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

la. A GIS file. Each
field is digitized as
a polygon with a unique
polycentre identifier.
Figure 1. A GIS file and
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lb. The corresponding
image file(s) and the
corresponding pixel(s)
to each polycentre in
the GIS file.
corresponding RSS file.
is selected and the crop type of this sample
is identified, by using remotely sensed data.
3.2 Approaches to the GIPS/ALP concept
3.2.1 In the GIS data base each polygon has a
unique identifier, i.e. the polycentre. And,
the coordinates and attributes (e.g. the crop
type, the acreage, the crop yield...etc) of each
polygon are stored at a high level of accuracy
and up dated as required, which can be retrieved
directly and easily if a proper data structure
is used.
Field
Identifier
Location of
Polycentre
Attributes
1
xl yl
la lb lc
2
x2 y2
2a 2b 2c
3.2.2 If the
position of
each pixel of the
image
is known to
a certain
level of accuracy,
the
corresponding
pixel to
each polycentre in
the
GIS can be identified and extracted at the
leve 1
of accuracy. For a multiband or multidate images,
registration techniques can be used to help define
i:he fieldcentre pixels. It is also possible to
estimate the purity of the polycentre pixel to
see whether the extracted pixel is a pure pixel,
composed of one major cover type, or a mixed
pixel, composed of more than one major crop types.
Figure 2. A flow chart showing the approaches to
the implementation of classification in GIPS/ALP.
containing this particular fieldcentre is identified
i.e. the crop type of the fieldcentre pixel is
assigned to the whole field. By comparing this
newly derived crop type with that originally
stored in the data base, the change, if any,
of
the
cover type
can be
detected. The
acreage
of
each
crop type
in
the
study area is
derived
or
computed from
the
GIS
data instead
of the
RSS
data
to achieve
the
accuracy at the GIS
accuracy
leve 1.
Identified Change
F.I. L.PC. A. L.PCP. P.PCP. New Cover Condition
1 x 1 y 1 lalblc L1S1 P Cl N (no ch)
2 x2y2 2a2b2c L2S2 M C2 C (chngd)
3.2.5 The field check or ground truth verification
if it is to be applied can be directed to those
areas which appear as mixed pixels or are detected
as changed cover. These location can be accurately
identified from a GIS generated map, on which
the suspected areas are marked automatically
on a user specified base map.
3.2.6 The resulted outputs can include (i) statistic
tables and diagrams, (ii) maps, and (iii) images,
whatever available functions in both the geographic
information system and remote sensing system.
Field Location of Location
Identifiar Polycentre Attribute PC Pixel
T' xl yl la lb lc LI SI
2 x2 y2 2a 2b 2c L2 S2
Purity
PC Pixel
P (pure)
M (mixed)
3.2.3 If the calibration data or training data
collected in their primitive form and stored
in GIS are structured properly, they can be
retrieved for deriving particular data sets to
meet a particular application. The derived data
sets are used in turn to assist the classification
of the extracted pixels along with some other
ancillary data available in the data base. One
of the derived data sets of particular importance
is related to the a priori probability distribution
of each existing cover type for a particular
time in the study area. This can be computed
from the archival land cover file in the data
base.
3.2.4 As soon as the crop tyr 2 of the field centre
pixel is identified, the crop type of the field
3.3 On the implementation of classification
Figure 2 is an example to demonstrate the specific
classification approach in GIPS/ALP. A brief
discussion is given here. The geographic information
system and remote sensing system are the two
supporting pillars of GIPS/ALP. The geographic
information system is for handling the fully
structured data base; the remote sensing system,
for collecting the most uptodate spatial data.
At the training stage, four tasks are involved,
namely (i) locating the fields with known land
use type during the time images were taken, (ii)
extracting image data corresponding the fields
located in the previous step, (iii) extracting
calibration data from GIS for modifying the image
data, and (iv) obtaining the training statistics
needed for a particular classification algorithm.
The a priori prabability distribution of each
cover type in study area can be estimated from
the archival land cover file in GIS.
The unknown image data, of which a value stands
for a field centre pixel, are derived from RSS
file and stored in a proper structure.
The result
the inputs tc
the cover t]
subsequent i
cover data
land cover fi
4. UNIQUE ADV.
This approac]
volume which
resolution i
pixels of e;
be kept cons
for a define
in a certaii
polygon data
that of the
The classif
improved due
and the hig
available in
by this systei
too.
The geometr
in the GIS ’
higher than
sophisticate!}
statistics r
on basis of
system.
5. CONCLUSION
Both geograp
sensing systi
spatial data,
special featx
image process
They are the
and geometric
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data. Geo]
964, usgs. ;
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information
R.N. Coweli
vol.I, p.92:
Tseng, W.T.
of computer
and the st
land covers
& Service Ox