Alpine grasslands have experienced significant changes over the last decades due to socio-economic shifts and a changing climate, often resulting in intensification of management or abandonment. The Habitats Directive aims to preserve these habitats, but effective conservation measures require up-to-date habitat maps, the availability of which often limited by resource constraints. Remote sensing can help to mitigate this issue, assisting in the development of up-to-date habitat maps and supporting the management of protected areas in the Alps. In this study, we used hyperspectral satellite data from the PRISMA mission (ASI), along with complementary data from Sentinel-1 and Sentinel-2, to achieve a two-step classification process in the Sciliar-Catinaccio Nature Park (South Tyrol, Italy). We exploited Random Forest (RF) and Support Vector Machine (SVM) classifiers to (i) classify land cover types within the park, and (ii) conduct a more detailed classification of grassland habitats, which we validated using field data. In the first step, the best results were achieved with RF and PRISMA data in combination with Sentinel-1 backscatter data (overall accuracy of 86%). SAR data played an important role, increasing sensitivity for shrubs (+15%) and woods (+9%), and improving discrimination between these two last classes. In the second step, preliminary results show that a combination of PRISMA and SAR data yielded the most accurate grassland classification, while with Sentinel-2 and SAR data we achieved lower accuracies. Our results show that a multi-sensor approach can lead to improved classification of Alpine habitats. However, significant challenges are still present. These are mostly related to grassland habitat fragmentation relative to sensor resolution, the need to collect substantial amounts of field data in areas which can be difficult to access, and frequent cloud cover. We also believe that a multitemporal approach should be tested to achieve better results whenever possible. However, low cloud-free image availability in alpine areas imposes strong limitations on this type of analyses and requires specific approaches to deal with partial cloud cover. Considering the limitations imposed by frequent cloud cover, a synergy among the missions (PRISMA, EnMAP, CHIME, SBG) could be a good way to improve the monitoring capabilities of hyperspectral satellite sensors in Alpine areas.
- Mapping Alpine Grasslands with PRISMA, Sentinel-1, and Sentinel-2: A Two-Step Classification Approach
- Emilio DorigattiMariapina CastelliEmanuela PatriarcaRuth SonnenscheinLaura StendardiBasil TufailBartolomeo VenturaClaudia Notarnicola
- 3rd WORKSHOP ON INTERNATIONAL COOPERATION IN SPACEBORNE IMAGING SPECTROSCOPY (Noordwijk, 13/11/2024–15/11/2024)
- (EURAC)30663375
991007112862001241 - Institute for Earth Observation
- English
- Conference poster
- none
- Scientific
- Scientific
- Dorigatti E, Castelli M, Patriarca E, Sonnenschein R, Stendardi L, Tufail B, Ventura B, Notarnicola C