Description

Land composition or co-occurrence of land use types, is a measure of spatial distribution of elements or components of a landscape. To quantify land composition we use the Landscape Mosaic (LM), model available in Guido's tool box (Vogt and Riitters 2017). A land mosaic is a tri-polar classification scheme that represents the land type dominance, the interface zone and the mix zone within a defined area. The classification uses the threshold values of 10%, 60%, and 100% along each axis to partition the tri-polar space into 19 classes. These threshold values are indicative for the presence (10%), dominance (60%), or uniqueness (100%) of each land cover type. The model measures land type heterogeneity and allows to consider trade‐offs occurring between intra-land type changes (i.e., modification of the area of a given land type) and inter land types changes (i.e., direction of change). It provides a measure of the relative contributions of the three key land types in percentage within a given neighborhood/observation area.

Land composition is used to assess the co-occurrence of land types within each Functional Urban Area. It represents the arrangements of ecosystem types within and around cities.

Scale as linked to D2.2 framework

• City
• Neighborhood

Data source [ link to data framework]

• Satellite

Data needs

• Corine Land Cover (CLC) 2000-2018, Version 20 - the model can be implemented using any land use land cover data

Expected Outputs

• Precision: 100 m

Calculation methods

• Dominant land types were extracted from Corine Land Cover. Agricultural areas include all agricultural land types identified in Corine, natural areas include all natural and semi-natural land types, developed areas include all artificial land types including urban green. Parameters applied for the analysis of 700 EU Functional Urban Areas.
• Dominant land types:
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Links and references

• Landscape Mosaic (LM), model available in Guido's tool box (http://forest.jrc.ec.europa.eu/download/software/guidos/)
• Vogt P, Riitters K (2017) GuidosToolbox: universal digital image object analysis. Eur J Remote Sens 50(1): 352–361. doi: 10.1080/22797254.2017.1330650
  • + next MAES report will include the methodology applied to all EU cities