{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1978"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1978","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Mapping urban forest extent and modeling sequestered carbon across Chattanooga, TN using GIS and remote sensing","abstract":"Chattanooga, Tennessee is among many cities experiencing rapid urbanization and subsequent losses to urban forest area. Using remote sensing and digital image processing, this research 1) applied supervised hybrid classification across Landsat imagery that quantified the extent of urban forest loss across Chattanooga between 1984 and 2021, 2) modeled the carbon sequestered in the biomass of Chattanooga’s urban trees using field data and vegetation indices, and finally 3) developed the first city-wide high-resolution land cover map across Chattanooga using SkySat imagery and object-based classification. Results found that Chattanooga has lost up to 43% of its urban tree canopy and gained up to 134% of urban land area. Additionally, a methodology for modeling sequestered carbon across urban forests was identified. Finally, using high-resolution imagery and the object-based workflow as described here, it is capable of producing accurate maps of urban tree canopy distribution with overall accuracy quantified in excess of 93%.","abstract_html":"Chattanooga, Tennessee is among many cities experiencing rapid urbanization and subsequent losses to urban forest area. Using remote sensing and digital image processing, this research 1) applied supervised hybrid classification across Landsat imagery that quantified the extent of urban forest loss across Chattanooga between 1984 and 2021, 2) modeled the carbon sequestered in the biomass of Chattanooga’s urban trees using field data and vegetation indices, and finally 3) developed the first city-wide high-resolution land cover map across Chattanooga using SkySat imagery and object-based classification. Results found that Chattanooga has lost up to 43% of its urban tree canopy and gained up to 134% of urban land area. Additionally, a methodology for modeling sequestered carbon across urban forests was identified. Finally, using high-resolution imagery and the object-based workflow as described here, it is capable of producing accurate maps of urban tree canopy distribution with overall accuracy quantified in excess of 93%.","abstract_has_math":false,"creators":["Stuart, William"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Hossain, A.K.M. Azad","Hunt, Nyssa; Qin, Hong","College of Arts and Sciences"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:47:13Z","subjects":["Urban forestry--Tennessee--Chattanooga","Trees in cities--Valuation--Tennessee--Chattanooga","Carbon sequestration--Tennessee--Chattanooga","Geographic information systems"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/813","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hossain, A.K.M. 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S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["Chattanooga, Tennessee is among many cities experiencing rapid urbanization and subsequent losses to urban forest area. Using remote sensing and digital image processing, this research 1) applied supervised hybrid classification across Landsat imagery that quantified the extent of urban forest loss across Chattanooga between 1984 and 2021, 2) modeled the carbon sequestered in the biomass of Chattanooga’s urban trees using field data and vegetation indices, and finally 3) developed the first city-wide high-resolution land cover map across Chattanooga using SkySat imagery and object-based classification. Results found that Chattanooga has lost up to 43% of its urban tree canopy and gained up to 134% of urban land area. Additionally, a methodology for modeling sequestered carbon across urban forests was identified. Finally, using high-resolution imagery and the object-based workflow as described here, it is capable of producing accurate maps of urban tree canopy distribution with overall accuracy quantified in excess of 93%."]},{"key":"dc:title","label":"Title","values":["Mapping urban forest extent and modeling sequestered carbon across Chattanooga, TN using GIS and remote sensing"]}]}],"canonical_facts":{"dc:contributor":["Hossain, A.K.M. Azad","Hunt, Nyssa; Qin, Hong","College of Arts and Sciences"],"dc:creator":["Stuart, William"],"dc:date":["2023-05-01T07:00:00Z"],"dc:description":["Dept. of Biological and Environmental Sciences","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["Chattanooga, Tennessee is among many cities experiencing rapid urbanization and subsequent losses to urban forest area. Using remote sensing and digital image processing, this research 1) applied supervised hybrid classification across Landsat imagery that quantified the extent of urban forest loss across Chattanooga between 1984 and 2021, 2) modeled the carbon sequestered in the biomass of Chattanooga’s urban trees using field data and vegetation indices, and finally 3) developed the first city-wide high-resolution land cover map across Chattanooga using SkySat imagery and object-based classification. Results found that Chattanooga has lost up to 43% of its urban tree canopy and gained up to 134% of urban land area. Additionally, a methodology for modeling sequestered carbon across urban forests was identified. Finally, using high-resolution imagery and the object-based workflow as described here, it is capable of producing accurate maps of urban tree canopy distribution with overall accuracy quantified in excess of 93%."],"dc:identifier":["https://scholar.utc.edu/theses/813"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Urban forestry--Tennessee--Chattanooga","Trees in cities--Valuation--Tennessee--Chattanooga","Carbon sequestration--Tennessee--Chattanooga","Geographic information systems"],"dc:title":["Mapping urban forest extent and modeling sequestered carbon across Chattanooga, TN using GIS and remote sensing"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:13Z"}