Spectral classes of urban phenomena identified from Earth Resources Technology Satellite (ERTS) multispectral scanner data in Milwaukee included suburban inner city, industry, grassy (open area), road, wooded suburb, water cloud, and shadow. The Milwaukee spectral class statistics were used to classify the Chicago area, within the same ERTS frame, and similar results were achieved. In another ERTS frame, Marion County (Indianapolis) data were classified into similar classes. The Marion County ERTS study was supported by a land use classification of an area near downtown Indianapolis that utilized 12-band MSS data collected by aircraft from 3000 feet. The results of the ERTS analyses suggest that satellite data will be a useful tool for the urban planner for monitoring urban land use

Mausel, P. E.

Todd, W.

Wenner, K. A.

English

NASA Technical Reports Server

Paper  L 19 
PREPARATION OF URBAN LAND USE INVENTORIES BY MACHINE- 
PROCESSING OF ERTS MSS DATA 
William Todd, Paul Mausel, Kenneth  Wenner, Laboratory for Applications of 
Remote Sensing, Purdue University, West La fayette, Indiana 
ABSTRACT 
Spectral classes of urban phenomena identified from Earth 
Resources Technology Satellite (ERTS) multispectral scanner data 
in Milwaukee included "Suburban", "Inner City", "Industry", 
"Grassy" (open area) , "Road", "Wooded Suburb", "Water", "Cloud", 
and "Shadow". The Milwaukee spectral class statistics were used 
to classify the Chicago area, within the same ERTS frame, and 
similar results were achieved. In another ERTS frame, Marion 
County (Indianapolis) data were classified into similar classes. 
The Marion County ERTS study was supported by a land use class- 
ification of an area near downtown Indianapolis that utilized 
12-band MSS data collected by aircraft from 3000 feet, The re- 
sults of the ERTS analyses suggest that satellite data will be 
a useful tool for the urban planner for monitoring urban land use. 
1. GENERAL DISCUSSION 
The Milwaukee County Subframe 
investigators interested in large urban complexes. On August 9, 1972 
ERTS passed over a large area in the Midwest, including portions of 
northern Illinois and southern Wisconsin. 
was chosen to assess the utility of ERTS data for urban land use studies. 
Four bands ( 4 , 5 , 6 , 7 )  of ERTS multispectral, digitized data were 
analyzed by computer programs developed at the Laboratory for Applica- 
tions of Remote Sensing (LARS)! Initially, the data were examined on a 
digital imaging display (e.g. Fig. 1-A, Fig. 1-B), Significant amounts 
of information may be learned by studying gray scale band images but 
their primary purpose was for orientation. 
toward obtaining a land use classification of the county from analysis 
of the four bands of ERTS MSS data. 
The nonsupervised clustering algorithm ($NSCLAS) was used to disect 
the data into the most spectrally separable classes, using all four bands 
of data. Statistics were calculated for the clusters delimited, using a 
maximum likelihood classifier ($CLASS), and the results displayed by a 
line printer with different alphanumeric symbols for each class ($DISPLAY). 
IT. L. Phillips, (Editor), LARSYS Users Manual, (West Lafayette, Indiana, 
Laboratory for Applications of Remote Sensing, Purdue University, 1973). 
Land use information systems are becoming increasingly important to 
The Milwaukee County sub frame 
The major effort was directed 
1031 
Original photography may be purchased from: 
EROS Data Center 
10th and Dakota Avenue 
Sioux Falls, SD 57198 
https://ntrs.nasa.gov/search.jsp?R=19730019582 2020-03-17T08:48:51+00:00Z
Most of the classes of ground cover types delineated by SNSCLAS were 
the computer manually t o  recognize many ear th  sur- 
not adequately rep Thus, it 
was  necessary t o  t 
face features. On c lass  chosen by SNSCLAS , representing grassy, 
open areas, was retained. Its f i e ld  coordinate cards wexe  combined with 
those chosen manually for  other classes. 
and $DISPLAY programs were used t o  c lassi fy  the study area. 
the $PHOTO program was used t o  display and photograph the c lass i f ica t ion  
r e su l t s  from the d i g i t a l  display. 
of the classes and the gray levels  used for  display. 
In the c lass i f ica t ion  r e su l t s  (Fig. 1-C) the white area i n  the cen- 
tral pa r t  of the study area, which contains Milwaukee's Central Business 
District, has a very high reflectance i n  the v is ib le  bands. The princi- 
pa l  component of t h i s  c lass  is concrete while rooftops are secondary 
features. This class, termed "Road", occurs a l so  as In te rs ta te  highways 
and sandy beaches along Lake Michigan. 
black, is termed "Inner City". 
the bungalow or  "two-flat" type of s t ruc ture ,  housing two or  more fami- 
lies, and were usually b u i l t  pr ior  t o  1940. Rooftops and roads a re  the 
primary consti tuents of t h i s  c lass  while green vegetation is a secondary 
Component . 
The class "Industry" (dark gray) was ident i f ied only where the lar- 
ger areas of heavy industry predominate. Two very Large indus t r ia l  re- 
gions were ident i f ied,  which together form an L-shaped region located 
j u s t  south of the Central Business District. One region is i n  the 
Menomonee River Valley and the other i n  the Kinnickinnick River Valley. 
Rooftops are the  primary, and roads are the secondary components of t h i s  
class. 
complex land uses. 
"Suburban", "Grass", and "Wooded Suburb". Most of t h i s  r ing is classi-  
f ied as suburban (shown as white) and includes the outer areas of the 
City of Milwaukee, northern Wauwatosa, West A l l i s ,  and other suburbs 
within Milwaukee County. "Suburb" consists primarily of single-family , 
middle-income residences b u i l t  a f t e r  World War 11 which are character- 
ized by a complex mixture of roads, rooftops, and green vegetation. 
The areas of the class "Wooded Suburb'' (very l i gh t  gray) are southern 
Wauwatosa, Fox Point, Whitefish Bay, and Shorewood which comprise the 
older upper income sectors  of Milwaukee County. 
primary component of t h i s  class, 
gray) manifests i t s e l f  i n  t h i s  r ing i n  the parks, golf courses, and 
cemeteries. 
"Grass". 
i f  icat ion of newer, upper-income areas as "Grass". 
which are located almost exclusively i n  Lake Michigan. 
t ive of the land use i n  the county. 
As before, the,$STAT, $CLASS, 
Finally, 
Table 1 lists the spectral  reflectance 
The f i r s t  r ing outward from the Central Business District, appearing 
A majority of the homes i n  th i s  area are 
The ring north, w e s t ,  and south of the  "Inner City" is an area of 
The three primary cover types i n  t h i s  area a re  
Green vegetation is the 
Finally, the class "Grass" (very dark 
Most of the outer areas of the county were c lass i f ied  a s  
A problem i n  the c lass i f ica t ion  of t h i s  area w a s  the misclass- 
Five classes of water were ident i f ied within the study area, four of 
There is a regu- 
1032 
lar succession of water classes eastward (Fig. 1-C), suggesting tha t  the 
various classes of water are indicators  of depth. However, reference t o  
U.S, Geological Survey topographic quadrangles shows only s l i g h t  associ- 
a t ion  between the  spec t r a l  classes of water and depth of water. 
and i n  the Milwaukee River. 
( inside the breakwater) and t o  the south along the coast. 
fluencing. the spec t r a l  differences i n  water may be color ,  turbidi ty ,  
and c l a r i t y .  
these are located i n  the southern p a r t  of Milwaukee County and are dis- 
played as white (level 16). 
( l eve l  1). 
The f i f t h  class of water ("Water 4") occurs io  small water bodies 
Factors in- 
This class also appears i n  Milwaukee Harbor 
Several small cumulus clouds were ident i f ied  within the study area; 
The cloud shadows are displayed as black 
Chicago Subframe 
Analysis of the Chicago subframe was undertaken t o  determine i f  the 
spec t ra l ly  separable classes which were used t o  c l a s s i fy  urban land use 
successfully i n  one metropolitan area could produce sa t i s fac tory  r e su l t s  
i n  another metropolitan area, within the same ERTS frame. 
Images representing spec t ra l  da ta  of Chicago from bands 5 (Fig. 2-A) 
and 7 (Fig. 2-B) were produced, using the same h i s tog raw generated fo r  
use i n  the Milwaukee area. 
applying the $CLASS through $PHOTO sequence of computer programs, using 
the same $STAT deck of computer cards as used i n  the Milwaukee study. 
Chicago c l a s s i f i ca t ion  results were s imilar  to  those of Milwaukee (Fig. 
2-C). The same gray leve ls  were used for  both urban c lass i f ica t ions .  
The f i r s t  major r ing  of land use outward from the Central  Business 
District w a s  c l a s s i f i ed  as "Inner City". 
c i t y  of Chicago, Cicero, Berwyn, and Blue Island. 
ies and la rge  indus t r i a l  areas were ident i f ied  within t h i s  ring. 
The next r ing  outward includes large areas of the classes "Suburb" 
and "Wooded Suburb". The l a rger  areas of "Suburb" are found i n  the muni- 
c i p a l i t i e s  of Oak Lawn, Hodgkins, Norridge, Harwood Heights, and Morton 
Grove. Four major regions of the class "Wooded Suburb" appeared: (1) the 
Glencoe, Wilmette, Evanston area along Lake Michigan; (2) the suburban 
agglomeration south of O'Hare Airport including Oak Park, Elmhurst, Lom- 
bard, and Wheaton; (3) the suburban agglomeration t o  the south of the 
second, including Hinsdale and Downers Grove; and (4) an area i n  Chicago 
bounded by the Dan Ryan Woods on the north, 119th S t r ee t  on the south, 
Beverly S t ree t  and Vincennes on the east, and Western Avenue on the w e s t .  
The classes "Cloud" and "Shadow" were iden t i f i ed  i n  the northwestern 
pa r t  of the  Chicago area, north of O'Hare Airpert. 
A problem i n  the Chicago c l a s s i f i ca t ion  arose i n  the cent ra l ,  older 
p a r t  of the urbanized area, most of which was c la s s i f i ed  as "Inner City". 
Too many data  points within t h i s  area were c l a s s i f i ed  as "Industry" 
The Chicago spec t ra l  da ta  were analyzed by 
This area includes most of the 
Larger parks, cemeter- 
1033 
suggesting t h a t  ye t  another type of r e s iden t i a l  area e x i s t s  i n  Chicago, 
which is not found i n  Milwaukee. 
areas are largely void of vegetation and consis t  mainly of rooftops and 
Marion County (Indianapolis) Subframe 
I n  another ERTS frame (pass of Sept. 30, 1972), similar spec t r a l  
classes were obtained f o r  the Indianapolis area, using the same procedures 
of analysis  developed f o r  the  Milwaukee area. 
vised (SNSCLAS) method of c l a s s i f i ca t ion  was implemented followed by the  
manual, o r ,  supervised approach, 
Figure 3-A, and Table 2 lists the mean ref lectance of classes and the 
gray leve ls  used i n  the  figure.  
Whereas a class fo r  heavy industry w a s  obtained i n  the Milwaukee 
area, a general class of heavy industry/conmterce was developed (shown as 
medium gray) f o r  Indianapolis. This c lass  includes .the Central Business 
District. As i n  Milwaukee, the c l a s s  "Suburban" was developed f o r  Indi- 
anapolis. Areas i n  t h i s  class are located on the eastern and western 
f lanks of Interstate 465. 
I n  the Milwaukee study, a class "Wooded Suburb" was obtained, but a 
more general class "Wooded" was used i n  Indianapolis. 
c i t y ' s  reservoirs  and cer ta in  upper income areas near Meridian H i l l s  and 
t o  the east were c l a s s i f i ed  as "Wooded". 
developed which was represented by the  primary water bodies of Geist and 
Eagle Creek Reservoirs. 
were ident i f ied  within the county. 
urb" and "Inner City". 
quently as "Grassy" (open) o r  "Wooded" . 
tween the "Suburban" and "Inner City" is not as w e l l  defined as i n  
Milwaukee. 
It is speculated tha t  such r e s iden t i a l  
roads. I 
I n i t i a l l y ,  the nonsuper- 
Classif icat ion r e s u l t s  are presented i n  
Areas around the 
Only one class of "Water" was 
Several small cumulus clouds and the i r  shadows 
A problem i n  the c l a s s i f i ca t ion  arose i n  some locations between "Sub- 
Areas i n  these classes  were misclassified fre- 
Apparently, the d is t inc t ion  be- 
Supporting the Indianapolis ERTS study was an analysis  of 12-band 
mult ispectral  scanner data  collected by aircraft from 2000 f t .  over a por- 
t i on  of the West Fork White River near downtown Indianapolis. The same 
procedure of analysis  was used as i n  Milwaukee, Chicago, and Marion 
County except tha t  only four of the twelve bands (#2, .46-.491~m; # 6 ,  .55- 
.601rm; #lo, lO0-1.4vm; #12, 9.3-11.7pm) were used t o  c l a s s i fy  the  study 
area. 
three photos of the c lass i f ica t ion .  
ear th  surface features.  
ter", "Bare Soil", "Road", "Fine Grass", "Coarse Grass" , "Trees", and 
"Rooftops", 
t u r a l  features  are displayed by one gray-tone, while the two classes of 
"Road" and three of "Rooftop" are given varying graytones. 
the  var ia t ion i n  na tura l  features.  A l l  cu l tu ra l  features are one gray- 
tone, while "Trees", two classes of "Coarse Grass", and two classes of 
"Fine Grass" vary i n  graytone. 
Table 3 lists the mean reflectance and gray leve ls  used i n  the 
Fig. 3-B shows the general classes of 
The classes, from darkest  t o  l i gh te s t ,  are "Wa- 
Fig. 3 4  shows the  var ia t ions i n  cu l tu ra l  features.  A l l  na- 
Fig. 3-D shows 
1034 
CONCLUSIONS 
Machine processing of ERTS MSS data i n  urban areas has produced spec- 
tral classes which may be of value t o  the urban planner. 
the future,  timely processing of such data  could r e su l t  i n  quick updating 
of land use inventories. The Chicago r e su l t s  indicate that  a careful 
analysis of one urban area can provide valuable information fo r  other 
urban areas, with no a p r i o r i  knowledge. 
cast the development of statistics, taking in to  account seasonal varia- 
t ions,  which may be used t o  c lass i fy  automatically many urban areas. 
Looking toward 
It is  not unreasonable to fore- 
1035 
Water 1 
Water 2 
Water 3 
Water 4 
Water 5 
Grass 1 
Grass 2 
Road 
Wood Sub. 
Inner City 
Suburban 
Industry 
Cloud 
Shadow 
Commerce 
Inner City 
Suburban 
Grassy 
Wooded 
Water 
Cloud 
Shadow 
Rooftop 1 
Rooftop 2 
Rooftop 3 
Road 1 
Road 2 
Fine Grass 
Fine Grass 
RELATIVE MEAN REFLECTANCE AND GRAY LEVELS USED FOR 
DISPLAY OF SPECTRAL CLASSES 
Table 1. Milwaukee (ERTS) 
Levell 
II
Bd. 7 -Bd. 6 Ed. 4 
35-20 21.41 9.94 1.49 5 
-Bdo 5 -I
24.23 
19.71 
21 . 47 
46.83 
27.01 
24-03 
47.42 
24.94 
30.16 
39.17 
25.98 
71.55 
17.70 
10.37 
9.42 
13.85 
46.50 
19.88 
16.25 
48.26 
18.66 
25.97 
37.09 
21.77 
69.50 
9.45 
5.40 
5.86 
12.65 
23.83 
53.16 
44.18 
46.84 
40.24 
34.59 
53.12 
21.30 
89.07 
16.15 
0.69 
1.10 
3.34 
2.83 
31.35 
26.59 
20.16 
22 91 
16.53 
26.99 
8.50 
44.38 
6.75 
Table 2. Indianapolis (ERTS) 
Bd. 7 Bd. 4 
34.28 30.70 28.17 11.88 
24.01 17.04 26.18 14.09 
31 87 25.82 41.07 22.51 
23.65 17.33 34.69 20.36 
19.79 12.01 32.95 21.10 
22.64 13.73 8.24 1.37 
82.41 78.88 85.08 41.17 
16.49 8.46 9.46 3.31 
-Bd. 6 -Bd. 5 --
3 
1 
1 
14 
6 
6 
16 
10 
1 
16 
8 
16 
1 
- Levell 
8 
1 
16 
5 
11 
1 
16 
1 
Table 3. Indianapolis (2000 Feet) 
Bd. 12 GC2 CC3 NC4 - - - -  Bd. 2 Bd. 6 Bd. 10 
130.04 1 m  88.40 199.23 16 16 3 
86.28 98.27 70.33 221.53 16 13 3 
47.34 44.75 37.63 248.43 16 10 3 
158.67 201.79 120.61 122.80 6 6 3 
90.59 102.96 71.39 152.58 6 8 3 
1 50.30 72.20 150 58 132.11 8 4 9 
2 44-82 60.98 146.43 124.81 8 4 11 
Coarse Grass 1 51.49 68.73 108.56 134.96 10 4 13 
Coarse Grass 2 44.74 57.87 105.80 128.21 10 4 16 
Trees 38.69 48.27 105.28 88.49 12 4 7 
86.93 106.52 80-57 153.01 4 4 5 Bare Soil 
Water 43.11 53.00 27.11 84.73 1 1 1 
Shadow 37.82 35.70 39.24 73.98 1 1 1 
'Gray levels used in land use classification. 
2Gray levels used in classification: general classes. 
'Gray levels used in classif ication: cultural features 
4Gray levels used in claseif ication: natural f eatures. 
1036 
.. - 
M 
d .A . 
1037 
Fr 
1038 
. 
rr) 
1039 


Preparation of urban land use inventories by machine processing of ERTS MSS data

Spectral classes of urban phenomena identified from ERTS multispectral scanner data in Milwaukee included Surburban, Inner City, Industry, Grassy (open area), Road, Wooded Suburb, Water, Cloud, and Shadow. The Milwaukee spectral class statistics were used to classify the Chicago area, within the same ERTS frame, and similar results were achieved. In another ERTS frame, Marion County (Indianapolis) data were classified into similar classes. The Marion County ERTS study was supported by a land use classification of an area near downtown Indianapolis that utilized 12-band MSS data collected by aircraft from 3000 feet. The results of the ERTS analyses suggest that satellite data will be a useful tool for the urban planner for monitoring urban land use

\- 34? ,
PREPARATION OF URBAN LAND USE INVENTORIES
BY MACHINE-PROCESSING OF ERTS MSS DATA
William Todd, Paul Mauscl, Kenneth Wonner
m^ &bor^ aory for Applications of Remote Sensing
Purdue University
West Lafayette, Indiana
ABSTRACT
Spectral classes of urban phenomena identified from Earth
Resources Technology Satellite (ERTS) multispectral scanner data
in Milwaukee included "Suburban", "Inner City", "Industry",
"Grassy" (open area), "Road", "Wooded Suburb", "Water", "Cloud",
and "Shadow". The Milwaukee spectral class statistics were used
to classify the Chicago area, within the same ERTS frame, and
similar results were achieved. In another ERTS frame, Marion
County (Indianapolis) data were classified into similar classes.
The Marion County ERTS study was supported by a land use class-
ification of an area near downtown Indianapolis that utilized
12-band MSS data collected by aircraft from 3000 feet. The re-
suits of the ERTS analyses suggest that satellite data will be
a useful tool for the urban planner for monitoring urban land use.
1. GENERAL DISCUSSION
The Milwaukee County Subframe
Land use information systems are becoming increasingly important to
investigators interested in large urban complexes. On August 9, 1972
ERTS passed over a large area in the Midwest, including portions of
northern Illinois and southern Wisconsin. The Milwaukee County subfrane
was chosen to assess the utility of ERTS data for urban land use studies.
Four bands (4,5,6,7) of ERTS multispectral, digitized data were
analyzed by computer programs developed at the Laboratory for Applica-
tions of Remote Sensing (LARS)i Initially, the data were examined on a
digital imaging display (e.g. Fig. 1-A, Fig. 1-B). Significant amounts
of information may be learned by studying gray scale band images but
their primary purpose was for orientation. The major effort was directed
toward obtaining a land use classification of the county from analysis
of the four bands of ERTS MSS data.
The nonsupervised clustering algorithm ($NSCLAS) was used to disecc
the data into the most spectrally separable classes, using all four bands
of data. Statistics were calculated for the clusters delimited, using a
maximum likelihood classifier ($CLASS), and the results displayed by s.
line printer with different alphanumeric symbols for each class ($DISPLAY)
1T. L. Phillips, (Editor), LARSYS Users Manual. (West Lafayette, Indiana,
Laboratory for Applications of Remote Sensing, Purdue University, 1973).
https://ntrs.nasa.gov/search.jsp?R=19730013620 2020-03-23T02:15:42+00:00Z
Most of the classes of ground cover types delineated by $NSCLAS v/cre
not adequately representative of the land use in the county. Thus, it
was necessary to train the computer manually to recognize many earth sur-
face features. Only one class chosen by $NSCLAS, representing grassy,
open areas, was retained. Its field coordinate cards were combined with
those chosen manually for other classes. As before, the $STAT, $CLASS,
and $DISPLAY programs were used to classify the study, area. Finally,
the $P1IOTO program was used to display and photograph the classification
results frora the digital display. Table 1 lists the spectral reflectance
of the classes and the gray levels used for display.
In the classification results (Fig. 1-C) the white area in the cen-
tral part of the study area, which contains Milwaukee's Central Business
District, has a very high reflectance in the visible bands. The princi-
pal component of this class is concrete while rooftops are secondary
features. This class, termed "Road", occurs also as Interstate highways
and sandy beaches along Lake Michigan.
The first ring outward from the Central Business District, appearing
black, is termed "Inner City". A majority of the homes in this area are
the bungalow or "two-flat" type of structure, housing two or more fami-
lies, and were usually built prior to 1940. Rooftops and roads are the
primary constituents of this class while green vegetation is a secondary
component.
The class "Industry" (dark gray) was identified only where the lar-
ger areas of heavy industry predominate. Two very large industrial re-
gions were identified, which together form an L-shaped region located
just south of the Central Business District. One region is in the
Menomonee River Valley and the other in the Kinnickinnick River Valley.
Rooftops are the primary, and roads are the secondary components of this
class.
The ring north, west, and south of the "Inner City" is an area of
complex land uses. The three primary cover types in this area are
"Suburban", "Grass", and "Wooded Suburb". Most of this ring is classi-
fied as suburban (shown as white) and includes the outer areas of the
City of Milwaukee, northern Wauwatosa, West Allis, and other suburbs
within Milwaukee County. "Suburb" consists primarily of single-family,
middle-income residences built after World War II which are character-
ized by a complex mixture of roads, rooftops, and green vegetation.
The areas of the class "Wooded Suburb" (very light gray) are southern
Wauwatosa, Fox Point, Whitefish Bay, and Shorewood which comprise the
older upper income sectors of Milwaukee County. Green vegetation is the
primary component of this class. Finally, the class "Grass" (very dark
gray) manifests itself in this ring in the parks, golf courses, and
cemeteries. Most of the outer areas of the county were classified as
"Grass". A problem in the classification of this area was the misclass-
ification of newer, upper-income areas as "Grass".
.Five classes of water were identified within the study area, four of
which are located almost exclusively in Lake Michigan. There is a regu-
lar succession of water classes eastward (Fig. 1-C), suggesting that the
various classes of water are indicators of depth. However, reference to
U.S. Geological Survey topographic quadrangles shows only slight associ-
ation between the spectral classes of water and depth of water.
The fifth class of water ("Water 4") occurs in small water bodies
and in the Milwaukee River. This class also appears in Milwaukee Harbor
(inside the breakwater) and to the south along the coast. Factors in-
fluencing the spectral differences in water may be color, turbidity,
and clarity.
Several small cumulus clouds were identified within the study area;
these are located in the southern part of Milwaukee County and are dis-
played as white (level 16). The cloud shadows are displayed as black
(level 1).
Chicago Subframe
Analysis of the Chicago subframe was undertaken to determine if the
spectrally separable classes which were used to classify urban land use
successfully in one metropolitan area could produce satisfactory results
in another metropolitan area, within the same ERTS frame.
Images representing spectral data of Chicago from bands 5 (Fig. 2-A)
and 7 (Fig. 2-B) were produced, using the same histograms generated for
use in the Milwaukee area. The Chicago spectral data were analyzed by
applying the $CLASS through $PHOTO sequence of computer programs, using
the same $STAT deck of computer cards as used in the Milwaukee study.
Chicago classification results were similar to those of Milwaukee (Fig.
2-C). The same gray levels were used for both urban classifications.
The first major ring of land use outward from the Central Business
District was classified as "Inner City". This area includes most of the
city of Chicago, Cicero, Berwyn, and^ Blue Island. Larger parks, cemeter-
ies and large industrial areas were identified within this ring.
The next ring outward includes large areas of the classes "Suburb"
and "Wooded Suburb". The larger areas of "Suburb" are found in the muni-
cipalities of Oak Lawn, Hodgkins, Norridge, Harwood Heights, and Morton
Grove. Four major regions of the class "Wooded Suburb" appeared: (1) the
Glencoe, Wilmette, Evanston area along Lake Michigan; (2) the suburban
agglomeration south of O'Hare Airport including Oak Park, Elmhurst, Lom-
bard, and Wheaton; (3) the suburban agglomeration to the south of the
second, including Hinsdale and Downers Grove; and (4) an area in Chicago
bounded by the Dan Ryan Woods on the north, 119th Street on the south,
Beverly Street and Vincennes on the east, and Western Avenue on the west.
The classes "Cloud" and "Shadow" were identified in the northwestern
part of the Chicago area, north of O'Hare Airport.
A problem in the Chicago classification arose in the central, older
part of the urbanized area, most of which was classified as "Inner City".
Too many data points within this area were classified as "Industry"
suggesting that yet another type of residential area exists in Chicago,
which is not found in Milwaukee. It is speculated that such residential
areas are largely void of vegetation and consist mainly of rooftops and
roads.
Marion County (Indianapolis) Subframe
In another ERTS frame (pass of Sept. 30, 1972), similar spectral
classes were obtained for the Indianapolis area, using the same procedures
of analysis developed for the Milwaukee area. Initially, the nonsuper-
vised ($NSCLAS) method of classification was implemented followed by the
manual, or, supervised approach. Classification results are presented in
Figure 3-A, and Table 2 lists the mean reflectance of classes and the
gray levels used in the figure.
Whereas a class for heavy industry was obtained in the Milwaukee
area, a general class of heavy industry/commerce was developed (shown as
medium gray) for Indianapolis. This class includes the Central Business
District. As in Milwaukee, the class "Suburban" was developed for Indi-
anapolis. Areas in this class are located on the eastern and western
flanks of Interstate 465.
In the Milwaukee study, a class "Wooded Suburb" was obtained, but a
more general class "Wooded" was used in Indianapolis. Areas around the
city's reservoirs and certain upper income areas near Meridian Hills and
to the east were classified as "Wooded". Only one class of "Water" was
developed vrfiich was represented by the primary water bodies of Geist and
Eagle Creek Reservoirs. Several small cumulus clouds and their shadows
were identified within the county.
A problem in the classification arose in some locations between "Sub-
urb" and "Inner City". Areas in these classes were misclassified fre-
quently as "Grassy" (open) or "Wooded". Apparently, the distinction be-
tween the "Suburban" arid "Inner City" is not as well defined as in
Milwaukee.
.Supporting the Indianapolis ERTS study was an analysis of 12-band
multispectral scanner data collected by aircraft from 2000 ft. over a por-
tion of the West Fork White River near downtown Indianapolis. The same
procedure of analysis was used as in Milwaukee, Chicago, and Marion
County except that only four of the twelve bands (#2, .46-.49ym; #6, .55-
.60yra; #10, 1.0-1.4um; #12, 9.3-11.7ym) were used to classify the study
area. Table 3 lists the mean reflectance and gray levels used in the
three photos of the classification. Fig. 3-B shows the general classes of
earth surface features. The classes, from darkest to lightest, are "Wa-
ter", "Bare Soil", "Road", "Fine Grass", "Coarse Grass", "Trees", and
"Rooftops". Fig. 3-C shows the variations in cultural features. All na-
tural features are displayed by one gray-tone, while the two classes of
"Road" and three of "Rooftop" are given varying graytones. Fig. 3-D shows
the variation in natural features. All cultural features are one gray-
tone,, while "Trees", two classes of "Coarse Grass", and two classes of
"Fine Grass" vary in graytone.
CONCLUSIONS
Machine processing of ERTS MSS data in urban areas has produced spec-
tral classes which may be of value to the urban planner. Looking toward
the future, timely processing of such data could result in quick updating
of land use inventories. The Chicago results indicate that a careful
analysis of one urban area can provide valuable information for other
urban areas, with no a priori knowledge. It is not unreasonable to fore-
cast the development of statistics, taking into account seasonal varia-
tions, which may be used to classify automatically many urban areas.
RELATIVE flEAN REFLECTANCE AND GRAY LEVELS USED FOR
DISPLAY OF SPECTRAL CLASSES
Table 1. Milwaukee (ERTS)
Water 1
Water 2
Water 3
Water 4
Water 5
Grass 1
Grass 2
Road
Wood Sub.
Inner City
Suburban
Industry
Cloud
Shadow
Bd. 4
35.2~0
24,23
19.71
21.47
46.83
27.01
24.03
47.42
24.94
30.16 '
39.17
25.98
71.55
17.70
Ikl. 5
21.41
10 . 37
9.42
13.85
46.50
19.88
16.25
48.26
18.66
25.97
37.09
21.77
69.50
9.45
Bd. 6
9.94
5.40
5.86
12.65
23.83
53.16
44.18
46.84
40.24
34.59
53.12
21.30
89.07
16.15
Table 2. Indianapolis
Commerce
Inner City
Suburban
Grassy
Wooded
Water
Cloud
Shadow
Rooftop 1
Rooftop 2
Rooftop 3
Road 1
Road 2
Fine Grass 1
Fine Grass 2
Coarse Grass
Coarse Grass
Trees
Bare Soil
Water
Shadow
AGray levels
2Gray levels
3
 Gray levels
**Gray levels
Bd. 4
34.28
24.01
31.87
23.65
19.79
22.64
82.41
16.49
Table
Bd. 2
130.04
86.28
47.34
158.67
90.59
50.30
44.82
1 51.49
2 44.74
38.69
86.93
43.11
37.82
Bd. 5
30.70
17.04
25.82
17.33
12.01
13.73
78.88
8.46
Bd. 6
28.17
26.18
41.07
34.69
32.95
8.24
85.08
9.46
Bd. 7
1.49
0.69
. 1.10
3.34
. 2.83
31.35
26.59
20.16
22.91
16.53
26.99
8.50
44.33
6.75
(ERTS)
Bd. 7
11.88
14.09
22.51
20.36
21.10
1.37
41.17
3.31
3. Indianapolis (2000 Feet)
Bd. 6
145.81
98.27
44.75
201.79
102.96
72.20
60.98
68.73
57.87
48.27
106.52
53.00
35.70
Bd. 10 Bd. 12
83.40
70.33
37.63
120.61
71.39
150.58
146.43
108.56
105.80
105.28
80.57
27.11
39.24
; 199.23
221.53
248.43
122.80
152.58
132.11
124.81
134.96
128.21
88.49
153.01
84.73
73.98
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Preparation of urban land use inventories by machine processing of ERTS MSS data

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