The Statistical Reporting Service of the U.S. Department of Agriculture is evaluating ERTS-1 imagery as a potential tool for estimating crop acreage. A main data source for the estimates is obtained by enumerating small land parcels that have been randomly selected from the total U.S. land area. These small parcels are being used as ground observations in this investigation. The test sites are located in Missouri, Kansas, Idaho, and South Dakota. The major crops of interest are wheat, cotton, corn, soybeans, sugar beets, potatoes, oats, alfalfa, and grain sorghum. Some of the crops are unique to a given site while others are common in two or three states. This provides an opportunity to observe crops grown under different conditions. Results for the Missouri test site are presented. Results of temporal overlays, unequal prior probabilities, and sample classifiers are discussed. The amount of improvement that each technique contributes is shown in terms of overall performance. The results show that useful information for making crop acreage estimates can be obtained from ERTS-1 data

Vonsteen, D. H.

Wigton, W. H.

English

NASA Technical Reports Server

Paper A 5 
CROP IDENTIFICATION AND ACREAGE MEASUREMENT UTILIZING ERTS 
IMAGERY 
William H. Wigton and Donald H. Von Steen, Statistical Reporting Service, U. S. Department 
of Agriculture 
The Sta t i s t ica l  Reporting Service of the U.S. Department of 
Agr i c i  
ERE-  : 
The S. 
casts and estimates throughout the year, across the U.S. A 
main data source for the estimates is obtained by enumerating 
small land parcels that have been randGmly selected f r o m  the 
to ta l  U.S. land'area. These small parcels are being used as 
ground observations in th i s  investigation. 
are located in Missouri, Kansas, Idaho, and South Dakota. The 
major crops of interest are wheat, cotton, corn, soybeans, sugar 
beets, potatoes, oats, alfalfa,  and grain sorghum. Some of the 
crops are unique to a given site while others are common in two 
or three States.  This provides an opportunity t o  observe crops 
grown under different conditions. 
site are presented i n  th i s  report. 
unequal pr ior  probabilities, and sample classif iers  are discussed. 
The amount ob improvement that each technique contributes is 
shown i n  t e r n  of overall performance. 
useful information for making crop acreage estimates can be 
obtained from E R E - 1  data. 
as a principle investigator for  NASA, is  evaluating 
sy as a potential tool for estimating crop acreage. 
c a l  Reporting Service makes crop and livestock fore- 
The test sites 
Results for the Missouri test 
Results of temporal overlays, 
The results show that 
INTRO WCl'ION 
SRS of the U,S. Department of Agriculture is the inain fact-gathering 
agency of the USDA- The name of the agency has changed several times, 
but the objective of collecting and disseminating primary data on agricul- 
ture has remained the same for  more than 100 years. Crop acreage and pro- 
duction as well as livestock, prices, labor, and farm expenditures are 
estimated. 
Many of these estimates are generated f r o m  a general purpose land 
area sample survey conducted in June and based on 17,000 segments selected 
a t  random from the t o t a l  U.S. land area. 
States and within s ta tes  by land use. Segments for a State are defined 
within each category of land use or stratum and a sample of these segmnts 
is selected. Stratif ication by land use has made it possible t o  sample 
more efficiently for al l  items because sample segments are allocated to 
each stratum individually. A t  the time of field enumeration, the inter- 
viewer must be ab2e t o  irientify the boundaries of the sample segment znd 
collect  infonnation which applies to  the land inside these boundaries. 
ERE imagery may also be helpful in stratif ication and in the segmeat 
selection process; we have not used ERTS for these pruposes yet, but plan 
t o  t r y  th i s  soon. 
This is a sample s t ra t i f ied  by 
https://ntrs.nasa.gov/search.jsp?R=19740022599 2020-03-17T15:22:35+00:00Z
Keep in mind that we use these segments to generate livestock and 
price estimates as well as crop acreages, and for this reason, ERTS will 
not replace our present system for major items. Secondly, our estimates 
have sampling errors between 2 percent and 5 percent at the.U.S. level, 
and between 5 and 10 percent at the state level for major commodities. 
We do not go much below the state level for our probability survey since 
the sample was not designed to provide estimates below the state level. 
PIWEDUFES 
Twenty-nine segments of approximately one square mile size were 
located in t w  ERTS frames covering most of Crop Reporting Districe No. 
9 in Southeast Missouri. The segments are located over a 10,000 square 
mile area. 
by SRS enumerators during the summer of 1972; this data has been used for 
training the classifier and testing its performance. 
dates was included in the analysis. 
October 2,  1972 was registered (overlaid) to data collected August 26, 1973. 
The temporal overlay alleviated the necessity of locating fields in three 
different data sets, as well as permitted a test of the utility of temporal 
data in the classification. 
Information on the crop and acreage of each field was obtained 
ERTS data from three 
Data collected September 14 and 
The E R E  data was also geometrically corrected to facilitate locating 
the coordinates of segments and fields. 
cess the MSS data is rotated, deskewed, and scaled to 1/24,000 scale. 
The geometrically corrected data was overlaid on 1/24,000 scale topographic 
maps on which the segments had been outlined. 
were then classified (clustered) using the ono-supervised classifier in 
LARSYS. Field coordinates were located on the map output from this 
classification. 
classifier in L4RSYS.U 
In the geometric correction pro- 
The individual segments 
Final classifications were carried out using the supervised 
The results are presented in the form of a classification matrix. 
Table 1 shows the classification results obtained when using quadratic 
discriminant functions with equal prior probabilities. That is, it is 
assumed that the probability of occurence of corn is the same as the pro- 
bability for cotton, and so forth. Because of the small size of the data 
set the whole data set was used in training the classifier. This is a 
nine channel classification with data from three ERTS passes. 
major classes, cotton, corn, soybeans, and grass were classified 74, 59, 
40, and 57 percent correctly, respectively. 
percent. 
The four 
Overall performance was 59 
- 1/ 
We gratefully acknowledge the assistance of Dr. Marvin Bauer, 
hrdue, LARS, for technical assistance and in reviewing this paper. 
LARS for giving support in providing the temporal overlays for this analysis. 
However the authors accept responsibility for the suitability of the methods 
employed and interpretation of the results. 
Also, 
88 
The assumption of eaua l  m i o r  p r o b a b i r i t i e s  is many times 
not v a l i d ,  b u t  . !d because of l a c k  of information.  
The p r i o r  probal 
survey,  t h e  Juni re Survey. Other  sources  of 
p r i o r  p r o b a b i l i t y  inrormation are h i s t o r i c  d a t a ,  f o r  example, 
last y e a r ' s  farm census.  C l a s s i f i c a t i o n  r e s u l t s  u s ing  unequal 
p r i o r  p r o b a b i l i t i e s  are shown i n  Table 2 .  Comparing t h e  r e s u l t s  
i n  Table 1 t o  those  i n  Table 2 it i s  seen t h a t  t h e  o v e r a l l  per- 
formance has  been increased  from 59 t o  7 1  p e r c e n t ;  and secondly,  
t h a t  t h e  t o t a l  number o f  p o i n t s  c l a s s i f i e d  i n t o  each class i s  
much c l o s e r  t o  t h e  a c t u a l  number of  po in t s  p r e s e n t .  For example 
from T a b l e  2 ,  t h e  t o t a l  number of po in t s  classified as c o t t o n  
is 9 0 6  which i s  cons iderably  c l o s e r  t o  9 2 7 ,  t h e  a c t u a l  number 
p r e s e n t .  The t o t a l  number of corn po in t s ,  43 is r a t h e r  c l o s e  
t o  t h e  a c t u a l  5 8  presen t .  For soybeans, t h e  t o t a l  of  8 6 6 ,  i s  
very c l o s e  t o  t h e  a c t u a l  8 5 2  present .  Two hundred seventy- 
seven ( 2 7 7 )  p o i n t s  were classified as g r a s s  compared t o  2 4 0  
a c t u a l  poi1 r, t h e  s ta t is t ical  p r o p e r t i e s  
of estimate b e t t e r  s i n c e ,  i f  t h e  assump- 
t i o n  of no] i s  c o r r e c t ,  and t h e  p r i o r  
p r o b a b i l i  t j n unbiased estimates. 
H o s t  c l a s s i f i c a t i o n s  repor ted  by o t h e r  r e s e a r c h e r s  have 
not used p r i o r  p r o b a b i l i t i e s .  
r e p o r t e d  h e r e  i s  h ighe r  than  repor ted  by some researchers, t h i s  
s tudy  w a s  pling o f  t h e  e n t i r e  land  
area i n  - on purposely s e l e c t e d  t e s t  
sites. 
t h i s  study came from an e a r l i e r  
While t h e  o v e r a l l  error rate 
Tablt: 3 3 1 1 ~ ~ 3  L - C ~ U A L ~  uA U D A l l &  a sample classifier r a t h e r  
than a p o i n t  c lass i f ier  used i n  t h e  above work. 
f ier  system each p o i n t  i n  a f i e l d  can  be a s s igned  t o  any o f  t h e  
groups.  With t h e  sample c l a s s i f i e r  a l l  p o i n t s  i n  t h e  f i e l d  are 
as s igned  t o  t h e  same class o r  crop. 
dure i s  t h a t  t h e r e  were a l a r g e  number of  f ields t h a t  were n o t  
classified because t h e  technique r equ i r e s  p + 1  data p o i n t s  i n  
order t o  form t h e  statist ics necessary t o  a s s i g n  it t o  a crop 
(where p is the length of the vector of measurements). Mver, 
if enough p o i n t s  are p resen t ,  c l a s s i f i c a t i o n  performance has  
g e n e r a l l y  been found t o  be bet ter  than f o r  the p o i n t  c l a s s i f i e r .  
I n  the work w e  have done i n  Missouri  u s i n g  t h e  sample classi- 
fier, about  40 pe rcen t  o f  t h e  f i e l d s  w e r e  n o t  c lass i f ied because 
t h e  r e q u i r e d  number o f  p o i n t s  f o r  t h e  classifier (10 i n  t h i s  
p a r t i c u l a r  ca se )  exceeded t h e  number of  p o i n t s  p r e s e n t  w i th in  t h e  
d e f i n e d  f i e l d s .  Of t h e  t o t a l  number of f i e lds  33 pe rcen t  were 
c o r r e c t l y  i d e n t i f i e d .  
classified, 5 4  percen t  were c l a s s i f i e d  c o r r e c t l y .  
but account  f o r  3 2  percent  of t h e  t o t a l  area. 
s i te ,  2 0  percen t  of t h e  f i e l d s  were l e s s  than  20 acres, bu t  
account  for only 1.5 percent  of t h e  t o t a l  land areas. 
I n  a p o i n t  classi- 
One drawback t o  t h i s  proce- 
Considering only those  f i e lds  which were 
I n  Wfssouri 71 pepcent of tbe f i e l d s  V e ~ e  less than  2 0  acres? 
I n  ou r  Kansas 
I n  South 
89 
Dakota, 40 p e r c e n t  of t h e  f i e l d s  were less than  20 acres, and 
account fo r  15  pe rcen t  of t h e  area. I n  Idaho, 74 p e r c e n t  of 
the  f i e l d s  were less than  20 acres, and account  f o r  25 p e r c e n t  
of t h e  area. I f  20 acres i s  a c r i t i ca l  f i e l d  s i z e  f o r  t h e  
c l a s s i f i e r ,  w e  would expect t o  do w e l l  i n  making acreage  es t i -  
nates, i n  Kansas, b u t  i n  Missour i  on ly  a l i t t l e  more than  50 
percent  of  t h e  acreage  would be accounted for .  
Next, t h e  informat ion  gained from t h e  temporal  ove r l ay  i s  
evaluated.  I n  Table 4 c l a s s i f i c a t i o n  r e s u l t s  fo r  s i n g l e  dates 
are compared t o  t h e  mult i temporal  c l a s s i f i c a t i o n s  a l r e a d y  pre-  
sented.  The o v e r a l l  c l a s s i f i c a t i o n  performance w a s  improved 
about 10 p e r c e n t  by t h e  a d d i t i o n  of  temporal data wi th  even 
g r e a t e r  improvement f o r  s e v e r a l  of t h e  i n d i v i d u a l  classes. 
DISCUS S I  ON 
The r e s u l t s  p re sen ted  do no t  show t h e  c l a s s i f i c a t i o n  
accuracy t o  be as h igh  as t h a t  found by o t h e r  i n v e s t i g a t o r s .  
The lower performance l e v e l  i s  premari ly  a t t r i b u t e d  t o  t h e  
g r e a t e r  v a r i a t i o n  i n  c rops ,  s o i l s ,  and weather  ove r  a 1 0 , 0 0 0  
square m i l e  area than  i s  found over  smaller areas. And, second- 
l y  t o  t h e  k ind  of crops which were be ing  d i sc r imina ted .  
t he  c l a s s i f i c a t i o n s  con ta in  enough informat ion  t o  be u s e f u l  i n  
e s t ima t ing  c rop  acreages  over  l a r g e  areas, p a r t i c u l a r l y  i f  re- 
g res s ion  o r  some o t h e r  technique i s  used t o  improve t h e  esti-  
mate. 
S t i l l ,  
A r e g r e s s i o n  estimator can be used t o  reduce  t h e  va r i ance  
We can 
of the estimate. For example, i f  a l a r g e  area i s  classified 
and t h e r e  i s  an r2 of .SO between t h e  d i sc r iminan t  f u n c t i o n  
c l a s s i f i c a t i o n  and what t h e  ground acreage  data shows. 
a d j u s t  o u r  area sample estimates by t h e  somplgte c lass i f ied 
da ta  and o b t a i n  a reduced var iance  of Cy (1-r )/n(n-2) where r2 
i s  the c o r r e l a t i o n  c o e f f i c i e n t  squared.  The estimate of t h e  
vaqiance of t h e  comparable s t a t i s t i c  wi thout  u s i n g  ERTS d a t a  i s  
Cy h(h-1) which would be n e a r l y  twice as l a r g e  when r2 = .SO. 
If w e  were t o  c l a s s i f y  a sample of p o i n t s  w e  would have a 
double sample and t h e  va r i ance  would be: 
where n = the sample 
ERTS . 
2 ~yn;nl;;r') + cy (r2) 
S ize  f r o m  JES and m = the  sample s i z e  f r o m  
n m  
I 90 
Table 1. C l a s s i f i c a t i o n  matrix of quadra t i c  d i scr iminant  funct ions 
w i t h  equal  p r i o r  p r o b a b i l i t i e s  us ing  data from three over- 
flights: Missouri Study Area. 
No. of  
sample Percent 
po in t s  Correct Cotton Corn Soybean Grass Misc. 
Number of samples classified i n t o  
Cotton 927 74.3 689 21 83 36 98 
corn 58 58.6 4 34 3 10 7 
Soybeans .o 1 49 338 137 227 
Grass 34 22 22 137 25 
Misc. 14 5 7 9 105 
I 
Tota ls  2 
Overal l  per 
t42 131 s5 3 329 46 2 
I t  
August 26, I-..uY ., 5, 7. 
September 14, 1972, MSS bands 5, 7. 
October 2 bands 4 ,  5, 6, 7. !, 1972, MSS 
-- .-. . .  Table 2 .  C l a s s i r i c a t i o n  matrix of quadra t ic  &kcriminant funct ions 
w i t h  unequal p r i o r  p r o b a b i l i t i e s  us ing  data f r o m  three 
C Missouri Study Area. 
1 N u m b e r  o f  samples classified i n t o  
sampre r e rcen t  
po in t s  Correct Cotton Corn Soybean Grass Misc. 
Cotton 927 79.7 7 39 2 137 26 23 
Corn 58 44.8 9 26 7 14 1 
Soybeans 852 71.8 99 12 612 96 23 
Grass 240 53.3 42 1 66 12 8 2 
Misc. 140 89.3 17 2 44 13 64 
Tota l s  2217 906 43 86 6 2 77 12 5 
Overal l  performance 70.8 percent  
August 26, 1972, MSS bands 4, 5, 7. 
September 14, 1972, MSS bands 5, 7. 
October 2, i972, HSS bands 4, 5 ,  6 ,  ?. 
91 
Table 3. Sample classification matrix based on data from 3 overflights. 
Missouri Study Area. 
No. of Per- No. of Per Cotton Corn Soy Grass Misc. Not 
f&elds cent points cent beans class- 
Group fields points if ied 
cor- cor- 
rect rect 
Cotton 38 63.2 927 85.0 24 0 2 0 1 11 
Corn 7 14.3 58 20.7 0 1 0 1 1 4 
Soybeans 58 25.9 852 44.2 9 3 15 3 8 20 
Grass 31 9.7 240 29.6 3 1 1 3 2 21 
Misc. 9 14.4 140 65.7 1 0 1 1 4 2 
Totals 143 32.9 2217 60.4 37 5 19 8 16 58 
Table 4. Comparison of multitemporal classification performance to 
classifications of single dates. 9 * fi~ssomi study Area. 
Group Multitemporal Aug. 26 Sept. 14 Oct. 2 
Cotton 79.7 60.6 69.7 73.2 
Corn 44.8 10.3 0.0 12.1 
Soybeans 71.8 86.0 67.6 62.4 
Grass 53.3 8.3 42.1 27.9 
Misc. 89.3 31.4 22.8 17.9 
Overall 70.8 61.6 61.1 59.2 
'Unequal prior probabilities were used for all classifications. 
'August 26, 1972, MSS bands 4, 5, 7. 
September 14, 1972, MSS bands 5, 7. 
October 2, 1972, MSS bands 4, 5, 6, 7. 
92 


Crop identification and acreage measurement utilizing ERTS imagery

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