[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.

This SRTM Slope Map was created from level 1 SRTM NASA data which was cleaned and had holes patched. The slope map was created in ArcMap (presumably using the simple 3x3 nearest neighbour method). The data does not include the Shetland Islands as SRTM data becomes unreliable at 60N. The cell size is close to 90m. Data was acquired between the 11th - 20th Feb 2000. SRTM Slope Map was created from level 1 SRTM NASA data, slope map generated in ArcGIS using a basic nearest neighbour approach. Digital Terrain Model. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-06-30 and migrated to Edinburgh DataShare on 2017-02-20.

Spatial datasets consider the lands contribution to preventing and mitigating climate change, through storage of carbon in the Soils (below ground). This below Ground Carbon spatial datasets represent a strategic resource for England, that indicate the range of carbon storage values in tonnes of carbon per hectare (t C Ha-1 ). At a local scale (e.g. 1:50 000). They are presented as a series of raster datasets for use in GIS Systems at a resolution of 25m2. These maps will assist users to find out where the most important carbon stores in soils in their areas. They are not suitable for field scale carbon mitigation as this would require field scale carbon assessment. As well as soil being an excellent natural carbon sink, locking carbon away from the atmosphere and reducing the amount of greenhouse gasses produced soil carbon has a number of other excellent benefits. The amount of carbon stored within mineral soil depends upon the soil type, with clay rich and silt rich soils storing more carbon than sandy soils. Within peat soils, carbon storage operates by a different process. In a non-compromised or fully functional state peat soils are fully saturated with water for most of the year. This leads to the minimal decomposition of plant biomass, so soil carbon builds up faster than decomposition can occur, so no equilibrium is reached, to form a very carbon-rich layer of peat. However, if the peats are damaged so leading to drying out the soil microbial activity can re-start, and as the carbon is utilised by the soil microfauna, carbon dioxide and methane are then released to the atmosphere, changing a carbon sink that is sequestering carbon, into a source of greenhouse gas emissions. (UK Peatland Strategy 2018) . Natural England produced a report in 2021 reviewing this research and compiling different land use. approximate values in tons per hectare of carbon for a wide variety of habitats in England (Gregg et al 2021) see Carbon Storage and Sequestration by Habitat 2021 (NERR094). Framework created from Soilscapes and NE Natural England Peat Map (Natural England 2008).Soilscapes- 1:250,000 scale soils dataset. [https://www.landis.org.uk/soilsguide/soilscapes_list.cfm ]. the 27 soils carbon figure was assigned. This data was split in 2; Mineral Soils; Organo mineral & Peat Soils. Mineral soil split by habitats. modified by: PHI habitat overlying the soil (more natural / semi-natural the higher the score) with 50% overlap = 30% uplift carbon; the Ancient Woodland (NE 2019) with 50% overlap add 30% uplift in carbon. Organo Mineral & Peat soils: NE Peat Map (2008) was used to describe the shallow and deep peat soils, inc. peaty pockets. then conflated with the Soilscape for organo -mineral soils and peat soils with the NE peat map having priority. Modifiers were used & included: Indications that the habitats might be in good ecological condition, the PHI and the SSSI was used as a proxy. If no PHI overlap a 10% reduction; If the habitat overlying the soil is Fen = 2 x carbon figure. If the habitat overlying the soil is Raised Bog = 2.5 x carbon figure; Arable = reduced carbon lost from peat soils under. The Mineral and Organo mineral & Peat Soils re-joined to single England layer. Then Soil depth & Slope adjustment. Soil depth important to carbon stored. Most carbon in the topsoil, lesser amount of carbon held deep in soil profiles. Put into the model each soil type was allocated to one of four depth classes: Shallow soils with a profile likely to be 15-50 cm or less; The models assumed a 30 cm depth for carbon calculations; Normal depth mineral soils with a profile between 1 m and 1.25 m. The models assumed a 1 m depth for carbon calculations. Blanket peat soils. The models assumed a 2 m depth for carbon calculations. Raised bog and fen peat soils. Model assumed 4 m depth for carbon calculations. Slope, habitats occurring on steep slopes have thinner soil. A value of over 18o was used to show as a proxy for thinner soils. Slope occurring on; on slopes between 0-11o = 0%; on slopes between 11o - 18o = -10%; on slopes over 18o = -20%. NE PHI/ Ancient Woodland - OGL NE Living England - OGL NE Peat Map [2008] - Non- commercial licence Soilscapes - Cranfield University- NE Bespoke Licence SRTM- NASA Shuttle Radar Topography- Open Topography Attribution statement: © Natural England [Year], reproduced with the permission of Natural England, www.gov.uk/natural-england. © Crown Copyright and database right [Year]. Ordnance Survey licence number AC0000851168. Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right [Year] Ordnance Survey 100021242 Soils Data © Cranfield University (NSRI) and for the Controller of HMSO [Year] Need to add text for SRTM NASA Shuttle Radar Topography Mission (SRTM)(2013). Shuttle Radar Topography Mission (SRTM) Global. Distributed by OpenTopography. https://doi.org/10.5069/G9445JDF. Accessed: 2024-05-17

Land Cover Map 2021 (LCM2021) is a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2021. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2021. Land cover maps describe the physical material on the surface of the country. For example grassland, woodland, rivers & lakes or man-made structures such as roads and buildingsThis is a 10 m Classified Pixel dataset, classified to create a single mosaic of national cover. Provenance and quality:UKCEH’s automated land cover classification algorithms generated the 10m classified pixels. Training data were automatically selected from stable land covers over the interval of 2017 to 2019. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.Land cover was validated by organising the pixel classification into a land parcel framework (the LCM2021 Classified Land Parcels product). The classified land parcels were compared to known land cover producing confusion matrix to determine overall and per class accuracy.View full metadata information and download the data at catalogue.ceh.ac.uk

Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data.

The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).

Files:

agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8) AGBstack.tif = covariates used for predicting AGB aggArea.tif = coarse grid used for simulation in the model-based methods ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (https://www.isric.org/explore/soilgrids) OCSstack.tif = covariates used for predicting OCS strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples TOTmask.tif = mask of the area covered by the covariates

Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:

Name

Description

Source

Note

ai

Aridity Index

https://chelsa-climate.org/downloads/

    Version 2.1

bio1

Mean annual air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio5

Mean daily maximum air temperature of the warmest month [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio7

Annual range of air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio12

Annual precipitation [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio15

Precipitation seasonality [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

gdd10

Growing degree days heat sum above 10°C

    https://chelsa-climate.org/downloads/
    Version 2.1

clay

Clay content [g/kg] of the 0-5cm layer

https://soilgrids.org/

Only used for AGB

sand

Sand content [g/kg] of the 0-5cm layer

    https://soilgrids.org/
    as above

pH

Acidity (Ph(water)) of the 0-5cm layer

    https://soilgrids.org/
    as above

glc2017

Landcover 2017

https://land.copernicus.eu/global/products/lc, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water

Categorical variable

dem

Elevation

https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem

cosasp

Cosine of slope aspect

Computed with the terra package from elevation

    Computed @25m resolution; next aggregated to 0.5km

sinasp

Sine of slope aspect

    Computed with the terra package from elevation
    as above

slope

Slope

    Computed with the terra package from elevation
    as above

TPI

Topographic position index

    Computed with the terra package from elevation
    as above

TRI

Terrain ruggedness index

    Computed with the terra package from elevation
    as above

TWI

Topographic wetness index

Computed with SAGA from 500m resolution (aggregated) dem

gedi

Forest height

https://glad.umd.edu/dataset/gedi

Zone: NAFR

xcoord

X coordinate

Using a mask created from the other covariates

ycoord

Y coordinate

    Using a mask created from the other covariates

Dcoast

Distance from coast

Using a land mask created from the other covariates

This dataset is the Derived Postcode Database issued as part of the GeoSure Insurance V7 incorporating postcode data from OS Code-Point Open version 2016.1. The GeoSure Insurance Product (including the Derived Postcode Database) represents the end of an interpretation process, starting with the BGS Digital Geological Map of Great Britain at the 1:50,000 scale (DiGMapGB-50). This digital map is the definitive record of the types of rocks underlying Great Britain (excluding the Isle of Man), as represented by various layers, starting with Bedrock and moving up to overlying Superficial layers. In 2003, the BGS also published a series of GIS digital maps identifying areas of potential natural ground movement hazard in Great Britain, called GeoSure. There are six separate hazards considered - shrink-swell clays, slope instability, dissolution of soluble ground, running sand, compressible and collapsible deposits. These maps were derived by combining the rock-type information from DiGMapGB-50 with a series of other influencing factors which may cause the geological hazards (e.g. steep slopes, groundwater). In 2005, the BGS used the GeoSure maps to make an interpretation of subsidence insurance risk for Great Britain property insurance industry, released as the new GeoSure Insurance Product. This represents the combined effects of the 6 GeoSure hazards on (low-rise) buildings in a postcode database - the Derived Postcode Database, which can be accompanied by GIS maps showing the most significant hazard areas. The combined hazard is represented numerically in the Derived Postcode Database as the Total Hazard Score, with a breakdown into the component hazards. The GeoSure Derived Postcode Database (DPD) is a stand-alone database, which can be provided separately to the full GeoSure Insurance Product V7. The methodology behind the DPD involves balancing the 6 GeoSure natural ground stability hazards against each other. The GeoSure maps themselves have a fivefold coding (A to E), and the balancing exercise involves comparing each level across the six hazards e.g. comparing a level C shrink-swell clay area with a level C running sand area. The comparison is done by a process involving expert analysis and statistical interpretations to estimate the potential damage to a property (specifically low-rise buildings only). Each level of each of the hazards is given a 'hazard score' which can then be added together to derive a Total Hazard Score at a particular location (e.g. within a given postcode).

Spatial data supporting appropriately located and designed woodland creation to help reduce flood risk by slowing flood flows and increasing the retention and infiltration of water on the land.

The layer shows where there is ‘Opportunity for Floodplain’ woodland creation and ‘Opportunity for Wider Catchment’ woodland creation.

Appropriately located and designed proposals can help reduce flood risk by slowing flood flows and increasing the retention and infiltration of water on the land by creating; woodland in the wider catchment – creating woodland here can help to, reduce fertiliser and pesticide usage, protect sensitive soils from disturbance and erosion, increase infiltration and reduce water runoff and intercept sediment and chemical pollutants in run-off, reducing the delivery of pollutants to watercourses.

Floodplain woodland – creating woodland here can act as a permeable partial barrier to a river when in flood, helping to slow flood flows.

Riparian woodland – creating woodland along watercourses can create a buffer between rivers and the adjacent land, reducing water temperature by providing shade and slowing flood flow water delivery to watercourses.

Cross-slope woodland – creating smaller areas (typically shelterbelts) of woodland (all types) across hill slopes can reduce rapid runoff from higher land. Trees also encourage infiltration and increase the soil’s water storage capacity.

Data input sources: - Spatial prioritisation of catchments suitable for Natural Flood Management (Environment Agency) - Flood Map for Planning (Rivers and Sea) - Flood Zone 3 (Environment Agency) - Soil-derived spatial prioritisation of woodland creation for NFM in the wider catchment (Forest Research)

Attributes: ‘LANDSCAPE’ – the targeting category: Opportunity for Floodplain Woodland / Opportunity for Wider Catchment Woodland. ‘AreaHa’ – Area of the feature in hectares.

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Digital Elevation Model for the Northern Experimental Forest

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

Howland DEM is a digital elevation model of the 10km X 10km area located within the Northern Experimental Forest. The contours and elevation benchmarks from the United States Geological Survey 7.5'quadsheets for Howland and Lagrange were digitized and then rasterized into a 10m X 10m grid.

The data was revised by projecting it into NAD83 datum by L. Prihodko at NASA Goddard Space Flight Center. Although the data was received at GSFC with an undeclared datum, it was assumed to be in North American Datum of 1927 (NAD27) because the original map from which the data were digitized was in NAD27. Also, the data fit exactly within the bounds of the FED site grid (even Universal Transverse Mercator projections) in NAD27. After projecting the data into NAD83 it was checked to insure that the change was a linear translation of the coordinates only and that the gridded values did not undergo any changes.

This dataset provides linear trends, over varying time periods, for the Collated Indices of individual butterfly species across the UK. The main statistical values derived from a linear regression (slope, standard error, P-value) are presented for the entire time series for each species (1976# to 2022), for the last 20 years, and for the last decade. In addition, trends are classified based on the direction and significance of a linear slope together with an estimated percentage change for that time period. These trend data are provided for all UK butterfly species for which we have sufficient data (58 species). Trends are calculated by performing a linear regression on the annual Collated indices for each species. Collated indices are calculated using a log-linear model incorporating individual site indices from all monitored sites across the UK for a given species in a given year. Trends across different time series allow us to determine the long and short-term status of individual species. This is enables us to focus conservation and research and also to assess species responses to conservation already in place. The UK Butterfly Monitoring Scheme is organized and funded by Butterfly Conservation (BC), the UK Centre for Ecology & Hydrology (UKCEH), the British Trust for Ornithology (BTO), and the Joint Nature Conservation Committee (JNCC). The UKBMS is indebted to all volunteers who contribute data to the scheme. 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. #Because the Collated indices are only calculated for each species in years in which it was recorded on five or more sites, the starting year for the series is later than 1976 for a number of rarer species.

The GeoSure Insurance Product (GIP) dataset identifies potential natural ground movement hazard within Great Britain by postcode. These data are available in GIS point feature and database format, updated on a 6 monthly basis. The GeoSure Insurance Product represents the end of an interpretation process starting with the BGS Digital Geological Map of Great Britain at the 1:50,000 scale (DiGMapGB-50). This digital map is the definitive record of the types of rocks underlying Great Britain (excluding Northern Ireland), as represented by various layers, starting with Bedrock and moving up to overlying Superficial layers. In 2003, the BGS published a series of GIS digital maps identifying areas of potential natural ground movement hazard in Great Britain, called GeoSure, demonstrating a grade of potential for 6 separate hazards: shrink-swell clays, slope instability, dissolution of soluble ground, running sand, compressible and collapsible deposits. These maps were derived by combining the rock-type information from DiGMapGB-50 with a series of other influencing factors which may cause the geological hazards (e.g. steep slopes, groundwater). In 2005, the BGS used the GeoSure maps to make an interpretation of subsidence insurance risk for the British property insurance industry, released as the GeoSure Insurance Product. This represents the combined effects of the 6 GeoSure hazards on buildings in a postcode database. The combined hazard is represented numerically in the database as the Total Hazard Score, with a breakdown into the component hazards. The methodology behind these data involves balancing the 6 GeoSure natural ground stability hazards against each other. The GeoSure maps themselves have a fivefold coding (A to E), and the balancing exercise involves comparing each level across the six hazards e.g. comparing a level C shrink-swell clay area with a level C running sand area. Each level of each of the hazards is given a 'hazard score' which can then be added together to derive a Total Hazard Score within a 300m radius from the population weighted postcode point.

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Elevation Contours for the Northern Experimental Forest

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

This data layer contains elevation contours for the 10 X 10 km area located within the Northern Experimental Forest. Contours and elevation benchmarks from the United States Geological Survey 7.5" Maine quadsheets for Howland and Lagrange were digitized, and elevation data in feet were added.

The data was revised by projecting it into NAD83 datum by L. Prihodko at NASA Goddard Space Flight Center. Although the data was received at GSFC with an undeclared datum, it was assumed to be in North American Datum of 1927 (NAD27) because the original map from which the data were digitized was in NAD27. Also, the data fit exactly within the bounds of the FED site grid (even Universal Transverse Mercator projections) in NAD27. After projecting the data into NAD83 it was checked to insure that the change was a linear translation of the coordinates.

The Most Probable Overland Flow Pathway dataset is a polyline GIS vector dataset that describes the likely flow routes of water along with potential accumulations of diffuse pollution and soil erosion features over the land. It is a complete network for the entire country (England) produced from a hydro-enforced LIDAR 1-metre resolution digital terrain model (bare earth DTM) produced from the 2022 LIDAR Composite 1m Digital Terrain Model. Extensive processing on the data using auxiliary datasets (Selected OS Water Network, OS MasterMap features as well as some manual intervention) has resulted in a hydro-enforced DTM that significantly reduces the amount of non-real-world obstructions in the DTM. Although it does not consider infiltration potential of different land surfaces and soil types, it is instructive in broadly identifying potential problem areas in the landscape. The flow network is based upon theoretical one-hectare flow accumulations, meaning that any point along a network feature is likely to have a minimum of one-hectare of land potentially contributing to it. Each segment is attributed with an estimate of the mean slope along it. The product is comprised of 3 vector datasets; Probable Overland Flow Pathways, Detailed Watershed and Ponding and Errors. Where Flow Direction Grids have been derived, the D8 option was applied. All processing was carried out using ARCGIS Pro’s Spatial Analyst Hydrology tools. Outlined below is a description of each of the feature class. Probable Overland Flow Pathways The Probable Overland Flow Pathways layer is a polyline vector dataset that describes the probable locations accumulation of water over the Earth’s surface where it is assumed that there is no absorption of water through the soil. Every point along each of the features predicts an uphill contribution of a minimum of 1 hectare of land. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Every effort has been used to digitally unblock real-world drainage features; however, some blockages remain (e.g. culverts and bridges. In these places the flow pathways should be disregarded. The Ponding field can be used to identify these erroneous pathways. They are flagged in the Ponding field with a “1”. Flow pathways are also attributed with a mean slope value which is calculated from the Length and the difference of the start and end point elevations. The maximum uphill flow accumulation area is also indicated for each flow pathway feature. Detailed Watersheds The Detailed Watersheds layer is a polygon vector dataset that describes theoretical catchment boundaries that have been derived from pour points extracted from every junction or node of a 1km2 Flow Accumulation dataset. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Ponding Errors The Ponding and Errors layer is a polygon vector dataset that describes the presence of depressions in the landscape after the hydro-enforcing routine has been applied to the Digital Terrain Model. The Type field indicates whether the feature is Off-Line or On-Line. On-Line is indicative of a feature that intersects with a watercourse and is likely to be an error in the Overland Flow pathways. Off-line features do not intersect with watercourses and are more likely to be depressions in the landscape where standing water may accumulate. Only features of greater than 100m2 with a depth of greater than 20cm have been included. The layer was derived by filling the hydro-enforced DTM then subtracting the hydro-enforced DTM from the filled hydro-enforced DTM. Please use with caution in very flat areas and areas with highly modified drainage systems (e.g. fenlands of East Anglia and Somerset Levels). There will occasionally be errors associated with bridges, viaducts and culverts that were unable to be resolved with the hydro-enforcement process. Attribution statement: © Environment Agency copyright and/or database right 2023. All rights reserved.

This data contains values of bare sand area, modelled wind speed, aspect and slope at a 2.5 m spatial resolution for four UK coastal dune fields, Abberfraw (Wales), Ainsdale (England), Morfa Dyffryn (Wales), Penhale (England). Data is stored as a .csv file. Data is available for 620,756.25 m2 of dune at Abberfraw, 550,962.5 m2 of dune at Ainsdale, 1,797,756.25 m2 of dune at Morfa Dyffryn and 2,275,056.25 m2 of dune at Penhale. All values were calculated from aerial imagery and digital terrain models collected between 2014 and 2016. For each location, areas of bare sand were mapped in QGIS using the semi-automatic classification plugin (SCP) and the minimum distance algorithm on true-colour RGB images. The slope and aspect of the dune surface at each site was calculated in QGIS from digital terrain models. Wind speed at 0.4 m above the surface of the digital terrain model at each site was calculated using a steady state computational fluid dynamics (CFD). Data was collected to statistically test the relationship between bare sand and three abiotic physical factors on coastal dunes (wind speed, dune slope and dune slope aspect). Vertical aerial imagery was sourced from EDINA Aerial Digimap Service and digital terrain models from EDINA LIDAR Digimap Service. Wind speed data was generated and interpreted by Dr Thomas Smyth (University of Huddersfield).

Stepping Stones Lidar

The PALEOMAP project produces paleogreographic maps illustrating the Earth's plate tectonic, paleogeographic, climatic, oceanographic and biogeographic development from the Precambrian to the Modern World and beyond.

A series of digital data sets has been produced consisting of plate tectonic data, climatically sensitive lithofacies, and biogeographic data. Software has been devloped to plot maps using the PALEOMAP plate tectonic model and digital geographic data sets: PGIS/Mac, Plate Tracker for Windows 95, Paleocontinental Mapper and Editor (PCME), Earth System History GIS (ESH-GIS), PaleoGIS(uses ArcView), and PALEOMAPPER.

Teaching materials for educators including atlases, slide sets, VHS animations, JPEG images and CD-ROM digital images.

Some PALEOMAP products include: Plate Tectonic Computer Animation (VHS) illustrating motions of the continents during the last 850 million years.

Paleogeographic Atlas consisting of 20 full color paleogeographic maps. (Scotese, 1997).

Paleogeographic Atlas Slide Set (35mm)

Paleogeographic Digital Images (JPEG, PC/Mac diskettes)

Paleogeographic Digital Image Archive (EPS, PC/Mac Zip disk) consists of the complete digital archive of original digital graphic files used to produce plate tectonic and paleographic maps for the Paleographic Atlas.

GIS software such as PaleoGIS and ESH-GIS.

This dataset contains height, foliage height diversity, mean crown area, tree count, bedrock, elevation, age, aspect and slope data for woodlands under 1ha in size that were also covered by Defra’s LiDAR survey in the year 2011 in the Isle of Wight. These data were collected to see if the presence of an adjacent older neighbour affects woodland structure and height in recently created woodlands. Data was processed by the author under NERC Grant NE/S007458/1 PANORAMA - A Yorkshire partnership for training in environmental careers

The data set consists of 1:25,000 topographic maps covering Lutzow-Holm Bukt coast and major bare rock areas and inland mountains. The contour interval is 10 m. Maps of Lutzow-Holm Bukt coast were published in 1965 - 1986, and those of Prince Olav coast in 1974 - 1985. Total number of map sheets for these areas is 61. Maps of Yamato Mountains were published in 1980 with 11 sheets. All maps have been digitized into raster data and are available with TIFF format.

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: County Soil Survey Data with Attributes

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

Additionally, almost 450 ha of the surrounding area consists of bogs and other wetlands. Generally, the soils throughout the forest are glacial tills, acid in reaction, with low fertility and high organic composition. These soils are classified primarily within three suborders: orthods, orchrepts, and aquepts. The climate is chiefly cold, humid, and continental and the region exhibits a snowpack of up to 2 m from December through March.

The original soil polygons were obtained by digitizing a 1963 USDA General Soil Map of Penobscot County, Maine. All of the soil symbols used were taken directly off of the county soil map. Data from the State Soil Geographic Database (STATSGO) were cross-matched. The county symbol was chosen as the identifier, and a STATGO identifier that best "fits" the county soil identifier was selected. The original maps used for the digitization came in 6 map sheets. All of the sheets were digitized, corrected, edge-matched, and appended. Once this was finished, topology was built, new items and attributes were added.

The data in its current form can be used to delineate basic soil groups. However, because the STATSGO map unit identifier is located in each polygon the user can link any of the other STATSGO data sets depending on the desired information. The identifier is the key for creating a very detailed and thorough soils data set. Once linked the data can be used for ecological modeling, resource management, and many other applications.

Ice sheet elevation data are collected over Greenland with NASA's Airborne Topographic Mapper (ATM). The data are known as the Greenland Airborne Precision Elevation Survey (GRAPES).

The ATM is a laser altimeter flown on NASA aircraft. The Global Positioning System (GPS) of orbiting satellites is used to navigate the aircraft's autopilot in order to provide precise location information for repeat coverage.

The data are collected yearly starting in 1991. The GRAPES data currently available include results from the 1993 mission, with other data to be included soon. Flight trajectory data are available for all years beginning with 1993.

The data collected by ATM form baseline measurements of ice elevation of Greenland. The data will be used in conjunction with the future elevation measurements of the Geoscience Laser Altimeter System (GLAS) instrument onboard the ICESat satellite (to be launched in 2001). Changes in ice sheet elevation measurements provide a better understanding of glacial changes that may be due to global climate change.

For more information, see http://atm.wff.nasa.gov/

[This summary was derived from the pages of the Observational Science Branch at NASA Wallops Flight Facility. The Observational Science Branch is a division of NASA's Goddard Space Flight Center Laboratory for Hydrospheric Processes.]