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20 urn:ogc:def:uom:EPSG::9001

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  • This dataset consists of computer code transcripts for two proprietary flood risk models from a study as part of the NERC Rural Economy and Land Use (RELU) programme. This project was conceived in order to address the public controversies generated by the risk management strategies and forecasting technologies associated with diffuse environmental problems such as flooding and pollution. Environmental issues play an ever-increasing role in all of our daily lives. However, controversies surrounding many of these issues, and confusion surrounding the way in which they are reported, mean that sectors of the public risk becoming increasingly disengaged. To try to reverse this trend and regain public trust and engagement, this project aimed to develop a new approach to interdisciplinary environmental science, involving non-scientists throughout the process. Examining the relationship between science and policy, and in particular how to engage the public with scientific research findings, a major diffuse environmental management issue was chosen as a focus - flooding. As part of this approach, non-scientists were recruited alongside the investigators in forming Competency Groups - an experiment in democratising science. The Competency Groups were composed of researchers and laypeople for whom flooding is a matter of particular concern. The groups worked together to share different perspectives - on why flooding is a problem, on the role of science in addressing the problem, and on new ways of doing science together. We aimed to achieve four substantive contributions to knowledge: 1. To analyse how the knowledge claims and modelling technologies of hydrological science are developed and put into practice by policy makers and commercial organisations (such as insurance companies) in flood risk management. 2. To develop an integrated model for forecasting the in-river and floodplain effects of rural land management practices. 3. To experiment with a new approach to public engagement in the production of interdisciplinary environmental science, involving the use of Competency Groups. 4. To evaluate this new approach to doing public science differently and to identify lessons learnt that can be exported beyond this particular project to other fields of knowledge controversy. This dataset consists of computer code transcripts for two proprietary flood risk models. Flood risk and modelling interview transcripts from this study are available at the UK Data Archive under study number 6620 (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).

  • This dataset consists of the vector version of the Land Cover Map 1990 (LCM1990) for Northern Ireland. The vector data set is the core LCM data set from which the full range of other LCM1990 products is derived. It provides a number of attributes including land cover at the target class level (given as an integer value and also as text), the number of pixels within the polygon classified as each land cover type and a probability value provided by the classification algorithm (for full details see the LCM1990 Dataset Documentation). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UK CEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. 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/d6a3588b-23a8-4715-88e9-e21ab0060727

  • This dataset reports metrics of plant growth, including height, total biomass and the biomass of component plant parts, and percentage root colonisation by mycorrhizas, for tree seedlings of eight tropical and seven subtropical growing in pots of soil that had been amended by addition of various sources of phosphorus (inorganic phosphate, adenosine monophosphate, phytic, or a mixture of all three) plus an unfertilized control treatment with no P additions. The aim of the experiment was to test the hypothesis that seedlings of species that associate with different types of root-inhabiting mycorrhizal fungi would respond differently to the range of P sources applied in the experiment. The experiments were conducted as part of a NERC Discovery Science project with the title Explaining niche separation in tropical forests: feedbacks from root-fungal symbioses and soil phosphorus partitioning led by Professor David Burslem (University of Aberdeen) reference NE/M004848/1. Full details about this dataset can be found at https://doi.org/10.5285/3ad644c9-e341-4a15-ab35-311076defc33

  • This is the 20m classified pixels 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 is the Random Forest classification result from classifying a 20m pixel raster containing multi-season spectral information combined with context layers, which help to resolve spectral confusion. It is provided as a 2-band, 8-bit integer raster. The band-1 is the UKCEH Land Cover Class identifier, band-2 is an indicator of classification confidence. For a fuller description please refer to the product documentation. LCM2017 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2017. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2017. LCM2017 was simultaneously released with LCM2018 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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/f6f86b1a-af6d-4ed8-85af-21ee97ec5333

  • This is the 20m classified pixels dataset for the UKCEH Land Cover Map of 2019 (LCM2019) representing Great Britain. It describes Great Britain's land cover in 2019 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset is the Random Forest classification result from classifying a 20m pixel raster containing multi-season spectral information combined with context layers, which help to resolve spectral confusion. It is provided as a two-band, 8-bit integer raster. Band 1 is the UKCEH Land Cover Class identifier, band 2 is an indicator of classification confidence. For a fuller description please refer to the product documentation. LCM2019 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2019. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2019. LCM2019 was simultaneously released with LCM2017 and LCM2018. These are one in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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/10.5285/643eb5a9-9707-4fbb-ae76-e8e53271d1a0

  • The dataset contains raw hemispherical photographs of canopy structure representative for 0.5 ha permanent plots (26) across Caatinga area in Brazil. The photographs were taken between March 2017 and August 2019, using a digital camera (NIKON D100) with a Sigma 4.5 mm F2.8 fisheye lens. When processed the photos provide a representative value of LAI (Leaf area index) for the plot. Full details about this dataset can be found at https://doi.org/10.5285/a1c0f869-6e03-4902-af94-31e90ba141a0

  • This is the 20m classified pixels dataset for the UKCEH Land Cover Map of 2018(LCM2018) representing Northern Ireland. It describes Northern Ireland's land cover in 2018 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset is the Random Forest classification result from classifying a 20m pixel raster containing multi-season spectral information combined with context layers, which help to resolve spectral confusion. It is provided as a 2-band, 8-bit integer raster. The band-1 is the UKCEH Land Cover Class identifier, band-2 is an indicator of classification confidence. For a fuller description please refer to the product documentation. LCM2018 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2018. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2018. LCM2018 was simultaneously released with LCM2017 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Northern Ireland (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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/cf5050d8-495d-45a6-9e2e-bba5239284c2

  • The dataset includes data on vegetation composition, flower counts, berry availability over winter, pollinator visitation rates, invertebrate, hedge structure and hedgerow regrowth from a set of long running hedgerow experiments. There were three experiments in total. Experiment 1 was based in Monks Wood, Cambridgeshire, and was used to investigate the long-term effects of timing and frequency of cutting on resource provision for wildlife. Experiment 2 was based at 5 sites across Oxfordshire, Buckinghamshire and Devon and was used to investigate the effect of timing, intensity and frequency of hedgerow cutting. Experiment 3 was based at 5 sites across Cambridgeshire, Northamptonshire, Buckinghamshire and Oxfordshire and was used to investigate the effects of different rejuvenation techniques on hedgerows. All three experiments were randomised plot experiments (full details of plots and their treatments can be found in the supporting documentation. The majority of the data was collected between 2010 and 2016 but for one experiment there is data from 2005. The long running hedgerow experiments had two linked aims focused on management to maintain and restore the hedgerow resource under the agri-environment schemes: • to examine the effects of simple cutting management regimes promoted by Entry Level Stewardship (ELS) and Higher Level Stewardship (HLS) on the quality and quantity of wildlife habitat, and food resources in hedgerows; and • to identify, develop and test low-cost, practical options for hedgerow restoration and rejuvenation applicable at the large-scale under both ELS and HLS. This research was funded by Defra (project number BD2114: Effects of hedgerow management and restoration on biodiversity) and managed by the UK Centre for Ecology & Hydrology (UKCEH). Full details about this dataset can be found at https://doi.org/10.5285/95259623-f0b6-4328-a0e3-4aec09ede5b5

  • This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2018(LCM2018) representing Great Britain. It describes Great Britain's land cover in 2018 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2018 20m classified pixels dataset. All further LCM2018 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 to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2018 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2018. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2018. LCM2018 was simultaneously released with LCM2017 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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/10.5285/9f7f7f70-5137-4bfc-a6a3-f91783d5a6a6

  • Data are presented for daily rainfall, stream discharge and hydraulic conductivity of soils from catchments located in the Upper Nilgiris Reserve Forest in the state of Tamil Nadu. The catchments are dominated by four land cover types, shola, grassland, pine and wattle. The data were collected between May 2014 and December 2016. Tipping bucket wired rain gauges were used to measure rainfall. Stream discharge was measured from stilling wells and capacitance probe-based water level recorders. A mini-disk infiltrometer was used to measure the hydraulic conductivity of soils. Dry season data has not been included in this dataset as its focus is on extreme rain events. The data were collected as part of a series of eco-hydrology projects that explored the impact of land cover on rain-runoff response, carbon sequestration and nutrient and sediment discharge. The dataset presented here was collected by a team of three to five researchers and field assistants who were engaged in the installation of the data loggers and their regular operation and maintenance. Four research agencies have partnered across multiple projects to sustain the data collection efforts that started in June 2013 and continue (June 2020). These are the Foundation for Ecological Research, Advocacy and Learning - Pondicherry, the Ashoka Trust for Research in Ecology and the Environment - Bangalore, the Lancaster Environmental Centre, Lancaster University - UK, and the National Centre for Biological Sciences - Bangalore. Funding was provided by Ministry of Earth Sciences Government of India from the Changing Water Cycle programme (Grant Ref: MoES/NERC/16/02/10 PC-II) and the Hydrologic footprint of Invasive Alien Species project (MOES/PAMC/H&C/85/2016-PC-II). Additional funding was provided by UKRI Natural Environment Research Council grant NE/I022450/1 (Western Ghats-Capacity within the NERC Changing Water Cycle programme) and WWF-India as part of the Noyyal-Bhavani program.This research took place inside protected areas in the Nilgiri Division for which permissions and support were provided continually by the Tamil Nadu Forest Department, particularly the office of the District Forest Officer, Udhagamandalam. Full details about this dataset can be found at https://doi.org/10.5285/9257a999-2844-4be1-80d1-fd29e2ccf9ef