Mapping management intensity types in grasslands with synergistic use of Sentinel-1 and Sentinel-2 satellite images

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.

Keywords: Backscatter; Ecosystem services; Extensive and intensive managed grassland; Machine learning; Multispectral bands; Satellite imagery.

Fig. 1

The process of mapping management intensity types in grasslands using the RF algorithm with satellite observations. Figure created by the authors using Inkscape (https://inkscape.org).

Fig. 2

Location of field surveys conducted at grasslands highlighted by bright red dots in Wielkopolskie (PL41) and Podlaskie (PL84) in Poland. Figure created by the authors using QGIS 3.36 Maidenhead software (https://qgis.org/).

Fig. 3

Types of grassland management intensity: (a) extensive, (b) intensive. Photos were taken at the Podlaskie study sites during the field campaign on June 30, 2021.

Fig. 4

Comparison of spectral curves for intensive and extensive grasslands averaged with the loess algorithm (span = 0.35, confidence interval = 0.95). Statistically significant differences between channels are highlighted in red. Figure created by the authors using R 4.7-1 (https://www.r-project.org/) and Inkscape (https://inkscape.org).

Fig. 5

Variable importance of the RF model measured by MDA between Sentinel-1/2 bands in Podlaskie and Wielkopolskie. Figure created by the authors using R 4.7-1 (https://www.r-project.org/) and Inkscape (https://inkscape.org).

Fig. 6

Maps of grassland management intensity classification for Wielkopolskie (left) and Podlaskie (right) Voivodeships. Figure created by the authors using QGIS 3.36 Maidenhead software (https://qgis.org/).