United States
Environmental Protection
Agency
Environmental Research
Laboratory
Duluth MN 55804
Research and Development
EPA/600/S3-85/034 July 1985
&ERA Project Summary
\ A .'.
Aerial Photography and Ground
Verification at Power Plant
Sites: Wisconsin Power Plant
Impact Study
Sarah L Wynn and Ralph W. Kiefer
This study demonstrated and evalu-
ated nine methods for monitoring the
deterioration of a large wetland on the
site of a newly-constructed coal-fired
power plant in Columbia County, Wis-
consin. Four of the nine methods used
data from ground sampling; two were
remote sensing methods without
ground verification; and three were
remote sensing methods which either
used ground verification or relied on the
analyst's "on-the-ground" knowledge
of the area.
These methods were evaluated on
the basis of whether they monitor
change at a species or a community
level, whether they monitor community
change in terms of area or location or
both, and whether they provide infor-
mation about trends in plant communi-
ties. They were also evaluated in terms
of time, cost, sensitivity, and reliability.
Changes in the wetland over a three-
year period are presented, as deter-
mined by each of the methods. Eight
appendices provide information and
raw data for several of the methods,
color/texture keys for interpreting air
photos, and an annotated bibliography
on remote sensing methods.
This Project Summary was devel-
oped by EPA's Environmental
Research Laboratory, Duluth, MN, to
announce key findings of the research
project that is fully documented in a
separate report of the same title (see
Project Report ordering information at
back).
Background
The full report describes part of a large
study documenting the environmental
impact of construction and operation of a
1050 MW coal-fired power plant in south
central Wisconsin. A major goal of the
study was to develop new, less expensive
and more effective methods for predict-
ing and measuring environmental
change.
This subproject had a dual purpose:
1. To document the nature and
extent of changes in vegetation by
a variety of ground-based and
remote sensing methods, and
2. To compare and evaluate the
methods on the basis of their effi-
ciency, sensitivity, and reliability.
Before construction, the site was an
extensive marsh/sedge meadow with
areas of floodplain forest and a few low,
semi-wooded knolls. The marsh included
small expanses of open water with emer-
gent vegetation, and pockets of shrub
carr and alder thicket. The soil was a peat
mat overlying sand. Construction and
operation of the power plant in this set-
ting resulted in the elimination of sea-
sonal fluctuations in water level, an
increased flow of ground water, and
year-round thermal loading of the ground
water. All plant communities responded
to these changed conditions.
Methods
Changes were monitored by nine dif-
ferent methods representing both tradi-
tional and new approaches. Four were
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ground based; five involved remote sens-
ing with or without ground verification.
Together, they provided information at
many levels. The evaluation of the results
and methods considered which methods
can best:
1. Detect changes at the species
level with time.
2. Detect changes at the community
level in the area and location of
each type of plant community.
3. Document trends in changes in
vegetation.
Ground Sampling Methods
The study site was an area of 33.5 ha.
marked off into transects at 50 m inter-
vals with sampling stations every 50 m
along these transects. Data were col-
lected each summer and fall, from 1974
through 1977.
Diversity Index
The diversity index gives information
about changes in the total number of spe-
cies. The number of species present at
each sampling station was counted, and
these values were summed for each year
of the study. The index was calculated as
the annual percentage gain or loss in
species or as a change in relation to some
base year.
Subjective Classification
This method classifies vegetation by
type of community. Twelve classes of
vegetation were defined empirically,
according to the most prominent species
in each one. (Classes identified in this
way and by the seven other ground-
based and remote sensing methods are
shown in Table 1.) Stem counts were
then made of each species found at a
sampling station, and on this basis the
station was assigned to the appropriate
class. For maximum reliability, the sub-
jective classification method requires
that the analyst adhere strictly to the
criteria established for each class.
Table 1 . Classes of Vegetation Identified by Eight Ground-Based and Remote Sensing
Methods
Vegetation class
Carex stricta
Degraded C. stricta
C. lacustris
Degraded C. lacustris
Transition
Degraded transition
Emergents
Degraded emergents
Spiraea alba
Shrubs
Open water
Open-emergents
Weedy annuals
Transition-emergents
Sedges and grasses
Grasslike
Tall-coarse
Grasslike-tall
Disturbed vegetation
Undisturbed vegetation
Degraded sedges
Shrubs and trees
Typha latifolia
Scirpus fluviatilis
Floating mat
Lemna minor
Trees
Spiraea/sedges
Spiraea/shrubs
* Not all of these classes
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classes
Association Analysis
Association analysis is a type of cluster
analysis which groups stations according
to their similarities in species composi-
tion. Thirty-four common, visually domi-
nant species were selected as attributes
in identifying clusters. Each station was
rated + or - for the presence or absence of
each attribute. A computer program then
divided the stations into two clusters of
maximum dissimilarity. Division was
based on presence or absence of the sin-
gle species which created the greatest
dissimilarity in species composition of
merged with others and not included as distinct classes in the final analysis.
the resulting clusters, using sum chi
square as a dissimiliarity coefficient.
Subdivision into further clusters con-
tinued on this basis until the desired
number of clusters had been created. The
result is a hierarchial structure that can
be displayed as a dendrogram. The clus-
ters created by association analysis were
named to correspond as well as possible
with other classifications of vegetation
used in this study (Table 1).
Vegetation Structure Analysis
Another way to monitor changes in the
study area with time is on the basis of
changes in gross vegetation structure.
Five categories of structure were defined
(Table 1). Each station was assigned to
one of these according to the results of
ground sampling, year by year.
Remote Sensing Methods
For the five remote sensing methods,
airphotos were interpreted or analyzed in
a variety of ways. The photos were taken
with both color and color infrared film, at
scales of from 1:11,500 to 1:120,000.
Color infrared film gave the best, most
-------
discriminating results; the scales that
permitted the most satisfactory interpre-
tations were 1:19,000 and 1:38,000. Air-
photo data were collected monthly during
the growing season and several times
during the rest of the year.
Airphoto Monitoring
Airphotos can be used without ground
verification to monitor sites which are
inaccessible from the ground. This
method may provide a high degree of
detail, but alone it does not map or quan-
tify information. It gives a simple photo-
graphic record of change, accompanied
by any interpretations the analyst is able
to make. Six types of features could read-
ily be interpreted from airphotos, and
with some ground verification, two addi-
tional classes of vegetation could be
identified (Table 1).
Airphoto Interpreted Disturbance
Maps
Disturbance mapping identifies only
three types of area: undisturbed vegeta-
tion, disturbed vegetation, and open
water. Disturbed areas represent
degraded vegetation resulting from ero-
sion of the peat mat. They are easy to
recognize on airphotos as changes in
appearance from earlier photos.
Maps were made by tracing the boun-
daries of the three types of area on mylar
overlying enlarged airphotos. No ground
verification is needed for this method.
The extent of change is easy to quantify,
for example, by use of a planimeter.
Methods Combining Airphotos
and Ground Sampling Data
Airphoto Interpreted Vegetation
Maps
Vegetation classification maps were
drawn in the same way as the distur-
bance maps, with mylar overlays show-
ing the locations of transects and
sampling stations. Patterns seen on the
photos were traced and labeled by refer-
ring to the ground-based data and classi-
fication for each station. Classes
recognizable on the airphotos did not
always correspond exactly to ground-
based classifications, and some classes
identified on the ground or in individual
airphotos had to be merged so that com-
parisons could be made among the air-
photo interpreted vegetation maps (Table
1).
A color and texture key was assembled
for each airphoto to facilitate consistent
mapping of the various communities. The
, total area of each community was deter-
mined as for the disturbance maps, and
expressed as a percentage of the study
area. One problem with this kind of map-
ping is that communities sometimes
grade into one another, and the boundar-
ies between them do not appear distinct.
This detracts from the precision of the
method.
Airphoto Grid Analysis
In. airphoto grid analysis, airphotos
were overlaid with grids to scale ( a cell
representing 50 m2 on the ground), with
the locations of ground stations and tran-
sectS also shown. Classes of vegetation
were identified (Table 1) and a color/tex-
ture key was assembled as in airphoto
interpreted vegetation mapping. The dif-
ferences between the two methods is
that, in grid analysis, assigning vegeta-
tion classes and determining the percen-
tage of cover of each class are done
simultaneously on a cell by cell basis.
Results are presented as percentage
cover or change in percentage cover for
each class of vegetation, when the
method is used to monitor change over
time.
Computer Assisted Vegetation
Mapping
Color and color infrared photos were
scanned with a densitometer using three
different filters to obtain analyses of color
bands in the red, green, and blue spectral
regions. The size of the scanning unit
(pixel) corresponded to a spot size of 1.9
m (3.6 m2) on the ground. Continuous
output from the photomultiplier tube of
the densitometer was converted to
integer values, pixel by pixel. Through a
series of computer programs, these data
underwent various transformations and
corrections. Sets of data were then
selected for "training" the computer to
distinguish among different classes of
vegetation. This part of the analysis
requires a high degree of interaction
between the analyst and the computer.
After the limits of each class had been
defined and the probability distribution of
data values within each class had been
determined, the spectral response signa-
ture of each pixel was compared to the
spectral response signatures of the final
training sets and classified for the best fit.
Three models for classification were
used in this study. They involved different
amounts of time and expense and gave
somewhat different results.
This method offers consistent classifi-
cation of vegetation and quantification of
the area of each class, once the criteria
for classification have been established.
Classes are named by reference to
ground-based data (Table 1). Results can
be displayed with computer-printed
maps or color photo maps.
Results
The combined results from all methods
give information at several levels. The
method of choice for any study depends
on what kind of information is desired.
Changes at the Species Level
Of the methods employed in this study,
only the diversity index provides direct
information on changes occurring in the
number of species in the area. However,
the diversity index gives no information
at the community level and cannot be
used to locate or quantify areas of
change. Furthermore, the method is use-
ful only if there is a definite trend toward
net gain or loss of species. If some spe-
cies disappear from parts of the study
area while others invade or increase, the
diversity index may show no change. This
particular study revealed a sharp
decrease in diversity, as summarized in
Table 2.
Changes at the Community
Level
All methods other than the diversity
index give some information on changes
in the area or the location of plant com-
munities or both. Ground sampling
methods show point locations of com-
munities and quantify changes in the rel-
ative percentage of stations in each class.
Airphoto methods, whether quantitative
or purely descriptive, are better able to
show the area and location of plant com-
munities than ground sampling methods.
Ground Sampling Methods
Both subjective classification and
association analysis demonstrated
changes with time in the number of sta-
tions assigned to each vegetation class
(Table 3). These changes could be corre-
lated with changes in water temperature,
volume of flow, and erosion of the peat
mat. The results show a successional
trend toward deeper water species. Both
methods indicate that Carex lacustris,
transition, and emergent communities
are more fragile than C. stricta and
shrubby communities.
Vegetation structure analysis revealed
that the grasslike and grasslike tall
classes were most sensitive to impact. Of
the 29 stations classified as grasslike in
1974, 21 were reclassified in 1977, and
only one of twelve stations that were
grasslike tall in 1974 retained this classi-
-------
Table 2. Diversity Index, 1974-1977
Number of species (sum
Year of numbers at 62 stations)
1974
1975
1976
1977
379
357
296
266
Change (number of
species/ year)
-22
-61
-30
Diversity index*
(% change/year)
-5.8
-17.1
-10.1
*A positive index number represents an overall gain in number of species; a negative number
indicates a loss.
fication three years later. The shift
toward a tall coarse and open water vege-
tation structure occurred throughout the
study area.
Remote Sensing Methods
The four remote sensing methods cor-
roborate results of the ground sampling
methods, that is, a trend from healthy,
predominantly marsh/sedge meadow
communities to eroded and disturbed
wetland vegetation. Airphoto monitoring
provides a photographic record of the
change. The increasing and enlarging
areas of open water and disturbed vege-
tation between September 1975 and
October 1977 are shown in the airphoto
interpreted disturbance maps (Figure 1).
Table 4 shows results for the same
period, for airphoto grid analysis and air-
photo interpreted mapping, in terms of
changes in percentage of cover of each
vegetation class.
The discrepancies between airphoto
interpretated vegetation mapping and
airphoto grid analysis result from the dif-
ference in scale at which the analyses
are done. Grid analysis, in which the per-
centage of each vegetation class is deter-
mined on a cell by cell basis, gives the
more accurate information on changes in
total area of each type of vegetation, but it
cannot reveal changes in the location of
the various communities. Vegetation
mapping shows the predominant type of
vegetation on a broader scale, and
changes in the location of a plant com-
munity can be seen at a glance on maps
similar to the disturbance maps of Figure
1.
Computer assisted mapping offers
some advantages of each of the other
Table 3. Changes in Classification of 62 Stations from 1974 TO 1977, by Methods of
Subjective Classification and Association Analysis
Classification in 1977
Classification
in 1974,
community
Carex lacustris
C. stricta
Transition
Emergents
Shrubs
Spiraea
Subjective classification
Community
C. lacustris
Weedy annuals
Degraded C. stricta
Degraded C. lacustris
C. stricta
Open water
Emergents
C. stricta
Degraded C. stricta
Transition
Transition
Degraded transition
Emergents
Open water
Emergents
Degraded emergents
Open water
Shrubs
C. stricta
Spiraea
No. of
stations
1
2
1
3
3
4
1
5
9
1
1
4
5
2
2
6
3
5
1
3
Association analysis
Community
Weedy annuals
Degraded C. stricta
Transition
Degraded transition
Emergents
Emergents-open
Degraded C. stricta
Spiraea
Weedy annuals
Shrubs
Transition
Degraded transition
Emergents
Emergents-open
Emergents
Emergents-open
Shrubs
Emergents-open
C. stricta
No. of
stations
0
2
8
2
4
1
3
4
10
1
1
1
1
1
4
1
2
3
2
2
1
2
remote sensing methods. Because the
analyst can choose the size of the unit
area to be classified (the pixel), the ana-
lyses can be made on as large or small a
scale as desired. Results can be pres-
ented as the overall area of each vegeta-
tion class (Table 5) or as a map on which
each pixel is shown in the color desig-
nated for the corresponding class of
vegetation. Comparison of Tables 4and 5
shows general agreement among the
three methods. Of the three, computer
assisted mapping offers the most con-
sistent classification of vegetation and
quantification of results and the most
readable visual product in the form of
maps.
Evaluation of Methods
The nine methods applied in this study
were evaluated according to the kind of
information provided, the expertise
required, efficiency (requirements in
time, capital equipment, materials), and
sensitivity and reliability.
The diversity index is the only method
which provides information on changes
in numbers of species, but it provides no
information at the community level.
Ground sampling methods show point
locations of communities and quantify
changes in the numbers of stations clas-
sified as to type of community, but they
cannot show changes in the area or loca-
tion of communities as well as remote
sensing methods can.
All methods require expertise in botany
and ecology. Computer facilities and
expertise are necessary for association
analysis and computer assisted mapping.
The five remote sensing methods
demand skills in the visual interpretation
of airphotos, and, in addition, computer
assisted mapping involves interpretation
of computer generated images. Drafting
skills are necessary for disturbance map-
ping and vegetation mapping.
Detailed analyses compared the effi-
ciency, sensitivity, and reliability of the
nine methods. Table 6 summarizes the
results. There is a fourfold difference in
cost between the least expensive and the
most expensive methods, and a twelve-
fold difference in the requirement for
time between the fastest and the most
time-consuming methods.
The greatest amount of information is
obtained by combining ground based
sampling methods with airphoto map-
ping methods. Ground sampling data
give information on the nature of the
occurring change, whereas mapping
methods show where the change is
occurring and how extensive it is. For
-------
September 25. 1975
D Undisturbed
H Disturbed
• Open Water
September 24, 1976
small data sets, subjective classification
is effective in demonstrating trends in
plant communities and can be combined
effectively with airphoto-interpreted
vegetation mapping. Both of these
methods are costly and time consuming,
however, and would not be the methods
of choice for large data sets. For large
amounts of data, association analysis
can be used effectively in conjunction
with grid analysis or computer assisted
mapping. The choice of remote sensing
method would depend on the nature of
the results desired - whether highly
quantitative in terms of community area,
or both quantifiable and visual, such as a
map.
Grid analysis is also compatible with
disturbance mapping, for use in large
areas. Grid analysis measures the per-
cent change in cover of each vegetation
class, and disturbance mapping shows
the extent and location of gross change.
Similarly, association analysis and dis-
turbance mapping provide complemen-
tary information for large data sets. If
facilities are available, the ideal combina-
tion of methods for extensive monitoring
would be association analysis, to identify
community change, and computer
assisted mapping, for visual displays and
quantitative presentation of results.
Figure 1. Changes in vegetation at site of Columbia Generating Station, 1975-1977, as
shown by airphoto disturbance mapping.
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Table 4.
Percentage Cover of Each Vegetation Class, as Determined by Airphoto Grid
Analysis and Airphoto Interpreted Mapping
% of each class
Grid analysis
Airphoto interpreted mapping
Vegetation class
Shrub
Spiraea
Open water
Transition-Emergents
Sedges
Degraded sedges
Other disturbance
Unclassified
Sept. 1975
7.4
9.5
5.2
25
50
-
2.2
-
Oct. 1977
7.8
9.2
10
25
11
12
24
-
Sept 7975
;/
11
1.5
22
45
-
7.5
1.9
Oct. 1977
5.7
7.5
10
15
22
20
17
1.3
Table 5. Summary of Results of Computer Assisted Vegetation Mapping
Class of vegetation
Shrubs
Spiraea/Sedges
Open water
Transition -Emergents
Sedges
Degraded Sedges
Other Disturbance
Other, and unclassified
September
9.7
18
0.5
22
39
-
1.8
9
% of total area
1975 June 1977
6.7
9.6
8.1
17
13
21
18
5.8
Table 6. Comparison of Methods Based on Efficiency. Sensitivity, and Reliability
Sensitivity2
Method
Diversity Index
Subjective Classification
Association Analysis
Vegetation Structure
Analysis
Airphoto Monitoring
Disturbance Mapping
Airphoto Grid Analysis
Airphoto Vegetation
Mapping
Computer Assisted
Mapping
Efficiency'
Time Cost
2
3
2
2
1
1
3
3
2
1
2
1
1
1
1
3
3
3
No. classes
defined
-
1
2
3
2
3
1
1
2
Type
of data
2
1
2
2
3
3
3
3
3
Reliability3
1
3
1
3
3
3
2
2
2
'A rating of 1 represents high efficiency, i.e.. low time or cost requirements. A rating of 3
represents relatively low efficiency.
2A rating of 1 represents a high degree of sensitivity to subtle changes. It requires that a large
number of vegetation classes be defined, or that data be based on stem counts. A rating of 3
represents few classes distinguished or airphoto data. Presence-absence data receive a rating of
2.
3The reliability rating is based on the repeatability of data collection, the level of subjectivity (the
degree to which the analyst must interpret the data), and whether results are quantitative or
qualitative. A rating of 1 represents a high level of repeatability, a high degree of objectivity, or
computer quantitative results. In this table, the methods were rated most reliable (11, moderately
reliable (2), and least reliable (31, according to their combined scores on the three criteria for
reliability.
U. S. GOVERNMENT PRINTING OFFICE: 1985/559-111/20618
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Sarah L Wynn and Ralph W. Kiefer are with the University of Wisconsin,
Madison, Wl 53706.
Gary E. Glass is the EPA Project Officer (see below).
The complete report, entitled "Aerial Photography and Ground Verification at
Power Plant Sites: Wisconsin Power Plant Impact Study," (Order No. PB 85-197
358/AS; Cost: $23.50, subject to change) will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Environmental Research Laboratory
U.S. Environmental Protection Agency
6201 Congdon Blvd.
Duluth, MN 55804
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
BULK RATE
POSTAGE & FEES PAIt
EPA
PERMIT No G-35
Official Business
Penalty for Private Use $300
EPA/600/S3-85/034
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