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Ceredigion, United Kingdom

Environment Systems Inc. | Date: 1991-10-29

lubricants for use in industrial machinery, automobiles, oil field tubular goods, all-purpose household lubricants.

Bell G.,Environment Systems Ltd. | Neal S.,Norfolk Biodiversity Information Service | Medcalf K.,Environment Systems Ltd.
Ecological Informatics | Year: 2015

Information on the extent, location and condition of semi-natural habitats is essential to deliver the national targets to achieve the UK commitment to Biodiversity 2020 (Defra, 2011). This strategy aims to halt overall biodiversity loss by 2020 and move towards a position of net gain. In order to achieve this, both local and national bodies need detailed information on the habitats present over their entire area.Remote sensing provides opportunities for cost-effective, rapid and repeatable habitat mapping. This paper presents a method used to produce a seamless habitat map of the county of Norfolk, UK, of sufficient detail to inform land management decisions. Key aspects of the method were the development of parallel classification systems using different input data combinations and a long-term, volunteer-based map validation and update procedure.The habitat classification method utilised multiple earth observation platforms characterised by differences in spatial resolution, spectral range and season of image capture. The combinations of image data used were very important for the success of the analyses. The classification process was guided by ecological principles and local knowledge, along with targeted ground-truthing to guide class associations, confirm underlying ecological processes and to assess accuracy, and map revision.The study found that automated methods of analysis were most effective when classifying habitats characterised by distinctive dominant cover species, or groups of dominant species. The methods were least effective at identifying habitats defined by the presence of low growth-form species at low frequency or where they form understorey vegetation; in such cases field checking is vital to confirm the habitat class assignment.This scale of mapping can be used in combination with targeted, sustainable field survey effort to provide the level of information needed by decision makers to support Biodviersity 2020 targets and a wide range of other policy needs. The map has already been adopted by a wide range of organisations and finding application in such areas as Green Infrastructure, Living Landscape and habitat suitability modelling. © 2015 Elsevier B.V. Source

Lucas R.,Aberystwyth University | Medcalf K.,Environment Systems Ltd. | Brown A.,Countryside Council for Wales CCW | Bunting P.,Aberystwyth University | And 6 more authors.
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2011

The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-orientated rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land. © 2010 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Source

Medcalf K.A.,Environment Systems Ltd. | Jarman M.W.,Environment Systems Ltd. | Keyworth S.J.,Environment Systems Ltd.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

Much of Scotland's uplands are covered by organic peat soils, these form a significant proportion of the global peat resource. Peat ecosystems play a key role in the global carbon cycle through sequestration of atmospheric carbon during peat accumulation, and release of carbon gases in the form of CO2 and methane when they erode. This project set out to find a cost-effective means of identifying peat erosion features within a study area of 320 sq km on the Monadhliath Mountains in Northern Scotland. An innovative, object-orientated classification system method was used. Within Definiens eCognition software, SPOT5, IRS P6 and ASTER satellite imagery were prepared, including full geometric and atmospheric correction. In order to obtain the spatial detail required, digital aerial photography was integrated into the automated processing chain. This image data was complemented by GIS datasets that provided a set of core thematic information. Using image segmentation and a rule-base the spatial details from the air photos were integrated with the spectral detail from the satellite imagery and the thematic attributes from the GIS layers. Two levels of classification were produced: 'core level' data and 'application level' data. The application level data was produced from the core level data in the form of peat erosion maps. Erosion features were successfully identified that ranged from small gullies only a metre across, to larger exposed bare peat areas. Overall map accuracy was calculated at over 84%, with clear visual coincidence between the classified map and both the in-situ field data and aerial imagery. Source

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