Multispectral scanner (MSS) digital data from ERTS-1 was used to delineate coastal land, vegetative, and water features in two portions of the Texas Coastal Zone. Data (Scene ID's 1037-16244 and 1037-16251) acquired on August 29, 1972, were analyzed on NASA Johnson Space Center systems through the use of two clustering algorithms. Seventeen to 30 spectrally homogeneous classes were so defined. Many classes were identified as being pure features such as water masses, salt marsh, beaches, pine, hardwoods, and exposed soil or construction materials. Most classes were identified to be mixtures of the pure class types. Using an objective technique for measuring the percentage of wetland along salt marsh boundaries, an analysis was made of the accuracy of areal measurement of salt marshes. Accuracies ranged from 89 to 99 percent. Aircraft photography was used as the basis for determining the true areal size of salt marshes in the study sites

Flores, L. M.

Hixon, S. B.

Paris, J. F.

Reeves, C. A.

English

NASA Technical Reports Server

UNSUPERVISED CLASSIFICATION AND AREAL MEASUREMENT OF LAND 
AND WATER COASTAL FEATURES ON THE TEXAS COAST 
L. M. Flores, C. A. Reeves, S. B. Hixon and J. F. Paris, LockheedElectronics 
Company, lnc., Houston Aerospace Systems Division 
ABSTRACT 
ERTS-1 multispectral scanner (MSS) digital data was used to 
delineate coastal land, vegetative, and water features in two 
portions of the Texas Coastal Zone. ERTS-1 MSS data (Scene ID'S 
1037-16244 and 1037-16251) acquired on August 29, 1972, were 
analyzed on NASA Johnson Space Center systems through the use of 
two clustering algorithms. Seventeen to 30 spectrally homogeneous 
classes were so defined. Many classes were identified as being 
pure features such as water masses, salt marsh, beaches, pine, 
hardwoods, and exposed soil or construction materials. Most 
classes were identified to be mixtures of the pure class types. 
Using an objective technique for measuring the percentage of 
wetland along salt marsh boundaries, an analysis was made o f  the 
accuracy o f  areal measurement of salt marshes. Accuracies ranged 
from 89 to 99 percent. ,NASA aircraft photography was used as the 
basis for determining the true areal size of salt marshes in the 
study sites. 
1. INTRODUCTION 
cation algorithms to ERTS-1 multispectral scanner (MSS) system corrected data 
acquired over the Galveston Bay (Texas) area on August 29, 1972 (Scene ID'S 
1037-16244 and 1037-16251). Specifically, two algorithms (ISOCLS and NSCLAS) 
available at the NASA Johnson Space Center (JSC) were used to produce classi- 
fication symbol maps of portions of the Texas coastal zone. The resulting 
classes then were related to an information hierarchy selected for the MSC 
ERTS-1 coastal estuarine investigation. 
The purpose of this paper is to report on the application of two classifi- 
HIERARCHY OF COASTAL FEATURES 
Level 1 
Water 
Nonwater 
Level 2 
Water Mass 
Features 
Bottom Features 
Sand (beaches, spoil) 
Wetland 
Other 
Level 3 
Turbid water 
Productive water. 
Nonproductive water 
Vegetated 
Nonvegetated 
Vegetated 
Open water 
Nonvegetated 
Photography obtained by the NASA WE-57 aircraft on August 3 0 ,  1972, was 
used to aid in the identification of the classes defined by the algorithms 
and to provide a basis for the measurement of the true area of selected 
features such as water bodies and marsh. 
1675 
Original photography may be Burchased 
EROS Data Center 
loth and Dakota Avenue \ 
iou Fails, Sp 57_198 - ' 
https://ntrs.nasa.gov/search.jsp?R=19730019651 2020-03-17T09:14:20+00:00Z
2. METHOD OF ANALYSIS 
Two study si tes were e d i t e d  from ERTS-1 MSS system corrected d a t a  contained 
i n  frames 1037-16244 and 1037-16251. These s i tes  covered (1) T r i n i t y  River 
Delta a rea  including s a l t ,  brackish,  and f r e s h  water marshes, and (2) Galveston 
I s l and ,  West Bay, Pe l i can  I s l and ,  and offshore Gulf waters .  
t o  Purdue Universi ty .  
t he  remote terminal  a t  JSC. 
t i o n  and unknown s p a t i a l  e x t e n t ,  it was decided t o  use c l a s s i f i c a t i o n  algorithms 
t o  produce c l a s s i f i c a t i o n  symbol maps of t h e  s i tes .  
(ISOCLS and NSCLAS) attempts t o  p a r t i t i o n  t h e  d a t a  i n t o  s p e c t r a l l y  homogeneous 
groups. 
regions i n  which these  homogeneous groups of a a t a  occur. A d e t a i l e d  descr ip-  
t i o n  of ISOCLS can be found i n  references 1 through 5. A desc r ip t ion  of NSCLAS 
i s  given i n  r e fe rence  6. 
Photography (f i lm 2443, co lo r  I R )  of t h e  study s i t e s  was obtained by the  
NASA WB-57 a i r c r a f t  from 60,000 feet  (MSL) on August 30, 1972, one day a f t e r  
t h e  sub jec t  ERTS-1 da ta .  
scale of 1:41,200. 
team members. 
a photographic d a t a  quan t i ze r  (planimeter).  F i e ld  t r i p s  i n t o  t h e  si tes were 
made t o  a i d  i n  the  i d e n t i f i c a t i o n  of classes defined by ISOCLS i n  t h a t  si’te. 
Photo i n t e r p r e t e r s  i d e n t i f i e d  classes i n  both s i tes  through the  use of t h e  
a i r c r a f t  photography. 
s i t e ,  an ob jec t ive  techniquk described i n  reference 7 i n  t h i s  symposium was 
used t o  count p i c t u r e  elements and p a r t i a l  p i c t u r e  elements on the  boundary of 
t he  marsh. 
One computer compatible t ape  (CCT) containing t h e  Galveston da ta  was s e n t  
This d a t a  set could then be analyzed through t h e  use of 
Both sets could be analyzed by JSC systems. 
Since t h e  two study s i tes  exhibi ted n a t u r a l  f e a t u r e s  of complex’configura- 
Each of t h e  two algorithms 
They then generate  c l a s s i f i c a t i o n  maps t o  d i sp l ay  t h e  geographical 
The photography was r e c t i f i e d  and enlarged t o  a 
Then, wetland areas  were de l inea ted  by c o a s t a l  ana lys i s  
The a rea  of each de l inea t ion  was determined through the  use of 
In  the  case of t he  areal measurement of t h e  wetland areas  i n  the  Galveston 
3. RESULTS 
Space does not permit t h e  inc lus ion  of a l l  of t he  color  coded c l a s s i f i c a -  
t i o n  maps produced during the  p re sen t  ana lys i s  e f f o r t .  Examples of l e v e l  two 
maps i n  each s i t e  a re  shown i n  f igu res  1 and 2 ( o r i g i n a l s  a r e  i n  co lo r ) .  In  
f i g u r e  2 ,  four  c l a s s e s  a r e  shown: water,  fo re s t ed  a reas ,  vegetat ion including 
marsh, and bare soil/beach/asphalt/concrete. In f i g u r e  3, t h r e e  c l a s ses  a re  
shown: water,  wetland (marsh), and other .  
4 .  WETLAND ANALYSIS 
the  areas  of t h r e e  wetlands were determined. The r e s u l t s  a r e  given i n  t a b l e  I .  
Using t h e  ob jec t ive  technique described i n  reference 7 ( t h i s  symposium), 
TABLE I.- RESULTS OF DETERMINING AREA OF WETLANDS FROM 
ERTS-1 DATA THROUGH AN OBJECTIVE TECHNIQUE 
ESTIMATED AREA PERCENT ACCURACY 
WETLAND (ACRES) TRUE AREA 
NUMBER ISOCLS NSCLAS (ACRES) ISOCLS NSCLAS 
1 864.13 871.56 866.0 99.6 99.4 
2 *  197.97 193.77 191.3 96.5 98.7 
3 150.86 165.94 149.6 99.2 89.1 
The accuracies  i n  wetland (marsh) area measurement a r e  q u i t e  high. Both 
algorithms accurately def ine wetland f ea tu res  and boundary c l a s ses .  I t  i s  not 
poss ib l e  a t  t h i s  time t o  determine t h e  more supe r io r  algorithm; however, 
ISOCLS on the  UNIVAC 1108 was ab le  t o  handle a l a r g e r  number of d a t a  po in t s  than 
NSCLAS on the  IBM 360/67 a t  Purdue University.  
5 .  MIXTURE PROBLEM 
A graph dep ic t ing  t h e  p o s i t i o n  of c l u s t e r  means i n  t h e  f i rs t  and fou r th  
channels of t he  ERTS-1 MSS is shown f o r  t he  T r i n i t y  s i te  i n  f i g u r e  4 f o r  a 
1676 
30-cluster  case, which w i l l  be the  sub jec t  f o r  t h e  remainder of t h i s  discussion.  
This f i g u r e  i s  a good r ep resen ta t ion  of t he  da t a  and of t h e  r e s u l t s  of t he  
c l u s t e r  ana lys i s .  With few exceptions the  second and t h i r d  channels of t h e  
MSS add l i t t l e  new information. 
The c l u s t e r s  obtained here  are, f o r  t h e  most p a r t ,  mixtures of some of t h e  
major c l a s s e s  t h a t  e x i s t  on t h e  su r face .  
r e l a t e d  t o  these major classes. 
can be so  r e l a t ed :  
The remaining c l u s t e r s  can be uniquely 
The following t a b l e  shows those c l u s t e r s  t h a t  
C lus t e r  I d e n t i f i c a t i o n  
B,R,D,N,S 
C 
Class 
Vegetation e E i i Z i n g  trees 
Pine Trees 
Hardwoods 
Beach/asphalt/concrete 
Bare s o i l  
Water 
The c l u s t e r s  have been i d e n t i f i e d  by assigning t o  them consecutive a r b i t r a r y  
symbols 1, 2 ?  ..., 9 ,  A ,  B,  ... 
Up t o  f i v e  c l u s t e r s  can be i d e n t i f i e d  as  belonging t o  water bodies. No 
q u a n t i t a t i v e  r e l a t i o n s h i p  can be assigned t o  these  c l u s t e r s  a t  t h i s  time 
because the re  was no concurrent water survey on t h e  d a t e  of the ERTS pass,  
The following is  a general  desc r ip t ion  of t he  type of water c l u s t e r s  and where 
they occur:  
. 
Clus te r  I d e n t i f i c a t i o n  Class o r  Description 
1Bay -tly 
tu rb id )  
G Turbid (fresh o r  s a l i n e )  
water ’ 
H Highly t u r b i d  water ,  very 
shallow water,  o r  under- 
water s p o i l  banks 
L Fresh water t h a t  occurs i n  
l akes  
S Turbid f r e s h  water o r  t u rb id  
water with a small  amount 
of vegetat ion ( l e s s  than 
1 0  pe rcen t ) .  
The c l u s t e r i n g  ana lys i s  d i f f e r e n t i a t e s  fo re s t ed  a reas  from o the r  types of 
Vegetation types o the r  than 
vegetat ion.  Within the fo re s t ed  a r e a s ,  pine t r e e s  and hardwoods can a l s o  be 
d i f f e r e n t i a t e d  and assigned t o  sepa ra t e  c l u s t e r s .  
t r e e s  cannot be separated.  S p e c i f i c a l l y ,  the same combination of c l u s t e r s  
occur over vegetated a reas  which a r e  known t o  contain d i f f e r e n t  c l a s s e s  such 
a s  Phra mites  communes, S a r t i n a  s a r t i n a e ,  and pas tu re  land. 
On: conspicuous f a c h i :  inves t iga t ion  has brought f o r t h  i s  t h a t  a 
good p a r t  of t he  da t a  can be r e l a t e d  t o  a mixture of two o r  more c l a s s e s  found 
on the  surface.  In  the  case of wetlands a rea ,  t he  proport ion of c l a s s e s  con- 
t r i b u t i n g  t o  a p a r t i c u l a r  mixture v a r i e s  i n  a continuous fashion over t he  
poss ib l e  range. Under these  circumstances the re  a r e  no n a t u r a l  groupings of 
the d a t a ;  so the  c l u s t e r i n g  program tends t o  p a r t i t i o n  the  da t a  a t  a r b i t r a r y  
p l aces .  
of c l a s s e s ,  can be obtained by l i n e a r l y  combining d a t a  assigned t o  c l u s t e r s  
i d e n t i f i e d  with one of t h e  major c l a s ses .  
inspect ing f igu re  4 where, f o r  example, d a t a  po in t s  i n  c l u s t e r  8 can be 
obtained by l i n e a r l y  combining d a t a  po in t s  i n  c l u s t e r s  5 and 6. In t h i s  manner, 
a range o f  r a t i o s  of a p a r t i c u l a r  mixture can be ex t r ac t ed  from t h e  data .  The 
following t a b l e  represents  t h i s  range f o r  the water /vegetat ion mixture. These 
numbers r ep resen t  an e s t ima te  only and a r e  not t he  r e s u l t  of a s t a t i s t i c a l  
s tudy . 
I t  may be noted t h a t  da t a  assigned t o  c l u s t e r s  which r ep resen t  a mixture 
This f a c t  may be e a s i l y  seen by 
1677 
Clus te r  I d e n t i f i c a t i o n  
6 
8 
E 
2 
7 
5 
N 
R 
Ratio of Water 
.6- 95 
.5-.8 
.3-.7 
.2-.6 
. l - * 5  
0-.35 
0-. 20 
0- .10 
0-.05 
Other mixtures could c o n s i s t  of almost any poss ib l e  combination of t h e  
major classes t h a t  have been i d e n t i f i e d .  The following t a b l e  p re sen t s  a l i s t  
of poss ib l e  rnixutres t h a t  have been v e r i f i e d :  
C lus t e r  
I d e n t i f i c a t i o n  
4 
C 
0 
8 
Mixture 
Hardwoods; p ines  and vegetat ion (excl.  trees) 
Pines;  hardwoods and water 
Dense p ines ;  trees and water 
Pines and vegetat ion (excl.  t r e e s ) ;  hardwoods vegetat ion 
(excl. water) and water 
Marsh; vegetat ion and water 
Water and bare  soi l /beach/asphal t  i n  increasing proportion of 
ba re  soi l /beach/  asphal t 
Vegetation and bare  soil/beach/asphalt/concrete i n  increasing 
proport ion of bare  soil/beach/asphalt/concrete 
Vegetation, water, and bare  soil/beach/asphalt/concrete i n  
increasing proportion of bare  soil/beach/asphalt/concrete 
6. CONCLUSION 
Two c l u s t e r i n g  algorithms were used t o  produce c l a s s i f i c a t i o n  maps of t h e  
T r i n i t y  River d e l t a  a r e a  (Scene I D  1037-16244) and the  Galveston a rea  (scene 
ID 1037-16251) from d a t a  obtained on August 29, 1972. Seventeen t o  t h i r t y  
classes were defined. Most of t hese  c l a s ses  represented mixtures of land, 
water,  and vegetat ion including f o r e s t .  Through the  use of a c l u s t e r  diagram, 
ranges of percent  of water and wetland were assigned t o  some c l a s s e s .  Improve- 
ments would r e s u l t  i f  a s t a t i s t i c a l  approach were used f o r  t he  mixture problem. 
7 .  RECOMMENDATIONS 
Further  ana lys i s  is needed t o  determine whether t he  pos i t i on  of c l u s t e r  
c e n t e r s  i n  the  c l u s t e r  diagram can be used t o  i d e n t i f y  the  c l u s t e r  a s  repre-  
s en t ing  a c e r t a i n  c l a s s  of c o a s t a l  f ea tu re s .  
1 678 
REFERENCES 
1. Ball, G. H., and Hall, D. J.: A Clustering Technique for Summarizing 
Multivariate Data, Behavioral Science, Volume 12, 1967. 
2. Ball, G. H., and Hall, D. J.: ISODATA. A Novel Technique for Data Analysis 
and Pattern Classification. TeJ 
Standard Research Institute, May 1965. 
3. Kan, E. P. F.: "The Latest Version of ISODATA(A)/ISOCLS", Lockheed 
Electronics Company, Inc., HASD, Houston, Texas, Technical Memorandum 
TM642-570, September 1972. 
Recommendations on ISODATA," Lockheed Electronics Company, Inc., Houston, 
Texas, Technical Rep. 640-TR-112, May 1972. 
Lockheed Electronics Company, Inc., HASD, Houston, Texas, Computer Program 
Documentation CPD202, October 1972. 
6. Swain, Pitt.: "Pattern Recognition: A Basis for Remote Sensing Data 
Analysis", LARS Information Note 11572, West Lafayette, Purdue University. 
7. Cousin, S. B., Anderson, A. C., Paris, J. F., Potter, J. F.: "Significant 
Techniques in the Processing and Interpretation of ERTS-1 Data", Lockheed 
Electronics Company, Inc., HASD, Houston, Texas. 
4 .  Kan, E. P. F.; and Holley, W. A. : "More on Clustering With Final 
5 .  Minter, R. T.: "ISOCLS, Iterative Self-organizing Glustering Program", 
1679 
Figure 1 
I 
I 
I 
I 
I 
I 
I 
I 
1 
I 
I 
0 1 1 1 I 1 
U 5 IO IS 20 2s 1 
c u s s  cmm a ms-1 
LOCatlO" d Cl.,I c m t l ,  167 Trlnlcy m1u. 
Figure 2 
1680 
1681 


Unsupervised classification and areal measurement of land and water coastal features on the Texas coast

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server