{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/36183"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/36183","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A GIS Model for Minefield Area Prediction: The Minefield Likelihood Procedure","abstract":"Existing minefields left over from previous conflicts pose a grave threat to humanitarian relief operations, domestic everyday life, and future military operations. The remaining minefields in Afghanistan, from the decade long war with the Soviet Union, are just one example of this global problem. The purpose of this research is to develop a methodology that will predict areas where minefields are the most likely to exist through use of a GIS model. The concept is to combine geospatial data layers to produce a scored raster output of the most likely regions where minefields may exist. It is a \"site suitability analysis\" for minefield existence. The GIS model uses elevation and slope data, observer and defensive position locations, hydrographic features, transportation features, and trafficability estimates to form a minefield prediction surface. Through use of the NATO Reference Mobility Model (NRMMII) and the Digital Topographic Support System (DTSS), trafficability estimates are generated for specific vehicles under specific terrain and weather conditions in specific areas of interest. The model could be used to create prioritized maps for minefield detection sensors, demining teams, or for avoidance. These maps could define the \"high payoff\" search areas for remote sensors, such as ASTAMIDS, and positively identify minefields. These maps could also be used by humanitarian relief agencies for consideration when planning movement into areas that may contain minefields. The analysis includes a model calibration and sensitivity analysis procedure and compares the model output to known training minefield locations taken from two US Army training centers. The resultant Minefield Likelihood Surface has a 91% accuracy rate when compared to known training minefield data.","abstract_html":"Existing minefields left over from previous conflicts pose a grave threat to humanitarian relief operations, domestic everyday life, and future military operations. The remaining minefields in Afghanistan, from the decade long war with the Soviet Union, are just one example of this global problem. The purpose of this research is to develop a methodology that will predict areas where minefields are the most likely to exist through use of a GIS model. The concept is to combine geospatial data layers to produce a scored raster output of the most likely regions where minefields may exist. It is a "site suitability analysis" for minefield existence. The GIS model uses elevation and slope data, observer and defensive position locations, hydrographic features, transportation features, and trafficability estimates to form a minefield prediction surface. Through use of the NATO Reference Mobility Model (NRMMII) and the Digital Topographic Support System (DTSS), trafficability estimates are generated for specific vehicles under specific terrain and weather conditions in specific areas of interest. The model could be used to create prioritized maps for minefield detection sensors, demining teams, or for avoidance. These maps could define the "high payoff" search areas for remote sensors, such as ASTAMIDS, and positively identify minefields. These maps could also be used by humanitarian relief agencies for consideration when planning movement into areas that may contain minefields. The analysis includes a model calibration and sensitivity analysis procedure and compares the model output to known training minefield locations taken from two US Army training centers. The resultant Minefield Likelihood Surface has a 91% accuracy rate when compared to known training minefield data.","abstract_has_math":false,"creators":["Chamberlayne, Edward Pye"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Civil Engineering","degree_department":"Civil Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Dymond, Randel L."],"committee_members":["Mouras, Victoria A.","Stevens, M. Merrill"],"year":2002,"date_issued":"2002-11-27","date_published":"2002-11-27","updated_at":"2026-07-22T22:19:32Z","subjects":["likelihood","MCS-Eng","ABCS","NRMM","IMSMA","prediction","minefield","GIS","DTSS"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12162002-135105"],"render_values":[{"text":"etd-12162002-135105","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/36183","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Dymond, Randel L."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Mouras, Victoria A.","Stevens, M. Merrill"]},{"key":"dc:contributor.department","label":"Department","values":["Civil Engineering"]},{"key":"dc:creator","label":"Author","values":["Chamberlayne, Edward Pye"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:50:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:50:01Z","2003-12-30"]},{"key":"dc:date.issued","label":"Date","values":["2002-11-27"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["likelihood","MCS-Eng","ABCS","NRMM","IMSMA","prediction","minefield","GIS","DTSS"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12162002-135105"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/36183"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Existing minefields left over from previous conflicts pose a grave threat to humanitarian relief operations, domestic everyday life, and future military operations. The remaining minefields in Afghanistan, from the decade long war with the Soviet Union, are just one example of this global problem. The purpose of this research is to develop a methodology that will predict areas where minefields are the most likely to exist through use of a GIS model. The concept is to combine geospatial data layers to produce a scored raster output of the most likely regions where minefields may exist. It is a \"site suitability analysis\" for minefield existence. The GIS model uses elevation and slope data, observer and defensive position locations, hydrographic features, transportation features, and trafficability estimates to form a minefield prediction surface. Through use of the NATO Reference Mobility Model (NRMMII) and the Digital Topographic Support System (DTSS), trafficability estimates are generated for specific vehicles under specific terrain and weather conditions in specific areas of interest. The model could be used to create prioritized maps for minefield detection sensors, demining teams, or for avoidance. These maps could define the \"high payoff\" search areas for remote sensors, such as ASTAMIDS, and positively identify minefields. These maps could also be used by humanitarian relief agencies for consideration when planning movement into areas that may contain minefields. The analysis includes a model calibration and sensitivity analysis procedure and compares the model output to known training minefield locations taken from two US Army training centers. The resultant Minefield Likelihood Surface has a 91% accuracy rate when compared to known training minefield data."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["A GIS Model for Minefield Area Prediction: The Minefield Likelihood Procedure"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Dymond, Randel L."],"dc:contributor.committeemember":["Mouras, Victoria A.","Stevens, M. Merrill"],"dc:contributor.department":["Civil Engineering"],"dc:creator":["Chamberlayne, Edward Pye"],"dc:date.accessioned":["2014-03-14T20:50:01Z"],"dc:date.available":["2014-03-14T20:50:01Z","2003-12-30"],"dc:date.issued":["2002-11-27"],"dc:description.abstract":["Existing minefields left over from previous conflicts pose a grave threat to humanitarian relief operations, domestic everyday life, and future military operations. The remaining minefields in Afghanistan, from the decade long war with the Soviet Union, are just one example of this global problem. The purpose of this research is to develop a methodology that will predict areas where minefields are the most likely to exist through use of a GIS model. The concept is to combine geospatial data layers to produce a scored raster output of the most likely regions where minefields may exist. It is a \"site suitability analysis\" for minefield existence. The GIS model uses elevation and slope data, observer and defensive position locations, hydrographic features, transportation features, and trafficability estimates to form a minefield prediction surface. Through use of the NATO Reference Mobility Model (NRMMII) and the Digital Topographic Support System (DTSS), trafficability estimates are generated for specific vehicles under specific terrain and weather conditions in specific areas of interest. The model could be used to create prioritized maps for minefield detection sensors, demining teams, or for avoidance. These maps could define the \"high payoff\" search areas for remote sensors, such as ASTAMIDS, and positively identify minefields. These maps could also be used by humanitarian relief agencies for consideration when planning movement into areas that may contain minefields. The analysis includes a model calibration and sensitivity analysis procedure and compares the model output to known training minefield locations taken from two US Army training centers. The resultant Minefield Likelihood Surface has a 91% accuracy rate when compared to known training minefield data."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-12162002-135105"],"dc:identifier.uri":["http://hdl.handle.net/10919/36183"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["likelihood","MCS-Eng","ABCS","NRMM","IMSMA","prediction","minefield","GIS","DTSS"],"dc:title":["A GIS Model for Minefield Area Prediction: The Minefield Likelihood Procedure"],"dc:type":["Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:32Z"}