Satellite and unmanned aerial vehicle data for the classification of sand dune vegetation

Abstract

Within coastal systems, sand dunes are the only natural barriers able to counteract erosive processes. Since their equilibrium is often threatened by human activities and high vulnerability of the coastal environment, dunes require increasing attention and specific monitoring. Located between the mainland and the sea, dunes are unique residue habitats for some plant and animal species. In particular, their vegetation is important because it has a consolidation function and promotes the vertical dune accretion. A georeferenced vegetation classification can be useful to define the advancements or erosion stage of the dune, usually based only on the geometric reconstruction. The proposed study aims to compare the classifications performed with some combinations of two of the last generation sensors and traditional image processing techniques. High spectral resolution satellite image (WorldView-2) and a multispectral orthophoto, obtained from data acquired by an unmanned aerial vehicle, were used. Objects and pixel algorithms were applied and the results were compared by a statistical test. Using the same bands, the findings show that both data are suitable for monitoring the evolutionary dune status. Specifically, the WorldView-2 pixel-based classification and UAV object-based classification provide the same accurate results.