Land-use Intensity

lui Grafik

In Lange et. al (2022) we quantified land-use intensity (LUI) and its key parameters - grazing intensity, mowing frequency and fertiliser application - across Germany. Key parameters were classified using Convolutional Neural Networks (CNN) and Copernicus Sentinel-2 satellite data with 20 m x 20 m spatial resolution. Predictions of LUI and its components were validated using comprehensive in situ grassland management data from the DFG Biodiversity Exploratories. A feature contribution analysis using Shapley values substantiated the applicability of the methodology by revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. We achieved an overall classification accuracy of up to 66% for grazing intensity, 68% for mowing, 85% for fertilisation and an r^2 of 0.82 for the subsequent LUI depiction. We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions. Spatial transferability was assessed by delineating the models' area of applicability (AOA). More information can be found in the related publication.

The data can be viewed in a web service, as well as downloaded as GeoTiff-files for further analysis.