Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series
نویسندگان
چکیده
Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill ecosystem services, such as carbon storage, water purification, provision habitats. These services usually depend on grassland management. central Europe, grasslands are grazed and/or mown, whereby management type intensity vary space time. Spatial information mowing timing frequency larger scales not available but would be required order to assess species composition, yields. Time series high-resolution satellite remote sensing data can used analyze temporal spatial dynamics grasslands. Within this study, we aim overcome drawbacks identified previous studies, optical availability lack comprehensive reference data, testing time various Sentinel-2 (S2) Sentinal-1 (S1) parameters combinations them detect events 2019. We developed a threshold-based algorithm using from dataset heterogeneously managed parcels Germany, obtained RGB cameras. The approach enhanced vegetation index (EVI) derived S2 led successful event detection (60.3% detected, F1-Score = 0.64). However, shortly before, during, or after cloud gaps were missed regions with lower orbit coverage fewer detected. Therefore, S1-based backscatter, InSAR, PolSAR features investigated during gaps. From these, entropy detected most reliably. For focus region, tested integrated combining S1 parameters. This additional events, also many false positive resulting reduction (from 0.65 0.61 + region). According our analysis, majority only mown zero two times (around 84%) probably additionally grazing. A small proportion is more often than four (3%). Regions generally higher located southern, south-eastern, northern Germany.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14071647