2004 Fiscal Year Final Research Report Summary
Studies on environment and resources information system for circulatory society
| Project/Area Number |
13460147
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
生物資源科学
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| Research Institution | Kyoto University |
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Principal Investigator |
SAKAI Tetsuro Kyoto University, Graduate School of Informatics, Professor, 情報学研究科, 教授 (10101247)
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| Co-Investigator(Kenkyū-buntansha) |
MORIYA Kazuyuki Kyoto University, Graduate School of Informatics, Professor, 情報学研究科, 教授 (90159195)
NUMATA Kunihiko Kyoto University, Graduate School of Informatics, Associate Professor, 情報学研究科, 助教授 (30026405)
ARAI Nobuaki Kyoto University, Graduate School of Informatics, Associate Professor, 情報学研究科, 助教授 (20252497)
YOSHIMURA Tetsuhiko Kyoto University, Graduate School of Informatics, Assistant Professor, 情報学研究科, 助手 (40252499)
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| Project Period (FY) |
2001 – 2004
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| Keywords | NOAA / high-resolution satellite / lidar-data / vegetation / land use / cluster analysis / GPS / NDVI |
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| Research Abstract |
As a monitoring of environment and resources information using satellite data, various satellite data were examined. The AVHRR data of NOAA is suitable for the monitoring throughout the long term in the wide region. Using the month maximum NDVI value calculated from AVHRR data, the classification method of land-use and vegetation was examined. Using this method, the land use division is possible in the countries with the unimproved database, easily. For Thailand and China, the usefulness of this method was examined. In the case of the classification using the data for the multiple years, the NDVI value is greatly influenced for the year fluctuation of climates such as temperature and precipitation. Therefore, there is a case in which it differs for the classification class even in the site where the land use does not change. As the countermeasure, the classification using standardization of monthly of the data and the classification considering degree of assignment to the each classification class were examined. As the result, it was possible to remove the effect of the year fluctuation in comparison with the classification using the living data. And, land use division using high-resolution satellite data of IKONOS and altitude data by the aircraft lidar was also examined. As the result, method of processing of the shadow of satellite data by the ratio calculation was useful. And, the classification technique using joint the lidar data and satellite data was effective. The surface high of the vegetation information, which are average, standard deviation, maximum value and minimum value, and satellite data, which are vegetation index and band value, were used. In the GPS positioning, accuracy was examined. As the result, next fact was proven. It is enough even in the independent positioning for the navigation and, DGPS are necessary in order to be correspondent to the high-resolution satellite.
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