Grassland mowing detection using sentinel-1 time series: potential and limitations
De Vroey, M.; Radoux, J.; Defourny, P. (2021). Grassland mowing detection using sentinel-1 time series: potential and limitations. Remote Sens. 13(3): 348. https://dx.doi.org/10.3390/rs13030348
In: Remote Sensing. MDPI: Basel. ISSN 2072-4292; e-ISSN 2072-4292, more
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grassland; mowing detection; Sentinel-1; backscattering; coherence; land use intensity |
| Authors | | Top |
- De Vroey, M.
- Radoux, J., more
- Defourny, P., more
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| Abstract |
Grasslands encompass vast and diverse ecosystems that provide food, wildlife habitat and carbon storage. Their large range in land use intensity significantly impacts their ecological value and the balance between these goods and services. Mowing dates and frequencies are major aspects of grassland use intensity, which have an impact on their ecological value as habitats. Previous studies highlighted the feasibility of detecting mowing events based on remote sensing time series, a few of which using synthetic aperture radar (SAR) imagery. Although providing encouraging results, research on grassland mowing detection often lacks sufficient precise reference data for corroboration. The goal of the present study is to quantitatively and statistically assess the potential of Sentinel-1 C-band SAR for detecting mowing events in various agricultural grasslands, using a large and diverse reference data set collected in situ. Several mowing detection methods, based on SAR backscattering and interferometric coherence time series, were thoroughly evaluated. Results show that 54% of mowing events could be detected in hay meadows, based on coherence jumps. Grazing events were identified as a major confounding factor, as most false detections were made in pastures. Parcels with one mowing event in the summer were identified with the highest accuracy (71%). Overall, this study demonstrates that mowing events can be detected through Sentinel-1 coherence. However, the performances could probably be further enhanced by discriminating pastures beforehand and combining Sentinel-1 and Sentinel-2 data for mowing detection. |
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