Impact analysis of pansharpening Landsat ETM+, Landsat OLI, WorldView-2, and Ikonos images on vegetation indices

Abstract

© 2016 SPIE. The aim of our study was to verify the impact that pansharpening (PS) methods produce on vegetation indices. We used images with both moderate (Landsat 7, Landsat 8) and high (World View2, Ikonos) spatial resolution on which we performed three methods of PS (Brovey transform, Gram-Schmidt and Principal component). The study is based on the differences of vegetation indices (VI) values before and after the pansharpening method is applied. The difference is quantified as an root mean square error. Vegetation indices used in this study were: NDVI, MSAVI2, EVI2, GNDVI, OSAVI and SAVI. Statistical analysis is carried out by calculating coefficients of correlation, root mean square errors and bias calculations for every vegetation index before and after pansharpening procedure is done. The results imply that the BT gave the most diverse results between original VI values and the PS VI values, while the GS and PC methods preserved the values of pixel bands, and that the effect of any PS method is most evident when using Ikonos bands.