Grassland birds are diminishing more steadily and rapidly than other North American birds in general. The nesting success of some grassland bird species depends on the amount of nonproductive vegetation (NPV). To estimate NPV land managers are currently using the Robel pole visual obstruction reading methods. Researchers with the USDA Agricultural Research Service's (ARS) Northern Great Plains Research Laboratory in Mandan, ND, recently established statistical relationships between photosynthetic vegetation (PV), NPV and spectral vegetation indices (SVIs) derived from more sensitive and more detailed, but less accessible and more costly hyperspectral aerial imagery. This study is an extension of this previous work using spectral vegetation indices collected using the Landsat TM sensor, including simple ratios SWIR-SR (ρ2215/ρ1650) and SR71 (ρ2215 /ρ485) to estimate the amount of NPV and bare ground cover, respectively.
","abstract_html":"<p>Grassland birds are diminishing more steadily and rapidly than other North American birds in general. The nesting success of some grassland bird species depends on the amount of nonproductive vegetation (NPV). To estimate NPV land managers are currently using the Robel pole visual obstruction reading methods. Researchers with the USDA Agricultural Research Service's (ARS) Northern Great Plains Research Laboratory in Mandan, ND, recently established statistical relationships between photosynthetic vegetation (PV), NPV and spectral vegetation indices (SVIs) derived from more sensitive and more detailed, but less accessible and more costly hyperspectral aerial imagery. This study is an extension of this previous work using spectral vegetation indices collected using the Landsat TM sensor, including simple ratios SWIR-SR (ρ2215/ρ1650) and SR71 (ρ2215 /ρ485) to estimate the amount of NPV and bare ground cover, respectively.</p>","abstract_has_math":false,"creators":["Collins, Marla"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Geography & Geographic Information Science","degree_department":null,"school":null,"contributors":["Bradley C. Rundquist"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T03:26:30Z","subjects":["grassland, non photosynethetic vegetation, remote sensing, Short-Wave InfraRed (SWIR), Vegetation Indices (VIs), vegetation mapping"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.und.edu/theses/1523","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bradley C. Rundquist"]},{"key":"dc:creator","label":"Author","values":["Collins, Marla"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Geography & Geographic Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["grassland, non photosynethetic vegetation, remote sensing, Short-Wave InfraRed (SWIR), Vegetation Indices (VIs), vegetation mapping"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.und.edu/theses/1523"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Grassland birds are diminishing more steadily and rapidly than other North American birds in general. The nesting success of some grassland bird species depends on the amount of nonproductive vegetation (NPV). To estimate NPV land managers are currently using the Robel pole visual obstruction reading methods. Researchers with the USDA Agricultural Research Service's (ARS) Northern Great Plains Research Laboratory in Mandan, ND, recently established statistical relationships between photosynthetic vegetation (PV), NPV and spectral vegetation indices (SVIs) derived from more sensitive and more detailed, but less accessible and more costly hyperspectral aerial imagery. This study is an extension of this previous work using spectral vegetation indices collected using the Landsat TM sensor, including simple ratios SWIR-SR (ρ2215/ρ1650) and SR71 (ρ2215 /ρ485) to estimate the amount of NPV and bare ground cover, respectively.
"]},{"key":"dc:title","label":"Title","values":["Evaluation Of Selected Spectral Vegetation Indices In Senescent Rangeland Canopy Using Landsat Imagery"]}]}],"canonical_facts":{"dc:contributor":["Bradley C. Rundquist"],"dc:creator":["Collins, Marla"],"dc:description.abstract":["Grassland birds are diminishing more steadily and rapidly than other North American birds in general. The nesting success of some grassland bird species depends on the amount of nonproductive vegetation (NPV). To estimate NPV land managers are currently using the Robel pole visual obstruction reading methods. Researchers with the USDA Agricultural Research Service's (ARS) Northern Great Plains Research Laboratory in Mandan, ND, recently established statistical relationships between photosynthetic vegetation (PV), NPV and spectral vegetation indices (SVIs) derived from more sensitive and more detailed, but less accessible and more costly hyperspectral aerial imagery. This study is an extension of this previous work using spectral vegetation indices collected using the Landsat TM sensor, including simple ratios SWIR-SR (ρ2215/ρ1650) and SR71 (ρ2215 /ρ485) to estimate the amount of NPV and bare ground cover, respectively.
"],"dc:identifier":["https://commons.und.edu/theses/1523"],"dc:subject":["grassland, non photosynethetic vegetation, remote sensing, Short-Wave InfraRed (SWIR), Vegetation Indices (VIs), vegetation mapping"],"dc:title":["Evaluation Of Selected Spectral Vegetation Indices In Senescent Rangeland Canopy Using Landsat Imagery"],"thesis:degree_discipline":["Geography & Geographic Information Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T03:26:30Z"}