The Provincial Land Use Atlas (LUA) is a collection of digital land use data displaying administrative boundaries, proposed/approved developments, and layers associated with land use policies/regulations/legislation. The LUA was originally created as a tool to process Crown land applications and an aide in resource planning to identify potential land use conflicts. Included with the LUA is topographic base-mapping, aerial photography, imagery, Crown Land titles/applications, Municipal Plans and land use/land use restriction data.

This is the INSPIRE Existing Land Use data set of the Netherlands. It is based on the topographical map of the Netherlands (BRT) and aerial photo's of summer of 2017.

Corine Land Cover (CLC) 2012 revised, CLC 2018 and CLC change 2012-2018 are datasets produced within the frame of the Copernicus programme on land monitoring. Corine Land Cover (CLC) provides consistent information on land cover and land cover changes across Europe. This inventory was initiated in 1985 (reference year 1990) and established a time series of land cover information with updates in 2000, 2006 and 2012, being the last one the 2018 reference year. CLC products are based on photointerpretation of satellite images by national teams of participating countries - the EEA member and cooperating countries – following a standard methodology and nomenclature with the following base parameters: 44 classes in the hierarchical three level Corine nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres; minimum mapping unit (MMU) for Land Cover Changes (LCC) for the change layers is 5 hectares. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. Land cover and land use (LCLU) information is important not only for land change research, but also more broadly for the monitoring of environmental change, policy support, the creation of environmental indicators and reporting. CLC datasets provide important datasets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, assessing developments in agriculture and implementing the EU Water Framework Directive, among others. More information about the Corine Land Cover (CLC) and Copernicus land monitoring data in general can be found at http://land.copernicus.eu/.

«Data was produced with funding by the European Union. Copyright Copernicus Programme.»
DISCLAIMER: Wageningen Environmental Research has undertaken to distribute the data on behalf of EEA under Specific Contract No 3436/R0-Copernicus/EEA.56950 implementing Framework service contract No EEA/IDM/R0/16/009/Netherlands. Wageningen Environmental Research accepts no responsibility or liability whatsoever with regard to the content and use of these data. The European Environment Agency accepts no responsibility or liability whatsoever with regard to the information on this site and the information does not necessarily reflect the official opinion of the EEA or other European Communities bodies and institutions.

This dataverse present a collection of global land use decision-making patterns spatial distributions. The collection consists of probabilities for two different objectives behind land use change (survival and livelihood, and economic objectives), probability maps for 6 decision-making types that can be found in land-use literature, a probability map for land use diversification, a categorical map combining all decision-making types, and 6 similarity maps (identifying the areas with high or low similarity for each decision-making type). All files are in the geotiff format and can be opened in standard GIS software. Legend files for ArcGIS and QGIS are provided as well, for the categorical map.

This land system map is a comprehensive and high resolution representation of European land systems on a 1-km2 grid integrating important land use and landscape characteristics. There are four main land systems that are dominated by one land cover : settlement systems, forest systems, cropland systems, and grassland systems. Under each of these systems, there are low, medium, and high land use intensity classes. There are also mosaic systems that are not dominated by any land cover, including forest/shrub mosaics and agricultural mosaics. Other land systems in this map include water and wetland systems, shrubs, and rocks and bare soil. Details of the map and applications on Species distribution models can be found in this article: Dou, Y., Cosentino, F., Malek, Z. et al. A new European land systems representation accounting for landscape characteristics. Landscape Ecol (2021). https://doi-org/10.1007/s10980-021-01227-5

The map summarizes the status of the land use and land cover at global scale. It was compiled according to the current GAEZ (Global Agro-Ecological Zones) -2009 approach, developed by FAO in collaboration with IIASA (International Institute for Applied Systems Analysis).The current Global AEZ (GAEZ-2009) offers a standardized framework for the characterization of climate, soil and terrain conditions relevant to agricultural production, which can be applied at global to sub-national levels. The map is based on six geographic datasets: - GLC2000 land cover database at 30 arc-sec (http://www-gvm.jrc.it/glc2000), using regional and global legends (JRC, 2006); - an IFPRI global land cover categorization providing 17 land cover classes at 30 arc-sec. (IFPRI, 2002), based on a reinterpretation of the Global Land Cover Characteristics Database (GLCC ver. 2.0), EROS Data Centre (EDC, 2000); - FAO’s Global Forest Resources Assessment 2000 and 2005 (FAO, 2001; FAO, 2006) at 30 arc-sec. resolution; - digital Global Map of Irrigated Areas (GMIA) version 4.0 of (FAO/University of Frankfurt) at 5’ by 5’ latitude/longitude resolution, providing by grid-cell the percentage land area equipped with irrigation infrastructure (Siebert et al., 2007); - a spatial population density inventory (30-arc seconds) for year 2000 developed by FAO-SDRN, based on spatial data of LANDSCAN 2003, with calibration to UN 2000 population figures. An iterative calculation procedure has been implemented to estimate land cover class weights, consistent with aggregate FAO land statistics and spatial land cover patterns obtained from remotely sensed data, allowing the quantification of major land use/land cover shares in individual 5’ by 5’ latitude/longitude grid cells.

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2017 (LCM2017) representing Great Britain. It describes Great Britain's land cover in 2017 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2017 20m classified pixels dataset. All further LCM2017 datasets for Great Britain are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help assessing classification confidence and accuracy; for a full explanation please refer to the dataset documentation. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/b77ce981-d038-4774-a620-f50da5dd3d31

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Field reconnaissance is intended to familiarize the photo interpreter with the Monument, patterns of vegetation distribution, and environmental factors. During map class and attribute development, the mapping ecologist uses all available information, professional experience, and an inspection of the aerial imagery to develop map classes and appropriate attributes. Mapping is the process during which the photo interpreter uses field data, field notes, and characteristic photo signatures to draw consistent, homogenous polygons on the base photography. During spatial database development, attributes (e.g., vegetation height, land use category) and ancillary datasets (e.g., photos, map class descriptions) are linked to each point or polygon in the spatial layer. Because TICA is a small park, the first three steps were accomplished simultaneously during the plot data collection visit in June 2007.

We present a forest management map for Europe. Forest is classified in 5 distinct forest management classes: unmanaged forest, close-to-nature forestry, combined objective forestry, intensive forestry and very intensive forestry. Data on disturbance area, disturbance frequency, forest age, forest age evenness, fast-growing tree species and primary forest is used to classify forest.

This dataset describes the software and data to quantify uncertainty in Pareto fronts arised from spatial data. This dataset contains the Python code for a multi-objective land use allocation optimization under uncertainty. The program is an extension to CoMOLA from Strauch et al. 2019 (https://doi.org/10.1016/j.envsoft.2019.05.003). For a detailed description of CoMOLA, we refer to the article. Before executing CoMOLA under uncertainty, extreme lower and upper bound samples need to be generated from the land use and soil fertility map with quantified uncertainty. That preprocessing is described reproducable with the following Mendeley Dataset: Hildemann, Moritz Jan; Verstegen, Judith (2021), “Sampling procedure of land use and soil fertility map under uncertainty”, Mendeley Data, V2, doi: 10.17632/6x6cccfc4x.1. For every produced extreme sample, CoMOLA needs to be performed with the corresponding land use and soil fertility map. As the computational effort and computation time are high (15-20 hours) and ten optimizations were performed for every extreme sample, the runs were performed in parallel on a high-performance Linux cluster (MEGWARE cluster with 15.120 cores, 412 nodes and Intel Xeon Gold 6140 18C 2.30GHz processors). The program is executable for Python 3.7 and 3.8 in a Linux environment. The changes compared to CoMOLA include: an update to Python 3.8, removal of R components in objecting the objective values, and the implementation of a seeding procedure to inject single-objective optima into the first generation of the Genetic Algorithm. The seeding procedure allowed faster and better convergence. The generated Pareto fronts can be used postprocessing to quantify the uncertainty in objective and solution space. Pseudo-random states are used to assure reproducibility despite the stochastic processes.

The dataset contains input information used to prepare exposure maps for 37 European countries and territories from 1870 to 2020. It includes baseline land cover/use map and population map, and Excel tables with national or regional-level data on the environment, population and economy. Inofrmation on currencies and inflation can be used to convert nominal value of natural hazard-related losses to present-value euro.

The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. The dataset corresponds to a map of potential soil loss (Mg/ha/yr) due to erosion risk. The map is the result of applying the Erosion cookbook developed in SERENA/EJP-Soil. The map is created by calculating the erosion factor by the RUSLE model and has 25 m spatial resolution.

Geographic raster dataset showing the land cover in Flanders, status 2015. This map has a focus on the soil and the loss of its essential ecosystem functions as soil and the irreversibility thereof. We speak here of 'Soil sealing'. Soil sealing is expressed as the area where the nature and/or condition of the soil surface has been changed by the application of artificial, (semi-) impermeable materials, resulting in the loss of essential ecosystem functions of the soil. The map is displayed in percentage coverage per pixel (5m resolution).

Deze dataset bestaat uit de 1km raster, percentage target class versie van de Land Cover Map 2015 (LCM2015) voor Groot-Brittannië. Het percentageproduct van 1 km geeft de procentuele dekking voor elk van de 21 landbedekkingsklassen voor 1 km x 1 km pixels. Dit product bevat één band per doelhabitatklasse (waarbij een 21-bandbeeld wordt geproduceerd). De 21 doelklassen zijn gebaseerd op de Brede Habitats van het Joint Nature Conservation Committee (JNCC), die het hele scala van Britse habitats omvatten. Deze dataset is afgeleid van de vectorversie van de Land Cover Map, die individuele percelen landbedekking bevat en de hoogste beschikbare ruimtelijke resolutie heeft. LCM2015 is een land cover kaart van het Verenigd Koninkrijk die werd geproduceerd in het Centre for Ecology & Hydrology door het classificeren van satellietbeelden van 2014 en 2015 in 21 Broad Habitat-gebaseerde klassen. LCM2015 bestaat uit een reeks raster- en vectorproducten en gebruikers moeten zich vertrouwd maken met het volledige assortiment (zie gerelateerde records, de CEH-website en de LCM2015 Dataset-documentatie) om het product te selecteren dat het meest geschikt is voor hun behoeften. LCM2015 werd geproduceerd in het Centre for Ecology & Hydrology door satellietbeelden van 2014 en 2015 te classificeren in 21 Broad Habitat-gebaseerde klassen. Het is een van een reeks landbedekkingskaarten, geproduceerd door UKCEH sinds 1990. Ze omvatten versies in 1990, 2000, 2007, 2015, 2017, 2018 en 2019. Volledige details over deze dataset zijn te vinden op https://doi.org/10.5285/505d1e0c-ab60-4a60-b448-68c5bbae403e

The potential soil erosion map per plot (2020) shows the total potential erosion of a certain agricultural plot on the basis of a classification. The total erosion potential does not take into account the current land use (grassland or cropland). The 'Erosion sensitivity collective application' field contains the information that corresponds to the erosion sensitivity on the collective application 2020. The approved objections were processed in both the 'Erosion sensitivity collective application' field and the 'Total erosion' field. The approved applications of the reduction of the erosion sensitivity class based on a high carbon content were processed in the 'Erosion sensitivity collective application' field with an indication of '/C' after the erosion sensitivity, but the 'Total erosion' field kept its original value for these lots. Water erosion is a process by which soil particles are loosened and transported by the impact of raindrops and run-off water, either in layers over a large area or concentrated in gullies and ravines. This leads, among other things, to a decrease in soil quality and productivity, but also to significant damage due to mud nuisance in (residential) areas located downstream. Soil erosion is one of the most important forms of soil degradation in Flanders. The potential soil erosion map per plot is based on the agricultural use plots ALV 2019 (export August 2019). The potential soil erosion map per plot was prepared by the Flemish Planning Bureau for the Environment using computer modeling with a spatial resolution of 5x5 m. The calculation of the erosion is based on the revised universal soil loss equation or R.U.S.L.E. (Revised Universal Soil Loss Equation, Renard et al, 1991). It concerns an empirical model with which the average annual soil erosion rate per unit area as a result of inter-channel and channel erosion is calculated as a product of 6 factors.