Spectral reflectance and principal component analysis on the distribution of clove vegetation using Landsat 8



  • I Made Yuliara Physics Department, Faculty of Mathematics and Science, Udayana University, Denpasar, Bali, Indonesia
  • Ni Nyoman Ratini Physics Department, Faculty of Mathematics and Science, Udayana University, Denpasar, Bali, Indonesia
  • Windarjoto Windarjoto Physics Department, Faculty of Mathematics and Science, Udayana University, Denpasar, Bali, Indonesia
  • Ni Komang Tri Suandayani Physics Department, Faculty of Mathematics and Science, Udayana University, Denpasar, Bali, Indonesia


clove distribution, Landsat 8, normalized difference vegetation index (NDVI), principal component analysis (PCA), spectral reflectance


This paper discusses the distribution of clove vegetation in Buleleng Regency, Bali using a vegetation index extracted from Landsat 8 imagery based on spectral reflectance and Principal Component Analysis (PCA). Data analysis used the Normalized Difference Vegetation Index (NDVI) transformation and PCA band transformation. Adjustment of the position of clove vegetation in the image is determined by the measurement results of the clove coordinate sample in the field. The results showed that the accuracy of the area of ?? clove vegetation distribution as measured as a percentage comparison to the area data of the Forestry and Plantation Service, Buleleng Regency, Bali in 2014, was 97.066% for the spectral reflectance-based vegetation index (NDVIref) and 97.072% for those based on PCA ( NDVIpca). The distribution class category with the dominant area identified into heavy class (NDVIref) of 7841.25 ha and moderate class (NDVIpca) of 7591.77 ha. There is a difference in the two determinant coefficient values ?? (R2), which is 0.2407% and at 5% significance, the variants of the B4 and B5 spectral reflectance image variable data variants, as well as the C1 and C2 component image variables simultaneously, can affect the NDVI vegetation index.


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Abd El-Kawy, O. R., Rød, J. K., Ismail, H. A., & Suliman, A. S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied geography, 31(2), 483-494. https://doi.org/10.1016/j.apgeog.2010.10.012

Adams, J. B., & Gillespie, A. R. (2006). Remote sensing of landscapes with spectral images: A physical modeling approach. Cambridge University Press.

Becker, F., & Choudhury, B. J. (1988). Relative sensitivity of normalized difference vegetation index (NDVI) and microwave polarization difference index (MPDI) for vegetation and desertification monitoring. Remote Sensing of Environment, 24(2), 297-311. https://doi.org/10.1016/0034-4257(88)90031-4

Bhandari, A. K., Kumar, A., & Singh, G. K. (2012). Feature extraction using Normalized Difference Vegetation Index (NDVI): A case study of Jabalpur city. Procedia technology, 6, 612-621. https://doi.org/10.1016/j.protcy.2012.10.074

Destefanis, G., Barge, M. T., Brugiapaglia, A., & Tassone, S. (2000). The use of principal component analysis (PCA) to characterize beef. Meat science, 56(3), 255-259. https://doi.org/10.1016/S0309-1740(00)00050-4

Dharani, M., & Sreenivasulu, G. (2019). Land use and land cover change detection by using principal component analysis and morphological operations in remote sensing applications. International Journal of Computers and Applications, 1-10.

Dinas Kehutanan dan Perkebunan Pemkab Buleleng. 2014. Laporan Triwulan Luas Areal dan Produksi Komoditas Perkebunan Kabupaten Buleleng Tahun 2014. Buleleng, Bali. [Indonesian]

Dou, Z., Cui, L., Li, J., Zhu, Y., Gao, C., Pan, X., ... & Li, W. (2018). Hyperspectral estimation of the chlorophyll content in short-term and long-term restorations of mangrove in Quanzhou Bay Estuary, China. Sustainability, 10(4), 1127.

Estornell, J., Martí-Gavliá, JM, Sebastiá, MT, & Mengual, J. (2013). Principal component analysis applied to remote sensing. Modeling in Science Education and Learning , 6 , 83-89.

Houborg, R., Fisher, J. B., & Skidmore, A. K. (2015). Advances in remote sensing of vegetation function and traits. https://doi.org/10.1016/j.jag.2015.06.001

Jagalingam, P., Akshaya, B. J., & Hegde, A. V. (2015). Bathymetry mapping using Landsat 8 satellite imagery. Procedia Engineering, 116, 560-566. https://doi.org/10.1016/j.proeng.2015.08.326

Lasaponara, R. (2006). On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series. Ecological modelling, 194(4), 429-434. https://doi.org/10.1016/j.ecolmodel.2005.10.035

Lemenkova, P. (2015). Analysis of Landsat NDVI time series for detecting degradation of vegetation. In Geoecology and Sustainable Use of Mineral Resources. From Science to Practice. Proceedings of 3rd International Conference of Young Scientists. Belgorod State University (BelSU), Ed. AN Petin, PV Goleusov, EI Makaseeva (pp. 11-13).

Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.

Liu, D., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), 187-194.

Malatesta, L., Attorre, F., Altobelli, A., Adeeb, A., De Sanctis, M., Taleb, N. M., ... & Vitale, M. (2013). Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen). Journal of Applied Remote Sensing, 7(1), 073527.

Ozdogan, M. (2010). The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis. Remote Sensing of Environment, 114(6), 1190-1204. https://doi.org/10.1016/j.rse.2010.01.006

Rees, W. G. (2013). Physical principles of remote sensing. Cambridge university press.

Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., & Egorov, A. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote sensing of Environment, 185, 57-70. https://doi.org/10.1016/j.rse.2015.12.024

Shahabi, H., Ahmad, B. B., Mokhtari, M. H., & Zadeh, M. A. (2012). Detection of urban irregular development and green space destruction using normalized difference vegetation index (NDVI), principal component analysis (PCA) and post classification methods: A case study of Saqqez city. International Journal of Physical Sciences, 7(17), 2587-2595.

Sukmono, A., & Ardiansyah. (2017). Identification of rice field using Multi-Temporal NDVI and PCA method on Landsat 8 (Case Study: Demak, Central Java). In IOP Conference Series: Earth and Environmental Science (Vol. 54, No. 1, p. 012001). IOP Publishing.

Sule, S. D., & Wood, A. (2020). Application of principal component analysis to remote sensing data for deforestation monitoring. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII (Vol. 11528, p. 1152806). International Society for Optics and Photonics.

USGS. 2019. Using the USGS Landsat Level-1 Data Product.

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.

Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: a review of developments and applications. J Sens 2017: 1353691.

Yuliara, I. M., & Kasmawan, A. (2017). The Reflectance Spectral Characteristic of Cloves Vegetation Using Landsat 8 in Buleleng, Bali. Journal of Food Security and Agriculture, 1(1), 15-18.

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.

Zaitunah, A., Samsuri, A. A., & Safitri, R. A. (2018, March). Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 126, No. 1, pp. 1-9).



How to Cite

Yuliara, I. M., Ratini, N. N., Windarjoto, W., & Suandayani, N. K. T. (2020). Spectral reflectance and principal component analysis on the distribution of clove vegetation using Landsat 8. International Journal of Physical Sciences and Engineering, 4(3), 27-37. https://doi.org/10.29332/ijpse.v4n3.611



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