Mountain grazing lands are key constituents of the natural, economical and cultural heritage, but at the same time sensitive to climate and land use change, hence requiring urgent adaptation and management strategies. These must be based on a better understanding of the distribution of mountain pastoral resources across space and time. In this study we model the distribution and the productivity of grassland surfaces in a topographically complex protected area (Gran Paradiso National Park, 710 km2) in north-western Italian Alps. The objective of our work was threefold: a) modelling the distribution of mountain grasslands across the entire park at a 20-meters spatial resolution, b) classify pastoral surfaces according to productivity classes, and c) according to thirteen pastoral categories. We used a random forest approach to combine a massive terrain vegetation survey as ground truth, with remote-sensing-derived, climatic and topographic layers as predictors. Grassland presence/absence was classified with high accuracy (up to 88%) and, compared to the standard Copernicus European Grassland Product, revealed the presence of extensive high altitude grassland areas potentially available for wild herbivores. Grassland productivity was modelled with remarkably high accuracy both according to three broad productivity classes (90% accuracy) and to a more detailed classification into thirteen pastoral categories (83% accuracy). Productivity estimates agree well with satellite-derived leaf area index maps and with area-averaged NDVI seasonal patterns. We conclude that combining tailored field campaigns and high-resolution remote sensing allows for robust prediction of grassland distribution and productivity even in complex terrains. This information can contribute to improve the management of pastoral resources and promote effective adaptation strategies.

On the distribution and productivity of mountain grasslands in the Gran Paradiso National Park, NW Italy: A remote sensing approach

Filippa G.
First
;
Poggio L.;Oddi L.;Argenti G.;
2022

Abstract

Mountain grazing lands are key constituents of the natural, economical and cultural heritage, but at the same time sensitive to climate and land use change, hence requiring urgent adaptation and management strategies. These must be based on a better understanding of the distribution of mountain pastoral resources across space and time. In this study we model the distribution and the productivity of grassland surfaces in a topographically complex protected area (Gran Paradiso National Park, 710 km2) in north-western Italian Alps. The objective of our work was threefold: a) modelling the distribution of mountain grasslands across the entire park at a 20-meters spatial resolution, b) classify pastoral surfaces according to productivity classes, and c) according to thirteen pastoral categories. We used a random forest approach to combine a massive terrain vegetation survey as ground truth, with remote-sensing-derived, climatic and topographic layers as predictors. Grassland presence/absence was classified with high accuracy (up to 88%) and, compared to the standard Copernicus European Grassland Product, revealed the presence of extensive high altitude grassland areas potentially available for wild herbivores. Grassland productivity was modelled with remarkably high accuracy both according to three broad productivity classes (90% accuracy) and to a more detailed classification into thirteen pastoral categories (83% accuracy). Productivity estimates agree well with satellite-derived leaf area index maps and with area-averaged NDVI seasonal patterns. We conclude that combining tailored field campaigns and high-resolution remote sensing allows for robust prediction of grassland distribution and productivity even in complex terrains. This information can contribute to improve the management of pastoral resources and promote effective adaptation strategies.
108
102718
102718
NDVI; Pastoral resources; Pasture categories; Random forest; Sentinel 2
Filippa G.; Cremonese E.; Galvagno M.; Bayle A.; Choler P.; Bassignana M.; Piccot A.; Poggio L.; Oddi L.; Gascoin S.; Costafreda-Aumedes S.; Argenti G.; Dibari C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1844269
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