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The use of antibiotics is not permitted in Switzerland; moreover, no other medication exists to combat European foul brood. Hives with symptoms must therefore be destroyed in order to limit the outbreak, and the contaminated material must be sanitised. Since prevention is the best cure, early detection is desirable. Numerous studies have been undertaken and documents have been created at the Swiss Bee Research Centre and elsewhere to draw the attention of beekeepers to these problems and inform them about the causes and the control measures to be implemented.

Merkblatt zur erkennung von Bienenbrutkrankheiten (German (PDF, 357 kB, 18.10.2017) and French (PDF, 226 kB, 02.02.2017))
M. Tschumi (2011)

Downscaling Switzerland land use/land cover data using nearest neighbors and an expert system.

Institutional Repository Agroscope

Giuliani G., Rodila D., Külling N., Maggini R., Lehmann A.

Downscaling Switzerland land use/land cover data using nearest neighbors and an expert system.

Land, 11, (5), 2022, 1-21.

Download englisch (10834 kB)

Zusammenfassung:

High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and calculation of indices. Downscaled LU/LC maps for Switzerland were obtained for three time periods by blending two inputs: the Swiss topographic base map at a 1:25,000 scale and the national LU/LC statistics obtained from aerial photointerpretation on a 100 m regular lattice of points. The spatial resolution of the resulting LU/LC map was improved by a factor of 16 to reach a resolution of 25 m, while the thematic resolution was increased from 29 (in the base map) to 62 land use categories. The method combines a simple inverse distance spatial weighting of 36 nearest neighbors’ information and an expert system of correspondence between input base map categories and possible output LU/LC types. The developed algorithm, written in Python, reads and writes gridded layers of more than 64 million pixels. Given the size of the analyzed area, a High-Performance Computing (HPC) cluster was used to parallelize the data and the analysis and to obtain results more efficiently. The method presented in this study is a generalizable approach that can be used to downscale different types of geographic information.

ISSN Print: 2073-445X
Digital Object Identifier (DOI): https://doi.org/10.3390/land11050615
Publikations-ID (Webcode): 49387 Per E-Mail versenden

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Downscaling Switzerland land use/land cover data using nearest neighbors and an expert system.