Change Detection in (Semi-) Natural Grassland Ecosystems for Biodiversity Monitoring Using Open Data

A crucial challenge among remote sensing communities is the availability of open data. Several European and international programs address this aim offering a useful support to many applications. The detection of changes in ecosystems for the monitoring of biodiversity is one of the main goal for the commitments undertaken not only by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also the UN Sustainable Development Goals (SDG). This work aims to show the usefulness of free available data for the detecting of changes in (semi-) natural grasslands ecosystem for three different test site in Italy and Portugal. Due to its request for only one multispectral remote sensed image at time T2 and a land cover map at time T1, the Cross Correlation Analysis (CCA) algorithm was preferred for the detection of changes from (semi-) natural grasslands to other land cover. A Sentinel-2 image as T2 and the (semi-) natural grasslands Copernicus layer as T1 were considered. The accuracy assessment of the change maps was based on stratified random sampling weighting the Overall Accuracy (OA) by the proportions of the study area represented by the map classes. The reported finding for OA % of 94.21±0.10, is encouraging and promising as a support to policy makers.