High Resolution Mapping of Vegetation Biodiversity by Hyperspectral Images and Convolutional Autoencoders

Giovanni Nico, Manilo Monaco, Olimpia Masci, Mario G. C. A. Cimino

Abstract: A methodology is presented to map the vegetation biodiversity based on the hypothesis of the spectral variation (SV) which has been proposed to assess the forest biodiversity by means of Earth Observation (EO) data. Hyperspectral data acquired by the PRecursore Iperspettrale della Missione Applicativa (PRISMA) mission of the Italian Space Agency to spectral signature with a high spectral resolution. The NDVI is computed from PRISMA data and used to identify pixels corresponding to vegetation cover. The spectral signatures at those pixels are then clusterized using the convolutional autoenconders technique and the final map with the location of pixels belonging to the different classes is produce. The methodology is applied to assess the vegetation biodiversity in National Parks of Gargano, Alta Murgia, Cilento-Vallo di Diano-Alburni, Appennino Lucano Val D’Agri Lagonegrese and Pollino, all located in Southern Italy.