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

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  • Gregory Giuliani

    (EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
    GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland)

  • Denisa Rodila

    (EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
    GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland)

  • Nathan Külling

    (EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland)

  • Ramona Maggini

    (Agroscope, via A Ramél 18, 6593 Cadenazzo, Switzerland)

  • Anthony Lehmann

    (EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland)

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.

Suggested Citation

  • Gregory Giuliani & Denisa Rodila & Nathan Külling & Ramona Maggini & Anthony Lehmann, 2022. "Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System," Land, MDPI, vol. 11(5), pages 1-21, April.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:615-:d:799280
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    References listed on IDEAS

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    Cited by:

    1. Parinaz Rashidi & Sopan D. Patil & Aafke M. Schipper & Rob Alkemade & Isabel Rosa, 2023. "Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium," Land, MDPI, vol. 12(9), pages 1-19, September.
    2. Hao Wang & Huimin Yan & Yunfeng Hu & Yue Xi & Yichen Yang, 2022. "Consistency and Accuracy of Four High-Resolution LULC Datasets—Indochina Peninsula Case Study," Land, MDPI, vol. 11(5), pages 1-19, May.
    3. Bojana Horvat & Nino Krvavica, 2023. "Disaggregation of the Copernicus Land Use/Land Cover (LULC) and Population Density Data to Fit Mesoscale Flood Risk Assessment Requirements in Partially Urbanized Catchments in Croatia," Land, MDPI, vol. 12(11), pages 1-22, November.

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