Copernicus Land Monitoring Service - High Resolution Layers - Imperviousness

Prod-ID: DAT-14-en
Created 22 Nov 2017 Published 15 May 2018 Last modified 16 May 2018
3 min read
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The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012 and 2015 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product.

GIS data

Imperviousness status products
  • Imperviousness 2015
  • Imperviousness 2012
  • Imperviousness 2009
  • Imperviousness 2006
Imperviousness change products
  • Imperviousness degree change 2012-2015
  • Imperviousness classified change 2012-2015
  • Imperviousness degree change 2009-2012
  • Imperviousness classified change 2009-2012
  • Imperviousness degree change 2006-2009
  • Imperviousness classified change 2006-2009
  • Imperviousness degree change 2006-2012 (CLC Synchronous)
  • Imperviousness classified change 2006-2012 (CLC Synchronous)


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Figures produced

Produced Indicators

Used in indicators

Landscape fragmentation pressure from urban and transport infrastructure expansion Landscape fragmentation pressure from urban and transport infrastructure expansion This indicator is based on the Effective Mesh Size (Jaeger 2000) method .  For some species, the effective mesh size (meff) can be interpreted as the area that is accessible when beginning to move from a randomly chosen point inside a landscape without encountering man-made barriers such as transport routes or built-up areas. However, it should be stressed that for many species that can fly, or are effective dispersers in others ways, man-made structures may not act as barriers. The combination of all barriers in a landscape is called Fragmentation Geometry (FG) hereafter. The meff value expresses the probability that any two points chosen randomly in an area are connected. Hence, meff is a measure of landscape connectivity, i.e. the degree to which movements between different parts of the landscape are possible. The larger the meff, the more connected the landscape. The indicator addresses structural connectivity of the landscape and does not tackle functional, species specific connectivity. The Effective Mesh Density (seff) is a measure of landscape fragmentation, i.e. the degree to which movement between different parts of the landscape is interrupted by Fragmentation Geometry. It gives the effective number of meshes (or landscape patches) per 1 000 km 2 , in other words, the density of the meshes. The seff value is calculated as 1 000 km 2 /meff, hence, the number of meshes per 1 000 km 2 . The more barriers fragmenting the landscape, the higher the effective mesh density. meff and seff are reported within the cells of a 1 km 2 regular grid. meff is area-proportionally additive, hence it characterises the fragmentation of any region considered, independently of its size, and thus can be calculated for a combination of two or more regions. The meff has several advantages over other metrics: It addresses the entire landscape matrix instead of addressing individual patches. It is independent of the size of the reporting unit and its values can be compared among reporting units of differing sizes. It is suitable for comparing the fragmentation of regions with differing total areas and with differing proportions occupied by housing, industry and transportation structures. It's reliability has been confirmed on the basis of suitability criteria through a systematic comparison with other quantitative measures. The suitability of other metrics was limited as they only partially met the following criteria: Intuitive interpretation; Mathematical simplicity; Modest data requirements; Low sensitivity to small patches; Detection of structural differences; Mathematical homogeneity (i.e., intensive or extensive).
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