The publication is part of the Showcase 4 (myEcoSystem) for which it is essential to have reliable data products over various scales to investigate ecosystem status and possible changes over time.

The study by Adamo et al. develops an approach based on very high spatial resolution (WV-2) data and accounting for the Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) hierarchical scheme. The latter allows to maintain a standardized LC while the data basis enables to detect even small features such as single trees and bushes that normally used data sets (MODIS, Sentinel) can merely detect due to their coarser  spatial resolution. On our endeavour to develop new products with higher significance for decision maker but also for local stakeholders such as protected area managers/long-term site coordinators, this is a step forward that could be implemented consistently on a larger spatial scale in terms of spatial coverage and would therefore serve SC4 service aims but also e-shape in general.

M.Adamo, V.Tomaselli, C.Tarantino, S.Vicario, G.Veronico, R.Lucas, P.Blonda (2020). Knowledge-Based Classification of Grassland Ecosystem Based on Multi-TemporalWorldView-2 Data and FAO-LCCS Taxonomy. Remote Sensing, 2020,12(09),1447.(pdf)

Pilot 4.1