385 
data shows 
:lassification 
cative of the 
ogies at this 
d the oilseed 
s low and the 
r, is not yet 
oung plants. 
the spring 
as and beans. 
emerged in 
soil class. 
ve occured in 
fusion is now 
ereals, grass 
nd beans; and 
racy of 51.5% 
al confusion, 
d the oilseed 
usion between 
eet, peas and 
confused with 
to the lower 
lanting. The 
the spring 
nfusion. The 
ively limited 
11 not found, 
of the winter 
ise to some 
g planted a 
op growth and 
However both 
1 established 
lassification 
and 73.6% for 
and now has a 
coincident 
still showing 
oodland class 
he deciduous 
efined with a 
now that the 
paration from 
esult in good 
sses. A mean 
strates this, 
ique spectral 
other class, 
100%, though 
hese were all 
lassification 
ed the same 
onfusion was 
ry pixels was 
entified with 
growth and 
good spectral 
d beans which 
anges in crop 
of confusion 
the winter 
accuracy of 
een the two 
lassification 
ing date have 
n between the 
assified with 
ey having the 
barley has a 
two woodland 
0% accuracy. 
As expected the multitemporal combination of data 
from April and May gives the best results. Overall 
class purity is now 69.78%. Classification purity is 
greatly increased for the cereal crops. The unique 
spectral response of the oilseed rape in May plus the 
reasonable separation of the two winter cereals in 
April combined with the good separation of the spring 
crops in May give class purities of 100% for the 
oilseed rape, 87.4% for spring barley, 82.9% for the 
winter wheat and 69.7% for the winter barley. The 
rather low figure for the winter barley highlights 
the difficulties of accurate separation of crops with 
closely matched phenologies. Inclusion of 
Multispectral data from late June and early July 
would in all probability resolve the winter wheat, 
winter barley confusion because of the difference in 
the time of crop scenescence. 
Comparison of the classification crop area estimates 
with proportions for the whole county and the 
parishes show large errors in the classification of 
the cereal crops. In particular the spring barley is 
wildly over predicted. Much of the spring barley 
class seems to be from boundary pixels, and so can be 
considered to be a mixed pixel class. Chhikara, 1984 
has shown that classification of mixed pixels is 
biased and that bias in the crop proportion estimate 
is increased by an increase in the relative size of 
the mixed pixel class. Such bias may account for the 
over estimation of the spring barley as this would 
appear to be a mixed pixel class of considerable 
size. 
Most of the other crop types also show considerable 
error in area estimation when compared with the June 
census data. This tends to show that the 
classification accuracy as predicted through the 
confusion analysis of the training areas is rather 
over optimistic. It must however be remembered that 
the crop area estimates for the county, the parishes 
and the independant crop area estimates used to 
weight the confusion matricies are based on the June 
census returns. Work as early as 1955 by Coppock 
highlights the fact that inaccuracies can emerge in 
such data due to the relationship between parish and 
farm boundaries, and more recently Wright, 1985, in 
his work on oilseed rape classification found errors 
in excess of 100% in the area recorded by the census 
return and the actual ground conditions. The possible 
errors from this source may go part of the way 
towards explaining the rather poor crop area 
estimations. 
5. CONCLUSIONS 
The thesis that the use of multitemporal Landsat data 
in crop classification will give greater 
classification accuracy than single date has been 
shown to hold for the United Kingdom. 
The classification accuracies as expressed through 
the analysis of the training data are generally 
satisfactory though problems in good discrimination 
between the winter cereals and grassland remain, even 
when multitemporal data is used. This is a problem 
associated with lack of suitable imagery. Landgrebe, 
1974, and Baur et al, 1979 both report decreases in 
classification accuracy through the use of mismatched 
multitemporal data. However, the data set used shows 
the clear advantages of using multitemporal data, 
though image aquisition corresponding to the 
scenescent phase of the winter cereal crops may well 
prove to be vital in the successful separation of 
these closely related crops. 
Alternative approaches to the analysis of 
multitemporal data may also improve the 
discrimination between the various cover types. Work 
by Richards (1984), has shown the value of using 
principal components transformation in the analysis 
of multitemporal data. Regions of localised change in 
constant cover types were found to be enhanced in the 
higher components. Simple image addition through the 
use of multitemporal Landsat colour composites has 
also been shown to be of value in change detection. 
Areas of change showing up as a series of colours and 
areas of no change showing as black and white (Eyton, 
1983). Vector change detection has also been 
sucessfully applied to multitemporal Landsat data. 
Engvall et al, 1977, showed good proportion estimates 
for winter wheat through the analysis of the temporal 
trends in the Landsat mean vectors from training 
areas. The problems of mixed pixels associated with 
field boundaries encountered in this study calls for 
the greater use of geographic information systems. 
The integration of map data such as field boundaries 
could considerably reduce the detrimental impact of 
mixed pixels on classification accuracy. 
The potential and the problems of crop identification 
and monitoring from multitemporal satellite imagery 
are illustrated in this study. With new developments 
in this area and improved frequency of satellite 
overpass allowing the aquisition of sufficient cloud 
free data many of the problems will be overcome and 
the potential for crop mapping and monitoring will be 
increased 
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