高分解能衛星データを用いた水田のタンパク含有率の推定
Contents
1. 65 68 6 0 65 5 68 70 7 60 65 20052. 1 2008 NDVI 2008 8 18 2 2009 NDVI 2009 8 25 3 2009 NDVI Np 2009 9 24 1 59 104 NDVI 2008 1 2009 7 sp 0 5 4 gt 3 Ww 850 900 950 1000 B4 1 2008 4 T WN T Ye 4 4 T
3. 4 6 2 6 6 58 62 5 4 5 8 5 0 5 4 5 7
4. 30 17 GeoEye 1 2008 4 2009 NDVI 4 NDVI GeoEye 1 2008
5. No 2 115 118 2007 6 17 2003 SPOT HRV 23 451 457 2007 No 1 143 147 2006 PS 500 66PP 1974 4 48 91 96 107
6. 2009 3 Y 2 1415x 5 4125 Y x NDVI r 0 67 4 4 5 7 2003 SPOT HRV HRVIR 3 NDVI
7. NDVI 5 2008 8 18 ERDAS IMAGINE 5
8. 2007 2003 2009 GeoEye 1 2008 IKONOS BL BL 5 8 9
9. GeoEye 1 2mx 2m 3 4 NDVI 4 5 IKONOS 4mx 4m GeoEye 1 2mx 2m ALOS 10m x 10m IKONOS 4mx 4m
10. Annual Report of Research Center for Natural Hazards and Disaster Recovery Niigata University No 3 2009 1 Estimating Rice Grain Protein Contents using High Resolution Satellite Data in case of Kawaguchi Town and Yamakoshi Ward by Nobuyuki ABE Toshinori FUKUYAMA Hiromi KAMIIE Abstract We presumed the rice grain protein content rate using image of IKONOS and GeoEye 1 sensors in Budokubo and Yamakoshi ward which were the stricken area of a Cyuuetsu great earthquake In both regions relations were admitted between the rice grain protein content rate and normalized difference vegetation index NDVI in 2009 data In addition we presumed the rice grain protein content rate and made it to the map using obtained regression The protein content rate was able to be understood from the map in the sight and the large area To obtain the regression of higher accuracy At is necessary to examine the method of acquiring the taking an image satellite date and extraction method of digital number And it is necessary to use high resolution satellite data for the protein content rate can be presumed in the case of small rice field
11. 9 24 NDVI 2 8 25 0 8 0 7 a 06 nh ie a 0 3 0 2 0 1 0 0 2 4 6 8 NDVI 2009 106 8 18 NDVI 1 8 24 SPAD 10 8 12 8 31 9 11 10 SPAD 8 31 SPAD 1 9 11 SPAD
12. IKONOS GeoEye 1 NDVI Keywords rice grain protein content rate IKONOS GeoEye 1 paddy field ND VI 16 10 23 I 2007 2008 2008 2009 1974 6 3
13. NO ww T 0 82 0 84 0 86 0 88 0 9 NDVI 2 2009 NDVI 8 7 sl 5 KO SN 4 gt 2N 88 a Ww 2 1 0 0 1 0 3 0 5 0 7 0 9 NDVI 3 2009 NDVI 2 3 4 1 4 3 NDVI 2008 1 2008 1 Y 0 00997x 3 0095 Y x Band4 r 0 767 2009 2 Y 33 6725x 23 0352 Y x NDVI r 0 615 2 2
14. 2 1 2 3 103 IKONOS 2008 8 18 2009 8 25 2009 9 24 GeoEye 1 IKONOS 4mx 4m GeoEye 1 2mx 2m ERDASIMAGINE8 7 ArcGIS9 1 9 19 9 18 9 22 9 28 10 7 2 PS 500
15. 2008 4 2009 NDVI 2007 2 2008 2003 1 2 2008 2009 8 25 9 24 1 049 0 50 2008 8 18 2009 105 202 50 54 54 5S 58 62 1 66 5 2009 0 fa a y EE 1
16. 3 0 1 2006 IKONOS GeoEye 1 E 8 25 9 24
17. lt A 6 2009 3 9 24 2 NDVI 2 NDVI 2003 20 2009 9 24 NDVI 8
18. 2009 2008 18 2009 31 2009 17 2008 31 2008 2009 1
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