Assessing the effect of band selection on accuracy of pansharpened imagery: application to young woody vegetation mapping

Abstract

Expansion of woody vegetation has adverse effects on ecosystem services, and thus it is desirable to contain the problem at the early developmental stages. This can be aided by using high spatial resolution remotely-sensed data. The study investigated the effect of band selection during pansharpening on the ability to discriminate young woody vegetation from coexisting land cover types. Red-green-blue (RGB) spectral bands (30 m) of Landsat 8 imagery was pansharpened using the panchromatic band (15 m) of the same image to improve spatial resolution. Near-infrared (NIR), shortwave-infrared 1 (SWIR1) and shortwave-infrared 2 (SWIR2), bands were used respectively as the fourth spectral band during pansharpening, resulting in three pansharpened images. Unsupervised classification was performed on each pansharpened image as well as non-pansharpened multispectral image. The overall accuracies of classification derived from the pansharpened image was higher (87% − 89%) than that derived from the non-pansharpened multispectral image (83%). The study shows that band selection did not affect the classification accuracy of woody vegetation significantly. In addition, the study shows the potential of pansharpened Landsat data in detecting woody vegetation encroachment at the early growth stage.

Keywords: Young woody vegetation, Landsat, pansharpening, unsupervised classification