renewal of data using the same method requires extensive 
man-power. 
Remote sensing technique can collect global land cover 
information timely. Satellites such as LANDSAT series especialy 
provide repetitive coverage with the advantages of a synoptic 
overview. Thus remote sensing techniques provide the method to 
resolve the above problems faced in carring out the renewal of 
NNLI. 
This paper describes feasibility studies on renewal of the 
land use data in NNLI using remote sensing data and digital 
processing techniques, which was carried out under contract with 
The National Land Ajency of Japan. 
As the first generation NNLI represents the landuse at about 
1972, LANDSAT MSS data can be used for change detection. However, 
the resolution of LANDSAT MSS is too large to use for classifica- 
tion of land use for NNLI which has about 10m ground resolution. 
Hence, high altitude aerial photographs were used for classifica- 
tion. 
II. STUDY AREA AND IMAGE DATA 
The study area is Sendai and its suburb(long. 142 52'30"E-143 
00'00"E, lati. 38 15'N-38 20'N) which covers approximately lOlkm'. 
Recentry, industrial developments and urban growth are active in 
this area. 
The smallest unit of NNLI for data collection and strage is 
10x10m“ UTM grid cell. Land use categories in NNLI are fifteen 
and are shown in Table l. Figure 5 shows the land use map of 
study area created from the NNLI which almost corresponds to the 
state at 1971. 
Two LANDSAT data covering the study area, dated on Novenber 
26, 1972 and December 14,1979, were used for this study. For 
classification of land use, high altitude infrared color 
photographs (9in.x9in.) were acquired on October 20, 1979 with 
12,000m altitude. The scale of this image is 1 to 80,000. 
III. CHANGE DETECTION WITH LANDSAT DATA 
A. PREPROCESSING 
The preprocessings needed for the change detection are divided 
into four stages, i.e. destriping, geometric correction, 
normalization and smoothing. The destriping was firstly applied 
for two LANDSAT images by means of histgram equalization 
algorithm. 
Secondly, each LANDSAT image is geometrically corrected using 
system correction with the aid of ground control points within the 
mean accuracy of one pixel using UTM projection.  Resampled pixel 
size is 50x50m^ using a nearest neighbour algorithm, so that 
rectified image of the study area contained 800x800 pixels. 
The normalization means a linear transformation which coincids 
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