Development and optimization of the ratio vegetation index on the visible and infrared spectrum
This study aims to find a suitable vegetation index model to analyze the distribution of clove vegetation in Buleleng regency, Bali. Vegetation index model Ratio Vegetation Index (RVI) extracted from Landsat 8 was developed in the visible spectrum (l = 0.450 - 0.680 ?m) and infrared (l = 0.845 - 2.300 ?m). Development methods are carried out on the basis of the spectral reflectance response characteristics of the dominant electromagnetic waves from the visible and infrared spectra of vegetation. Created a multiple regression relationship results from scattergram that links RVI vegetation index with band 3 = B3, band 5 = B5, band 6 = B6, and band 7 = B7. Optimization strategy is carried out by dividing the development of RVI with a variable number factor. There are 4 forms of RVI vegetation index models from the development and optimization of the visible and infrared spectra. Of the 4 new vegetation index forms, which provide optimal results and close to extensive data from the Forestry and Plantation Service, Buleleng regency, Bali is RVInew4 = 0.0022 + 0.00142 * B3 + 0.00028 * B5 + 0.00054 * B6 + 0.00096 * B7. The area produced by this vegetation index model in analyzing the distribution of clove vegetation is 7667.82 ha. Is this area 99.40% of the average data area? the Forestry and Plantation Service, Buleleng regency, Bali in 2014, which is 7622.32 ha. The dominant distribution of clove vegetation is in the rare category with an area of 7441.74 ha.
Adams, J. B., & Gillespie, A. R. (2006). Remote sensing of landscapes with spectral images: A physical modeling approach. Cambridge University Press.
Barbosa, H. A., Huete, A. R., & Baethgen, W. E. (2006). A 20-year study of NDVI variability over the Northeast Region of Brazil. Journal of arid environments, 67(2), 288-307.
Forestry and Plantation Service of Buleleng Regency. (2014). Quarterly Area Report and Production of Buleleng Regency Plantation Commodities in 2014.
Forestry and Plantation Service of Buleleng Regency. (2015). Quarterly Area Report and Production of Buleleng Regency Plantation Commodities in 2015.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation. John Wiley & Sons.
Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: an introduction. John Wiley & Sons.
Shao, Y., Fan, X., Liu, H., Xiao, J., Ross, S., Brisco, B., ... & Staples, G. (2001). Rice monitoring and production estimation using multitemporal RADARSAT. Remote sensing of Environment, 76(3), 310-325.
Small, C. (2004). The Landsat ETM+ spectral mixing space. Remote sensing of Environment, 93(1-2), 1-17.
Song, C., Gray, J. M., & Gao, F. (2011). Remote sensing of vegetation with landsat imagery. Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, 3-29.
Strahler, A. H., Boschetti, L., Foody, G. M., Friedl, M. A., Hansen, M. C., Herold, M., ... & Woodcock, C. E. (2006). Global land cover validation: Recommendations for evaluation and accuracy assessment of global land cover maps. European Communities, Luxembourg, 51(4).
USGS. (2017). Using the USGS Landsat 8 Product, U.S. Geological Survey. http://landsat.usgs.gov/Landsat8_Using_Product.php Retrieved on June 1, 2017.
Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: a review. Journal of plant ecology, 1(1), 9-23.
Yuliara, A. Kasmawan. (2017). The Reflectance Spectral Characteristic of Cloves Vegetation Using Landsat 8 in Buleleng Bali, Journal of Food Security and Agriculture (Vol. 1). 15 – 17 . IRCS. Udayana University Press.
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