Land-use intensity depiction of grasslands in Germany by using optical satellite data (Sentinel-2) and machine learning methods (see Lange et al 2021). Land-use intensity product includes:

(1) Livestock density (livestock units per day and ha);

(2) Mowing frequency count;

(3) Fertilisation (boolean: 0=no, 1=yes);

(4) Field usage (1=mown pasture, 2=pasture, 3=fertilized meadow, 4=meadow, 5=others);

(5) Land-use intensity index (see Bluethgen et al 2012);

(6) Land-use classes derived from ATKIS data (see BKG 2015);

References:

[Bluethgen 2012]: Bluethgen, N., Dormann, C.F., Prati, D., Klaus, V.H., Kleinebecker, T., Hoelzel, N., Alt, F., Boch, S., Gockel, S., Hemp, A., Mueller, J., Nieschulze, J., Renner, S.C., Schoening, I., Schumacher, U., Socher, S.A., Wells, K., Birkhofer, K., Buscot, F., Oelmann, Y., Rothenwoehrer, C., Scherber, C., Tscharntke, T., Weiner, C.N., Fischer, M., Kalko, E.K.V., Linsenmair, K.E., Schulze, E., Weisser, W.W., 2012. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic and Applied Ecology 13, 207–2020. doi:10.1016/j.baae.2012.04.001;

[BKG 2015]: Bundesamt für Kartographie und Geodaesie (2015): Digitales Basis-Landschaftsmodell (AAA-Modellierung). GeoBasis-DE. Geodaten der deutschen Landesvermessung. Bundesamt für Kartographie und Geodaesie

[Lange 2021]: Lange M, Doktor D (2021): Mapping land-use intensities of grasslands in Germany with machine learning algorithms using Copernicus Sentinel-2 optical satellite data. Remote Sensing of Environment. In preparation;