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Mapping management intensity types in grasslands with synergistic use of Sentinel-1 and Sentinel-2 satellite images
被引:1
|作者:
Bartold, Maciej
[1
]
Kluczek, Marcin
[1
]
Wroblewski, Konrad
[1
]
Dabrowska-Zielinska, Katarzyna
[1
]
Golinski, Piotr
[2
]
Golinska, Barbara
[2
]
机构:
[1] Remote Sensing Ctr, Inst Geodesy & Cartog, 27 Modzelewskiego St, PL-02679 Warsaw, Poland
[2] Poznan Univ Life Sci, Dept Grassland & Nat Landscape Sci, 11 Dojazd St, PL-60632 Poznan, Poland
来源:
SCIENTIFIC REPORTS
|
2024年
/
14卷
/
01期
关键词:
Extensive and intensive managed grassland;
Machine learning;
Backscatter;
Multispectral bands;
Satellite imagery;
Ecosystem services;
INTRAANNUAL TIME-SERIES;
INFORMATION;
D O I:
10.1038/s41598-024-83699-4
中图分类号:
O [数理科学和化学];
P [天文学、地球科学];
Q [生物科学];
N [自然科学总论];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
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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页数:17
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