Automatic determination of grassland use intensities by means of satellite image time series
Assessing the potentials of remote sensing data in the fields of land use, agricultural economics and biodiversity
Nationwide information on grassland use intensity is still unknown in Germany. Remote sensing data can help to close this knowledge gap in the future…
Background and Objective
The high spatial and temporal resolution of the Sentinel satellites (one image taken every 2 to 3 days) of the European Copernicus Earth Observation Programme makes it possible to acquire information on land use with high spatial and temporal resolution. Based on the remote sensing data, the intensity of use of permanent grassland is to be determined nationwide throughout Germany.
This comprehensive information is an important basis for the assessment of grassland, in particular with regard to the provision of feedstuffs and questions of nature and resource conservation. On the basis of the targeted data set, it is possible, for example, to estimate the extent of the potential of individual areas and regions and, in particular, to improve policy impact assessments for the Common Agricultural Policy (CAP).
The following objectives are to be achieved:
- development of methods for the automatic determination of usage intensities in permanent pastureland from sentinel remote sensing data
- Germany-wide mapping and provision of current spatial information on the intensity of use of permanent pastureland in Germany
The following steps will be carried out in this project:
- Creation of a database for validation data,
- merge different data types and develop indicators to describe the intensity of use in grassland
- Development of a method for determining the usage intensity of permanent grassland Germany-wide
- Transfer of the method
- Validation of the methods based on the established database
Links and Downloads
Involved external Thünen-Partners
- Technische Universität Berlin
7.2017 - 6.2025
Lobert F, Holtgrave A-K, Schwieder M, Pause M, Gocht A, Vogt J, Erasmi S (2021) Detection of mowing events from combined Sentinel-1, Sentinel-2, and Landsat 8 time series with machine learning. Grassl Sci Europe 26:123-125
Lobert F, Holtgrave A-K, Schwieder M, Pause M, Vogt J, Gocht A, Erasmi S (2021) Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series. Remote Sens Environ 267:112751, DOI:10.1016/j.rse.2021.112751
Holtgrave A-K, Röder N, Ackermann A, Erasmi S, Kleinschmit B (2020) Comparing Sentinel-1 and -2 data and indices for agricultural land use monitoring. Remote Sensing 12:2919, DOI:10.3390/rs12182919
Holtgrave A-K, Ackermann A, Röder N, Kleinschmit B (2020) Towards a dual-polarisation radar vegetation index for Sentinel-1 for grassland monitoring. Grassl Sci Europe 25:596-598
Holtgrave A-K, Röder N, Kleinschmit B (2019) Detecting grassland management strategies with sentinel-1 and fuzzy data in different regions of Germany. In: Living Planet Symposium, Milan (Italy), May 13-17 2019.