This dissertation seeks to investigate hyperspectral and waveform LiDAR datasets through a new analytical framework called Moment Distance method that uses a metric derived from the shape of the curve (spectral or waveform). In the case of hyperspectral data, the shape of the reflectance curve should unmask fine points of the spectra usually not considered by existing band-specific indices. To explore the advantages and shortcomings of this new approach, leaf and canopy hyperspectral reflectance samples were simulated using the physicallybased models PROSPECT (a leaf model) and SAIL (a canopy model). Sensitivity analysis was conducted with the goal of understanding the sensitivity of the new framework to leaf and canopy parameters relative to other existing and widely-used vegetation indices. The analysis evaluated the efficiency of the new approach to overcome, for instance, the decreased sensitivity of the NDVI at moderate to high Chl content or LAI. With waveform LiDAR data, the new approach was tested through the characterization of the canopy height without the typical step of fitting a series of Gaussian curves to the waveform to identify key peaks. Furthermore, the effects of noise and smoothing procedures on the metrics framework were assessed by introducing different types and levels of noise and various smoothing window sizes.
","abstract_html":"<p>This dissertation seeks to investigate hyperspectral and waveform LiDAR datasets through a new analytical framework called Moment Distance method that uses a metric derived from the shape of the curve (spectral or waveform). In the case of hyperspectral data, the shape of the reflectance curve should unmask fine points of the spectra usually not considered by existing band-specific indices. To explore the advantages and shortcomings of this new approach, leaf and canopy hyperspectral reflectance samples were simulated using the physicallybased models PROSPECT (a leaf model) and SAIL (a canopy model). Sensitivity analysis was conducted with the goal of understanding the sensitivity of the new framework to leaf and canopy parameters relative to other existing and widely-used vegetation indices. The analysis evaluated the efficiency of the new approach to overcome, for instance, the decreased sensitivity of the NDVI at moderate to high Chl content or LAI. With waveform LiDAR data, the new approach was tested through the characterization of the canopy height without the typical step of fitting a series of Gaussian curves to the waveform to identify key peaks. Furthermore, the effects of noise and smoothing procedures on the metrics framework were assessed by introducing different types and levels of noise and various smoothing window sizes.</p>","abstract_has_math":false,"creators":["Salas, Eric Ariel L."],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation - Open Access","degree_discipline":"Geospatial Science and Engineering","degree_department":null,"school":null,"contributors":["Geoffrey M. Henebry"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T04:28:36Z","subjects":["Geographic Information Sciences","Physical and Environmental Geography","Remote Sensing"],"languages":["en"],"rights":["Copyright © 2014 Eric Ariel L. Salas"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/1080","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Geoffrey M. Henebry"]},{"key":"dc:creator","label":"Author","values":["Salas, Eric Ariel L."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-10-07T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geospatial Science and Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Geographic Information Sciences","Physical and Environmental Geography","Remote Sensing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © 2014 Eric Ariel L. Salas"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/1080"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation seeks to investigate hyperspectral and waveform LiDAR datasets through a new analytical framework called Moment Distance method that uses a metric derived from the shape of the curve (spectral or waveform). In the case of hyperspectral data, the shape of the reflectance curve should unmask fine points of the spectra usually not considered by existing band-specific indices. To explore the advantages and shortcomings of this new approach, leaf and canopy hyperspectral reflectance samples were simulated using the physicallybased models PROSPECT (a leaf model) and SAIL (a canopy model). Sensitivity analysis was conducted with the goal of understanding the sensitivity of the new framework to leaf and canopy parameters relative to other existing and widely-used vegetation indices. The analysis evaluated the efficiency of the new approach to overcome, for instance, the decreased sensitivity of the NDVI at moderate to high Chl content or LAI. With waveform LiDAR data, the new approach was tested through the characterization of the canopy height without the typical step of fitting a series of Gaussian curves to the waveform to identify key peaks. Furthermore, the effects of noise and smoothing procedures on the metrics framework were assessed by introducing different types and levels of noise and various smoothing window sizes.
"]},{"key":"dc:title","label":"Title","values":["Evaluation and Application of a New Shape-sensitive Metric Useful for Characterizing Both Spectral Curves and Lidar Waveforms"]}]}],"canonical_facts":{"dc:contributor":["Geoffrey M. Henebry"],"dc:creator":["Salas, Eric Ariel L."],"dc:date.available":["2016-10-07T07:00:00Z"],"dc:description.abstract":["This dissertation seeks to investigate hyperspectral and waveform LiDAR datasets through a new analytical framework called Moment Distance method that uses a metric derived from the shape of the curve (spectral or waveform). In the case of hyperspectral data, the shape of the reflectance curve should unmask fine points of the spectra usually not considered by existing band-specific indices. To explore the advantages and shortcomings of this new approach, leaf and canopy hyperspectral reflectance samples were simulated using the physicallybased models PROSPECT (a leaf model) and SAIL (a canopy model). Sensitivity analysis was conducted with the goal of understanding the sensitivity of the new framework to leaf and canopy parameters relative to other existing and widely-used vegetation indices. The analysis evaluated the efficiency of the new approach to overcome, for instance, the decreased sensitivity of the NDVI at moderate to high Chl content or LAI. With waveform LiDAR data, the new approach was tested through the characterization of the canopy height without the typical step of fitting a series of Gaussian curves to the waveform to identify key peaks. Furthermore, the effects of noise and smoothing procedures on the metrics framework were assessed by introducing different types and levels of noise and various smoothing window sizes.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1080"],"dc:language":["en"],"dc:rights":["Copyright © 2014 Eric Ariel L. Salas"],"dc:subject":["Geographic Information Sciences","Physical and Environmental Geography","Remote Sensing"],"dc:title":["Evaluation and Application of a New Shape-sensitive Metric Useful for Characterizing Both Spectral Curves and Lidar Waveforms"],"thesis:degree_discipline":["Geospatial Science and Engineering"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:28:36Z"}