 m in length. Beginning at the 0.50 m point,
vegetation and disturbance data are sampled at 1 m intervals.
Further details on LCTA field methods can be found in
Tazik et al. (1992). In 1993, LCTA data were collected
from 23 May to 6 October at 74 of the 129 permanent
transects.
For this analysis, the number of disturbed points along
each transect of the 1993 data set were summed (Fig. 2).
The tails of the bimodal distribution were categorized as
12
10
8
6
4
2
0
0
20
40
60
80
100
% disturbance
Figure 2
Distribution of disturbance across all LCTA transects surveyed
in 1993. Disturbance is measured as percentage of 100 points
along a 100 m transect. Bars represent 5 percent increments of
disturbance.
disturbed and undisturbed. The middle values of the
histogram were held out for a posteriori categorization. Of
these transects, 67% were in the disturbed class when
mapped. A decision was made to categorize these middle
values as disturbed. Transects with greater than 45%
disturbance were categorized as disturbed. Transects with
less than 45% disturbance were categorized as undisturbed.
In September 1996, 80 locations were randomly generated
in Erdas Imagine across the study area. During the sampling
period only units south of the northern border of the Impact
Zone (Fig. 1 ) were open. Two observers were used for
most of the field work to avoid over or under estimating
plant cover and disturbance (Edwards et at., in press). Field
work was conducted during the last two weeks of September
to avoid conflicting with military training which is
concentrated in the summer months.
The coordinates for the random points were entered into
a Magellan Promark X GPS receiver. The receiver was
then used to navigate to each point. While estimating
cover, the receiver collected position data for at least three
minutes at each point. Data collection stopped if PDOP
(Position Dilution of Precision) readings were greater than
4.0. Positional data were post processed using Magellan’s
Mstar software to an accuracy of 3 to 10 m. At each point a
100 by 100 m area was estimated around the observers to
approximate the scale of the LCTA transect and to
approximate Gap Analysis Program (GAP) methodology
(Edwards et al. in press). Amount of disturbance was
quantified using modified Daubenmire cover classes
(Daubenmire 1949). Types of disturbance are based on the
two major types of vehicles used on Ft. Riley, wheeled
vehicles with rubber tires and tracked vehicles such as
tanks with metal treads. Type of disturbance was placed
into five categories: no disturbance, light wheeled, heavy
wheeled, light tracked, and heavy tracked disturbance.
Amount of disturbance was placed into 5 categories; 0-5, 625, 26-50, 51-75, and 76-100% disturbance. Initially points
that were at least 50% disturbed and had evidence of
tracked vehicles were categorized as disturbed. This
approximates the 45% cut-off defined in the 1993 LCTA
data set. Later analysis defined undisturbed points as 0-5%
of any disturbance type.
To identify the two cover classes, the 1993 LCTA
transects were overlaid onto the disturbance coverage (Fig.
3). The overall accuracy of the error matrix, user’s and
producer’s accuracies, and kappa statistic were calculated.
The overall accuracy is the sum of the diagonal of the
matrix divided by the sum of the values in the entire matrix.
The user’s accuracy is the probability that a pixel classified
on the map actually represents that class on the ground. The
producer’s accuracy is the probability of a reference pixel
being properly classified (Congalton 1991).
Because these data are categorical and not continuous,
standard statistical techniques will not work for comparing
matrices and testing for differences in accuracies of different
classification schemes. For classified data, discrete
multivariate techniques are appropriate (Congalton and
9
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N*Σxii - Σ(xi+*x+i)
Khat = ---------------------------------------N2 - Σ (xi+*x+i)
Figure 3
Disturbance coverage of Ft. Riley.
Oderwald 1983). These techniques do not assume
independence of data points, do not require data
transformations, and can incorporate information from the
entire data matrix. The most commonly used of these
measures is the Kappa statistic (Cohen 1960). The kappa
statistic is indicative of the improvement of a classification
over a random model.
The equation for the estimate of the Kappa statistic,
Khat is:
Table 1
where N is the number of points, xii is the number of
observations in row_i and column_i, and xi+ and x+i are
marginal totals for the row and column. The Kappa statistic
incorporates all cells in the matrix in it’s calculation
(Congalton 1991).
Error matrices for the class identification were generated
from four data sets (Table 1). The first data set used LCTA
data collected from across the 1993 growing season. The
second data set used only LCTA transects sampled within
two weeks of the date of image acquisition, 30 July 1993.
These two matrices could be compared to identify there
was significant change in land cover within the time period
of the field data collection which would reduce the accuracy
of the analysis. The third data set used LCTA data collected
from 15 July to 15 August 1994. Comparing this matrix to
the second matrix tested whether there were dramatic
changes in land cover from year to year. The last data set
was collected by the authors in September of 1996 to test
the feasibility of supplementing the LCTA data sets with
independently collected field data. For final presentation of
all images, ERDAS Clump and Eliminate were used to
remove any polygon less than 30 pixels (Price et al. 1996).
NDVI. Normalized Difference Vegetation Index
(NDVI) ((band 4 - band 3)/(band 4 + band 3)) has been used
by researchers to estimate biomass production and/or annual
aboveground net primary productivity (ANPP) in grassland
regions (Turner et al.1992, Paruelo et al. 1997). Vegetation
in the heavily disturbed areas on Fort Riley often has a
prostrate growth form (pers. obs). ANPP in 1995 and 1996
List of the error matrices for the classifications from 4 time periods. The main diagonal reports correctly classified transects/points. The offdiagonal reports errors. The right hand column reports user’s accuracy. The bottom row reports producer’s accuracy (see text). UND represents
undisturbed. DIS represents disturbed. Only LCTA transects sampled in 1993 were used in this analysis.
(1993) LCTA data collected 15 July - 15 August
Undisturbed
Disturbed
Producer’s Acc
Undisturbed
8
2
0.80
Disturbed
3
9
0.75
User’s accuracy
0.73
0.82
Overall Accuracy
0.77
Kappa
0.55
6
19
0.76
0.76
0.58
0.66
0.33
0
8
1.00
1.00
0.38
0.56
0.33
10
27
0.73
0.71
0.64
0.71
0.35
(1993) LCTA data collected 23 May - 6 October
Undisturbed
Disturbed
Producer’s Acc
19
14
0.58
(1994) LCTA data collected 15 July - 15 August
Undisturbed
Disturbed
Producer’s Acc
3
13
0.18
(1996) collected by authors 10 - 15 September
Undisturbed
Disturbed
Producer’s Acc
10
25
15
0.63
240
220
200
NDVI
180
160
140
120
100
80
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Figure 5
Figure 4
NDVI image of Ft. Riley Kansas. In this image brighter tones
represent greater biomass. Note the west central area has the
same dark tones as the road network indicating little vegetative
cover.
0
20
40
60
80
100
Regression of NDVI and disturbance along LCTA transects
sampled within 2 weeks of the imagery. As disturbance
increases the amount of vegetation decrease which is reflected
in the lower NDVI values.
forests (l0.l%), undisturbed grasslands (47.9%), and disturbed
grasslands (40.8%).
Discussion
averaged 448 and 315 g/m2 on undisturbed and disturbed
areas respectively (Rubenstein 1998). The image was
converted to NDVl (Fig. 4) and NDVI values at each
transect location were recorded. NDVI was then regressed
against the arcsine percent disturbance at each transect
(Fig. 5).
Results
The classification using the LCTA transects was able to
discriminate between disturbed grasslands and undisturbed
grasslands (Table 1). However, the timing of the ground
data collection significantly affected the results of the
analyses (Fig. 6). The analysis of the data set based on all
LCTA data collected in 1993 had an overall accuracy of
66% and a Khat of 0.33. When using data collected within
two weeks of the date of the satellite imagery, the overall
accuracy and Khat improved to 77% and 0.55 respectively.
Using the 1994 LCTA data set collected over the same 4
week interval, the overall accuracy and Khat are 56% and
0.33. Using the 1996 data set, the overall accuracy and Khat
were 52% and 0.11 respectively. If the criteria for this last
analysis are changed so that disturbance is defined as any
point with greater than 5% disturbance, the overall accuracy
and Khat improve to 71% and 0.35 respectively. The linear
regression of NDVI versus percent disturbance over all
transects had a slope that was significant (p = 0.001) but had
low predictive power (r2 = 0.0751). Again, if only transects
sampled within 2 weeks of the satellite image are analyzed,
the results improve dramatically (p=0.001, r2= 0.412) (Fig.
5).
Based on these results Ft Riley can be divided into four
general classes; roads and bare ground ( l.l%), riparian
The wet conditions of 1993 may have helped distinguish
the two disturbance cover classes. Annual net primary
productivity (ANPP) for burned lowlands at KPRNA in
1993 were the highest recorded since 1975 (Briggs and
Knapp 1995). The military traffic would have had a greater
impact on the vegetation and exposed more bare soil in the
spring and summer of 1993 than in a drier year (Wilson
1988). Thus, there may have been a greater contrast in plant
canopy cover and soil reflectance between disturbed and
undisturbed areas in 1993 than in other years with normal
precipitation.
In the error matrix for the 1993 data set using transects
from across the summer months (Table l), there are almost
twice as many points misidentified as undisturbed than as
disturbed. There are two possible explanations. First, these
points could represent areas with lightly impacted vegetation
from wheeled vehicles but were not disturbed enough to
remove the plant canopy. Second, over half of these
misclassified points from the 1993 data set were sampled
between the middle of May and the first week of June. It is
reasonable to hypothesize that these areas could have been
sampled as undisturbed early in the summer and were
subsequently disturbed prior to the date of the imagery, 30
July. The second hypothesis would explain the dramatic
improvement in the Khat when data collected early and late
in the growing season are removed from analysis.
Timing of field data collection is very important where
land cover (ie, disturbed or undisturbed) can change on a
weekly basis. This would be especially true if a major
exercise was conducted in the middle of the sampling
period. It would be difficult to compare field data collected
before and after the exercise. The data would include
11
Temporal Range of Ground-Truth Data
Versus Khat Statistic
0.6
0.5
Khat
0.4
0.3
0.2
0.1
0.0
Mar
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Figure 6
Time period of data collection versus classification accuracy.
Accuracy was highest when data are collected in a narrow
time window near the date of the imagery. Accuracy generated
from data from across the growing season or other years had
low classification accuracy.
‘untreated’ plots (before disturbance) and ‘treated’ plots
(after disturbance) and thus could not be combined. More
error would be introduced into the analysis if the satellite
image was from a third date, again due to cumulative
disturbance, ie. land cover change, between the field
sampling dates and date of the imagery. Field data should
be collected over as short a time period as possible to
minimize land cover change within the sampling period.
The analysis using the l 994 LCTA data demonstrates
that disturbance can change dramatically between years.
There is a correlation of 0.6l0 between 1993 and 1994 when
disturbance at all transects is measured. Significant amounts
of error are introduced into these analyses if ground data
are not collected during the same year as the imagery. Any
future studies at Ft. Riley or other military installations
should use field data from the same year as the imagery to
get a reasonable level of classification accuracy. This is
further demonstrated by the analysis using the data collected
in 1996 which had accuracies similar to the 1994 analysis.
Due to the small number of LCTA transects used in the
analysis, only 2 general land cover classes could be
identified, disturbed and undisturbed. However, it is clear
from raw band and NDVI imagery (Fig. 4) that there are
multiple levels of disturbance intensity on Ft. Riley.
Combining LCTA data with additional ground truth data
should allow land managers to identify different levels of
disturbance within this landscape.
As with the classification analysis, results of the NDVl
analysis from across the entire growing season were poor.
However when only those transects sampled within 2 weeks
of the imagery are considered the proportion of the variance
explained by the model improves significantly. These values
12
agree with the results of similar analyses done at KPRNA
(Turner et al. 1992).
NDVI is generally considered to be an indicator of
biomass. Thus the disturbed areas would be those with low
biomass. In the NDVI image (Fig. 4) the disturbed areas in
the west central part of the study area are the same dark
tone as the road network which we know to be bare ground.
In 1995 and 1996 disturbed LCTA transects had significantly
lower NPP, standing crop biomass, and graminoid biomass.
These transects also had seven times more bare ground than
undisturbed transects (Rubenstein 1998). Thus the
classification is probably discriminating between areas with
lower vegetation reflectance and/or a higher soil reflectance.
Ft. Riley is an extremely dynamic landscape where
detectable land cover change can happen over a time scale
of weeks. This research demonstrates that field data sampling
must be within the time constraints of land use dynamics.
The research clearly illustrates the errors involved in land
cover identification if ground truth data, collected in the
field or from aerial photos, are not simultaneous with the
satellite imagery.
The results and levels of accuracies obtained in this
study were typical of grassland remote sensing. Previous
classification studies (Glenn et al. 1993, Lauver and Whistler
1993) have been able to discriminate between two grassland
classes with the two classes differing primarily in biomass.
Levels of accuracy are also similar to these studies.
Conclusions
These results point to two factors critical to land cover
identification at Ft. Riley and presumably other military
installations. Data need to be collected over as short a time
as possible to eliminate land cover change within the
sampling period. Also, the year to year variability is great
enough that ground truth data from years other than the
year of the satellite imagery would be of limited utility
(Hoch 1998).
Disturbed and undisturbed are very general classes. These
data can not clearly identify exactly what the sensor is
detecting. Is the classification seeing bare ground/vegetation
mixed pixels or plant communities indicative of disturbance
or biomass? And to what extent are these and other variables
correlated? In this dynamic environment only real-time
ground data will answer these questions. The techniques
developed here might then be used to identify and quantity
various levels and types of disturbance.
For best results, LCTA data should be used in
combination with supplemental ground truth data. Both
ground truth and image data should be collected from the
end of the growing season. Since much of the training at Ft.
Riley occurs during the summer months this would minimize
the amount of disturbance occurring after satellite image
acqusition. It would also minimize the amount of land
cover change occurring within the sampling period. The
heaviest rains in this area occur in the fall and spring. By
analyzing land cover from late in the growing season
managers can identify the areas most susceptible to erosion
during these times. Using mid-summer imagery complicates
this problem as disturbance that occurs later in the summer
could be missed and areas with high erosion potential
would not be identified. Although there were complications
due to the long period of data collection across the summer,
LCTA data can be used to accurately ground truth
disturbance from military activity.
Acknowledgements
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We would like to thanks the Natural Resources Division
at Ft. Riley Army Reservation for funding this project.
Imagery was provided by the Konza Prairie Long-Term
Ecological Research program at Kansas State University.
Previous versions of this manuscript were reviewed by
John Briggs and Kevin Price. We would also like to thank
an anonymous reviewer for helpful suggestions.
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