Scientific article
OA Policy
English

Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System

Published inLand, vol. 11, no. 5, 615
Publication date2022-04
First online date2022-04
Abstract

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.

Keywords
  • Land cover
  • Land use change
  • Downscaling approach
  • Switzerland
  • Geographic information system
  • Aerial photo interpretation
  • Topographic map
  • Inverse distance weighting
  • Expert system
Citation (ISO format)
GIULIANI, Gregory et al. Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System. In: Land, 2022, vol. 11, n° 5, p. 615. doi: 10.3390/land11050615
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Article (Published version)
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Additional URL for this publicationhttps://www.mdpi.com/2073-445X/11/5/615
Journal ISSN2073-445X
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Technical informations

Creation21/04/2022 18:41:00
First validation21/04/2022 18:41:00
Update time16/03/2023 06:23:50
Status update16/03/2023 06:23:50
Last indexation12/11/2024 09:04:35
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