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

I Made Yuliara; Ni Nyoman Ratini; Windarjoto Windarjoto; Ni Komang Tri Suandayani

doi:10.29332/ijpse.v4n3.611

Authors

I Made Yuliara
imdyuliara@unud.ac.id
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

Keywords:

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

Abstract

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 (NDVI_ref) and 97.072% for those based on PCA ( NDVI_pca). The distribution class category with the dominant area identified into heavy class (NDVI_ref) of 7841.25 ha and moderate class (NDVI_pca) 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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