A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy)

Earth Observation services guarantee continuous land cover mapping and are becoming of great interest worldwide. The Google Earth Engine Dynamic World represents a planetary example. This work aims to develop a land cover mapping service in geomorphological complex areas in the Aosta Valley in NW Italy, according to the newest European EAGLE legend starting in the year 2020. Sentinel-2 data were processed in the Google Earth Engine, particularly the summer yearly median composite for each band and their standard deviation with multispectral indexes, which were used to perform a k-nearest neighbor classification. To better map some classes, a minimum distance classification involving NDVI and NDRE yearly filtered and regularized stacks were computed to map the agronomical classes. Furthermore, SAR Sentinel-1 SLC data were processed in the SNAP to map urban and water surfaces to improve optical classification. Additionally, deep learning and GIS updated datasets involving urban components were adopted beginning with an aerial orthophoto. GNSS ground truth data were used to define the training and the validation sets. In order to test the effectiveness of the implemented service and its methodology, the overall accuracy was compared to other approaches. A mixed hierarchical approach represented the best solution to effectively map geomorphological complex areas to overcome the remote sensing limitations. In conclusion, this service may help in the implementation of European and local policies concerning land cover surveys both at high spatial and temporal resolutions, empowering the technological transfer in alpine realities.

Automated summary

The Earth Observation services play a crucial role in continuous land cover mapping and have gained significant attention worldwide. The Google Earth Engine Dynamic World serves as a global example in this field. This study focuses on developing a land cover mapping service in the geologically complex areas of Aosta Valley in NW Italy, following the latest European EAGLE legend starting from 2020. The research utilizes Sentinel-2 data processed in the Google Earth Engine, combining multispectral indexes and k-nearest neighbor classification for accurate mapping of various land cover classes. Deep learning and GIS updated datasets, along with SAR Sentinel-1 SLC data, are also employed for mapping urban and water surfaces. The effectiveness of the implemented service and methodology is tested by comparing the overall accuracy with other approaches, and the mixed hierarchical approach proves to be the most effective for mapping geologically complex areas.