In Japan, the rice suffered damage due to cold weather in 1980. We carried out a digital analysis of the Landsat MSS data (116-30, 1980. 9. 19) to obtain a geographical distribution map of the damaged rice brought about by the cold weather. The area to be analyzed (53 × 65 km) includes the Ishikari plain where is one of the very large paddy field areas in Japan. As a result of this analysis, we were able to make a distribution map of the extent of rice damage and rice yield of the analyzed area.
(1) We used the Landsat MSS data for land-use classification, and we classified the area to be analyzed into eight land-use classes (paddy fields, dry fields, grass land, forest, strip ground, towns, sea, river or lake) by a supervised classification. In this case, we used the maximum likelihood decision rule. The classification performance was 96. 7% on the average for the training fields.(
Photo. 1, 2; Table 1)
(2) The color of the leaves and stalks of non damaged rice was “Koganeiro (golden or yellow color) ”, and the heads were heavily laden with seed. But, the appearance of the damaged rice was “Aodachi (poor stand and green in color) ” because of the immaturity and sterility. So, we investigated the spectral reflectance of the damaged rice concerning the data from the handy spectrometer and Landsat MSS. Then, we found that the reflectance in the red wave decreased and VI (Vegetation Index) value increased in proportion as the damage enlarged.(
Fig. 2-4)
(3) We were able to make the distribution map of the extent of the rice damage by level-slice classi-fication of band 5 or VI. This level-slice classification was performed on the all pixels which were classified into paddy field by the land-use classification. This map shows the distribution characteristics of the rice damage from cold weather in the Ishikari plain.(
Photo. 3)
(4) We carried out a multiple regression analysis for the rice yield estimation. Then, we made a multiple regression model of the rice yield in which the multiple correlation coefficient was 0. 91**.
Y (kg/10a) =7673.5-61.4. band 5+67.2·Eband 6-62.4 VI (R=0.91**)
As a result of the application of this model to the Landsat MSS data, we were able to make a distribution map of the rice yield of the area under analysis.(
Photo. 4)
(5) Much information of various types could be extracted from the distribution map of the rice yield which was made by the digital analysis of Landsat MSS data. This shows that Remote Sensing is available for a large area survey technique.
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