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7 results for “ipaast”
IPAAST East Heslerton FDEM
<p>FDEM Dualem21Hs dataset constituting a case study in interoperable archaeological prospection and precision agricultural data at the Landscape Research Centre's site 070 and site 120 at Manor Farm in East Heslerton in context of the IPAAST-CZO project (British Academy - Knowledge Frontiers Programme: kf5210407) by UGlasgow.</p> <p>Contains:<br> -Unprocessed point data: all_orig.csv<br> -Processed point data: all.csv<br> -Interpolated data data of elevation, horizontal coplanar (HCP) coil pair at 0.5 (HCPH), 1 (HCP1) and 2 m (HCP2) transmitter-receiver separation and perpendicular (PRP) coil pair at 0.6 (PRPH), 1.1 (PRP1) and 2.1 m (PRP2) separation in respective (geo)tif files<br> -Scope notes regarding applications of EMI data in precision agricultural management decisions & data in precision agriculture potentially relevant to the interpretation of EMI datasets in archaeology</p>
ipaast-czo case study: Dehesa Boyal de Botija
<p>VV01 (“Cerca del cementerio” sector). Data set description.</p> <p>One single geophysical method was used:</p> <ul> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Although topography obliged to split the survey area in 3 independent data sets, after correction and merging of all the data in one single data set it was observed that, considering the small time lapse between each one and homogeneous weather conditions, no significant discontinuity in measured data was observed.</li> </ul> <p>The interest on testing EMI survey in this dehesa environment within the framework of the IPAAST project was motivated with a triple objective:</p> <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> <p>VV01 sector corresponds to an area of dehesa named “Cerca del cementerio” (“cemetery enclosure”): it is a land plot 70m SW of the hillfort with a surface of 5240 sq. m. Previous knowledge of the area revealed the presence of a high density of archaeological materials, but there was no clear evidence of buried structures. A particular feature of this sector was the great depth of soil deposits within the walled enclosure, in great contrast with the surrounding fields. Data sets:</p> <ul> <li> EMI04. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> <p>VV02 (Mercadillo sector). Data set description.</p> <p>Two main methods were used:</p> <ul> <li>Magnetic survey: a dual-sensor gradiometer system (Grad602 Bartington)</li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Dense vegetation and topography obliged to split the survey area in 3 independent data sets.</li> </ul> <p>Additionally several areas were surveyed with GPR (Nogging system of Sensor&Software).</p> <p>The interest on testing different survey methods in the framework of the IPAAST project was motivated with a triple objective:</p> <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> <p>VV02 sector corresponds to an area of dehesa named “El Mercadillo (“little market”). It is located 150 m south of the hillfort of Villasviejas and covers a surface of approx. 3500 sq m. In this sector was excavated in the 1980s a funerary area corresponding to the earliest period of the hillfort (IV-II centuries B.C). Analysis of LiDAR data revealed that in the same area there were several topographic features that could be linked with off-site activity during the protohistoric and early roman period. Data sets:</p> <ul> <li> EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li> EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>EMI03. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>MAG01. <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul> <p> </p>
ipaast-czo case study: The Rinconada Estate.
<p>The interest in testing different survey methods within the framework of the IPAAST project was motivated by three objectives:</p> <ul> <li>identifying evidence of rural life in the hinterland of the Roman colony of Augusta Emerita, with special attention to forms of resilience and diversification of agrarian activities beyond the areas of highest productivity of the alluvial plain of the Guadiana River.</li> <li>assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and perform a regression analysis to assess the potential distribution of arable lands in the area from Roman times to the present.</li> </ul> <p>combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</p> <p> </p> <p>Attention was focused on a sector of the estate where two elements were coincident 1) preliminary evidence suggested the estate’s highest concentration of archaeological finds. 2) land plots within the estate used for grazing where LIFE Adapt experiments were undertaken. This area encompassed approximately. 6.5 ha.</p> <p> </p> <p>2 geophysical methods for the exploration were used:</p> <ul> <li>Magnetic survey: a 2 sensors gradiometer system was used (Grad602 Bartington). Data sets: <ul> <li>RC_MAG <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul> </li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Data sets: <ul> <li>RC_EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data (raw data)</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>RC_EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
ipaast case study: Wiltshire yield and EM survey data
<p>These data were collected by Soyl Ltd as part of a commercial agricultural soil survey. They were reprocessed by the University of Ghent ipaast team. </p>
ipaast-czo case study OptRX data: Montalcino (SI, Italy)
<p>These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture.</p> <p>The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2: </p> <p> </p> <p>NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE</p> <p> </p> <p>The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation). </p> <p>To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha. </p> <p>Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster. </p>
ipaast - agrivation collaboration EMI survey Manor Farm Field 70
<p>These data were collected as part of the ipaast-Agrivation collaboration to develop survey workflows that produce data compatible with common applications across archaeological, agricultural, and environmental domains. The project is described in the report at: <a href="http://ipaast-czo.glasgow.ac.uk/index.php/ipaast-agrivation-ltd-collaboration/">https://ipaast-czo.glasgow.ac.uk/index.php/ipaast-agrivation-ltd-collaboration/</a>. The ipaast project is funded by the British Academy Award KF5210407.</p>
ipaast project - community stakeholder survey data and basic analysis
<p>This data provides the basis for the report titled</p> <p>"Ready for integrated sustainable agricultural land management? </p> <p>Are practitioners in archaeology and agriculture informed, willing, enabled, and motivated to change how they work with remote and near-surface sensing data to collaboratively address contemporary challenges in sustainable agricultural land management? "</p> <p>Data were collected in compliance with the University of Glasgow's Research Ethics Policy (Application #100200154).</p> <p>As stated in the Methods section of this report:</p> <p>"The participatory survey was conducted between May 2021 and October 2022. </p> <p>Location: The preponderance of stakeholders engaged with are professional practitioners or researchers based in the UK, Belgium, Italy, Cyprus, Spain and France. Sessions occurred remotely (online/phone), as well as on site, during workshops at the University of Glasgow, the Dalswinton Estate, Dumfries, and Manor Farm, Yedingham. </p> <p>Participants </p> <p>Selection: A sub-group of 51 high-level participants were selected from a greater network of 86 stakeholders who were engaged with during the ipaast project. </p> <p>Sector: Farmers, researchers, heritage managers, geophysicists, remote sensing specialists, statisticians, soil scientists, service providers, sensor developers, and data archivists, who all deal directly, or indirectly with datasets relating to the measurement of soil and/or plant properties (physical, chemical, microbial) were represented (Table 1) </p> <p>Expertise: Engagement with mid- to late- career specialists was prioritised, with many participants having over 20 years of experience and most having over 10 years of experience (including time during the PhD). </p> <p> </p> <p>Interview method </p> <p>Engagement with stakeholders was primarily through one-to-one interviews and structured workshop discussions, conducted either in person, or remotely over video conference or phone. In some instances, participants provided written input (see Table 2 summary). Follow-up interviews or written exchanges were used to clarify or continue discussions when required. A semi-structured approach to interviews and discussions was preferred, with a mix of general questions (see sample questions), as well as questions specifically tailored to the participants specialist background and experience. </p> <p>Sample Questions: </p> <ul> <li> <p>What types of sensing data do you use/collect? </p> </li> </ul> <ul> <li> <p>Where/how do you access/collect these data? </p> </li> <li> <p>What are your main aims/applications in using or collecting these data? </p> </li> <li> <p>How often do you access/collect, or anticipate accessing/collecting, these data to be useful to you? </p> </li> <li> <p>What spatial resolution is necessary for these data to be useful to you? </p> </li> <li> <p>What, if anything, would encourage/discourage you from sharing your data? </p> </li> </ul> <ul> <li> <p>What kinds of additional data types or additional information (metadata) might help you to better understand and use data which you have previously collected or received? </p> </li> <li> <p>What do you see as the main impacts, if any, of ecosystem service frameworks and/or recent changes to rural/environmental regulations on your work? </p> </li> <li> <p>What attitudes to sensing data do you see from other stakeholders in rural affairs? </p> </li> </ul> <p>Documentation: Where viable, interviews and workshop discussions were recorded and transcribed; alternatively, notes were made during engagement by either the interviewer and/or dedicated participant observers (e.g. at workshops). Where notes were used, specific quotes and summary reports were checked with the participants for accuracy. "</p>
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