Detection of critical areas prone to land degradation using prisma: the Metaponto coastal area in South Italy test case

Land cover, or the biophysical cover of the earth's surface, plays an essential role in climate and environmental dynamics. Processes involving land cover change, are among the factors that most threaten the ecosystems sustainability and services. The objective of the work is to explore the potential of the PRISMA multi-temporal hyperspectral imagery in generating new EO products to complement/improve the products provided by Copernicus' Land Monitoring Service for the analysis and monitoring of complex and fragile ecosystems such as the coastal Metaponto (Southern Italy) by estimating of the land biological and economic productivity loss and land degradation vulnerability. Preliminary results showed that an improvement in ecosystem mapping is supported by the use of Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN) and Support Vector Machines (SVM) and a hybrid approach to define the vegetation trait, leads to significant improvement in the damage assessment and land degradation assessment.