Maintaining representative sampling of biologically rich and rare ecosystems has become an important means to preventing biodiversity loss. A limitation in indentifying and quantifying ecosystems is the cost of obtaining high resolution imagery necessary for a high resolution land cover assessment. This research shows how free, different resolution imagery (orthoimages and LANDSAT ETM+) could be combined to produce a hybrid dataset with enhanced spectral, spectral and temporal properties, and processed to obtain a object-based classification of land cover of bottomland and pine hardwood forest in south eastern Arkansas. Three classification techniques were evaluated: 1) a human derived, rule based method, 2) A nearest neighbor classification using only the infrared orthoimage (SRGB), and 3) A nearest neighbor classification using the infrared orthoimage and LANDSAT ETM+ derived multitemporal NDVI values (SNDVI). Overall accuracy of the rule based method and SNDVI were comparable, and significantly higher (~10-20%) than the SRGB. Further, when compared to existing land cover maps, both the rule based method and SNDVI had far greater visual appeal and accuracy.
","abstract_html":"<p>Maintaining representative sampling of biologically rich and rare ecosystems has become an important means to preventing biodiversity loss. A limitation in indentifying and quantifying ecosystems is the cost of obtaining high resolution imagery necessary for a high resolution land cover assessment. This research shows how free, different resolution imagery (orthoimages and LANDSAT ETM+) could be combined to produce a hybrid dataset with enhanced spectral, spectral and temporal properties, and processed to obtain a object-based classification of land cover of bottomland and pine hardwood forest in south eastern Arkansas. Three classification techniques were evaluated: 1) a human derived, rule based method, 2) A nearest neighbor classification using only the infrared orthoimage (SRGB), and 3) A nearest neighbor classification using the infrared orthoimage and LANDSAT ETM+ derived multitemporal NDVI values (SNDVI). Overall accuracy of the rule based method and SNDVI were comparable, and significantly higher (~10-20%) than the SRGB. Further, when compared to existing land cover maps, both the rule based method and SNDVI had far greater visual appeal and accuracy.</p>","abstract_has_math":false,"creators":["McFee, David"],"institution":null,"degree_name":"Master of Arts in Geography (MA)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Tullis, Jason A.","Huxel, Gary R."],"advisors":["Dixon, John C."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-05-01T07:00:00Z","date_published":"2012-05-01T07:00:00Z","updated_at":"2026-07-24T00:59:58Z","subjects":["Earth sciences","Biological sciences","Classification","Gis","Land cover","Mapping","Geographic Information Sciences","Remote Sensing"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/266","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tullis, Jason A.","Huxel, Gary R."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Dixon, John C."]},{"key":"dc:creator","label":"Author","values":["McFee, David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-08-18T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Arts in Geography (MA)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Earth sciences","Biological sciences","Classification","Gis","Land cover","Mapping","Geographic Information Sciences","Remote Sensing"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/266"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Maintaining representative sampling of biologically rich and rare ecosystems has become an important means to preventing biodiversity loss. A limitation in indentifying and quantifying ecosystems is the cost of obtaining high resolution imagery necessary for a high resolution land cover assessment. This research shows how free, different resolution imagery (orthoimages and LANDSAT ETM+) could be combined to produce a hybrid dataset with enhanced spectral, spectral and temporal properties, and processed to obtain a object-based classification of land cover of bottomland and pine hardwood forest in south eastern Arkansas. Three classification techniques were evaluated: 1) a human derived, rule based method, 2) A nearest neighbor classification using only the infrared orthoimage (SRGB), and 3) A nearest neighbor classification using the infrared orthoimage and LANDSAT ETM+ derived multitemporal NDVI values (SNDVI). Overall accuracy of the rule based method and SNDVI were comparable, and significantly higher (~10-20%) than the SRGB. Further, when compared to existing land cover maps, both the rule based method and SNDVI had far greater visual appeal and accuracy.
"]},{"key":"dc:title","label":"Title","values":["An Alternate Approach to Ecosystem Mapping: Fusing Orthophotography with LANDSAT ETM+ Data for a Object-Based Classification, South Eastern Arkansas."]}]}],"canonical_facts":{"dc:contributor":["Tullis, Jason A.","Huxel, Gary R."],"dc:contributor.advisor":["Dixon, John C."],"dc:creator":["McFee, David"],"dc:date":["2012"],"dc:date.available":["2016-08-18T07:00:00Z"],"dc:description.abstract":["Maintaining representative sampling of biologically rich and rare ecosystems has become an important means to preventing biodiversity loss. A limitation in indentifying and quantifying ecosystems is the cost of obtaining high resolution imagery necessary for a high resolution land cover assessment. This research shows how free, different resolution imagery (orthoimages and LANDSAT ETM+) could be combined to produce a hybrid dataset with enhanced spectral, spectral and temporal properties, and processed to obtain a object-based classification of land cover of bottomland and pine hardwood forest in south eastern Arkansas. Three classification techniques were evaluated: 1) a human derived, rule based method, 2) A nearest neighbor classification using only the infrared orthoimage (SRGB), and 3) A nearest neighbor classification using the infrared orthoimage and LANDSAT ETM+ derived multitemporal NDVI values (SNDVI). Overall accuracy of the rule based method and SNDVI were comparable, and significantly higher (~10-20%) than the SRGB. Further, when compared to existing land cover maps, both the rule based method and SNDVI had far greater visual appeal and accuracy.
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