1992 Fiscal Year Final Research Report Summary
Environmental Evaluation of Seto Inland Sea by Remote Sensing
| Project/Area Number |
02302086
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| Research Category |
Grant-in-Aid for Co-operative Research (A)
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| Allocation Type | Single-year Grants |
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| Research Field |
Informatics
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| Research Institution | The University of Tokushima |
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Principal Investigator |
OMATU Sigeru University of Tokushima, Faculty of Engineering, Professor, 工学部, 教授 (30035662)
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| Co-Investigator(Kenkyū-buntansha) |
YOSHIDA Tomoji Tokushima Bunri University, Faculty of Engineering, Research Associate, 工学部, 助手 (80220656)
NARITA Kenich Hiroshima University, Faculty of Engineering, Lecturer, 工学部, 講師 (20189210)
UKITA Masao Yamaguchi University, Faculty of Engineering, Professor, 工学部, 教授 (60035061)
TAZAKI Saburo Ehime University, Faculty of Engineering, Professor, 工学部, 教授 (00036394)
SUGA Yuzo Hirosima Institute of Technology, Faculty of Engineering, Associate Professor, 工学部, 助教授 (20133548)
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| Project Period (FY) |
1990 – 1992
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| Keywords | remote sensing / environmental data base / LANDSAT / NOAA / SPOT / neural networks / ERS-1 / JERS-1 |
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| Research Abstract |
Recently, various environmental problems such as acid rain problem,green house effect, and ozonn layer damage have been focused on sice those problems are serious for our lives in the future. In this study, we have considered environmental evaluation in Seto Inland Sea by remote sensing data analysis. Especially, a green house effect problem has become important from global environmental viewpoints. For such a problem, the remote sensing technique is suitable for getting an environmental situation in a wide, range. But in order to get the environmental situation as a two-dimensional image, we need the environmental data on the surface, which are called grand truth data. Therefore, in 1990 we have gathered the water pollution data in Seto Inland Sea which have been distributed in Chugoku or Shikoku areas and constructed the data base about the ground truth data. Then in 1991 we have analysed the LANDSAT TM data in Seto Inland Sea by using the remote sensing analysis and ground truth data. Finally, in 1992 we have discussed the error between the obtained image and the ground truth data. Furthermore, we have introduced a new remote sensing method based on neural networks in order to obtain more precise classification results compared with a conventional Bayesian method.
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