88
Table 1 Quantification of vegetation and landslide-
A comparison of unsupervised classification of MSS
data with ground truth.
area
veg.l
plain
veg.2 sunfacing
veg.3 poorly
veg.4 poorly
surfacing
Alfalfa
farms/orchards
i 1 luminateci
Illuminated
Landslide
farms
orchards
wood lands
CO
S3
v £
v £
C *->
0)
+J u
4. £
C 4-»
V
+J L-
C 4->
g ^
1 g
h ®
■ match
¡ 8
U 0)
§ rt
I
£
h
is £
M 3
x:
o
£
p
1 \
x:
0
4->
1
1 £
*9 £
a §
JZ
o
+J
g
O 0
£ s
i S'
*
£ s
i S
b*
a u
b*
map
18750
134375
306250
16000
53000
Sept.
~1572
B7-B4
22500
120
150000
112
230000
75
20000
125
B7-B5
20000
107
145000
108
185000
60
7500
47
VI
B7tB4
12500
67
142500
106
485000
158
30000
187
72500
137
B7+B5
90000
170
July
T577
B7-BS
7500
40
182500
136
445000
145
30000
187
VI
2500
13
135000
100
440000
144
25000
156
Note - In the vegetation index of the September image
185,000 square metre of shadowed areas were
classified as vegetation and in the vegetation index
of the July image 145,000 square metre of shadow
were misclassified as vegetation (see Fig 1 for
details of selected features).
2 CLASSIFICATION METHOD FOR THE LANDSAT MSS DATA
The results of the evaluation of the MSS data (
shown in Table 1) were not very satisfactory.
Various manipulation techniques were explored in
order to find an appropriate method of classifying
the MSS data. Several combinations of techniques
were applied and the results examined. The method
which yielded optimum results is described below.
Table 2 Pixel values and characteristics of the
training areas Mean standard deviation and range
of each band or combination are recorded under the
name of each selected features of the training
areas
T77]
B 5
B 4 1 B7+B5
VEGETATION
B7-B5
VI
B7+B4
B7-B4
mean
48.30
20.29
22.07
39.78
61.01
15.66
39.39
59.24
SD
6.51
5.88
5.87
5.57
7.89
2.67
5.29
8.14
meant,-
39-58
11-29
13-31
34-52
50-70
11-21
32-48
50-70
1.5 SD
SCREE
mean
64.41
85.97
86.16
87.16
11.45
1.29
84.31
11.26
SD
4.34
5.76
6.15
5.67
3.03
0.56
2.23
3.97
meant,-
1.5 SD
58-71
77-95
77-95
76-96
7-14
1-2
76-92
6-16
COARSE LAVA
mean
54.23
65.37
64.54
69.41
21.87
3.19
66.58
22.69
SD
2.30
3.00
3.08
2.68
2.83
0.51
2.23
3.66
Meant,-
] .5 SD
51-58
61-70
60-69
66-74
18-27
3-4
63-69
19-26
LIMESTONE
mean
69.53
72.19
72.47
82.22
30.34
3.68
79.57
30.00
so
4.53
3.91
4.40
4.62
2.71
0.47
4.65
3.53
meant,-
1.5 ED
63-76
66-78
66-79
76-91
27-36
3-4
72-87
24-35
LOWER JURASSICS
mean
23.71
40.05
41.36
36.96
16.66
4.62
36.41
15.35
SD
3.92
4.54
4.80
4.45
3.74
1.28
4.19
4.65
meant,-
1-5 SD
18-30
33-47
34-49
31-45
12-24
3-7
31-41
10-24
LANDSLIDE
mean
62.28
75.91
77.13
80.16
19.38
2.41
78.00
18.16
SD
4.56
6.44
4.76
6.03
4.00
0.71
4.89
3.39
mean+
1.5 SD
55-69
66-86
70-84
72-87
15-24
2-3
72-85
13-21
SHADOW
r—
mean 1 0.00
14.31
15.50
8.31
18.67
27.60
8.90
17.50
SD 1 0.00
4.37
4.76
2.52
4.37
19.48
2.66
4.76
meant,- 0.00
0-18
0-19
5-13
12-26
10-50
6-14
8-24
1.5 SD 1
Table 3 Feature separation using Landsat MSS band
combinations•
2.1 Methodology
A sample of 35 subscenes along the Haraz road was
used to develop a classification method. The most
dominant features observed across a majority of the
subscenes were chosen for classification. The
features comprised:
1- (VEG) Vegetation: orchards and farms
2- (SC) Scree: sunfacing steep slopes, usually
slumps resulting from landslides.
3- (CL) Coarse lava: poorly vegetated quaternary
volcanic conglomerate mixed with
volcanic tuffs.
4- (LIME) Light limestone
5- (SHEM) Shemshak formation: dark coaly lower
Jurassics
6- (LANDS) Landslide: unstable grounds
7- (SHAD) Shadow
By combining data acquired by field work with
existing maps, a geological map of the Haraz Valley
was compiled at a scale of 1:23,000. Subscenes were
then plotted at the same scale and superimposed on
the map. Several appropriate training areas,
containing the seven features listed above, were
selected from the map and were superimposed on the
plots of the subscenes. The pixel values of each of
the selected areas were transferred to separate data
files. The files contained bands 4, 5 and 7 of the
two images for the seven selected categories.
To simplify the training area data sets, the mean
and standard deviation of the data were calculated,
and only pixels falling within the range of three
standard deviations ( mean, plus and minus 1.5
standard deviations) were reselected. The pixels
selected by this sieving procedure were more
MSS band canbination
seperated features (and their values
band 7
band 5
band 4
band 7 t band 5
band 7 - band 5
Vgetation Index
band 7 t band 4
band 7 - band 4
SHAD (0-1) and SHEM (18-30)
SHEM (33-47)
SHEM (34-49)
SHAD (5-13) and CL(66-74)
SC (7-14), LIME (27-36) and VEG (50-70)
SC (1-2) and LANDS (2-3)
SHAD (6-14) and CL (63-69)
LIME (24-35) and VEG (50-70)
homogenous than before, and were less biased by
outlying values (Table 2).
Images of the training areas were examined using the
simplified sets of data for each of 3 bands.
Various combinations of bands were also examined
employing the same gains and offsets which were used
for the subscene. The combinations were: addition
of band 7 with band 5 (B7 + B5), band 7 with band 4
(B7+B4), subtraction of band 5 from band 7 (B7-B5),
band 4 from band 7 (B7-B4) and the vegetation index
(B7-B5)/(B7+B5). Means and standard deviations
were calculated and histograms plotted for each of
the newly derived data sets corresponding to each of
the 8 combinations of bands. Table 2 shows the
range of pixel values for each seven classes.
According to Table 3 dark shadow and lower Jurassics
can be distinguished in band 7. In bands 5 and 4,
lower Jurassics can also be delineated in the middle
part of the histogram, while vegetation and shadow
coincide in the lower part of the histogram,
preventing definitive classification. Subtraction
of band 5 from band 7 (B7-B5) and band 4 from band 7
(B7-B4) separates vegetation and limestone in the
upper and middle paart of the histogram
respectively, while the other features tend to
overlap.
Figure 1
of (B7-B5)
colours re
Index (B7-
lowest va]
while hi
vegetatio
Addition c
permits s
across th
features
middle pa
previousl
vegetation
histogram
associate
delineate
The veget
suitable
mountainoo
a plain ti
shadow.
the appro
classes
together \
five combi
A detailec
Tables 2 <
nature of
how, for
shadowed ;
on the ve
areas a mo
in the th
using the
Taken as
bands used
and cone:
band/combi
informati
The numbe
represents
the featu
classifi
diagrammat