Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.1.1. Germany
2.1.2. Focus Region
2.2. Ground Data
2.2.1. Parametrization Data
2.2.2. Validation Data for National Mowing Event Detection
2.2.3. Validation Data for the Mowing Event Detection in the Focus Region
2.3. Satellite Data
3. Methods
3.1. Pre-Processing of Satellite Data
3.1.1. Pre-Processing of Sentinel-2 Data
3.1.2. Pre-Processing of Sentinel-1 Data
3.2. Mowing Detection Approach Development
3.2.1. Investigation of Satellite Time Series and Variable Selection
3.2.2. Rule-Set Development and Parametrization
EVI-Based Mowing Detection Algorithm
EVI + Entropy-Based Mowing Detection
3.3. Validation Procedure
3.3.1. Validation Procedure for the Detection of Grassland Mowing Events
3.3.2. Accuracy Assessment of the Mowing Event Detection
3.4. Uncertainty Information
4. Results
4.1. Accuracy Assessment on Parametrization Sites
4.1.1. EVI-Based Mowing Detection Algorithm
4.1.2. EVI + Entropy-Based Mowing Detection
4.2. Mowing Detection Validation of the Focus Region
4.3. Germany-Wide Validation of S2-Based Mowing Detection
4.4. Germany-Wide Application of S2-Based Mowing Event Detection
5. Discussion
5.1. Relationships between S1 and S2 Parameters and Mowing Events
5.1.1. The Relationship of S2-Based EVI to Mowing Events
5.1.2. The Relationship of S1-Based Backscatter to Mowing Events
5.1.3. The Relationship of S1-Based InSAR Coherence to Mowing Events
5.1.4. The Relationship of S1-Based PolSAR Decomposition Parameters to Mowing Events
5.2. Spatial Patterns of Detected Mowing Events in Germany
5.3. Importance and Drawbacks of Optical and SAR Data Fusion
6. Conclusions
- The detection of grassland mowing events is possible with optical data; however, only if dense time (period < 10 to 14 days) series are available;
- A pixel-based approach is possible and advantageous as parcels are at times not used homogenously;
- The temporal signal of InSAR and PolSAR parameters for mown grasslands are inconsistent and do not reveal a clear relation to mowing events. Most probably they depend on additional drivers (i.e., moisture), for which general assumptions are difficult to make;
- Complementing the optical mowing detection approach based on EVI time series by the PolSAR entropy, led to an increase in detected mowing events by 9.2%. However, more false positives also occurred, resulting in a drop of the F1-Score (F1-Score = 0.65 for S2 only, F1-Score = 0.61 for S2 + S1);
- Use intensity and timing of the first mowing event of grasslands in Germany are heterogeneously distributed with more often mown parcels in the south/south-east and the north;
- In Germany, 13% of grasslands are not mown at all and a majority is only mown one (38%) to two times (33%), which might be grazed as well. Only 3% of all grasslands are mown four to six times, according to our analysis.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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EVI | ENT | K0 | K1 | BS VV | BS VH | COH VV | COH VH | |
---|---|---|---|---|---|---|---|---|
Normalized difference (Raw data) | 0.29 | 0.14 | 0.002 | 0.06 | 0.03 | 0.08 | 0.08 | 0.09 |
Normalized difference (Smoothed data) | 0.11 | 0.06 | 0.0009 | 0.02 | 0.008 | 0.02 | 0.06 | 0.06 |
Actual Condition (Validation) | |||
---|---|---|---|
Mown | Not Mown | ||
Predicted Condition (Satellite-based detection) S2 only | Mown | 148 | 76 |
Not mown | 81 | ||
Total | 229 | ||
Predicted Condition (Satellite-based detection) S2 + S1 | Mown | 169 | 157 |
Not mown | 60 | ||
Total | 229 |
Actual Condition (Validation) | |||
---|---|---|---|
Mown | Not Mown | ||
Predicted Condition (Satellite-based detection) | Mown | 179 | 94 |
Not mown | 104 | ||
Total | 283 |
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Reinermann, S.; Gessner, U.; Asam, S.; Ullmann, T.; Schucknecht, A.; Kuenzer, C. Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series. Remote Sens. 2022, 14, 1647. https://doi.org/10.3390/rs14071647
Reinermann S, Gessner U, Asam S, Ullmann T, Schucknecht A, Kuenzer C. Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series. Remote Sensing. 2022; 14(7):1647. https://doi.org/10.3390/rs14071647
Chicago/Turabian StyleReinermann, Sophie, Ursula Gessner, Sarah Asam, Tobias Ullmann, Anne Schucknecht, and Claudia Kuenzer. 2022. "Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series" Remote Sensing 14, no. 7: 1647. https://doi.org/10.3390/rs14071647