The Effect of Minimum Noise Fraction on Multispectral Imagery Data for Vegetation Canopy Density Modelling

Abstract

Minimum Noise Fraction (MNF) is known as one of the method to minimize noise on hyperspectral imagery. In addition, there are not many studies have tried to show the effect of MNF transform on multispectral data. This study purposes to determine the effect of MNF transform on the accuracy level of vegetation density modeling using 10 meters Sentinel-2A spatial resolution (multispectral data) and to know the cause. The study area is located in parts of Sapporo City, Hokkaido, Japan. Vegetation density is modelled through vegetation index approach, Normalized Difference Vegetation Index (NDVI). The results show that the coefficient correlation of vegetation density data and vegetation index regression after MNF transformation (0.801623) has higher value than the same regression without the MNF (0.794481). However, better correlation does not represent the better accuracy on vegetation density modeling. Accuracy calculation through standard error of estimate shows the use of MNF in multispectral data for vegetation density modeling causes the decrease of model accuracy value. The accuracy of vegetation density model without involving MNF transformation reached 91.40 %, while the model accuracy through MNF transformation before vegetation density modeling reached 90.89 %. The insignificant increased accuracy is occurred due to the limited number of multispectral image information compared to hyperspectral image data.