EST
BY
Hiroo TARUMI
Shuji FUJII
Hajime KENMOTSU
LAND
MAPPER DATA
THE
TOKYO
Dr. Eng., Associate
Dr. Eng., Associate Professor
M.
Eng., Graduate Student
Tokyo Institute of Technology
Department of Architecture and Building Engineer
2-12-1 Ookayama, Meguro
,Tokyo 152, Japan
( Commission Number : 7 )
g
1 ..
Since the Meiji Restoration(1867), Tokyo has continued to
expand and develop as the capital city of Japan. In the Tokyo
Metropolitan Area, the daytime population is 11000000 as
against 8500000 of resident population,
presenting a
remarkable doughnut phenomenon. Particularly, during the
period of high economic growth of the 1960' s,
various
functions concentrated in Tokyo in an accelerated pace. As a
re suI t, Tokyo has grown into a mammoth, over crowded city
burdened with many strains of urbanization: for example,
above-mentioned decline of population in central Tokyo due to
the extension of business district, disorderly sprawl of
built-up areas, outlying location of residence, deterioration
of the living environment. All these problems are urged to be
solved.
In this paper, therefore, the correspondence of the Detailed
Digi tal Land Use Informa tion da ta and the Landsa t Thema tic
Mapper da ta is analyzed by digi tal image processing for the
estimation of the land use. The area used for analysis is the
Tokyo Metropolitan Area: gross area is nearly 600 square kilometers.
2 .. DATA BASES
The National Capital Region Development Law in 1956 aimed at
first to restrict the expansion of the urban district by
providing the Suburban Zone(green belt). But owing to the
unexpected concentration of population and industries,
the
urban area continued to expand, for exceeding the initial
expectation. Thus, the Law was extensively amended in 1965.
The revi sed law a boli shed the green bel t concept, and laid
down to establish the Suburban Development Area in the suburbs
of the existing built-up area. In connection with this, land
use informa tion is collected and opened by the Geographical
Survey Institute in every five years, 1974, 1979 and 1984.
This Land Use da ta is DDLUI (the Detailed Digi tal Land Use
Information) data containing digital data on the state of land
utilization for each 10-meter square mesh. The data, though
the unit of information is small enough, is not suited for
monitoring of land use changes due to the survey period of 5
years . Effective urban and regional planning depends on
accurate information regarding current land usage. It is
considered satellite imagery can contribute substantialy to
this information base. Landsat Thematic Mapper data considered
in this study was acquired on November 1984.
Table 1 shows 16 c sses of DDLUI data, inc
residential
area, industrial area, commercial a
business area, etc.
Whereas
data has a 10m
1 size, a s
t
resolution of
Landsat Thematic Mapper
ata is ap
mately 30m. This
requires
units of the DDLUI
in a neighbo
ode
A method of c
is as fo
x 3
ts.
1) A me
x 3
is
to bo that of th
2) L
use
m sh
lar st
r.
use selected at a mesh, the
3)
f the two or more 1
use which the
rc
e is
priority
to
low in a
study area.
The resul ts
combining are
own in
2
e 3.
This process is considered to be e ective to preserve the
original profile.
The data image of the To
0 Metropolitan Area is shown in
Fig.1( approximately 32km
east and west, 30km in north and
south ). The commercial and business areas are in the central
right( Chiyoda and Chuo wards ) and central left( Shinjuku
ward ) regions. The town greenery in the central regions are
the Imperial Palace, the Meij i Shrine, The Shinj uku Imperial
Gardens, etc. Low-rise residential areas are in the left and
top right, as are low-rise densely residential area in the
just right of the center.
Table 1
Classes of the Detailed Di
tal Land Use Information data.
CJass Land use
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
fields, mountain greenery
paddy fieJds
plowed fields
fields under creation and reclamation
unoccupied grounds, parking zone
industrial area
low-rise residential area
(buildings Jess than 3 stories, a plottage above 100 m2 )
low-rise densely residential area
(buildings less than 3 stories, a plottage below 100 m2 )
residential area(buildings over 3 stories)
commercial and business area
roads
pub1 ic space I (park, town greenery, sports ground)
pub) ic spaceIT(rai I roads, ai rport, harbor faci I i ties)
revers, lakes and marshes
Forces
sea
Table 2
The change of area proportions by data combination.
Class
0.)5
30m mesh
0.10
169,720( 1.18%)
999,985( 6.95%)
13%)
954,
6.63%)
573,631( 3.98%)
432, 167( .89%)
687,658( 4.78%)
,842( 2.08%)
49,619(
,725(
,366(
34,331(
109,657(
64,609(
286,304(
3.10%)
1.23%)
7.59%)
2.15%)
6.86%)
4.04%)
.90%)
4.95%)
,208( 2.14%)
7.10%)
,658(10.48%)
548(
,266( 7.21%)
,04I( 3.94%)
8,
0.54%)
.57%)
BAND 1
BAND 4
0.05
jl!l
0
0
Fig. 2
50
J l
TOO
150
CCT COUNT
Probability of pixels
belonging to low-rise
residential area.
Table 3
Land use areas in the Tokyo Metropolitan 23 wards by the
Detailed Digital Land Use Information data (unit:ha).
Class
Ward
Chiyoda
Chuo
Minato
Shinjuku
Bunkyo
Taito
Sumida
Koto
Shinagawa
Meguro
ota
Setagaya
Shibuya
Nakano
Suginami
Toshima
Kita
Arakawa
Itabashi
Nerima
Adachi
Katsushika
Edogawa
Total
2
3
4
0.09
22.59
8.64
6.93
0.18
3.78
47.97
6.84
16.38
10.80
145.26
6.93
21.42
64.11
3.96
9.90
5.13
3.51
1.80
1.98
2.88
5.67
0.09 576.00
4.14 52.38
3.42
13.50 699.21
340.92
3.69
0.18
22.77
1.35
95.58
0.45
0.18
0.18
0.99
5.40
85.77
2.16
781.56 26.28
292.77 21.24
7.20
142.20
259.20 234.27
5
6
7.11
21.15
63.00
35.91
7.92
4.14
36.90
559.53
95.58
37.17
211.86
266.40
35.28
47.70
132.03
26.28
40.32
22.32
241.92
434.34
734.04
246.24
548.01
0.09
21.78
26.73
10.17
9.18
2.88
153.00
495.18
86.76
17.73
392.85
29.43
11.43
7.56
27.36
13.41
138.51
67.86
235.53
31.68
266.13
201.15
307.98
7
29.43
1.80
206.82
383.58
115.14
9.63
17.73
128.34
424.98
671.40
1330.92
2686.86
355.50
567.27
1769.94
358.65
319.41
113.40
621.90
1485.00
703.89
754.65
993.15
8
9
10
11
12
13,15
14
Total
7.92
29.16
127.98
131.58
95.13
74.52
258.30
172.98
217.89
120.87
251.01
245.43
176.40
143.73
108.81
167.76
314.28
148.95
363.42
386.37
602.55
307.17
122.22
10.35
18.45
96.75
108.09
30.60
4.23
29.97
163.08
54.27
47.61
83.97
189.18
74.43
60.93
55.80
30.69
131.40
15.30
169.47
73.17
230.85
92.79
89.37
311.04
399.78
488.97
311.88
185.58
386.28
274.23
529.38
481.05
162.00
416.88
365.58
160.92
110.61
207.45
212.31
192.78
187.83
335.34
263.25
569.25
278.55
335.25
273.96
269.37
364.32
413.46
300.60
257.94
288.72
561.69
282.24
141.93
693.00
542.97
288.27
352.53
478.71
270.36
301.41
186.30
502.38
674.28
810.81
502.92
671.04
88.02
44.82
169.38
134.55
127.35
138.69
74.25
148.95
151.92
46.80
275.58
358.65
186.93
55.80
188.82
59.13
158.67
56.52
224.01
171.90
279.72
211.23
245.97
334.17
51.39
440.01
262.80
188.55
96.93
114.39
406.80
380.34
204.89
796.23
552.96
205.83
161.46
264.24
166.32
297.27
158.04
287.46
364.50
353.97
305.10
261.81
72.99
106.65
2.79
7.02
1.98
38.34
112.95
226.53
23.40
8.73
287.91
77.67
0.45
5.04
12.87
0.36
169.29
53.01
126.18
10.80
441.90
403.56
695.16
1135.11
964.35
2014.47
1817.19
1128.96
1016.64
1369.89
4016.52
2261.79
1473.93
5465.52
5806.98
1502.55
1558.17
3406.23
1309.59
2074.23
1014.93
3227.76
4812.39
5325.03
3479.58
4782.60
31.95
108.99
4.32
23.94
14.58
0.27
0.27
13.59
2.88
4.59
559.62
25.38 2043.27 1647.00 3855.15 2554.38 14103.40 4574.43 1860.7 7172.19 9429.21 3597.66 6650.46 2885.58 60964.50
3. ANALYTICAL METHOD
The purpose of analysis is to be able to identify land
utilization by Landsat Thematic Mapper data. If for each pixel
measurements of reflection at several wavelengths are
available, then it shall be a rela ti vely easy rna tter to
discriminate fundamental land cover types. However, DDLUI data
dealt in this paper is a data of land utilization, not a data
of cover types. Therefore , it becomes necessary to treat a
mixel data itself as a training data.
To categorize pixels in an image into land use classes, the
maximum likelihood classification method, using Mahalanobis
classifier as the discriminant function, was taken in this
study. The percentage of correct classifications is calculated
by the matching of land use data and the Landsat TM data,
pixel by pixel ..
In order to correct geometric distortion, SO ground control
points are selected, for example, small lakes, bends in
rivers, prominent coastline features. The chosen mapping
polynomial has order of third degree.
f(x,y)=aO+a1x+a2y+a3x2+a4y2+a5xy+a6x3+a7y3+asx2y+a9xy2
Errors of the coordinate conversion were within plus or minus
1 pixel. Moreover, nearest neighbor method was used in the
resampling.
For training data, 21 samples were obtained and used in each
class. Since spectral classes in remote sensing data are
modeled by mul tidimensional normal distribution, one of the
distributions of training data is shown in Fig. 2.
4. ANALYTICAL RESULTS
Table 4 is a confusion matrix based on 16 classes. The
percentages listed in Table 5 represent the proportion of
ground truth pixels, in each case, correctly labeled by the
classifier. Fig. 3 shows a portion of the Tokyo Metropolitan
Area image classified into 16 land use types. The upper image
is DDLUI data and the lower image is an analyzed result. The
percentages of correct classifications are low allover. Even
in "low-rise densely residential area", only the percentage of
36% is obtained.
Therefore , combination of the classes was
done and S classes were newly laid down as follows; (1) field,
(2) industrial area, (3) low-rise residential area, (4)lowrise densely residential area, (5) residential area, (6)
commercial area and road, (7) park, (S) water surface.
The results are shown in Table 6, 7 and Fig.4. The percentages
of correct classifications rose approximately 50% in "low-rise
residential area" and "low-rise densely residential areal!.
However,
judging from the Mahalanobis distance between the
classes listed in Table 7, re-combination of the classes were
seemed to be required. On this account, the classification
based on the 5 classes, namely, (1) field, (2) residential
area, (3) commercial area and road, (4) park, (5) wa ter
surface, were put to the analysis ( Table Sand Fig.5 ).
vll . . 5ao
Whereas the percentage of the correct classifications
concerned wi th residential area became to indica te a higher
value of 79%, that of commercial area remained a low value of
31%.
A maj or cause of this turned to the characteristics of the
training classes that the classes don't mean land covers, but
land utilization. For example, in central regions, "commercial
area" is almost covered by concrete buildings. However, in
suburbs,
"commercial area" is sometimes formed by detached
houses with tiled roofs. Consequently, maximum likelihood
classification was applied to each ward( Table 9 ). In the
midtown area such as Chiyoda and Chuo ward, the percentage of
correct classifications in class "commercial area and road"
attained 58% and 70% in each.
Table
4
Clsss 1
1 1706
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
8
3205
684
2187
805
12141
1943
1367
2086
4611
6271
3809
1558
677
489
Confusion matrix resulting from classifying DDLUI data.
2
567
-.00
4036
4332
6950
1894
6612
1571
1011
2459
5110
7433
7790
2695
245
1472
4
5
6
422
2
481
3138
1866
85
678
107
59
102
511
3098
1812
746
225
269
124
26
739
2314
3006
918
1154
288
257
987
1380
982
2525
522
105
292
7
2
59
322
500
1375
1229
791
233
2242
1823
125
2682
301
19
732
3
775
87
6898
2397
7682
3042
17133
4918
1789
4273
7538
5618
6868
2322
408
539
Table
5
8
7
1009
27
4108
347
6881
3544
45277
7615
3963
7509
15244
4028
7551
443
235
138
184
6
1024
182
3444
4585
30141
18504
1997
14310
17112
1205
5082
282
99
83
9
10
11
367
7
490
308
1188
714
6908
1091
2293
2770
4870
2582
3795
1361
193
603
40
2
61
907
1057
1786
2433
1988
1098
14233
13392
519
4949
3649
24
2804
40
4
137
421
1506
1971
3353
982
982
10019
9758
492
3954
2176
77
1857
12
13
14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Pixels of OOLUI data
VII
Correctly
labelled pixels
1,706(27.44%)
90(31.91%)
6,898(30.38%)
3,138(17.15%)
3,006( 7.02%)
1,375( 4.84%)
45,277(28.88%)
18,504(36.41%)
2,293(11.09%)
14,223(17.85%)
9,758( 9.32%)
474( 1.19%)
19,301(27.42%)
9,328(29.10%)
531(15.17%)
59,505(77.90%)
6,217
282
22,702
18,300
42,831
28,380
156,752
50,821
20,672
79,667
104,749
39,970
70,384
32,059
3,501
76,394
1
16
36 734
1 205
0
0
21
0
0
0
0
18 1381
65
0
5 2672 165
3 103
9
46
22 6463
24
o 7632
8
6
15
24 29514
4 147
4
34
1 5578
5
5
5
34
1 5578
5
4 18513
37
67
66
22 23004
65 165 164
474 5258
39
53 1793
47 19301
36 276
87
13 2775 9328
96 3792
201 450
11 531
1
7 59505
1 1151 5463
Pixels labelled correctly in 16 classes.
Class
15
Table 6
Pixels labelled correctly in 8 classes.
Class
Pixels of
DDLUI data
1,2,3,4,5
6
7
8
90,332
28,380
156,752
50,821
23,031(25.50%)
4,080(14.38%)
79,416(50.66%)
25,201(49.59%)
9
10,11,13
,672
254,800
43,471
108,453
5,249(25.39%)
66,676(26. 17%)
2,511 ( 5.78%)
69,641(64.21%)
1 field
2 industrial area
3 low-rise residential area
4 Jow-rise densely
residential area
5 residential area
6 commercia1 area and road
7 park
8 water surface
Table 7
Class 1
1
2
3
4
5
6
7
8
Table 8
65.66
9.99
17.73
11.57
27.55
8 76
75.75
Mahalanobis distances.
2
3
4
8.09
9.02
6.17
9.62
6.03
19.20
23.
8.16
21.08
5.02
4.64
10.10
17.11
35.64
8.99
7.72
3.33
5.75
6
18.66
6
5
7
8
5.49 9.19
6.09
31.12 4.60 86.91 10.03
3.30 8.53 10.36 20.52
10.04 5.26 27.00 17.06
5.02 9.76 16.65
- 37.00 5.48
12.90
- 19 •
. 38 18.18
39.97 12. 89.96
Pixels labelled correctly in 5 classes.
Class
Reference code No.
of 16 classes
1 field
2 residential area
3 commercia1 area, road
4 park
5 water surface
Table 9
Correctb'
f abe tIed pi xe 1s
Reference code No.
of 16 classes
1,2,3,4,5
7,8,9
6,10,11,13
,15
14,16
Correctly
labelled pixels
Pixels of
DDLUI data
90,332
228,245
283,180
43,471
108,453
44,052(48.77%)
181,853(79.67%)
89,286(31.53%)
6,343(14.59%)
,292(73.11%)
The correct percentages in Chiyoda and Chuo wards.
Class
The correct percentages
Chiyoda ward
1 field
2 industrial area
3 low-rise residential area
4 low-rise densely
residential area
5 residential area
6 commercial area and road
7 park
8 water surface
Chuo ",lard
5.00%(
°
%(
29.66%(
41 235)
01 1)
971 327)
13.64%( 121 88)
11.91%( 281 235)
12.40%( 301 242)
25.00%( 51 20)
22.53%( 731 324)
35.65%( 411 115)
58.92%(5360/9097)
24.32%( 435/1789)
11.84%( 961 811)
21.46%( 441 205)
70.48%(5643/8006)
12.85%( 641 498)
70.73%(1629/2303)
Fig.3
Classification map based on 1 classes. The lower image
is a classified map and the upper is DDLUI data.
Fig.5
Classification map based on 5 classes.
VII-se3
5 .. CONCLUSIONS
This paper is summarized as fo ows;
1) As one example
the applications
the Landsa t
Thema tic
r da ta to a
c
DDLUI data in the
Tokyo Metropo i
Area was
as
ground truth data.
2) In the
rvised c ss ication of the
sat TM data,
combination of
use c sses were analyzed by the
percentage of correct c
ssifications and Mahalanobis
distances.
se in correct
o
to a
3) An
perc
use to the ward.
Analyses in this
r were
by
1 by
• However,
judging from t
conditions in urban areas and
uncertainties on the process of correction of geometric
distortion, analysis assuming divergence of near 1 pixel
remains as a coming problem. It is considered such a analysis
method may be e ective to estimate the thermal environment in
urban areas.
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