The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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of parcel characteristics within an image and (ii) the level of 
experience of one operator when interpreting image. 
Concerning “parcel#image properties”, only “image” (F=6.11 
p<0.0157) and “parcel size” (F=58.06 p<0.0001) were single 
factors significantly affecting the area buffer. Then, 2 nd order 
interactions between the shape of the parcel (“image* parcel 
shape” F=33.79 p<0.0001), the size of the parcel (‘image * 
parcel size” F=11.83 p=0.001) and “image” were significant. 
From ANOVA, orthophoto counted for 23% of the outliers and 
was significantly underestimated (mean value = -16.7m; F=6.50 
p=0.0025) when Cartosat-1 Aft and Fore counted respectively 
for 18% and 58% with mean values of -2.6m and +7.5m. 
As discussed in the next paragraph, this result did not signify 
that the orthophoto is a major source of underestimation (or 
Cartosat-1 Fore as a source of overestimation). In fact, the 
operator’s interpretation was mainly the source of 
underestimation. Concerning parcel size, small and large 
parcels counted respectively for 22% and 26% of the outliers 
when medium parcels represented 56% of the outliers. 
Difference of buffer was significant between parcel sizes 
(F=26.2 p<0.0001) with mean buffer values of +12.6m, +8.5 
and -28.1m respectively for small, medium and large parcels. 
Independently of the image, of the parcel shape or of the 
operator, small parcel areas were often overestimated and large 
parcel areas were underestimated. One would suggest that this 
effect was a consequence of the magnification function used 
during the measurement: each parcel was enlarged and fitted to 
the full screen size to facilitate boundary recognition. The 
smaller the parcels, the higher the magnification was and the 
higher the dilution of the contrast. On the other hand, one could 
imagine a “compensation” effect from the operator which, 
unconsciously, searched to not disadvantage small parcels and 
tended to put boundaries off and inversely. 
As explained previously, few outliers came from orthophoto 
when most of them were due to panchromatic Cartosat-1 
images: Cartosat-1 Aft and Fore images were respectively 
responsible of 18.5 and 58% of the outliers detected. Clarity of 
the object displayed on the screen and true colour (RGB) 
composition seemed essential first for a good recognition of the 
object, second for the better delineation of the parcel boundaries. 
Thus, “operator’s recognition capacity” and “operator’s object 
memorisation” could be of prime importance, at least to explain 
outlier’s existence. They both compose “operator’s experience” 
and condition the interpretation of the size and shape of the 
parcel (and of possible contained object to be excluded). With 
Cartosat-1 images, we assumed that parcel boundaries 
recognition was more difficult and more deductive for operator. 
We showed that “operator* image” effect on buffer 
measurement was significant (F=3.6 p=0.018). Over the five 
operators, four were responsible for the outliers; among those, 
three were skilled. The unskilled operator was responsible of 
most of the outliers (70%) and generally underestimated the 
buffer (mean value = -6.2m) when others always overestimated 
the buffer (from +12.2 to +14.6m). All together, the results 
obtained from the analysis of the outliers’ population clearly 
suggested a tripartite relationship existing between (i) the parcel 
with its particular characteristics, (ii) the image as the 
information vector conveying parcel characteristics and (iii) the 
operator as the place of interpretation where his personal visual 
recognition capacity and object memorisation interact and 
determine the accuracy of the measurement. Here we showed 
that the unskilled operator was mainly responsible of 
underestimation of parcel area certainly because of a too limited 
object recognition experience on image, this, whatever the type 
of image considered. On the contrary, the three skilled 
operators often overestimated parcel areas. They were 
responsible for the major part of overestimation on Cartosat-1 
image. This should be related to the fact that their experience 
initially concerned true colour composition imagery. 
Consequently, (i) effect of magnification, (ii) unconscious 
parcel size related “compensation effect” and/or (iii) lost of 
reference when passing from orthophoto to panchromatic could 
explain outliers from skilled operators. 
By analysing extreme area discrepancies, we showed that area 
measurement accuracy is mainly conditioned by the 
relationship between operator and the image properties. CAPI 
training for panchromatic and true colour images should be 
organised to reduce risk of wrong delineation of the agricultural 
parcels. 
4.2 Final buffer population 
4.2.1 Normality test 
A normality test (KSL/Kolmogorov-Smimov-Lilliefors) was 
performed on each type of image to determine if observed 
buffer values were normally distributed and consequently to 
decide if an analysis of variance was relevant to identify the 
main significant factors and differences between modalities. 
Whatever the type of image, small p-values were obtained 
(W=0.951 p-value<0.001; W=0.989 p-value<0.001; W=0.996 
p-value=0.001 for Orthophoto, Cartosatl Fore and Aft 
respectively) and the null hypothesis that observed buffer 
values have a normal distribution were rejected. However, 
buffer value distribution was very close to the normal 
distribution, being relatively symmetrical around the mean. 
Furthermore, the number of observations by type of image was 
sufficiently high (Table 2) to allow for processing SLS 
procedures and analysis of variance without restriction and to 
limit the risk of misinterpretation of factors’ effects. 
Buffer values obtained from the survey were significantly 
different between image modalities; mean values and standard 
deviation are given in table 2. Consequently, the SLS procedure 
and analysis of variance have been conducted for each type of 
image independently and will be discussed separately. 
Parameters 
orthophoto 
Cartosat 
Cartosat 
[m] 
Aft 
Fore 
Mean 
-0.059 
0.041 
0.515 
Std Dev 
0.990 
1.848 
2.857 
Std Err Mean 
0.024 
0.046 
0.074 
upper 95% Mean 
-0.011 
0.131 
0.661 
lower 95% Mean 
-0.106 
-0.049 
0.370 
Number of observations 
1642 
1613 
1485 
Table 2.Distribution parameters for each image 
4.2.2 SLS and ANOVA: main significant effects 
The SLS procedure applied separately to each image dataset 
showed that the factors responsible for the observed variability 
of the buffer were different between images. Whereas 
“operator”, “shape” and “size” of the parcel or even “visibility” 
were the single factors to be retained for the orthophoto, “land 
cover” should also be considered of interest for Cartosat-1 Fore. 
Concerning Cartosat-1 Aft, only “visibility” was retained as the