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Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series
Remote Sensing of Environment  (IF10.164),  Pub Date : 2021-10-22, DOI: 10.1016/j.rse.2021.112751
Felix Lobert, Ann-Kathrin Holtgrave, Marcel Schwieder, Marion Pause, Juliane Vogt, Alexander Gocht, Stefan Erasmi

The intensity of land use and management in permanent grasslands affects both biodiversity and important ecosystem services. Comprehensive knowledge about these intensities is a crucial factor for sustainable decision-making in landscape policy. For meadows, the management intensity can be described by proxies such as the mowing frequency, usually, a higher number of cuts indicate higher intensities. Dense time series of medium resolution (10–30 m) remote sensing data are suitable for the detection of mowing events. However, existing studies revealed a general lack of consensus about the most appropriate input data set for a consistent and reliable mowing detection.

We systematically evaluated the synergistic use of acquisitions from Sentinel-1, Sentinel-2, and Landsat 8 to detect the occurrence, frequency, and date of mowing events as an indicator of grassland management intensity. Dense time series of NDVI (Sentinel-2 and Landsat 8), γ0 backscatter, backscatter cross-ratio, backscatter second-order texture metrics as well as 6-day interferometric coherence (Sentinel-1) were used as input features. All possible combinations of input features were tested to train a one-dimensional convolutional neural network, which enables enhanced exploitation of the temporal domain of the data. The evaluation was conducted on 64 meadows for an overall of 257 mowing events from 2017 to 2019 in Germany.

Our results revealed that the combination of input features improves the detection performance. The highest overall accuracy was reached by a combination of NDVI, backscatter cross-ratio, and interferometric coherence with an F1-Score of 0.84. The mowing frequency was predicted with a mean absolute error of 0.38 events per year, while the date of the events was missed by 3.79 days on average. NDVI time series alone mostly underperformed in comparison to optical/SAR combinations but clearly outperformed input-sets that were solely based on SAR features. The proposed model performed well for meadows with low to medium management intensities but further testing is recommended for highly intensive managed parcels.

The results clearly demonstrate the additional value of fusing time series of the three present Earth observation systems that deliver a freely available global coverage of the land surface at medium resolution.