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Title
Remote sensing for resources development and environmental management
Author
Damen, M. C. J.

462
are either mature (non-green) or harvested in
March. Possible spectral confusion classes for
boro in March are forested areas, aquatic
vegetation such as water hyacinth in ponds or
reservoirs and homesteads.
The best available Landsat scene of sufficient
quality during the time of greatest boro green
biomass was 3 March (scene number E-2406-03404).
Maps and statistical information on the location
and amount of boro rice were obtained from the
Landsat MSS data using both digital and visual
analysis strategies.
3 DIGITAL PROCESSING
A variety of digital processing techniques for
mapping boro rice were investigated as a part of
this project. These techniques ranged from rather
simple and inexpensive, to more complex and/or
costly procedures. The objective was to determine
the technique with the best combination of
accuracy and cost effectiveness. The various
techniques examined for mapping the boro are
presented following.
3.1 Single date single band level slicing
Graymaps from individual MSS bands 5 and 7
produced for the study area using system derived
level slicing did not discriminate features of
interest. Manual level slicing maps improved on
the maps and provided indications of some of the
boro locations but also confused water and boro
areas.
MSS 5
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MSS 7
Figure 1. Table look-up classification for boro
rice(B;, water (W), homestead (H), and
other (0).
signature map had a boro recognition similar to
the look-up table but with a slightly lower amount
of boro acreage.
3.6 Multidate processing
3.2 Ratio
Ratio graymaps using bands 5 and 7 provided
reasonable recognition of boro pixels but confused
other cover types and did not adequately delineate
the water features.
3.3 Look-up table
A spectral scatter plot of the study area was
generated in MSS bands 5 and 7. The plot was
spatially divided on the basis of known spectral
signatures to recognize four cover types; boro,
water, homestead and other, see Figure 1.
Emphasis was then placed on examining those
boundary pixels between the known signatures. In
these spectral boundary areas there were
considerable numbers of reflectances involved
where the signatures might be from boro, from
homesteads or very frequently, mixed pixels. The
classified map generated by the look-up table was
compared to available ancillary data and ground
truth information. A reasonable map showing the
four cover types was obtained by this technique.
3.4 Unsupervised classification
Unsupervised signature extraction was accomplished
with an automatic clustering algorithm that used a
sample of the pixels and all four Landsat MSS data
channels. Initially 24 clusters were obtained and
then reduced to 17. The clusters were identified
using available ground truth and a four category
map was generated. The percentage of mapped area
identified as boro was slightly less in the
cluster map than in the table look-up map.
3.5 Supervised classification
Twenty-six sites were selected for signature
extraction for boro, homestead, water and other to
represent the scene classes. Using these
signatures, a maximum liklihood classifier was
employed to produce a map for comparison to a map
generated the look-up table. The supervised
Since the classes for possible spectral confusion
with boro rice in March, forests, aquatic
vegetation and homesteads, are largely spectrally
invariant, one way to separate them may be by use
of multidate Landsat data in which boro areas
appear non-green on one of the dates. Multidate
clustering was accomplished on a registered data
set including bands 5 and 7 for March and December
(when many of the boro fields are non-green) and
bands 5,6 and 7 for the same months (Colwell,
1977). Cluster classifications were attemped by
use of a zoom transfer scope to merge the cluster
maps and available ancillary maps. The cluster
spectral signatures were also examined. Multidate
clustering and classification indicated an
improved recognition of homesteads, but this was
accompanied by poorer recognition of other land
classes due to decreased resolution caused by
slight misregistration of the merged data.
Misregistration is particularly troublesome in
areas such as Bangladesh where many of the
features, such as homesteads and water areas, are
small and often linear.
For mapping boro rice, a table look-up procedure
has the greatest cost-effectiveness. The 3 March
Landsat data were used, and Landsat MSS 5 and 7
data space was divided into boro, water, homestead
and other categories. The data was then processed
using the subjectively established non-linear
boundaries between the various classes in this
data space to produce maps. By using the table
look-up, as in any traditional multivariate
recognition processing not based on resolution
decomposition, some pixels that contain less than
100 percent boro will be called entirely boro, and
some pixels in which a fraction of the pixel is
occupied by boro area will be called non-boro.
The effects should be partially compensating
(Horwitz, 1971).
4 BORO STATISTICS
The average field size in Bangladesh is quite
small, usually less than the spatial resolution
(0.4 h
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