Land cover in countries and regions
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Land cover: European Space Agency and Université catholique de Louvain Geomatics (2017) Climate Change Initiative - Land Cover (CCI-LC) Annual Maps 1992-2015

Political and administrative boundaries: FAO (2015) Global Administrative Unit Layers (GAUL)

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March 2018
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Land cover and land cover change

Methodology: http://dx.doi.org/10.1787/72a9e331-en

Loss of biodiversity and pressures on ecosystem services are among the most pressing global environmental challenges. Changes in land cover and land use are the leading contributors to terrestrial biodiversity loss.

Loss of natural and semi-natural vegetated land is presented as a proxy for pressures on biodiversity and ecosystems. The indicator is defined as the percentage of tree cover, grassland, wetland, shrubland and sparse vegetation converted to any other land cover type. Gains of natural and semi-natural vegetated land are conversions in the opposite direction. The denominator used is the ‘stock' of natural and semi-natural land at the start of the period.

Changes to and from 9 individual land cover types are also presented. These include conversions from natural and semi-natural vegetated land to cropland, and conversions from cropland to artificial surfaces, among others.

Land cover 'snapshots'for a given year provide context against which the conversions detailed above can be evaluated.

This multi-class dataset allows for analysis of changes in land cover consistently at the global scale. It builds on decades of Earth observation missions by different national and supranational space organisations. Important limitations for this dataset include the temporal heterogeneity of sensor inputs which frustrates comparison of rates of change from one period to the next (comparison of rates of change between different periods is not recommended) and the relatively more coarse resolution of change detection (approx. 1km) which means that smaller changes are not recorded. Furthermore, summary results for smaller countries and regions are more susceptible to errors caused by mis-classification and should be interpreted carefully.

For users interested specifically in urbanisation or surface water, there are indicators based on higher-resolution datasets of built-up area and surface water that are likely to be more suitable for more focused applications.

Note

These indicators are calculated by intersecting political, administrative or functional urban area boundaries with raster datasets using GIS software. They provide accessible tabular statistics of the underlying datasets for a variety of geographic output areas that can be used immediately without performing the spatial analysis that would otherwise be required.

Maps of the underlying geographic data set/s used can be viewed in a web browser at the above links. Prospective users are encouraged to examine the underlying data for their area of interest and familiarise themselves with the methodology used in their production so as to better understand what the data show and what kinds of conclusions they can be used to support. Irrespective of the underyling data, all earth-observation derived statistics come with caveats such as scale dependence, limitations associated with classification of continuous phenomena into discrete classes, and uneven geographical and temporal accuracy.

For more details on the methodology see: 

Methodology: http://dx.doi.org/10.1787/72a9e331-en

Known issues:

  • Global: conversions between the wetland definition (Shrub or herbaceous cover, flooded, fresh-saline or brackish water) and the flooded forest classes (Tree cover, flooded, fresh or brackish water, Tree cover, flooded, saline water) are typically spurious
  • New Zealand: High-elevation areas misclassified as cropland in the early 1990's
  • Northern Europe and Scandinavia: spurious change detection from trees to cropland in mosaic agricultural areas with mixed cropland/tree cover
  • Norway: Urban areas misclassified around Bergen
Land cover in countries and regionsContact person/organisation
env.stat@oecd.org
Data source(s) used

Land cover: European Space Agency and Université catholique de Louvain Geomatics (2017) Climate Change Initiative - Land Cover (CCI-LC) Annual Maps 1992-2015

Political and administrative boundaries: FAO (2015) Global Administrative Unit Layers (GAUL)

Date last updated
March 2018
Key statistical concept

Land cover and land cover change

Methodology: http://dx.doi.org/10.1787/72a9e331-en

Loss of biodiversity and pressures on ecosystem services are among the most pressing global environmental challenges. Changes in land cover and land use are the leading contributors to terrestrial biodiversity loss.

Loss of natural and semi-natural vegetated land is presented as a proxy for pressures on biodiversity and ecosystems. The indicator is defined as the percentage of tree cover, grassland, wetland, shrubland and sparse vegetation converted to any other land cover type. Gains of natural and semi-natural vegetated land are conversions in the opposite direction. The denominator used is the ‘stock' of natural and semi-natural land at the start of the period.

Changes to and from 9 individual land cover types are also presented. These include conversions from natural and semi-natural vegetated land to cropland, and conversions from cropland to artificial surfaces, among others.

Land cover 'snapshots'for a given year provide context against which the conversions detailed above can be evaluated.

This multi-class dataset allows for analysis of changes in land cover consistently at the global scale. It builds on decades of Earth observation missions by different national and supranational space organisations. Important limitations for this dataset include the temporal heterogeneity of sensor inputs which frustrates comparison of rates of change from one period to the next (comparison of rates of change between different periods is not recommended) and the relatively more coarse resolution of change detection (approx. 1km) which means that smaller changes are not recorded. Furthermore, summary results for smaller countries and regions are more susceptible to errors caused by mis-classification and should be interpreted carefully.

For users interested specifically in urbanisation or surface water, there are indicators based on higher-resolution datasets of built-up area and surface water that are likely to be more suitable for more focused applications.

Note

These indicators are calculated by intersecting political, administrative or functional urban area boundaries with raster datasets using GIS software. They provide accessible tabular statistics of the underlying datasets for a variety of geographic output areas that can be used immediately without performing the spatial analysis that would otherwise be required.

Maps of the underlying geographic data set/s used can be viewed in a web browser at the above links. Prospective users are encouraged to examine the underlying data for their area of interest and familiarise themselves with the methodology used in their production so as to better understand what the data show and what kinds of conclusions they can be used to support. Irrespective of the underyling data, all earth-observation derived statistics come with caveats such as scale dependence, limitations associated with classification of continuous phenomena into discrete classes, and uneven geographical and temporal accuracy.

For more details on the methodology see: 

Methodology: http://dx.doi.org/10.1787/72a9e331-en

Known issues:

  • Global: conversions between the wetland definition (Shrub or herbaceous cover, flooded, fresh-saline or brackish water) and the flooded forest classes (Tree cover, flooded, fresh or brackish water, Tree cover, flooded, saline water) are typically spurious
  • New Zealand: High-elevation areas misclassified as cropland in the early 1990's
  • Northern Europe and Scandinavia: spurious change detection from trees to cropland in mosaic agricultural areas with mixed cropland/tree cover
  • Norway: Urban areas misclassified around Bergen