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Different reasons may be responsible for the differences in
accuracies. One reason may have to do with accurate spatial
registration of the intrinsic characteristics of the images. For
instance Landsat has a spatial resolution of 30metres, NigeriaSat-1
32 metres and the SPOT lOmetres. Chen, et al., (2002) has shown
that these can variously have effect on the levels of accuracies
obtained from the images. Another reason could be that, spectral
characteristics among the different land cover types (e.g. built-up,
bare rock) are similar, while spectral variation within the same land
cover type or even within the same image might be high (Cushine,
1987).
5. CONCLUSION
This study was conducted with the intention of evaluating the
difference in landuse characterization, relative accuracy of feature
definitions and the usage of spatial data with Landsat, NigeriaSat -1
and SPOT images. The result of the study supports the knowledge
that each image has certain relative advantage over the other. For
stance, while NigeriaSat-1 images are shown to be very efficient in
the analysis of information within the visible portion of the
electromagnetic spectrum, SPOT images are better in the Near
Infrared. Information from Landsat images was rather weak at both
portions (Visible and NIR) of the Electromagnetic Spectrum. The
study also showed that SPOT images have the lowest level of data
redundancy of the three image providers. This observation is similar
to that of Kuplich, et al (2000), which concluded that SPOT has
relatively smaller data redundancy than ‘some’ other image
producers. This is because high correlation between spectral bands
is indicative of high degree of information. Spectrally adjacent
bands in a multispectral remotely sensed image are often highly
correlated. Multiband visible/near-infrared images of landuse areas
will show negative correlations between the near-infrared and
visible red bands and positive correlations among the visible bands
because the spectral characteristics of land use are such that as the
vigour or tone of the feature increases, the red reflectance
diminishes and the near-infrared reflectance increases.
The study also showed that SPOT images have higher level of
accuracy (> 97%) than Landsat and NigeriaSat-1. The reasons for
this may be the intrinsic characteristics of the images. Another
reason of course, is that spectral characteristics among the different
land cover types (e.g. built-up, bare rock) could be similar.
In addition, it was also revealed that the distinguishing spectral
characteristics between “farmland” and “rock surfaces” and
“farmland” and “secondary forest” in the Landsat images were
relatively poor. However, seamed data sets of NigeriaSat-1 and
Landsat images on the one hand, and NigeriaSat-1 and SPOT on the
other produce landuse classifications of better accuracies (>80%)
than the individual images i.e. SPOT, NigeriaSat-1 or Landsat
especially when representing land uses such as built-up area, bare
rock, water body and farmland.
Finally, the images differ in their ability to reveal landuse
characteristics and differences in spatial resolution may not be a
challenge to accuracy and details of reporting depending on the
subject of interest.
ACKNOWLEDGEMENT
We are grateful to the National Centre for Remote Sensing and
FORMECU for releasing of the satellite images. The authors wish
also to thank the department of geography of Obafemi Awolowo
University for intellectual support during this research work. Also
an appreciation to African Regional Centre for Space Science and
Technology Education for their supportive ways to see to the
completion of this research work. The authors and the researchers
of the literatures we referred to should also get our appreciations.
We extend our sincere appreciation to the editor and anonymous
reviewers, whose comments are valuable in improving the quality
of the article.
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