LANDSAT frames were geometrically corrected and data sets from six different dates were overlaid to produce a 24 channel (six dates and four wavelength bands) data tape. Changes in the extent of the snowpack could be accurately and easily determined using a change detection technique on data which had previously been classified by the LARSYS software system. A second phase of the analysis involved determination of the relationship between spatial resolution or data sampling frequency and accuracy of measuring the area of the snowpack

Bartolucci, L. A.

Hoffer, R. M.

Luther, S. G.

English

NASA Technical Reports Server

Paper No. 20 K'76-16581
SNOW COVER MONITORING BY MACHINE PROCESSING OF
MULTITEMPORAL LANDSAT MSS DATA
S. G. Luner, L. A. Bartolucci and R. M. Hoffer, Laboratory for Applications of
Remote Sensing, Purdue University. W. Lafayette, Indiana 47906
ABSTRACT
LANDSAT frames were geometrically corrected
and data sets from six different dates were
overlaid to produce a 2 4 channel (six dates
and four wavelength bands) data tape. Changes
in the extent of the snowpack could be accu-
rately and easily determined using a change
detection technique on data which had pre-
viously been classified by the LARSYS soft-
ware system.
A second phase of the analysis involved
determination of the relationship between
spatial resolution or data sampling fre-
quency and accuracy of measuring the area of
the snowpack.
INTRODUCTION
Climate and subsurface geology combine to make
water a scarce and valuable commodity in the western
United States. Most of the water supply comes from
the spring and summer runoff of winter snow accumu-
lations in the Rocky Mountains. A network of reser-
voirs has been constructed to conserve this resource
to satisfy the water needs of urban communities and
to provide water for irrigation. In addition, this
water is used for generating hydroelectric power
and for providing recreational facilities. The net-
work is operated by various state and federal govern-
ment agencies and private corporations.
In order to regulate these reservoirs properly,
these agencies must have an estimate of both the
discharge required downstream and the concommitant
recharge needed from the reservoir's source, which
This research was funded by NASA Contract NAS5-21880,
279
https://ntrs.nasa.gov/search.jsp?R=19760009493 2020-03-22T18:06:52+00:00Z
in most cases is a mountain stream. The objective
of this research is to investigate methods by which
MSS data can be applied to the process of snow cover
monitoring, thereby enabling more accurate predic-
tions of runoff from mountain watersheds.
Since the methods to be studied may vary in
time, cost and practicality of application, para-
meters must be established to insure that the tech-
niques involved are economically advantageous, i.e.,
that similar information is provided at less cost
than conventional methods or that increased cost is
accompanied by additional information. To meet these
objectives this analysis has purposely been kept
simple and hence does not reflect the most accurate
classification possible, i.e., misclassification
errors in shadowed areas do occur.
Computer Aided Analysis Techniques
The Animas River watershed above Howardsville,
Colorado was selected as the test site for this
phase of the study because 1) it is located in the
headwaters of the Colorado River, 2) it is wholly
contained in the San Juan Mountain Test Site, and 3)
good records exist for the gaging station at Howards-
ville. The watershed boundary was physically located
on four USGS 7 1/2' topographic maps (see Figure 1).
The boundary was defined by elevations, the stream
network and the location of the gaging station in
relation to the stream.
Once determined and delineated, the boundary was
transferred from the map to a gray scale printout at
the same scale (1/24,000), by aligning the stream
networks. The watershed was then defined on the
LANDSAT imagery? using a series of line and column
coordinates as individual "test areas", thus provid-
ing an accurate estimation of the total area of the
watershed (Figure 2).
The data for frames 1101-17203 (1 November,
1972), 1119-1720U (19 November, 1972), 1173-17202
(12 January, 1973), 1191-1720U (30 January, 1973),
1299-17205 (18 May, 1973) and 1317-17204 (5 June,
1973) were all overlaid, rotated and rescaled there-
by eliminating the need for repetition of the out-
lining process. These frames are cloud free over
the Howardsville watershed.
280
Figure 1. Animas River Watershed at
Howardsville, Colorado
281
REPRODUCBILITY OF THE
ORIGINAL PAGE IS POOR
Figure 2. Location of Animas Watershed on LANDSAT
Imagery
282
REPRODUCIBILITY OF TEL
ORIGINAL PAGE IS POOR
In order to ascertain the accuracy of this_type
of areal calculation, the relative size of a printer
element must be known. Previous investigations have
indicated that each LANDSAT element contains .43
hectares. Furthermore, a total of 32,405 elements
was contained within the watershed, so the area was
calculated to be 147 square kilometers. Since the
U.S. Geological Survey had estimated the area to be
55.9 square miles (145 square kilometers) (see
WSP 1925) and this coincided with an estimate ob-
tained by planimetering the area, the error intro-
duced by the computer tabulation and human errors
in outlining the boundary was 1.5%. Band five
digital display imagery for one of the frames
(1119-17204) which contains the watershed is shown
in Figure 3. This image consists of sixteen gray
levels defined by the computer program, based on
relative reflectance histograms of the area.
Figure 3. Digital Display Photograph of LANDSAT
Frame 1119-17204, Band Five.
To determine the areal extent of the snow cover,
two channels, (one in the visible (.6-.7pm) and one
in the IR (.8-l.lym), were used in conjunction with
LARSYS. LANDSAT MSS channels four and five were
generally saturated by the snow cover and the infor-
mation provided by each resulted in a singular cor-
relation matrix. This required the elimination of
one of these channels in order to implement the
LARSYS processor.
283
Two classes, "snow" and "other", were then
requested from the clustering processor, to generate
the statistics for these classes which are shown
below in Table 1.
TABLE 1. Spectral Response of Snow
Snow Other
Mean
Relative
LANDSAT Reflec-
Date Band tance
1 Nov. 1972
19 Nov. 1972
12 Jan. 1973
30 Jan. 1973
18 May 1973
5 June 1973
5
7
5
7
5
7
5
7
5
7
5
7
124.73
56.75
123.43
121.73
52.32
122.45
52.70
125.33
53.50
125.98
51.72
Std.
Dev.
7.01
9.23
9.21
10.66
11.17
11.13
10.32
10.78
11.02
17.42
10.30
15.26
Mean
Relative
Reflec-
tance
37.29
13.15
32.21
18.22
31.36
18.59
30.04
10.34
50.44
21.12
45.89
22.30
Std.
Dev.
18.21
7.11
10.35
6.96
10.31
7.03
17.21
6.72
18.83
5.50
18.66
5.90
These numbers are dimensionless and indicate the
relative reflectance of each class. Note that the
mean of the class snow does not vary significantly
between dates and approaches the saturation level,
which is 128 for band five and 64 for band seven.
Each frame was then classified separately into
two classes, snow and other according to these
statistics. Display maps of the classification
results were obtained, along with a table showing
the number of resolution elements within the water-
shed that were classified as snow and the percentage
of snow cover (an example of this type of output is
shown in Figure 4). By multiplying the number of
resolution elements classified as snow by the .43
hectares represented by each resolution element or
by multiplying the percentage of snow cover, the
areal extent of the snowpack can be quickly and
easily calculated by this computer analysis procedure.
284
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Table 2 gives a summary of the areal fluctuations
of the snowpack.
TABLE 2. Snow Acreage Fluctuations
Date
1 Nov. 1972
19 Nov. 1972
12 Jan. 1973
30 Jan. 1973
18 May 1973
5 June 1973
Percent
Snow Cover
76.1
68.3
62.6
65.1
81.0
87.5
Total Area
(hectares)
11189
10037
9203
10023
12U71
13U71
Figure 5 shows the last two classification
results taken from the digital display. These in-
clude that portion of the area surrounding the
Howardsville watershed.
The relationship between area, snowpack density
and total water content of the snowpack requires
much additional study. However, LANDSAT can provide
accurate, rapid measurements of the areal extent of
the snowpack.
Several intervening dates between January 30,
1973 and May 18, 1973 have not been included in this
investigation due to cloud conditions. Thus, this
study clearly indicated that LANDSAT-1 frequency of
coverage can rapidly decrease from once to every
18 days to once every 36 or 5U days, a totally
unacceptable condition for studying hydrological
and other dynamic phenomena.
Temporal Analysis
The general technique for determining ground
cover changes between two dates is known as change
detection. There are at least four change detection
methods; Delta Transformation, Spectral/Temporal
Concurrent Classification, Spectral/Temporal Layered
Classification and Post-Classification Comparison.
For this study classifications were made for
the area on the various dates and then these classi-
fications were compared. Before the classifications
were attempted an overlay was made which aligned the
data so a ground point could be located by the same
286
line and column coordinates. Channels I-1* are the
first date and channels 5-8 are the second date so
that two classifications exist for the same run
number, the same area, but different sets of channels,
A. May 18, 1973
%m
B. June 5, 1973
Figure 5. Classification Results
REPRODUdBILITY OF THE
ORIGINAL PAGE IS POOR
287
This program was used to analyze the snow
classification results as an aid to analyzing re-
distribution patterns of snow within watersheds and
to display the results as a third 4-class classifi-
cation. Figure 6 below illustrates this process.
Result 1
Class
Comparisons
S-S
0-0
S-S Snow cover on both dates
S-0 Snow cover changing to non-snow
cover
0-S Other changing to snow cover
0-0 Other on both dates
Figure 6. Flow Diagram of Post-Classification
Change Detection Technique
This program, was run on the six classification
results from the Animas River watershed. An example
of this temporal analysis technique for a portion of
the Animas watershed is shown in Figures 7, 8 and 9.
Classification Sampling Rate
The multispectral classification of enormous
quantities of data gathered by earth orbiting satel-
lites (to determine the areal extent of snow cover),
requires a relatively large amount of computer CPU
(Central Processing Unit) time. The purpose of this
phase of the investigation was to compare the
accuracy of the multispectral classification of snow
cover using different data sampling rates in order
to reduce the amount of computer processing time.
288
SYPBCl CLASS
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289
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REPRODUCBILITY OF THE
ORIGINAL PAGE IS POOR
291
Approximately 60% of a LANDSAT-1 MSS frame
(scene ID 1317-17204) was classified into four
spectral classes (one snow class and three non-snow
classes) using bands 5 and 6. In essence, five
classifications of the area were performed each at
a different data sampling rate. Then, the areal
extent of the snow cover was computed for each one
of the five classifications. Table 3 shows the
percent of the area classified as snow cover and the
corresponding CPU time used by each one of the five
different data sampling rate classifications. Note
in Table 3 that the difference between the area of
snow for the 1x1 and 16x16 sampling rates is only
0.4 of one percent.
TABLE 3. Determination of Areal Extent of Snow Cover
from LANDSAT MSS Data using Different
Sampling Intervals
Number
Sample Data
Interval Points
Number
of Points
Classified
as Snow
1x1 4,330,561 1,385,126
2 x 2 1,083,681 345,417
4 x 4 271,441 86,433
8 x 8 68,121 21,646
16 x 16 17,161 5 ,422
% of
Area
in Snow
31.99
31.87
31.84
31.78
31.60
Classifi-
cation
CPU* Time
(minutes)
54.34
13.20
4.21
1.52
0.65
*IBM 360 Model 67
Figure 10 shows a graph of the CPU time involved
in the classification of snow cover versus the data
sampling rates.
Statistical analysis of the results shown in
Table 3 has indicated that the percent area of snow-
cover determined by the 16x16 data sampling rate is
not significantly different from the percent area
covered by snow as determined by the 1x1 data sampl-
ing rate at both a 95% and 99% confidence level. In
other words, the 31.60% area (in Table 3) is not
statistically different from the 31.99% area, even
though the sample number was decreased from over
4 million points to just over five thousand data
points.
292
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293
Conclusions
Six LANDSAT frames over the test site were over-
laid, geometrically corrected and rescaled. The
dates ranged from November 1, 1972 through June 5,
1973. Computer processing techniques were utilized
to make an accurate determination of snow cover with-
in a watershed at a scale of 1:24,000. A post-
classification change detection processing technique
determined and located changes in snow cover. For
large geographic areas, snow cover can be accurately
mapped without incurring the cost of classifying
every data sample.
294


Snow cover monitoring by machine processing of multitemporal LANDSAT MSS data

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