Scale Change Distortion Projection 7 tation/R :
Ponaval Change ranslat on/ otation
Coordinate
Fig.5 Fundamentals of Geometric Manipulation
cently the transfer on university from
center of Tokyo have brought the new land
development and resulted the environmental
change for aiming new city planning. Here
can reach in one hour drive by car forward
western direction from center of Tokyo.
4.1 Change Detection
Two temporal Landsat TM data were
prepared for the change detection analy-
sis. One was observed on Dec.4, 1984 and
other was Dec.5, 1990. As the preprocess-
ing, geometric correction was carried out
for both of TM data in order to overlap on
the map coordinate. Each corrected imagery
consists of 512 pixels and 400 lines data
with 4 bands of band 1,2,3,4 and is stored
in one floppy disk.
In this area, typical environmental
change pattern is supposed that forest
has been changed to bare soil by land
development.
Therefore using two temporal NVI
data, which concerns the status of vegeta-
tion cover, land cover change detection
that is from vegetation to non-vegetation
(i.e. land development ) is carried out.
NVI is calculated by following formula;
(“Band à - Band 3 }
NVI = *Gain + Offset
(€ Band 4 +“ Band: 3°)
Gain = 100, Offset = 50
Here, this change detection means where
and how many area of land cover change has
been occurred in six years from '84 to
"90.
Actual detection procedure is as
follows; (1) Calculation of Normalized
Vegetation Index (2) Histogram Measure-
ment for NVI data (3) Define threshold
value for separating land cover type into
vegetation and non-vegetation classes.
(4). Classification to. two classes by
Density slicing. These processes from (1)
to (4) must to be executed for each data.
Finally change detection is carried out
for two results of (4) by logical opera-
tion.
4.2 Regional Characteristics Investigation
In this practice, every trainee can
understand how GIS will be used with
remote sensing data.
At the first practice, town condition
evaluation by population density is exe-
cuted as an analysis using polygon and its
attribute GIS data. This analysis proce-
dure is as follows; (1) Calculation of
population density using town population
data as the attributes for each town (2)
Grade for each town by the above calculat-
ed data (3) Coloring for each grade using
town boundary raster typed data (polygon
type) (4) Computation the statistics for
the result of grading.
At the second practice, the relation-
ship between population density land cover
change is examined by combining the result
of grading of population density and
result of change detection. This process
is the one of the summations of results,
obtained by remote sensing.
5. Conclusion
In this developed system mentioned in
this paper, there are two main training
categories such as fundamentals and
applications.
In the fundamental training, the
image data structure, format and its own
characteristics as some of basic image
processing techniques could be understood
through histogram measurement and density
slicing practices. As fundamentals for
GIS, GIS data structure and geometric
manipulation could be learnt through GIS
data generation by digitizer and vector-
raster data type conversion.
In the application training, through
the analysis of land cover change detec-
tion as an application of remote sensing,
what kind of informations extract from
remote sensing data could be learnt. And
how combine with the result of an analysis
of remote sensing data and GIS data such
as town boundary polygon data, town popu-
lation as its attribute data. could be
understood through regional characteris-
tics investigation.
This system has been developed in a
continuing form as a series of educational
tool for remote sensing and GIS. There are
still some revising points in order to
teach and learn easily. The authors hope
to have your suggestions on this matter.