Keyword

Boundaries

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  • A modelled dataset derived from a range of national datasets, describing the distribution of woody linear feature boundaries in Great Britain. The dataset presents linear features which have a high likelihood of being a woody linear feature. The dataset was created by a predictive model developed at the Centre for Ecology & Hydrology, Lancaster in 2016. Full details about this dataset can be found at https://doi.org/10.5285/d7da6cb9-104b-4dbc-b709-c1f7ba94fb16

  • This dataset consists of soil data for 64 field sites on paired farm sites, with 29 variables measured for soil texture and structural condition, aggregate stability, organic matter content, soil shear strength, fuel consumption, work rate, infiltration rate, water quality and hydrological condition (HOST) data. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

  • Auroral oval boundary locations derived from IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite FUV (Far Ultra Violet imager) data covering the period from May 2000 until October 2002. Three sets of boundary data were derived separately from the WIC (Wideband Imaging Camera) and SI12/SI13 (Spectrographic Imager 121.8/135.6 nm) detectors. For each image, the position of each pixel in AACGM (Altitude Adjusted Corrected Geomagnetic) coordinates was established. Each image was then divided into 24 segments covering 1 hour of magnetic local time (MLT). For each MLT segment, an intensity profile was constructed by finding the average intensity across bins of 1 degree magnetic latitude in the range of 50 to 90 degrees (AACGM). Two functions were fit to each intensity profile: a function with one Gaussian component and a quadratic background, and a function with two Gaussian components and a quadratic background. The function with a single Gaussian component should provide a reasonable model when the auroral emission forms in a continuous oval. When the oval shows bifurcation, the function with two Gaussian components may provide a better model of the auroral emission. Of the two functions fit to each intensity profile, we determine the one with the lower reduced chi-square goodness-of-fit statistic to be the better model for that profile. For the version 1.1 boundary location data, the fitting process was performed over 200 iterations to achieve each fit. The auroral boundaries were then determined to be the position of the peak of the poleward Gaussian curve, plus its FWHM (full-width half-maximum) value of the Gaussian, to the peak of the equatorward Gaussian, minus its FWHM. In the case of the single Gaussian fit, the same curve is used for both boundaries. A number of criteria were applied to discard poorly located auroral boundaries arising from either poor fitting or incomplete data. A further correction can be applied to the data, to estimate the location of the Earth''s magnetic field''s OCB (open-close boundary). These corrections have been tabulated in a separate file; if this correction is required the adjustments should be made to the poleward boundary value.

  • Ionospheric boundary locations derived from IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite FUV (Far Ultra Violet) imager data covering the period from May 2000 until October 2002. These include poleward and equatorward auroral boundary data derived directly from the three imagers, WIC (Wideband Imaging Camera), SI12 (Spectrographic Imager 121.8 nm), and SI13 (Spectrographic Imager 135.6 nm). These also include the OCB (open-closed magnetic field line boundary) and EPB (equatorward precipitation boundary) derived indirectly from the auroral boundaries. The data set also includes model fitted circles for all the boundary data sets for all measurement times. Chisham et al. (2022) also describe that the v2 data set also includes estimates of the OCB at each time, derived from a combination of the poleward auroral boundary measurements in combination with modelled statistical offsets between the auroral boundary and the OCB as measured by the DMSP spacecraft. The v2 data set also includes estimates of the EPB at each time, derived from a combination of the equatorward auroral boundary measurements in combination with modelled statistical offsets between the auroral boundary and the EPB as measured by the DMSP spacecraft. The v2 data set also includes model circle fit boundaries for all times for all eight raw data sets. These model circle fits were estimated using the methods outlined in Chisham (2017) and Chisham et al. (2022), which involves fitting circles to the spatial variation of the boundaries at any one time. The raw auroral boundaries were derived as outlined in Longden et al. (2010) (the original v1 data set) with the application of the additional selection criteria outlined in Chisham et al. (2022). For the creation of the original v1 data set, for each image, the position of each pixel in AACGM (Altitude Adjusted Corrected Geomagnetic) coordinates was established. Each image was then divided into 24 segments covering 1 hour of magnetic local time (MLT). For each MLT segment, an intensity profile was constructed by finding the average intensity across bins of 1 degree magnetic latitude in the range of 50 to 90 degrees (AACGM). Two functions were fit to each intensity profile: a function with one Gaussian component and a quadratic background, and a function with two Gaussian components and a quadratic background. The function with a single Gaussian component should provide a reasonable model when the auroral emission forms in a continuous oval. When the oval shows bifurcation, the function with two Gaussian components may provide a better model of the auroral emission. Of the two functions fit to each intensity profile, the one with the lower reduced chi-square goodness-of-fit statistic was deemed to be the better model for that profile. The auroral boundaries were then determined to be the position of the peak of the poleward Gaussian curve, plus its FWHM (full-width half-maximum) value of the Gaussian, to the peak of the equatorward Gaussian, minus its FWHM. In the case of the single Gaussian fit, the same curve is used for both boundaries. A number of criteria were applied to discard poorly located auroral boundaries arising from either poor fitting or incomplete data. Following Chisham et al. (2022), additional criteria were used to refine the data for the v2 auroral boundary data sets. These included dealing with anomalous data at the edges of the image fields of view, and dealing with anomalous mapping issues. Funding was provided by: STFC grant PP/E002110/1 - Does magnetic reconnection have a characteristic scale in space and time? NERC directed grant NE/V002732/1 - Space Weather Instrumentation, Measurement, Modelling and Risk - Thermosphere (SWIMMR-T). NERC BAS National Capability - Polar Science for Planet Earth.

  • A vector polyline at 60 deg S which is the northern limit for ADD datasets.

  • A coastline of Kalaallit Nunaat/ Greenland covering all land and islands, produced in 2017 for the BAS map ''Greenland and the European Arctic''. The dataset was produced by extracting the land mask from the Greenland BedMachine dataset and manually editing anomalous data. Some missing islands were added and glacier fronts were updated using 2017 satellite imagery. The dataset can be used for cartography, analysis and as a mask, amongst other uses. At very large scales, the data will appear angular due to the nature of being extracted from a raster with 150 m cell size, but the dataset should be suitable for use at most scales and can be edited by the user to exclude very small islands if required. The projection of the dataset is WGS 84 NSIDC Sea Ice Polar Stereographic North, EPSG 3413. The dataset does not promise to cover every island and coastlines were digitised using the data creator''s interpretation of the landforms from the images.

  • These catchment boundaries define upland catchments and subcatchments at the headwaters of rivers Upper Hafren (Severn) and Upper Gwy (Wye). They identify the area of study of the Plynlimon research catchment project.