1. Grazing detection based on Copernicus data
Grazing detection from Copernicus data for agricultural subsidy checks is one of our recently finished projects, carried out in collaboration with the European Space Agency (ESA) and with our partner, Czech EO company Gisat s.r.o. We developed a grazing detection methodology based on Sentinel-1 and Sentinel-2 imagery time series. This work is important for the reform of the Common Agricultural Policy, which will replace on-the-spot grazing checks with satellite monitoring. As about 20% of grasslands in Europe are being grazed, our detection methodology will help to complete the grassland maintenance checks alongside the mowing detection.
Fieldwork was a significant part of ground reference data collection required for the development of grazing detection methodology. We have collected the data about grazing and mowing events and grass height weekly during summer 2021 from several grasslands in central Estonia. Summary of the data together with S2 NDVI, NDWI, and PSRI indices and calculated grazing intensity for two grasslands is visualized in the figure below.
On these grasslands, cows moved from one field to another throughout the summer, staying on each grassland for up to 5 weeks. In Figure 2 it is visible that grassland V2 was separated by the farmer into two parts. The larger part was mowed on the 30th of June for silage, which is visible from the NDVI, NDWI, and grass height drop on the time series. Animals were located on the smaller part of the V2 before and after the mowing event. Due to the mowing event in between, it is quite hard to distinguish changes caused by grazing activity, even though the grazing intensity is high (4.2 LU/ha).
Changes in biomass on the grasslands are mostly visible on Sentinel-2 NDVI rasters, as the increase or decrease of NDVI values are more distinguishable by the human eye (Figure 2). In comparison, the visual analysis of Sentinel-1 raster parameters is more complex. The changes in VV or VH coherence values are difficult to detect if the exact location of animals is missing (Figure 3). However, the machine learning approach can be implemented in future works. As grazing is not distinguishable for the human eye on Sentinel-1 images, some of the signatures can be detected by the AI model.
Jelizaveta Vabištševitš, EO analyst, project manager
2. KappaZeta is going to Kemira and Valtra hackathons in Finland
KappaZeta and CarbonEye Global are to deliver new breed innovative solutions that fuse Carbon, Space and Agricultural markets to Kemira and Valtra hackathons in Finland in March 2022. Hackathons are organized by Jyväskylä University (JAMK) in cooperation with global segment leader Kemira Oyj (Chemicals company) and Valtra Inc. (Smart Tractors company). More here. For further enquiries please contact jurgen.lina@kappazeta.ee and lauri.karp@carboneye.ai.
Read more about the hackathons:
https://www.jamk.fi/en/project/biopaavo/biopaavo-hackathon/kemirahackathon
https://www.jamk.fi/en/project/biopaavo/biopaavo-hackathon/valtrahackathon
Kaupo Voormansik, CEO, SAR expert
