The Crop Map of England (CROME) is a polygon vector dataset mainly containing the crop types of England. The dataset contains approximately 32 million hexagonal cells classifying England into over 15 main crop types, grassland, and non-agricultural land covers, such as Woodland, Water Bodies, Fallow Land and other non-agricultural land covers. The classification was created automatically using supervised classification (Random Forest Classification) from the combination of Sentinel-1 Radar and Sentinel-2 Optical Satellite images during the period late January 2020 – September 2020. The dataset was created to aid the classification of crop types from optical imagery, which can be affected by cloud cover. The results were checked against survey data collected by field inspectors and visually validated. The data has been split into the Ordnance Survey Ceremonial Counties and each county is given a three letter code. Please refer to the CROME specification document to see which county each CODE label represents.

This spatial dataset is an output of the Natural England County & City Natural Capital Atlas project (July 2020). It shows variation in ecosystem service flow for habitats across England, based on indicators identified by NE in the 2018 Natural Capital Indicators project. The dataset comprises a hexagonal grid which summarises indicator values across the country (each unit = 5km²).Natural Capital is an important aspect of current environmental policy and management. This dataset, in combination with the other project outputs, will support understanding of Natural Capital in England and serve as a valuable engagement tool to communicate concepts of the Natural Capital approach to a wide variety of stakeholders.

Two vector (.shp) files are provided. The first, (macro assemblages.shp) shows the modelled (random forest) macrofaunal assemblage type based on a clustering of abundance data from the OneBenthic database (see https://sway.office.com/HM5VkWvBoZ86atYP?ref=Link). The second file, (macro assemblages confidence.shp) shows associated confidence in the modelled output, with darker shades (high values) indicating higher confidence and lighter shades (lower values) indicating lower confidence. Both layers can be viewed in the OneBenthic Layers tool (https://rconnect.cefas.co.uk/onebenthic_layers/), together with further details of the methodology used to produce them.

The modelled layer for macrofaunal assemblage is based on a random forest modelling of point sample data from the OneBenthic (OB, https://rconnect.cefas.co.uk/onebenthic dashboard/) dataset, largely following the methodology in Cooper et al. (2019), but with an expanded dataset covering the Greater North Sea and including data from the EurOBIS (https://www.eurobis.org/) data repository. Of the 44,407 samples within OB, we selected a subset of 31,845 for which data were considered comparable (i.e. sample acquired using a 0.1 m2 grab or core, processed using a 1 mm sieve and not taken from a known impacted site). Colonial taxa were included and given a value of one. To take account of potential differences in taxonomic resolution between surveys, macrofaunal data were aggregated to family level using the taxonomic hierarchy provided by the World Register of Marine Species (https://www.marinespecies.org/). This reduced the number of taxa from 3,659 to 750. To address spatial autocorrelation in the data, and in keeping with the previous approach, samples closer than 50 m were removed from the dataset, reducing the overall number to 18,348. A fourth-root transformation was then applied to the data to down weight the influence of highly abundant taxa. Data were then subjected to clustering using k-means. A species distribution modelling approach, based on random forest, was then used to model cluster group (i.e. macrofaunal assemblage or biotope) identity across the study area (Greater North Sea). Cross-validation via repeated sub-sampling was done to evaluate the robustness of the model estimate and predictions to data sub-setting and to extract additional information from the model outputs to produce maps of confidence in the predicted distribution, following the approach described in Mitchell et al. (2018). The cross-validation was done on 10 split sample data sets with 75% used to train and 25% to test models, randomly sampled within the levels of the response variable to maintain the class balance. The final model output was plotted as the cluster class with the majority vote of all 10 model runs. An associated confidence map was produced by multiplying map layers for 1) the frequency of the most common class and ii) the average probability of the most common class. Model outputs are used in the OneBenthic Layers Tool (https://rconnect.cefas.co.uk/onebenthic_layers/).

Cooper, K.M.; Bolam, S.G.; Downie, A.-L.; Barry, J. 2019. Biological-based habitat classification approaches promote cost-efficient monitoring: An example using seabed assemblages. J. Appl. Ecol. 56:1085–1098. https://doi.org/10.1111/1365-2664.13381

Mitchell, P.J., Downie, A.-L., Diesing, M. How good is my map? 2018. A tool for semi-automated thematic mapping and spatially explicit confidence assessment. Env. Model. Softw. 108, 111–122. https://doi.org/10.1016/j.envsoft.2018.07.014

In 2024, over nine million people lived in Greater London, making it the most populated ceremonial county in England. The West Midlands Metropolitan County, which contains the large city of Birmingham, was the second-largest county at just over 3.03 million, closely followed by Greater Manchester at three million, and then West Yorkshire with a population of 2.4 million. Kent, Essex, and Hampshire were the three next-largest counties in terms of population, each with just over 1.9 million people. A patchwork of regions England is just one of the four countries that compose the United Kingdom of Great Britain and Northern Ireland, with England, Scotland and Wales making up Great Britain. England is therefore not to be confused with Great Britain or the United Kingdom as a whole. Within England, the next subdivisions are the nine regions of England, containing various smaller units such as unitary authorities, metropolitan counties and non-metropolitan districts. The counties in this statistic, however, are based on the ceremonial counties of England as defined by the Lieutenancies Act of 1997. Regions of Scotland, Wales, and Northern Ireland Like England, the other countries of the United Kingdom have their own regional subdivisions, although with some different terminology. Scotland’s subdivisions are council areas, while Wales has unitary authorities, and Northern Ireland has local government districts. As of 2024, the most-populated Scottish council area was Glasgow City, with over 650,000 inhabitants. In Wales, Cardiff had the largest population among its unitary authorities, and in Northern Ireland, Belfast was the local government area with the most people living there.

The National Forest Inventory (NFI) woodland map covers all forest and woodland area over 0.5 hectare with a minimum of 20% canopy cover, or the potential to achieve it, and a minimum width of 20 metres. This includes areas of new planting, clearfell, windblow and restock. The woodland map excludes all 'tarmac' roads and active railways, and forest roads, rivers and powerlines where the gap in the woodland is greater than 20 meters wide. All woodland (both urban and rural), regardless of ownership, is 0.5 hectare or greater in extent, with the exception of Assumed woodland or Low density areas that can be 0.1 hectare or greater in extent. Also, in the case of woodland areas that cross the countries borders, the minimum size restriction does not apply if the overall area complies with the minimum size. Woodland less than 0.5 hectare in extent, with the expectation of the areas above, will not be described within the dataset but will be included in a separate sample survey of small woodland and tree features.

The woodland map is updated on an annual basis and the changes in the woodland boundaries use the Ordnance Survey MasterMap® (OSMM) as a reference where appropriated. The changes in the canopy cover have been identified on:

• Sentinel 2 imagery taken during spring/summer 2021 or colour aerial orthophotographic imagery available at the time of the assessment; • New planting information for the financial year 2020/2021, from grant schemes and the sub-compartment database covering the estate of Forestry England;

Woodland areas, greater than 0.5 hectares, are classified as an interpreted forest type (IFT) from aerial photography and satellite imagery. Non-woodland areas, open areas greater than 0.5 hectare completely surrounded by woodland are described according to open area types.

IFT categories are Conifer, Broadleaved, Mixed mainly conifer, Mixed mainly broadleaved, Coppice, Coppice with standards, Shrub, Young trees, Felled, Ground prep, Cloud \ shadow, Uncertain, Low density, Assumed woodland, Failed, Windblow.

IOA categories are Open water, Grassland, Agricultural land, Urban, Road, River, Powerline, Quarry, Bare area, Windfarm, Other vegetation.

For further information regarding the interpreted forest types (IFT) and the interpreted open areas (IOA) please see NFI description of attributes available on www.forestresearch.gov.uk

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

The landscape Change Program is an archive of paired historic and recent photos of Vermont landscapes. The program is funded by the National Science Foundation to digitally document how the Vermont landscape has changed over time.

The landscape of Vermont has changed considerably since it first emerged from the ocean during the collision of huge tectonic plates. For a time, geologically speaking, sediments that became Vermont had been in a warm tropical sea at the equator. Slowly they had moved north. Mountains were born and began to erode. Massive glaciers more than a kilometer thick blanketed Vermont. Soon after the glaciers left, Native Americans inhabited the area. Colonial settlers moved in, clearing the land and leaving just a quarter of the total area forested, making way for agriculture, then sheep, then dairy. Hundreds of hill farms sprang up and many were later abandoned as western soils called. Now the Vermont landscape is mostly forested and yet increasingly developed. The face of Vermont has changed dramatically over time. The shared appreciation and acknowledgement of this rich landscape history is the goal of this project.

[Summary provided by the University of Vermont.]

This 1 km resolution 41-class land cover classification map of South America was produced from 1-15 km National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data over the time period 1987 through 1991.

These data were originally acquired from Woods Hole Research Center ("http://terra.whrc.org/science/tropfor/setLBA.htm") and were modified as described in documentation provided when data are ordered from EOS-WEBSTER.

Digital images of these data are also available from the EOS-WEBSTER Image Gallary. Please see the Data Tab at the following URL: "http://eos-earthdata.sr.unh.edu/". These images can be downloaded as JPEGs and used directly in a document or printed.