URBAN VEGETATION COVER EXTRACTION FROM HYPERSPECTRAL REMOTE SENSING IMAGERY AND GIS-BASED SPATIAL ANALYSIS TECHNIQUES: THE CASE OF ATHENS, GREECE

Information on the urban vegetation cover spatial coverage is important in sustainable urban planning and resourceful environmental management, whereas the same time it plays a very important role in human-environment interactions. The present study aims at evaluating the combined use of Hyperion hyperspectral imagery with the Support Vector Machines (SVMs) and Spectral Angle Mapper (SAM) pixel-based classifiers for discriminating different land-cover classes at a typical urban setting focusing particular in urban vegetation cover. As a case study, the city of Athens Greece was used. Evaluation of the derived land cover maps was performed on the basis of the error matrix statistics which was assisted by co-orbital higher resolution imagery available and field visits conducted in our study region. To ensure consistency and comparability of our results, the same set of training and validation points were used. Our analyses showed that SVMs outperformed SAM in terms of both overall classification and urban vegetation cover mapping accuracy. In particular, an overall accuracy of 86.53% and Kappa 0.823 was reported for the SVMs' results, whereas for SAM were 75.13% and 0.673 respectively. The SVMs' ability to identify an optimal separating hyperplane for classes' separation exemplified the algorithm's ability to perform better, in comparison to SAM, at least this appears to be the case in our study. However, both techniques were influenced by the relatively coarse spatial resolution of the sensor, which resulted to misclassification cases due to spectral mixing effects. Yet, the potential of hyperspectral remote sensing for efficient and up-to-date derivation of the spatial representation of urban vegetation presence was evidenced, providing supportive results to efforts currently ongoing globally towards the development of accurate and robust techniques in mapping the spatiotemporal distribution of urban vegetation cover and dynamics from space.