{"id":{"repo_id":"wvu","oai_identifier":"oai:researchrepository.wvu.edu:etd-2071"},"canonical_url":"https://search.dev.ndltd.org/etd/wvu/oai:researchrepository.wvu.edu:etd-2071","repository":{"repo_id":"wvu","name":"West Virginia University","base_url":"https://researchrepository.wvu.edu/do/oai/"},"display":{"title":"Evaluation of the use of remotely sensed images to speciate mixed Appalachian forests","abstract":"Advancements in technology have led to development of various new sensors and platforms. Among them, the airborne digital camera has potential for use in detailed forest vegetation management, and they offer a number of advantages over conventional aerial photographs. The aim of the study was to determine the ability to speciate mixed Appalachian hardwood forests using the Airborne Data Acquisition and Registration (ADAR) System 1000, and to determine the optimal spatial resolution and the optimal scale of the acquired images. Digital images of a 200-ha area in west central West Virginia were acquired for the study.;The first objective of the study was to determine the optimal spatial resolution of Central Appalachian hardwoods images through analysis of spatial autocorrelation and semivariance.;The next objective of the study was to determine the optimal scale of the acquired images, which represents the pixel size at which the classification accuracy was optimized.;The third objective of the study was to evaluate the ability to determine the species composition of the study area using the acquired images. (Abstract shortened by UMI.).","abstract_html":"Advancements in technology have led to development of various new sensors and platforms. Among them, the airborne digital camera has potential for use in detailed forest vegetation management, and they offer a number of advantages over conventional aerial photographs. The aim of the study was to determine the ability to speciate mixed Appalachian hardwood forests using the Airborne Data Acquisition and Registration (ADAR) System 1000, and to determine the optimal spatial resolution and the optimal scale of the acquired images. Digital images of a 200-ha area in west central West Virginia were acquired for the study.;The first objective of the study was to determine the optimal spatial resolution of Central Appalachian hardwoods images through analysis of spatial autocorrelation and semivariance.;The next objective of the study was to determine the optimal scale of the acquired images, which represents the pixel size at which the classification accuracy was optimized.;The third objective of the study was to evaluate the ability to determine the species composition of the study area using the acquired images. (Abstract shortened by UMI.).","abstract_has_math":false,"creators":["Pacurari, Doru Ioan"],"institution":null,"degree_name":"MS","degree_level":"Thesis","degree_discipline":"Wood Science and Technology","degree_department":null,"school":null,"contributors":["Linda Gribko."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2000,"date_issued":"2000-08-01T07:00:00Z","date_published":"2000-08-01T07:00:00Z","updated_at":"2026-07-24T06:15:16Z","subjects":["Forestry","Physical geography","Remote sensing"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://researchrepository.wvu.edu/etd/1068"],"render_values":[{"text":"https://researchrepository.wvu.edu/etd/1068","href":"https://researchrepository.wvu.edu/etd/1068","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.33915/etd.1068","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Linda Gribko."]},{"key":"dc:creator","label":"Author","values":["Pacurari, Doru Ioan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-01-17T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Wood Science and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Forestry","Physical geography","Remote sensing"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.33915/etd.1068","https://researchrepository.wvu.edu/etd/1068"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Advancements in technology have led to development of various new sensors and platforms. Among them, the airborne digital camera has potential for use in detailed forest vegetation management, and they offer a number of advantages over conventional aerial photographs. The aim of the study was to determine the ability to speciate mixed Appalachian hardwood forests using the Airborne Data Acquisition and Registration (ADAR) System 1000, and to determine the optimal spatial resolution and the optimal scale of the acquired images. Digital images of a 200-ha area in west central West Virginia were acquired for the study.;The first objective of the study was to determine the optimal spatial resolution of Central Appalachian hardwoods images through analysis of spatial autocorrelation and semivariance.;The next objective of the study was to determine the optimal scale of the acquired images, which represents the pixel size at which the classification accuracy was optimized.;The third objective of the study was to evaluate the ability to determine the species composition of the study area using the acquired images. (Abstract shortened by UMI.)."]},{"key":"dc:title","label":"Title","values":["Evaluation of the use of remotely sensed images to speciate mixed Appalachian forests"]}]}],"canonical_facts":{"dc:contributor":["Linda Gribko."],"dc:creator":["Pacurari, Doru Ioan"],"dc:date.available":["2019-01-17T08:00:00Z"],"dc:description.abstract":["Advancements in technology have led to development of various new sensors and platforms. Among them, the airborne digital camera has potential for use in detailed forest vegetation management, and they offer a number of advantages over conventional aerial photographs. The aim of the study was to determine the ability to speciate mixed Appalachian hardwood forests using the Airborne Data Acquisition and Registration (ADAR) System 1000, and to determine the optimal spatial resolution and the optimal scale of the acquired images. Digital images of a 200-ha area in west central West Virginia were acquired for the study.;The first objective of the study was to determine the optimal spatial resolution of Central Appalachian hardwoods images through analysis of spatial autocorrelation and semivariance.;The next objective of the study was to determine the optimal scale of the acquired images, which represents the pixel size at which the classification accuracy was optimized.;The third objective of the study was to evaluate the ability to determine the species composition of the study area using the acquired images. (Abstract shortened by UMI.)."],"dc:identifier":["https://doi.org/10.33915/etd.1068","https://researchrepository.wvu.edu/etd/1068"],"dc:subject":["Forestry","Physical geography","Remote sensing"],"dc:title":["Evaluation of the use of remotely sensed images to speciate mixed Appalachian forests"],"thesis:degree_discipline":["Wood Science and Technology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T06:15:16Z"}