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Method for Estimation of Damage Grade and Damaged Paddy Field Areas due to Salt Containing sea Breeze with Typhoon Using Remote Sensing Satellite Imagery Data
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International Journal of Applied Sciences (IJAS)
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Volume:  2    Issue:  3
Pages:  NULL
Publication Date:   July / August 2011
ISSN (Online): 2180-1258
Pages 
84 - 92
Author(s)  
Kohei Arai - Japan
 
Published Date   
05-08-2011 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Typhoon Disaster, NIR Radiometer, Remote Sensing Satellite, Sea Breeze, Salt Amount Attached to Rice Crop Leaves, Paddy Field 
 
 
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Methods for estimation of damage grade and damaged paddy field areas due to salt containing sea breeze with typhoon using remote sensing satellite imagery data is proposed. Due to a fact that Near Infrared: NIR camera data is proportional to vitality of vegetation, it is possible to estimate damage grade and damaged paddy field areas due to salt containing sea breeze with typhoon using NIR channels of remote sensing satellite imagery data. Through regressive analysis between measured and estimated damage grade and damaged paddy field areas, it is found that there is a good correlation between both. Also it is found that there is a proportional relation between salt amount attached to the rice crop leaves and NIR reflectance measured with NIR channels of remote sensing satellite imagery data. Thus it is validated the proposed estimation method for damage grade and damaged paddy field areas due to salt containing sea breeze with typhoon using NIR channels of remote sensing satellite imagery data.  
 
 
 
1 Ramachandran, Justice, Abrams Edt., Kohei Arai, et al., Land Remote Sensing and Global Environmental Change, Part-II, Sec.5: ASTER VNIR and SWIR Radiometric Calibration and Atmospheric Correction, 83-116, Springer 2010.
2 Anatoly A. Gitelson,* Yoram J. Kaufman, + and Mark N. Merzlyak, Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS, REMOTE SENS. ENVIRON. 58:289-298 (1996)
3 Tucker, J. C. (1979), Red and photographic infrared linear combination for monitoring vegetation. Remote Sens. Environ.8:127-150.
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5 Sellers, P. J. (1987), Canopy reflectance, photosynthesis and transpiration. II. The role of biophysics in the linearity of their interdependence. Remote Sens. Environ. 21:143-183.
6 Andrieu, B., and Baret, F. (1993), Indirect methods of estimating crop structure from optical measurements, In Crop Structure and Light Microclimate. Characterization and Applications (C. Varlet-Grancher, R. Bonhomme, H. Sinoquet, Eds.), INRA Edition, Paris, pp. 285-322.
7 Baret, F., and Guyot, G. (1991), Potential and limits of vegetation indexes for LAI and APAR assessment. Remote Sens. Environ. 35:161-173.
8 Curran, P. J., Dungan, J. L., Macler, B. A., and Plummer, S. E. (1991), The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sens. Environ. 35:69-76.
9 Horler, D. N., Dockray, M., and Barber, J. (1983), The red edge of plant leaf reflectance. Int. J. Remote Sens. 4(2): 273-288.
10 Huete, A. R., and Liu, H. Q. (1994), An error and sensitivity analysis of the atmospheric and soil correcting variants of the NDVI for the MODIS-EOS, IEEE Trans. Geosci. Remote Sens. 32:897-905.
 
 
 
 
 
 
 
 
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