Land-use intensity quantification and management classifications in grasslands of Germany 2017/2018

Published: 9 March 2022| Version 1 | DOI: 10.17632/m9rrv26dvf.1
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Description

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. Data is provided in GeoTiff format (projection EPSG:32632). Grazing classification bases on grazing intensity (G), given as livestock units (depending on species and age) per ha and day (Class 0: G=0, Class 1: 0 < G <= 0.33, Class 2: 0.33 < G <=0.88, Class 3: G > 0.88). Mowing counts represent the number of moving events in the respective year. Fertilisation information were aggregated into two classes: fertilised and not fertilised. Each model's AOA is given in a separate GeoTiff file, with values of 0 for areas outside and 1 for areas inside the model's AOA. Please note: Grassland pixels were selected according to the digital landscape model 2015 of the official topographic-cartographic information system (© GeoBasis-DE / BKG 2015).

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Institutions

Helmholtz-Zentrum fur Umweltforschung UFZ

Categories

Remote Sensing, Machine Learning, Fertilization, Grassland, Pasture, Land Use Analysis, Germany, Satellite Mapping, Meadow, Vegetation

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