Dynamic Landscapes in the UK Driven by Pressures from Energy Production and Forestry—Results of the CORINE Land Cover Map 2018

Abstract

The CORINE Land Cover (CLC) map was established in 1985 and is now one of the most widely used products from the Copernicus Land Monitoring Service. As the world’s longest consistent operational land cover monitoring product, CLC maps have been produced for reference years 1990, 2000, 2006, 2012 and now for 2018. This paper presents the results from the CLC2018 mapping project in the UK and analyses the results of the land cover status layer and the change layer from the period 2012–2018. It sets this change in context with the change results from the period 2006–2012 and finds that the rate of change between the subsequent CORINE land cover maps is continuing to increase. Changes mapped for the period 2012–2018 covered 76,032 ha greater than the change mapped between 2006 and 2012, an increase of 26% of mapped change. The area of changes mapped covered an area equivalent to 1.16% of the total land area of the UK. The number of different types of changes also continue to diversify; however, the dominance of rotational forestry is consistent with the previous map. The process of urban land take has been highlighted in the results between 2012 and 2018 and is a trend identified in previous iterations of the CLC inventories. The largest gain is in industrial or commercial units (an increase of 14.4%). This growth is mainly attributed to renewable energy infrastructure. As well as the descriptive analysis, the results have been analysed to identify the likely pressures being experienced on the land in the UK. Although the CLC mapping approach is consistent, there have been improvements to the input EO data used to map the changes. For 2018, the Copernicus Sentinel-2 system offered a consistent and reliable image source for the first time. This increased the spatial resolution of the source datasets to 10 m, allowing for more accurate identification of small features and those with fine spatial textures such as suburban, road networks and windfarms. We also look forward to the development of CLC+, the new generation of CORINE land mapping, and the improvements it could make.

Geographical heterogeneity across England in associations between the neighbourhood built environment and body mass index

“…Following assignment of participants to LAs, we undertook additional linkage based on the LA boundaries to three external, publicly available data sources. As administrative units, LAs are well described in publicly available datasets spanning multiple domains, enabling us to obtain the following LA-level variables for analysis: (1) percentage of land cover in the LA classified as 'natural' based on CORINE Land Cover data from 2012 48 , as compiled in the Land Cover Atlas of the UK 49 ; (2) estimated adult obesity prevalence in 2003-05 derived from the Health Survey for England 50 ; and (3) gross disposable household income (GDHI) per capita for 2006 51 .…”

Sustainable and Equitable Land Management

“…Fifth, one can question the land-use categories used to calculate land take in the light of its underlying aim to conserve and improve soil health. One example is renewable energy (often coded as industry), which is often temporary and can leave soils intact (Cole et al 2022).…”

The Value of Agricultural Areas: An Estimation Model of the Area to the Southeast of the City of Bari

“…Collected data was processed using the geographic information system (GIS) [17] utilizing open source "QGis" software to manage the information gathered [18] and produce an objective representation of the areas identified; through "virtual" surveying and the querying of Corine cover land maps [19], the database was implemented by adding the typology of cultivations effectively practiced on the selected terrains (Appendix A, Dataset). For the structuring of the database, in addition to discarding acts of sale for terrains with different land uses than agricultural use, we also eliminated all acts for sales with a quota of ownership less than 100%.…”

“…This operation reduced the number of observations (Appendix A) considered in the model to 50 (Figure 6). Collected data was processed using the geographic information system (GIS) [17] utilizing open source "QGis" software to manage the information gathered [18] and produce an objective representation of the areas identified; through "virtual" surveying and the querying of Corine cover land maps [19], the database was implemented by adding the typology of cultivations effectively practiced on the selected terrains (Appendix A, Dataset).…”