Comparative Study on NDVI with RVI for Estimated Area and Class Distribution
Keywords:class distribution, comparative study, estimated area, NDVI, RVI
This study aims to determine the differences and comparison of the results of the estimated area, the distribution of clove vegetation using the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) and to choose a vegetation index that is more suitable for clove vegetation analysis in Buleleng district, Bali. The method used is to compare statistically descriptive area and distribution class produced by the NDVI and RVI models with area data from the Forestry and Plantation Service (FPS), Buleleng regency, Bali in 2014, amounting to 7622.32 ha. The estimated area of ??clove vegetation by the NDVI model was 7852.68 ha and the RVI model was 7669.44 ha. There is an estimated difference in the area of ??clove vegetation of 183.24 ha and a difference in the broad class category of 2453.85 ha for the Rare class (NDVI > RVI) category, for the Medium class of 1611.45 ha (RVI > NDVI), and for the Dense class of 659.16 ha (RVI > NDVI). Comparison of the area with FPS data obtained 97.07% for the NDVI model and 99.39% for the RVI model. This shows that the RVI model vegetation index is more suitable for use in the estimation of the area and class of clove vegetation distribution in Buleleng regency, Bali.
Adams, J. B., & Gillespie, A. R. (2006). Remote sensing of landscapes with spectral images: A physical modeling approach. Cambridge University Press.
Beeri, O., Phillips, R., Hendrickson, J., Frank, A. B., & Kronberg, S. (2007). Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110(2), 216-225. https://doi.org/10.1016/j.rse.2007.02.027
Dinas Kehutanan dan Perkebunan Pemkab Buleleng. (2014). Laporan Triwulan Luas Areal dan Produksi Komoditas Perkebunan Kabupaten Buleleng Tahun 2014.
Dinas Kehutanan dan Perkebunan Pemkab Buleleng. (2015). Laporan Triwulan Luas Areal dan Produksi Komoditas Perkebunan Kabupaten Buleleng Tahun 2015.
Gao, G., & Wang, S. (2012, June). Compare Analysis of Vegetation Cover Change in Jianyang City Based on RVI and NDVI. In 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering (pp. 1-4). IEEE. https://doi.org/10.1109/RSETE.2012.6260516
Gu, W., Ma, W., Zhou, L., Tang, L., & Huai, H. (2011). RS estimation of chlorophyll-a concentration based on RVI regionalization during algae blooming period in Dianshan Lake. Research of Environmental Sciences, 24(6), 666-672.
Li, H., Zheng, L., Lei, Y., LI, C., & ZHOU, K. (2007). Comparison of NDVI and EVI based on EOS/MODIS data. Progress in geography, 1.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.
Luo, H., Fang, J., Li, H., Wang, L., Dai, S., & Chen, S. (2014, August). Comparison and analysis NDVI and RVI changes before and after typhoon in Hainan based on HJ-1CCD satellite images. In 2014 The Third International Conference on Agro-Geoinformatics (pp. 1-4). IEEE. https://doi.org/10.1109/Agro-Geoinformatics.2014.6910615
Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: an introduction. John Wiley & Sons.
Purevdorj, T. S., Tateishi, R., Ishiyama, T., & Honda, Y. (1998). Relationships between percent vegetation cover and vegetation indices. International journal of remote sensing, 19(18), 3519-3535. https://doi.org/10.1080/014311698213795
Rees, W. G. (2013). Physical principles of remote sensing. Cambridge University Press.
USGS. (2019). Using the USGS Landsat Level-1 Data Product. https://www.usgs.gov/land-resources/nli/landsat/using-usgs-landsat-level-1-data-product
Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108(3), 290-310. https://doi.org/10.1016/j.rse.2006.11.021
Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: a review. Journal of plant ecology, 1(1), 9-23. https://doi.org/10.1093/jpe/rtm005
Xu, D., & Guo, X. (2014). Compare NDVI extracted from Landsat 8 imagery with that from Landsat 7 imagery. American Journal of Remote Sensing, 2(2), 10-14.
Yuliara, I. M., Sutapa, G. N., & Kasmawan, G. A. (2018). Development and optimization of the ratio vegetation index on the visible and infrared spectrum. International journal of physical sciences and engineering, 2(2), 101-110. https://doi.org/10.29332/ijpse.v2n2.172
How to Cite
Articles published in the International Journal of Physical Sciences and Engineering (IJPSE) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJPSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJPSE can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJPSE volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.