Spatial and temporal modelling of pastoral value in the Eastern Alps using Sentinel-2 and satellite embeddings
4 affiliations
- 1Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
- 2Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy; Corresponding author.
- 3University Grenoble Alpes, University Savoie Mont Blanc, CNRS, LECA, 38000 Grenoble, France
- 4Climate Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, 66 Boulevard Carl Vogt, CH-1205 Geneva, Switzerland; Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, 66 Boulevard Carl Vogt, CH-1205 Geneva, Switzerland
Abstract
Semi-natural mountain pastures are multifunctional systems that provide key ecosystem services, including forage for livestock. Their long-term preservation depends on appropriate grazing management, a condition increasingly challenged in the European Alps by declining stocking rates and the resulting undergrazing of pastoral areas. In this context, we tested a remote sensing-based approach to spatially and temporally model pastoral value (PV), a comprehensive indicator of pasture quality and applicable for determining grasslands carrying capacity, in two alpages located in the Italian Eastern Alps. Using 111 botanical field observations, we applied Partial Least Squares regression to temporal composites of Sentinel-2 and Google Embeddings data to predict PV at 10 m spatial resolution. We then compared predictions for recent conditions (2023–2025 imagery) with those obtained by applying the same models to 2017–2018 imagery, examining their relationship with slope and distance from night barns as proxies of management intensity. Models achieved R 2 values between 0.53 and 0.75, with RMSE ranging from 9.11 to 12.08, and were able to spatially distinguish lower-quality pastures (PV around 15) from more productive grasslands (PV > 25). Temporal comparisons indicated a reduction in predicted PV at one of the two sites, detected only by the embeddings-based models, while Sentinel-2 predictions showed no change. A stronger decline was predicted in steeper areas, whereas no clear relationship emerged with distance from night barns. Overall, the results show that remote sensing is a promising approach for mapping PV and supporting grazing management in mountain grasslands, while detecting changes over time requires further improvements before operational use.
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BibTeX
@article{guttry2026spatial,
title = {{Spatial and temporal modelling of pastoral value in the Eastern Alps using Sentinel-2 and satellite embeddings}},
author = {Luca De Guttry and Laura Stendardi and Andrea Confessore and Chiara Aquilani and Carolina Pugliese and Francesco Sirtori and Nicolina Staglianò and Philippe Choler and Arthur Bayle and Giovanni Argenti and Camilla Dibari},
journal = {Ecological Indicators},
year = {2026},
month = oct,
doi = {10.1016/j.ecolind.2026.115753},
url = {https://doi.org/10.1016/j.ecolind.2026.115753},
note = {CC BY},
}
RIS
TY - JOUR
TI - Spatial and temporal modelling of pastoral value in the Eastern Alps using Sentinel-2 and satellite embeddings
AU - Luca De Guttry
AU - Laura Stendardi
AU - Andrea Confessore
AU - Chiara Aquilani
AU - Carolina Pugliese
AU - Francesco Sirtori
AU - Nicolina Staglianò
AU - Philippe Choler
AU - Arthur Bayle
AU - Giovanni Argenti
AU - Camilla Dibari
PY - 2026
DA - 2026/10/30
JO - Ecological Indicators
DO - 10.1016/j.ecolind.2026.115753
UR - https://doi.org/10.1016/j.ecolind.2026.115753
AB - Semi-natural mountain pastures are multifunctional systems that provide key ecosystem services, including forage for livestock. Their long-term preservation depends on appropriate grazing management, a condition increasingly challenged in the European Alps by declining stocking rates and the resulting undergrazing of pastoral areas. In this context, we tested a remote sensing-based approach to spatially and temporally model pastoral value (PV), a comprehensive indicator of pasture quality and applicable for determining grasslands carrying capacity, in two alpages located in the Italian Eastern Alps. Using 111 botanical field observations, we applied Partial Least Squares regression to temporal composites of Sentinel-2 and Google Embeddings data to predict PV at 10 m spatial resolution. We then compared predictions for recent conditions (2023–2025 imagery) with those obtained by applying the same models to 2017–2018 imagery, examining their relationship with slope and distance from night barns as proxies of management intensity. Models achieved R 2 values between 0.53 and 0.75, with RMSE ranging from 9.11 to 12.08, and were able to spatially distinguish lower-quality pastures (PV around 15) from more productive grasslands (PV > 25). Temporal comparisons indicated a reduction in predicted PV at one of the two sites, detected only by the embeddings-based models, while Sentinel-2 predictions showed no change. A stronger decline was predicted in steeper areas, whereas no clear relationship emerged with distance from night barns. Overall, the results show that remote sensing is a promising approach for mapping PV and supporting grazing management in mountain grasslands, while detecting changes over time requires further improvements before operational use.
SN - 1470-160X
SN - 1872-7034
ER -