Digital analysis of August 15 ERTS-I imagery for southeastern South Dakota was performed to determine the feasibility of conducting crop surveys from satellites. Selected areas of bands 4, 5, 6, and 7 positive transparencies were converted to digital form utilizing Signal Analysis and Dissemination Equipment (SADE). The optical transmission values were printed out in a spatial format. Visual analysis of the printouts indicated that cultivated areas were readily distinguished from non-cultivated areas in all four bands. Bare soil was easily recognized in all four bands. Corn and soybeans, the two major crops in the area, were treated as separate classes rather than as a single class called row crops. Bands 6 and 7 provided good results in distinguishing between corn and soybeans

Heilman, J. L.

Horton, M. L.

English

NASA Technical Reports Server

Paper A 3 
CROP IDENTIFICATION USING ERTS IMAGERY~ 
Maurice L. Horton and Jarnes L. ~eilman.2 Remote Sensing Institute, Water 
Resourn Institute, and Rant Skienm D.am~nmt, South Dakota Stare 
University, Brookings, South Dakota 
ABSTRACT 
Digital analysis of Ausst 15 ERTS-I imagery for 
southeastern South Dakata was performed to determine 
the ftasibility of conducting crop surveys from 
satellites. Selected areas of bands 4, 5, 6, and 7 
positive transparencies were converted to digital 
form utilizing Signal Analysis and Dissemination Equip- 
ment (SADE). The optical transmission values were 
printed out in a spatial format. Visual analysis of 
the printouts indicated that cultivated areas were 
readily distinguished from ron-cultivated areas in all 
four bands. Bare soil wa.s easily recognized. in all 
four bands. Corn and soybeans, the two major crops 
in the area, were treated as separate-classes rather 
than as a single class called row crops. Bands 6 and 
7 provided good results in distinguishing between 
corn and soybeans. 
1. INTRODUCTION 
Crop resources are the food and fiber which we use every day. 
Satellite surveillance of crops planted, crop condition, yield and 
harvest offers significant advance in the management and reporting 
of crop resources. 
SDSU-RSI-J-02 Contract #NAS 5-21774 
Contribution from the Remote Sensing Institute, the Water Resources 
Institute, and the Plant Science Department, South Dakota State 
University. Approved for publication by the Director of the South 
Dabta Agricultural Experiment Station as Journal Paper No. 1173. 
Associate Professor of Plant Science, South Dakota State University, 
and Graduate Research Assistant, South Dakota State University, 
Brookinge, South Dakota 57006. 
https://ntrs.nasa.gov/search.jsp?R=19730019479 2020-03-17T09:05:29+00:00Z
Considerable backgr~una  has a l r eady  been developed f o r  i n t e rp re -  
Lat ian of remote m u l t i s p e c t r a l  imagery of a g r i c u l t u r a l  crops (Hoffer,  
1967) and i n  crop spec i e s  d i s c r imina t i on  from Apollo 9 imagery 
(Viegand e t .  a l .  1969). Mapping of crop,  so i l ,  and geologica i  f e a t u r e s  
using d i g i t i z e d  m u l t i s p e c t r a l  s a t e l l i t e  photography has been discussed 
by Anuta e t .  al. 1971. 
Recent s t u d i e s  on t he  use  of nonparametric methods f o r  pa t t e rn  
recogni t ion  (Hahn and Nelson, 1972) show cons iderab le  promise f o r  
aachine c l a s s i f i c a t i o n  of crops. 
The s tudy reported here  u t i l i z e d  ERTS-I imagery, ground t r u t h ,  
and l i -c lass i f ica t ion  using SADE (Signal  Analysis  and Dissemination 
Equipment). 
2. PROCEDURE 
The s tudy a r e a  i nves t i ga t ed  was a 3.2 km by 14.5 h s t r i p  loca ted  
south of Cen te sv i l l e ,  South Dakota. The ~l lajor  c rops  krovn i n  t he  a r ea  
were corn,  soybeans, and oa t s .  During t h e  month of June, 1972, t he  s tudy 
a r ea  was surveyed and mapped t o  de t e rn ine  f i e l d  s i z e s  and crop spec i e s  
planted i n  t he  var ious  f i e l d s .  Ground t r u t h  information was gathered 
during t he  time of t he  f i r s t  ERTS overpass  on August 15,  1972. 
The i n i t i a l  a n a l y s i s  was performed on August 1 5  ERTS-I b a g e r y .  
An a i e a  corresponding t o  t he  s tudy a r e a  was masked of f  f o r  bands 4 ,  5 ,  
6, and 7 on n ine  inch p o s i t i v e  t racsparenc ies .  The mask was prepared 
by applying opaque photogr lp t ic  t ape  d i r e c t l y  t o  t h e  t ransparenc ies .  
Each of t h e  fou r  t ransparenc ies  was d i g i t i z e d  a t  a r e so lu t i on  of 
36 da t a  po in t s  per  mm using S igna l  Analysis  and Dissemination Equipment 
(SXDE). The coded ou tpu t s  propor t iona l  t o  o p t i c a l  t ransmission fo r  
each band were 2rin'LeZ oclt i n  a s p a t i a l  format. T t e  format divided t h e  
O t o  255 range of coded outputs  i n t o  55 increments of f i v e  va lues  each. 
A s p e c i f i c  cha rac t e r  was assigned t o  each increment. 
Three c l a s s e s  cons i s t i ng  of corn, soybeans, and fa l low were 
s e l ec t ad  f o r  t h e  i n i t i a l  ana ly s i s .  Oats were no t  used because a l l  o a t s  
had been harvested by August 15. Three corn  f i e l d s ,  two soybean f i e l d s ,  
scd t ~ v  f a l l ow  f i e l d s  r ep re sen t a t i ve  of t he  t h r e e  c l a s s e s  i n  t h e  s tudy  
a r e a  wera l oca t ed  on t h e  p r in tou t s .  An 8 poin t  by 8 point  matr ix 
i n s ide  each f i e l d  boundary was used a s  t h e  s t a t i s t i c a l  sampl? of da t a  
po in t s  f o r  t h e  f i e l d .  Since t he  d a t a  p o i c t s  on t he  p r i n t o u t s  were i n  
terms of cha rac t e r s  assigned t o  t he  va r ious  output  increments,  t he  
po in t s  had t o  be converted t o  a c t u a l  output  values.  This  was accomplished 
using a computer program wi th  i npu t s  being t h e  i n i t i a l  x and y coord ina tes  
of t h e  matrix along wi th  t h e  s i z e  of t h e  matrix. The coord ina tes  were 
only mearured on the  t.and 6 p r in tou t  under the  aeeumption t h a t  e r r o r e  i n  
the  marking operat ion would not be g rea t  enougl; :O caase some of t he  
s a p l e d  pointe t o  be ou t r ide  the  f i e l d  boundariee in  bande 4 ,  6, and 7. 
Non3arametric K-class c l a s e i f i c a t i o n  (Zagaleky, 1968 and Hahn and Nelson, 
1972) was performed on the  data.  
3.  RESULTS 
Results  obtained with the  K-classif ier  a r e  shown i n  Table 1. 
Using one f ea tu re  the  h ighes t  percent co r rec t  c l a s s i l i c a t i o n  was 
obtained using band 6. Using two f ea tu re s ,  bes t  r e s u l t s  were obcained 
using bands 4 and 6 o r  bande 5 and 6. Using th ree  f ea tu re s ,  good 
r e s u l t s  were obtained using bands 4,  5,  and 6; bands 4, 6, and 7; and 
bands 5, 6, and 7. Xeesults obtained using a l l  four  feazures were a l s o  
sa t i s f ac to ry .  
TABLE 1. Percent t o t a l  co r r ec t  c l a s s i f i c a t i o n  of the  
t r a in ing  sample6 
Feature in terms 
of ERTS bando 
Percent t o t a l  co r r ec t  
c l a s s i f i c a t i o n  
Figures 1, 2 ,  3, and 4 show the  p robab i l i t y  dens i ty  func t ions  of 
each c l a s s  i n  bands 4 ,  5, 6, and 7 respec t ive ly .  The peaks and va l l eys  
i n  t h e  curves a r e  due t o  t he  small  number of d a t a  po in t s  t h a t  were 
sampled. It is sssumed t h a t  a l a r g e r  sample of da t a  poin ts  would r e s u l t  
i n  smoother curves. 
L i t t l e  separa t ion  of c l a s s e s  was achieved i n  band 4 except f o r  one 
corn f i e l d  represented by the  ctirve i n  t he  l e f t  hand por t ion  of f i gu re  
1. The d i f f e r ence  between t h i s  corn f i e l d  and the  o the r  corn f i e l d s  
does not  appear t n  be r e l a t e d  t o  s o i l  d i f fe rences .  No explanat ion of 
t he  d i f f e r ences  between the  corn f i e l d s  has been found. The sharp 
fa l low peaks i nd i ca t e  t h a t  some d i s c r i m i n ~ t i o n  of bars  s o i l  was achieved. 
Also, some separa t ion  of soybeans was achieved. 
Figure 2 shows some s e p a r a b i l i t y  of corn f o r  lower output values.  
It a l s o  shows s i g n i f i c a n t  overlap of t he  t h r ee  c l a s se s .  Some d i s -  
cr iminat ion of fa l low and l i t t l e  d i scr imina t ion  of soybeans was 
obtained i n  band 5. 
Figure 3 shows t h a t  almost ccmplete separa t ion  of c l a s s e s  was 
obtained i n  band 6. The exce l l en t  separa t ion  of c l a s s e s  achieved i n  
band 6 is refLected i n  K-c lass i f ie r  r e s u l t s  where t h i s  band i s  included. 
nn ana lys i s  of t he  band 7 p r in tou t  ind ica ted  t h a t  d i scr imina t ion  of 
t he  t h r e e  c l a s s e s  was poss ib le  i n  tand 7.  This does not completely 
agree wi th  f i g u r e  4.  The reason f o r  t h e  discrepancy l i e s  i n  t he  masking 
opera t ion  on t h e  band 7 transparency. When the  matrix coordinates  based 
on band 6 measurements were fed i n t ~  t h e  computer, t h e  masking e r r o r  
caused some of t h e  d a t a  po in t s  i n  band ? t o  be sampled outs ide  t he  f i e l d  
boundaries. 
The ERTS-I imagery se l ec t ed  fo:: de t a i l ed  processing has been of 
exce l l en t  qua l i t y .  Imagery from baud 6 (0.7-0.8 um) has given g r e a t e r  
than 90% accuracy i n  nonparametric X-c lass i f ica t ion  of t h r ee  c l a s s e s  - 
fallow, corn, and soybeans. The number of f i e l d s  c l a s s i f i e d  t o  da t e  
have been l imi ted  due t o  development of techniques. 
One 3f t h e  g r e a t e s t  d i f A i c u l t i e s  encountered has been i n  accura te ly  
l oca t ing  f i e l d  boundaries and i n  masking o f f  t h e  appropr ia te  a r ea  of the  
t ransparenc ies  f o r  d i g i t i z a t i o n .  
It appears t h a t  through the  use of sequent ia l  ERTS imagery, machine 
processing (SADE), and l imi ted  ground t r u t h  a t imely and accura te  
p i c t u r e  of crop resources can be prepared. 
.!$ 0 - - -  FALLOW 
L 
W CORN 
------ SOV8E AN8 i l  
i i r  i ; 
I iii : !! 
BAND 4 
CODED OUT PUT PROPORTIONAL TO T RANSYISSION 
Figure 1. Probability density functions of corn, soybeans, 
axd fallow in band 4 (0.5-0.6 urn) 
u I 2  1 CALLOW 1 -.-.-.- - ~ 
0 
W g lo -  ------ I O ~ A W b  
3 
0 - 
0 
0 0 -  
IL - 
0 6 - 
0 - 
Z 
w 4 -  
3 
0 -  
- 
- - 
CODE0 OUTPUT PROPORTIONAL TO TRANSMISSIOH 
Figure 2. Probability density functions of corn, soybeans. 
and fallow in band 5 (0.6-3.7 urn) 
- -.- - - CALLOW BAND 6 
J 10'  CORN 
2 - 
W ------ SOY8CANB g 10- 
3 
0 - ii n 8 a -  riil 
u - 
0 I' 
6 - 
> 
0 - 
L I I I I I  \ I l l I  5' 4 -  I I I I I  (11 I  
0 - I I I l '  # Id I I t ?  1 a 1 4  ! :  u P -  I  I I  
- i f  I I \ I  
O 
I I  1 1 .  1 I l !  , I 
60 UO I00 I PO 140 
CODED OUTPUT PROPORTIONAL TO TRANSMISSION 
Figure 3. Probability density functions of corn, soybeans, 
and fallow in band 6 (0.7-0.8 urn) 
P 
-.-.-.- 
OAMO 7 
I4 - FliLLOW 
W -  coal 
I * -  
I 
---- -- 8OVBtbl8 
b - 
COOLO OUTPUT CROP00TK)NAL TO 1 RANSYISSIOU 
Figure 4.  Probability density function8 of corn, eoybeane, 
and fallow in band 7 (0.8-1.1 urn) 
5. REFERENCES 
huts, P. E., S. J. Kristof, D. W. Levandowski, T. L. Phillips, and 
R. B. McDonald. 1971. Crop, soil, and geological mapping from 
digitized multispectral satellite photography. LARS Information 
Note 1061371. 33 pages. Laboratory for Agricultural Remote Sensing, 
Purdue University, Lafayette, Indiana. 
Hahn, S. F. and G. D. Nelson, 1972. A comparative study of nonparametric 
methods for pattern recognition. Interim Technical Report SDSU-RSI-72-19 
to NASA. 88 pages. Remote Sensing Institute, S o ~ r h  Dakota State 
University, Brookings, South Dakota. 
Hoffer, R. M. 1967. Interpretation of remote multispectral imagery 
of agricultural crops. LARS Research Bulletin No. 831, 36 pages. 
Laboratory for Agricultural Remote Sensing, Purdue University, 
Lafayette, Indiana. 
Wiegand, C. L., R. W. Learner, and A. H. Gerbennann. 1969. Crop 
species and soil condition dfscriminat-ion on Ectachrome Infrared 
Apollo 9 imagery. USDA, Weslaco, Texas. 
Zagaleky, N. 1968. A new formulation of a classification procedure. 
M. S. Thesis, University of ~dinnesota, St. Paul, Minnesota. 


Crop identification using ERTS imagery

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