Mowing detection using Sentinel-1 and Sentinel-2 time series for large scale grassland monitoring

被引:22
作者
De Vroey, Mathilde [1 ]
de Vendictis, Laura [2 ]
Zavagli, Massimo [2 ]
Bontemps, Sophie [1 ]
Heymans, Diane [1 ]
Radoux, Julien [1 ]
Koetz, Benjamin [3 ]
Defourny, Pierre [1 ]
机构
[1] Catholic Univ Louvain, Earth & Life Inst, 2 Croix Sud Bte L7-05-16, B-1348 Louvain La Neuve, Belgium
[2] e Geos, Prod Dev & Innovat Serv, Via Tiburtina 965, I-00156 Rome, Italy
[3] European Space Agcy, ESA ESRIN, Via Galileo Galilei,Casella Postale 64, I-00044 Frascati, Rome, Italy
关键词
Grasslands; Sen4CAP; Sentinel-1; Sentinel-2; Mowingdetection; ECOSYSTEM SERVICES; SOIL-MOISTURE; USE INTENSITY; BIODIVERSITY; MANAGEMENT; IMPACT;
D O I
10.1016/j.rse.2022.113145
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Managed grasslands cover about one third of the European utilized agricultural area. Appropriate grassland management is key for balancing trade-offs between provisioning and regulating ecosystem services. The timing and frequency of mowing events are major factors of grassland management. Recent studies have shown the feasibility of detecting mowing events using remote sensing time series from optical and radar satellites. In this study, we present a new method combining the regular observations of Sentinel-1 (S1) and the better accuracy of Sentinel-2 (S2) grassland mowing detection algorithms. This multi-source approach for grassland monitoring was assessed over large areas and in various contexts. The method was first validated in six European countries, based on Planet image interpretation. Its performances and sensitivity were then thoroughly assessed in an independent study area using a more precise and complete reference dataset based on an intensive field campaign. Results showed the robustness of the method across all study areas and different types of grasslands. The method reached a F1-score of 79% for detecting mowing events on hay meadows. Furthermore, the detection of mowing events along the growing season allows to classify mowing practices with an overall accuracy of 69%. This is promising for differentiating grasslands in terms of management intensity. The method could therefore be used for largescale grassland monitoring to support agri-environmental schemes in Europe.
引用
收藏
页数:14
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