{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/33656"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/33656","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Estimation of Important Scenic Beauty Covariates from Remotely Sensed Data","abstract":"The overall objective of this study was to determine if remotely sensed data could be used to model scenic beauty. Terrestrial digital images from within forest stands located in Prince Edward Gallion State Forest near Farmville, Virginia were rated for their scenic beauty by a group of students to obtain scenic beauty estimates (SBEs). Since the inter-rater reliability was low for the SBEs, they were not used in the modeling efforts. Instead, stand parameters (collected on tenth acre plots) that have been used in scenic beauty prediction models, like mean diameter at breast height (dbh), were the dependent variables in regression analyses. A color-infrared aerial photograph from the National Aerial Photography Program (NAPP) was scanned to achieve a pixel ground resolution of one meter. The digital aerial photograph was rectified and used as the remotely sensed data. Since the aerial photograph was taken in April, only conifer stands were used in the analyses. Summary statistics were obtained from a 23 by 23 window around plot locations in three images: the original image, a texture image created with the variance algorithm and a 7x7 window, and the first principal component image. The summary statistics were used as the independent variables in regression analyses. The mean texture digital number for the green band predicted the mean dbh of a plot with an R2 of 0.623. A maximum of 44.3 and 27.4 percent of the variability in trees per acre and basal area per acre, respectively, was explained by the models developed in this study. It seems unlikely that the remotely sensed forest stand variables would perform well as surrogates for field measurements used in scenic quality models.","abstract_html":"The overall objective of this study was to determine if remotely sensed data could be used to model scenic beauty. Terrestrial digital images from within forest stands located in Prince Edward Gallion State Forest near Farmville, Virginia were rated for their scenic beauty by a group of students to obtain scenic beauty estimates (SBEs). Since the inter-rater reliability was low for the SBEs, they were not used in the modeling efforts. Instead, stand parameters (collected on tenth acre plots) that have been used in scenic beauty prediction models, like mean diameter at breast height (dbh), were the dependent variables in regression analyses. A color-infrared aerial photograph from the National Aerial Photography Program (NAPP) was scanned to achieve a pixel ground resolution of one meter. The digital aerial photograph was rectified and used as the remotely sensed data. Since the aerial photograph was taken in April, only conifer stands were used in the analyses. Summary statistics were obtained from a 23 by 23 window around plot locations in three images: the original image, a texture image created with the variance algorithm and a 7x7 window, and the first principal component image. The summary statistics were used as the independent variables in regression analyses. The mean texture digital number for the green band predicted the mean dbh of a plot with an R2 of 0.623. A maximum of 44.3 and 27.4 percent of the variability in trees per acre and basal area per acre, respectively, was explained by the models developed in this study. It seems unlikely that the remotely sensed forest stand variables would perform well as surrogates for field measurements used in scenic quality models.","abstract_has_math":false,"creators":["Blinn, Christine Elizabeth"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Forestry","degree_department":"Forestry","school":null,"contributors":[],"advisors":[],"committee_chairs":["Wynne, Randolph H.","Buhyoff, Gregory J."],"committee_members":["Hull, Robert Bruce IV"],"year":2000,"date_issued":"2000-06-13","date_published":"2000-06-13","updated_at":"2026-07-22T22:18:47Z","subjects":["Scenic Beauty","Forest Measurements","Aerial Photography","Remote Sensing"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-06202000-11050038"],"render_values":[{"text":"etd-06202000-11050038","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/33656","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Wynne, Randolph H.","Buhyoff, Gregory J."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Hull, Robert Bruce IV"]},{"key":"dc:contributor.department","label":"Department","values":["Forestry"]},{"key":"dc:creator","label":"Author","values":["Blinn, Christine Elizabeth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:40:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:40:15Z","2001-06-26"]},{"key":"dc:date.issued","label":"Date","values":["2000-06-13"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Forestry"]},{"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":["Scenic Beauty","Forest Measurements","Aerial Photography","Remote Sensing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"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-06202000-11050038"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/33656"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The overall objective of this study was to determine if remotely sensed data could be used to model scenic beauty. Terrestrial digital images from within forest stands located in Prince Edward Gallion State Forest near Farmville, Virginia were rated for their scenic beauty by a group of students to obtain scenic beauty estimates (SBEs). Since the inter-rater reliability was low for the SBEs, they were not used in the modeling efforts. Instead, stand parameters (collected on tenth acre plots) that have been used in scenic beauty prediction models, like mean diameter at breast height (dbh), were the dependent variables in regression analyses. A color-infrared aerial photograph from the National Aerial Photography Program (NAPP) was scanned to achieve a pixel ground resolution of one meter. The digital aerial photograph was rectified and used as the remotely sensed data. Since the aerial photograph was taken in April, only conifer stands were used in the analyses. Summary statistics were obtained from a 23 by 23 window around plot locations in three images: the original image, a texture image created with the variance algorithm and a 7x7 window, and the first principal component image. The summary statistics were used as the independent variables in regression analyses. The mean texture digital number for the green band predicted the mean dbh of a plot with an R2 of 0.623. A maximum of 44.3 and 27.4 percent of the variability in trees per acre and basal area per acre, respectively, was explained by the models developed in this study. It seems unlikely that the remotely sensed forest stand variables would perform well as surrogates for field measurements used in scenic quality models."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Estimation of Important Scenic Beauty Covariates from Remotely Sensed Data"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Wynne, Randolph H.","Buhyoff, Gregory J."],"dc:contributor.committeemember":["Hull, Robert Bruce IV"],"dc:contributor.department":["Forestry"],"dc:creator":["Blinn, Christine Elizabeth"],"dc:date.accessioned":["2014-03-14T20:40:15Z"],"dc:date.available":["2014-03-14T20:40:15Z","2001-06-26"],"dc:date.issued":["2000-06-13"],"dc:description.abstract":["The overall objective of this study was to determine if remotely sensed data could be used to model scenic beauty. Terrestrial digital images from within forest stands located in Prince Edward Gallion State Forest near Farmville, Virginia were rated for their scenic beauty by a group of students to obtain scenic beauty estimates (SBEs). Since the inter-rater reliability was low for the SBEs, they were not used in the modeling efforts. Instead, stand parameters (collected on tenth acre plots) that have been used in scenic beauty prediction models, like mean diameter at breast height (dbh), were the dependent variables in regression analyses. A color-infrared aerial photograph from the National Aerial Photography Program (NAPP) was scanned to achieve a pixel ground resolution of one meter. The digital aerial photograph was rectified and used as the remotely sensed data. Since the aerial photograph was taken in April, only conifer stands were used in the analyses. Summary statistics were obtained from a 23 by 23 window around plot locations in three images: the original image, a texture image created with the variance algorithm and a 7x7 window, and the first principal component image. The summary statistics were used as the independent variables in regression analyses. The mean texture digital number for the green band predicted the mean dbh of a plot with an R2 of 0.623. A maximum of 44.3 and 27.4 percent of the variability in trees per acre and basal area per acre, respectively, was explained by the models developed in this study. It seems unlikely that the remotely sensed forest stand variables would perform well as surrogates for field measurements used in scenic quality models."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-06202000-11050038"],"dc:identifier.uri":["http://hdl.handle.net/10919/33656"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Scenic Beauty","Forest Measurements","Aerial Photography","Remote Sensing"],"dc:title":["Estimation of Important Scenic Beauty Covariates from Remotely Sensed Data"],"dc:type":["Thesis"],"thesis:degree_discipline":["Forestry"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:47Z"}