A comparison of airborne hyperspectral-based classifications of emergent wetland vegetation at lake Balaton, Hungary

Abstract

Earth observation has rapidly evolved into a state-of-the-art technology providing new capabilities and a wide variety of sensors; nevertheless, it is still a challenge for practitioners external to a specialized community of experts to select the appropriate sensor, define the imaging mode requirements, and select the optimal classifier or retrieval method for the task at hand. Especially in wetland mapping, studies have relied largely on vegetation indices and hyperspectral data to capture vegetation attributes. In this study, we investigate the capabilities of a concurrently acquired very high spatial resolution airborne hyperspectral and lidar data set at the peak of aquatic vegetation growth in a nature reserve at Lake Balaton, Hungary. The aim was to examine to what degree the different remote-sensing information sources (i.e. visible and near-infrared hyperspectral, vegetation indices and lidar) are contributing to an accurate aquatic vegetation map. The results indicate that de-noised hyperspectral information in the visible and very near-infrared bands (400–1000 nm) is performing most accurately. Inclusion of lidar information, hyperspectral infrared bands (1000–2500 nm), or extracted vegetation indices does not improve the classification accuracy. Experimental results with algorithmic comparisons show that in most cases, the Support Vector Machine classifier provides a better accuracy than the Maximum Likelihood.