ArticleScientific reports2024
Mapping management intensity types in grasslands with synergistic use of Sentinel-1 and Sentinel-2 satellite images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Ranking land degradation drivers in eastern Inner Mongolia using partial order theory and Hasse diagram analysis.Scientific reports · 2026Article
- Four decades of hydroclimate-driven change and ecological condition in a Baltic raised bog assessed with Landsat and RSEI.Scientific reports · 2026Article
- Predicting species diversity and community traits from remote sensing in species-rich grasslands.BMC ecology and evolution · 2026Article
- Assessing Changes in Grassland Species Distribution at the Landscape Scale Using Hyperspectral Remote Sensing.Sensors (Basel, Switzerland) · 2025Article
- Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning.Scientific reports · 2025Article
- Improved estimation of forage nitrogen in alpine grassland by integrating Sentinel-2 and SIF data.Plant methods · 2025Article
- Value Realization of Grassland Ecosystem Products in the Karst Desertification Control Area: Spatial Variability, Drivers, and Decision-Making.Ecology and evolution · 2025Article
- Unlocking the synergistic potential of sensor technologies in grassland research.Discover sensors · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
Grasslands, being vital ecosystems with significant ecological and socio-economic importance, have been the subject of increasing attention due to their role in biodiversity conservation, carbon sequestration, and agricultural productivity. However, accurately classifying grassland management intensity, namely extensive and intensive practices, remains challenging, especially across large spatial extents. This research article presents a comprehensive investigation into the classification of grassland management intensity in two distinct regions of Poland, NUTS2 - namely Podlaskie (PL84) and Wielkopolskie (PL41), by integrating data from Sentinel-1 and Sentinel-2 satellite imagery. The study leverages the unique capabilities of Sentinel-1, a radar satellite, and Sentinel-2, an optical multispectral satellite, to overcome the limitations of using a single data source. Preprocessed Sentinel-1 and Sentinel-2 data were combined to extract spectral and textural features, providing valuable insights into grassland characteristics and patterns. Supervised classification using the Random Forest algorithm was used, and ground truth data from field surveys facilitated the creation of training samples. In Podlaskie, extensive grasslands achieved an overall accuracy (OA) of 84%, while intensive grasslands attained an OA of 83%. In Wielkopolskie, extensive grasslands exhibited an OA of 84%, while intensive grasslands achieved an OA of 83%. Additionally, the classification metrics, including user's accuracy (UA), F1 score, and producer's accuracy (PA), further highlighted the variations in classification accuracy. This comprehensive mapping of grassland management intensity using combined Sentinel-1 and Sentinel-2 data provides valuable insights for conservation agencies, agricultural stakeholders, and land managers. The study's findings contribute to sustainable land management and decision-making processes, facilitating the identification of ecologically valuable areas, optimizing agricultural productivity, and assessing the impacts of different management strategies. Furthermore, the research highlights the potential of Sentinel missions for grassland monitoring and emphasizes the importance of advanced remote sensing techniques for understanding and preserving these crucial ecosystems.
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Registered trials
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