Continuing interpretation of the test area in Finney County, Kansas, has revealed that winter wheat can be successfully identified. This successful identification is based on human recognition of tonal signatures on MSS images. Several different but highly successful interpretation strategies have been employed. These strategies involve the use of both spectral and temporal inputs. Good results have been obtained from a single MSS-5 image acquired at a critical time in the crop cycle (planting). On a test sample of 54,612 acres, 89 percent of the acreage was correctly classified as wheat or non-wheat and the estimated wheat acreage (19,516 acres) was 99 percent of the actual acreage of wheat in the sample area

Barker, B.

Coiner, J. C.

Morain, S. A.

Williams, D. L.

English

NASA Technical Reports Server

Paper A 1 
IDENTIFICATION OF WINTER WHEAT FROM ERTS-1 IMAGERY 
Donald L. Willians, Stanley A. Morain, Bonnie Barker, and Jerry C. Coiner, 
The University of Kansas Center of Research, Inc., The Un imi ty  of Kansas, 
Lawenoe. Kansas 66044 
ABSTRACT: Continuing interpretation of the test area in Finney County, Kansas, h a  
revealed that winter wheat can be successfully identified. This successful identification 
i s  based on human reco~i t ion of M c !  sign&sns on MSS images. kveral different 
but h ighl successful in terpretot ior strategies have been employed. These strategies 
involve use of both spectral a d  temporal inputs. Good results have been obained 
from a single MSS-5 h g e  acquired at a critical time in the crop cycle (plonting). 
On a test sample of 54,612 ac-es (22,101 hectares), 89 percent of the acreage was 
correctly classified as wheat or non-wheat and the estimated wheat acreow (19,516 acres, 
7.898 ha.) was 99 percent of thc octwl acreage of wheat in the sample area. 
Continuing interpretaticn of ERTS-1 MSS 'hages of Finney County, Kansas, has 
established that ha~d red winter wheat may be successfully distinguished from all other 
~ f i d  ciopping mu'it iom by a simpie human intupntation technique. This 
C:%ique was initially developed for irrigated wheat, but has proven ap licable to c non-irrigated wheat as well. On a test sample of 54,612 ocres (22,101 ectares) for 
which surface observations were available, 89 percent of the acreage was corrediy 
classified as wheat or non-wheat. The error terms canfonned to the Co:+ral Limit 
TI . .  
I treorem. Tire estimate of wheat in this test was 19,516 acres (7,898 ha.), W percent 
of the actual amount of wheat (19,674 A., 7,962 ha.) in the sample. This estimate 
was based on a single badtime-fmme image, MSS-5, acquired September 21 and 
Sepemkr 22, 1972. The estimate i s  therefore based on imagery acquired during the 
planting period . 
The sample analyzed fsr this report represents 6.5 percent of the land area 
of Finney Cwnty and includes all environmental and agicultural types in the oounty 
except for the intensive irrigation area in the northwestern pa* at the county, where 
wheat i s  not a siwifioant canponent of the landsape. Finney County (Figure 1) was 
originally selected as a test area kcarse of the magnitude and diversity of agiculture 
in d.e county. Most d the 1308 quare mile (3,388 sq . krn .) area i s  typical of the 
large field a~iculhrrol system of the winter wheat belt of the Great Plains. However, 
large areas of rangeland exist in the county and extensive and intensive irrigation is  
widely pmcticad. The single most important crop in the catnty is what. In 1971, 
Finney County ranked third a O n g  Kansas counties with wheat production of 6,921,000 
8n'gi.al .:!icfograptiy may 6e purchased fromi 11 i;,iJL; :12f3 Ctijter 
1C:n asd 9akola Avenue 
S I ~ J X  Falls, SO 57198 
https://ntrs.nasa.gov/search.jsp?R=19730019477 2020-03-17T09:05:21+00:00Z
bushels (188,326 metric tom!. In the sumo year, ths county w=i iirt~tmg ?hz ten mod 
productive counties in K a ~ a s  forrorghumfor grain, corn for groin, corn for sllap, 
alfalfa hay, and sugar beets while ranking seventh in ncmkr of cattle on farms 
(Kansas State Board of Agtculture, 1972). 
Five sample areas were selected in Finney County in such a way that the areas 
are nadily located on ERTS-1 irnagory and al l  environmental and agricultural typa 
in the coun have been Included. Surface observations of crop t y p  and condition 
haw k e n  o 'I, toined for mch field in these test arms . Env ironmental parameters, 
prlnclpally m i l  type and topography, have born obtained from tho shndard roil 
sumy map (USDA, So!! Consewation Service, 1%5) and available topographtc mops. 
Four of the five sample oroar were used in this analysis (Figure 1). The 
fifth sample i s  located in the small field irrigated area of northwestern Finney County 
and contains a Iimlted amount of wheat. Samples 1 and 2 represent the wrt of the 
coun with sa roils. Mort of this area is  cmpowd of large irrigate iblds. 7 f d Somp es 3A and B npewnt  tho area with nearly level loamy roils. Mort of this 
area Is compomd of largo non-irrigatd fields. Sample 4A represents tho area of 
rolling lands with mostly loamy rolls. Al l  cultivation in thtr area i s  large-field 
dryland. Sam Ie 48 rapsants the area of nearly Ievel clayey roils. Mort of this P area Is largo- ield dryland cultivation. 
To obtain ERTS-1 data for analysis of thow samples, a simple human imago 
inhrpntatlon technique was employad. The gmy scale i0bl.t alon tho bottom of 
th. in. ra divided !n(c ?!vr rkpi xh!ch th. three intarpeter&, Ri~~iams, Coiner 
and 6or P er, amad  wore distinct and dehdable in the Imclga context (Flgure 2). A 
map rhowino field boundale8 and field numbers for wch rampla a-ma hod k e n  
pnviously pnpand. The interprater ncordod the apparent )om of each f k ld  as 
c m l w d  by cornparim to gwy scale tablet. Al l  inhrpretatbns were replicatad, a!! tkne in)sr?saton In .asst !r;skntas. Ha*ovar, 'S/:ll!am p r f m o d  the data 
analpis. To avoid porsiMm bior, aftor ho kgan the analysis for a given saple a m ,  
he took no further imago data for that arm. 
Tho initial wha t  &tectlon exprimen? w n  & s i p ?  on a multi-image baris 
(Williams, et al, 1973). Tonal data hod been taken from )our images for sample areas / 
1 and 2. These images m r e  MSS -5 aquimd Augrst 16, September 21, and December 2, 
and MSS-7 acquired December 2. A decision matrix (scattergmm) was constructed 
for the data fram sample area 1 and a wheat/non-wheat boundary was drawn through the 
decision matrix. This boundary resulted i,. 93 percent separation of wheat fields from 
non-whoat fkldr. Dva to partially offsett'ng e m ,  the estimated numkr of wheat 
fields (47) was 98 porcant of the actual number. When thir decirim bounda 
applird to tho data from sample arm 2, 86 percent of the field, wete correct 7" y classifid 
and the estlmatod numbor of whoat fields (14) equaled tho actual numkr. However, the 
method (1) did not appor applicable to the dota from Saple Arm 4, (2) war cumbmomo, 
and (3) -mod unsuitable for a I icatiom to large are-. Further analysis of the dectslon 
of any field. 
PP matrix rewaled that the MSS- imago hod not contributed to the succedul discrimination 
Thls fact Id b tho concept of monitoring temporal chon- as a method of 
Identiflwtion. The MSS-5 lmagi acquired Decembor 21, 1972, was added to tho &?a 
sot at thir the .  Although this methoa' of obrorving tonal chon 
ar the &r multl-Im-• approach. 
Iff thmJ* a'm suc~a fu l l y  dieahinatad wheat, the method was plagwd by l r a m  shortcomings 
While the temporal change analysis was in progress, further study of the 
original decision matrix revealed that most of the wheat/non-wheat separation had 
been due to a single image, MSS-5 acquired September 21, 1972. Part of the county 
was cloud covered on that date. However, al l  cloud covered sample areas were clear 
on September 22. The cloud free coverage for each test area was used for the inter- 
pretat ion. 
The hypothesis under which any binary discrimination is a< hieved is  that the 
two conditions are more or less distinct in the data space (Figure 3). In the case 
undcr m~aiderc";on, the hypothesis i s  h o t  \ $eat fields have ligkter tones than mn- 
wheut fields. This conclusion may be confirmed by inspection ~f the graphs in 
Figure 5. However, the significant degree of overlap in tone it-.;;! 2 :s obvious. Ti].- 
averlap constitutes a serious error i f  the tone i s  assigend to either wheat or non-wheat. 
Inspection of the data subdivided according to environmental area (Figure 4) 
reveals that this serious w e r l a ~  does not exist in the individual areas. Instead, most 
fields assigned to tone level 3 are wheat in sample areas 1, 2, and 48, whiL most 
fields assigned to tone level 3 are non-wheat in sample areas 3 and 4A. That is, 
error i s  spatially distributed as a function of the spatial distributim of environmentn! 
variables. 
Based on these spatial results, the following optimum rule for wheat indentification 
in Finney County, Kansas, was devised: on MSS-5 imagery acquired during the wheat 
planting period in 1972, all fields with light and medium tones (tone levels 1, 2, and 3) 
on sandy soils and nearly level clays are wheat and all fields with light tones on nearly 
level to rolling loamy soils are wheat. 
This rule was initially developed for and applied to al l  fields 80 acres (32 ha.) 
or larger, because these fields were consistently detectable as discrete entities in the 
image. The four sample areas contained 377 fields 80 acres or greater in size. These 
377 fields contained 54,612 acres (22,101 ha.), for an average size of 145 acres (59 IIO.). 
The accuracies (Table 1) of classification and estimation of wheat were identical for 
both number of fields and acreage. Eighty-nine percent of a l l  fields and acreages 
were correctly classified ns whwt 3: son-wheat. The estimated number and acreage of 
wheat fields was 99 percent of the actual number and aaeage. Use of a single classifica- 
tion rule for al l  sample areas results in slightly decreased accuracy of classification and 
serious errors in the estimation of wheat acreage. For example, if on1 y fields having 
tones 1 and 2 are assigned to wheat, the accuracy of the classification i s  86 
but wheat acreage i s  underestimated by 26 percent. On the other hand, i f  a rrFent I ftelds 
having tones 1, 2 and 3 are assigned to wheat, the classification accuracy drqs to 
82 percent and wheat acreage is overestimated by 35 percent. 
Fields smaller than 80 acrp: had been omitted from the initial analysis because 
(1) they were often hard to separate from adjacent fields, and (2) moat of the resolution 
cells contain boundaries and are, therefore, averages of often disparate tones. However, 
tones were also assigned for smaller fields in oreas 1, 2, and 3A. These areas contained 
202 large and 78 small fields. The largs fields contained 23,896 acres (9,670 ha.). 
This aaeage was classified with 91 percent accuracy and the wheat acreage estimate 
was 100 percent correct. The 78 small fields cmtained only 2,925 acres (: 184 ha .) . 
Only 74 percent of this acreage was correctly classified and wheat acreage was wer- 
estimated by 13 percent. But when t h i s  modest acreage was added to that of the large 
fields, the resulting ncreage classification was still 89 percent accurate and the over- 
estimation of wheat acreoge was 2 percent. 
13 
Although temporal data were not required for the identification of winter wheat, 
these data may serve two important roles. The estimates of  wheat acreage presented 
here are estimates of acreage planted and are, therefore, significantly larger than the 
acreage harvested. In the fa1 l of 1970, 192,000 acres (77,702 ha .) were seeded in 
Finney County. In  June 1971, 189,000 acres (76,488 ha.) were harvested. Although 
this difference was small, the amount of wheat destroyed is quite variable from year +o 
year and must be removed from the original acreage estimate. Furthermore, temporal 
data may provide information on the state of the crop. For example, tones of wheat 
fields are highly variable on MSS-5 images acquired in December. This variability 
i s  an indication of the degree of fall growth, which varies greatly from one field to 
another. This tonal variability makes identification of wheat very difficult, but, i f  
a field has already been identified as wheat, the variability provides useful data on 
the state of the crop in that field. 
The results resented here demonstrate that a simple method for winter wheat 
identification may k dewloped given an adequate prior knowledge of local environ- 
ment and crop cycle. The method appears to be applicable to other crops if suitable 
distinct cro cycle events may be defined. Knowledge of the local environment i s  
critical if t e e interpretation i s  to be successfully mndvcted. Components of the 
local environment dtrta set can be taken directly from the ERTS-1 imager (Will iams 
and Coiner, 1973) but other components are best developed at the lccal r evel. 
Furthermore, surface observations for a small number of frelds from each environmentai 
area would be a necessity. The necessity for (1) surface observation, (2) knowledge of 
the local environment, (3) knowiedg~ of local crop cycles, ard (4) 5r: ,nudest arno?int 
of equipment and training required to perform the% interpretatlow make this 
method suitable for implementation at the local (county) level. 
REFERENCES: 
ERTS- I Imagery 
DATE 
-
FRAME NUMBER 
8- 16-72 1024-1651 1-5 
9-21 -72 1060-1651 2-5 
9-22- 72 1061 - 16564-5 
9-22-72 1061 - 16570-5 
12-2-72 1132-16514-5 
i2-2-73 1132-16514-7 
12-21 -72 1 151 -16572-5 
12-2 1 -72 1151-16575-5 
SCALE 
1:1,000,000 
!:?,!m,rn 
1 :3,300,000 
1 :3,300,000 
1:1,000,oOo 
1:1,000,000 
1:1,000,000 
1:1,000,000 
QUALIPY 
Excel lent 
Good, partiai haze cover 
Good 
Good 
Good 
Good 
Good 
Good 
Reports: 
kanxrs State k r d  of Agriculture. 1972. Farm Facts, 1971-1972. Topeka, Kansas: 
Kansas State Printer. 
U.S.D .A., Soil Consemtion Service. 1965. Soil Survey, Finney County, Kansas. 
Washington: Government Printing Of f  ice. 
Williams, Donald L., and Jerry C. Coiner. 1973. Land Use Map, F inney County, Kcmsas. 
Center for Research, Inc. CRINC - DIIR 2264-3. 
Williams, Donald L., Bonnie Borker, and Jerry C. Coiner. 1973. Discrimination of 
winter wheat on irrigated land in southern Finney Cv~nty, Kansas. Center for 
Research, Inc. CRlNC - DIIR 2264-4 
14 
TABLE 1: Contigency table for discrimination of wheat from non-wheat fields 2 80 
acres, all test areas, Finney County, Kansas, September 21, 22, 1972. 
Rule: Field i s  wheat (areas 1, 2, 48) i f  tone i s  < 3 
Field i s  wheat (areas 3, 4A) i f  tone i s  4 2 
Nu &r of fie Ids assigned Acres assigned 
wheat non-wheat wheat non-wheat 
Actual 
wheat 14 22 16,710 2,964 
Actua I 
non-wheat 20 1 95 
Total = 377 fields Totol = 54,612 acres 
Accuracy of Assignment = 89% Accuracy of  Assignment = 89% 
Accuracy of estimation wheat = 99% Accuracy of estimation of 
wheat acreage = 99% 
r--- 3 
I I 
ir 
I 
I--... 
2 
m FIGURE 1. FlNNEY COUNTY, KANSAS. AREAS ANALYZfO FOR ME PRESENCE OF W I N m  WHEAT. F1O.D DATA WERE COLLECTED ON 
OCTOBfR 6 AND 7, !Pn. AND UPC:tTED ON JANUARY 22. 1973, 
Tone CatEgories 
Figure 2. ERTS-1 MSS-5 image acquired 
Smternbr ?Z, 1972 Part of Finney Counk 
Kansas, is in the southeast cwner d this 
I rnage. 
Figure I. tmquonoy with uhiuh WI-5 Lmram tonal wrrw raroo i r tad  
wLth whurt and non-whmrt f i r l d  wondltianm In t1nn.y County, Kanmmm, 
Imptmmbmr I1 ui5 32, lPYa, 
w 1 
3.001 
I I I be I 
P 0 
L . 1  1 4 . 5  1 - 1  1 I . ,  1 . 1  I 1 - 1  


Identification of winter wheat from ERTS-1 imagery

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