{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/14074"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/14074","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"Representing complex multisector, human systems into land use and land cover mapping to inform sustainability and future societal development.","abstract":"Humanity’s evolution into modern society has required the Earth’s surface and its natural systems to change drastically and in-step with technological progress. Although societal development is necessary to maintain the human population, it directly impacts the land with large-scale deforestation and habitat fragmentation. Human-earth interactions such as these are studied in the field of Multi-Sector Dynamics (MSD). MSD divides the earth’s natural systems into sectors and records their connection with/influence from human sectors (i.e. land, water, ecosystems, energy, urbanization, agriculture, transportation, and health) at various spatial and temporal resolutions. The novelty of Multi-Sector Dynamic modeling is its ability to characterize muti-sectoral information spatially within the physical landscape. When sectoral information is put in a spatial context, it allows research to access unique perspectives on spatial consequences. This is particularly important for quantifying and characterizing land use, i.e. land used for economic development that cannot be surveyed purely from satellite imagery. The geospatial quantification of land use not only aids current characterization of humanity’s influence on the natural environment but can look at trends and project future societal development based on human resource-use scenarios. However, scenario development and looking at patterns and trends begins with reliable and detailed land use datasets depicting our current society. Understanding current land use begins with improvement in our understanding and characterization of three distinct but interconnected sectors: energy, urbanization, and livestock agriculture through dynamic geospatial processing and machine learning techniques.","abstract_html":"Humanity’s evolution into modern society has required the Earth’s surface and its natural systems to change drastically and in-step with technological progress. Although societal development is necessary to maintain the human population, it directly impacts the land with large-scale deforestation and habitat fragmentation. Human-earth interactions such as these are studied in the field of Multi-Sector Dynamics (MSD). MSD divides the earth’s natural systems into sectors and records their connection with/influence from human sectors (i.e. land, water, ecosystems, energy, urbanization, agriculture, transportation, and health) at various spatial and temporal resolutions. The novelty of Multi-Sector Dynamic modeling is its ability to characterize muti-sectoral information spatially within the physical landscape. When sectoral information is put in a spatial context, it allows research to access unique perspectives on spatial consequences. This is particularly important for quantifying and characterizing land use, i.e. land used for economic development that cannot be surveyed purely from satellite imagery. The geospatial quantification of land use not only aids current characterization of humanity’s influence on the natural environment but can look at trends and project future societal development based on human resource-use scenarios. However, scenario development and looking at patterns and trends begins with reliable and detailed land use datasets depicting our current society. Understanding current land use begins with improvement in our understanding and characterization of three distinct but interconnected sectors: energy, urbanization, and livestock agriculture through dynamic geospatial processing and machine learning techniques.","abstract_has_math":false,"creators":["Sturtevant, Jillian, 1998-"],"institution":"Baylor University.","degree_name":"Ph.D.","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["McManamay, Ryan."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-24T01:07:54Z","subjects":["Geospatial modeling.","Land use modeling.","Multi-sector dynamics."],"languages":["en"],"rights":["Baylor University works are protected by copyright. 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MSD divides the earth’s natural systems into sectors and records their connection with/influence from human sectors (i.e. land, water, ecosystems, energy, urbanization, agriculture, transportation, and health) at various spatial and temporal resolutions. The novelty of Multi-Sector Dynamic modeling is its ability to characterize muti-sectoral information spatially within the physical landscape. When sectoral information is put in a spatial context, it allows research to access unique perspectives on spatial consequences. This is particularly important for quantifying and characterizing land use, i.e. land used for economic development that cannot be surveyed purely from satellite imagery. The geospatial quantification of land use not only aids current characterization of humanity’s influence on the natural environment but can look at trends and project future societal development based on human resource-use scenarios. However, scenario development and looking at patterns and trends begins with reliable and detailed land use datasets depicting our current society. Understanding current land use begins with improvement in our understanding and characterization of three distinct but interconnected sectors: energy, urbanization, and livestock agriculture through dynamic geospatial processing and machine learning techniques."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Representing complex multisector, human systems into land use and land cover mapping to inform sustainability and future societal development."]}]}],"canonical_facts":{"dc:contributor.advisor":["McManamay, Ryan."],"dc:creator":["Sturtevant, Jillian, 1998-"],"dc:date.accessioned":["2026-01-21T16:14:03Z"],"dc:date.issued":["2025-12"],"dc:description.abstract":["Humanity’s evolution into modern society has required the Earth’s surface and its natural systems to change drastically and in-step with technological progress. Although societal development is necessary to maintain the human population, it directly impacts the land with large-scale deforestation and habitat fragmentation. Human-earth interactions such as these are studied in the field of Multi-Sector Dynamics (MSD). MSD divides the earth’s natural systems into sectors and records their connection with/influence from human sectors (i.e. land, water, ecosystems, energy, urbanization, agriculture, transportation, and health) at various spatial and temporal resolutions. The novelty of Multi-Sector Dynamic modeling is its ability to characterize muti-sectoral information spatially within the physical landscape. When sectoral information is put in a spatial context, it allows research to access unique perspectives on spatial consequences. This is particularly important for quantifying and characterizing land use, i.e. land used for economic development that cannot be surveyed purely from satellite imagery. The geospatial quantification of land use not only aids current characterization of humanity’s influence on the natural environment but can look at trends and project future societal development based on human resource-use scenarios. However, scenario development and looking at patterns and trends begins with reliable and detailed land use datasets depicting our current society. Understanding current land use begins with improvement in our understanding and characterization of three distinct but interconnected sectors: energy, urbanization, and livestock agriculture through dynamic geospatial processing and machine learning techniques."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2104/14074"],"dc:language.iso":["en"],"dc:rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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