{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/110234"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/110234","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Small-volume monogenetic igneous landforms and edifices statistics (SMILES) of Earth and Mars: one morphometric framework to classify them all","abstract":"Small-volume (<1 km3) monogenetic volcanoes are the most abundant volcanic landforms on Earth and Mars. The remote classification of these volcanoes remained limited in scope and number due to methodological limitations. This dissertation develops, validates, and applies a dimensionless morphometric framework, known as SMILES (Small-volume Monogenetic Igneous Landform and Edifice Statistics), to improve the classification of these landforms on Earth and to evaluate the transferability of the SMILES method from terrestrial targets to planetary contexts, specifically Mars. By emphasizing dimensionless parameters, the database provides a standardized method to distinguish scoria cones, spatter landforms, and related landforms. SMILES improves the classification of volcanic landforms by distinguishing them from morphologically similar features of non-volcanic origin, such as mud volcanoes.The reliability of terrestrial Digital Elevation Models (DEMs) derived from uncrewed aerial system (UAS) photogrammetry was evaluated by an iterative approach and by comparing these DEM to LiDAR datasets. UAS DEMs are reasonably accurate and reproducible for morphometric analysis. These results reinforce the usage of terrestrial DEMs and further validate the SMILES methodology, while also identifying the limitations of photogrammetry when working with small landforms. The validated SMILES framework was applied to Martian volcanic landforms using Digital Terrian Models created from the High-Resolution Imaging Science Experiment and Context Camera imagery. The SMILES framework successfully classifies Martian scoria cones and spatter landforms from each other and non-volcanic features. The results demonstrate the utility of the SMILES framework for planetary classification. Additionally, the results infer that eruptive style is the primary control on morphology, while gravity and atmosphere function as secondary modifiers. Future studies can adapt this method and apply it to additional targets.","abstract_html":"Small-volume (&lt;1 km3) monogenetic volcanoes are the most abundant volcanic landforms on Earth and Mars. The remote classification of these volcanoes remained limited in scope and number due to methodological limitations. This dissertation develops, validates, and applies a dimensionless morphometric framework, known as SMILES (Small-volume Monogenetic Igneous Landform and Edifice Statistics), to improve the classification of these landforms on Earth and to evaluate the transferability of the SMILES method from terrestrial targets to planetary contexts, specifically Mars. By emphasizing dimensionless parameters, the database provides a standardized method to distinguish scoria cones, spatter landforms, and related landforms. SMILES improves the classification of volcanic landforms by distinguishing them from morphologically similar features of non-volcanic origin, such as mud volcanoes.The reliability of terrestrial Digital Elevation Models (DEMs) derived from uncrewed aerial system (UAS) photogrammetry was evaluated by an iterative approach and by comparing these DEM to LiDAR datasets. UAS DEMs are reasonably accurate and reproducible for morphometric analysis. These results reinforce the usage of terrestrial DEMs and further validate the SMILES methodology, while also identifying the limitations of photogrammetry when working with small landforms. The validated SMILES framework was applied to Martian volcanic landforms using Digital Terrian Models created from the High-Resolution Imaging Science Experiment and Context Camera imagery. The SMILES framework successfully classifies Martian scoria cones and spatter landforms from each other and non-volcanic features. The results demonstrate the utility of the SMILES framework for planetary classification. Additionally, the results infer that eruptive style is the primary control on morphology, while gravity and atmosphere function as secondary modifiers. Future studies can adapt this method and apply it to additional targets.","abstract_has_math":false,"creators":["Nolan, Joseph Ashton"],"institution":"University of Missouri-Kansas City","degree_name":"Ph.D. (Doctor of Philosophy)","degree_level":"Doctoral","degree_discipline":"Geosciences (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Graettinger, Alison H."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T05:17:53Z","subjects":[],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/110234","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Graettinger, Alison H."]},{"key":"dc:creator","label":"Author","values":["Nolan, Joseph Ashton"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-16T23:07:38Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-16T23:07:38Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geosciences (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. (Doctor of Philosophy)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri-Kansas City"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/110234"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page viewed January 22, 2026","Dissertation advisor: Alison H. Graettinger","Vita","Includes bibliographical references (pages 152-177)","Dissertation (Ph.D.)--Department of Earth and Environmental Sciences and Division of Energy, Matter, and Systems. University of Missouri--Kansas City, 2025"]},{"key":"dc:description.abstract","label":"Abstract","values":["Small-volume (<1 km3) monogenetic volcanoes are the most abundant volcanic landforms on Earth and Mars. The remote classification of these volcanoes remained limited in scope and number due to methodological limitations. This dissertation develops, validates, and applies a dimensionless morphometric framework, known as SMILES (Small-volume Monogenetic Igneous Landform and Edifice Statistics), to improve the classification of these landforms on Earth and to evaluate the transferability of the SMILES method from terrestrial targets to planetary contexts, specifically Mars. By emphasizing dimensionless parameters, the database provides a standardized method to distinguish scoria cones, spatter landforms, and related landforms. SMILES improves the classification of volcanic landforms by distinguishing them from morphologically similar features of non-volcanic origin, such as mud volcanoes.The reliability of terrestrial Digital Elevation Models (DEMs) derived from uncrewed aerial system (UAS) photogrammetry was evaluated by an iterative approach and by comparing these DEM to LiDAR datasets. UAS DEMs are reasonably accurate and reproducible for morphometric analysis. These results reinforce the usage of terrestrial DEMs and further validate the SMILES methodology, while also identifying the limitations of photogrammetry when working with small landforms. The validated SMILES framework was applied to Martian volcanic landforms using Digital Terrian Models created from the High-Resolution Imaging Science Experiment and Context Camera imagery. The SMILES framework successfully classifies Martian scoria cones and spatter landforms from each other and non-volcanic features. The results demonstrate the utility of the SMILES framework for planetary classification. Additionally, the results infer that eruptive style is the primary control on morphology, while gravity and atmosphere function as secondary modifiers. Future studies can adapt this method and apply it to additional targets."]},{"key":"dc:title","label":"Title","values":["Small-volume monogenetic igneous landforms and edifices statistics (SMILES) of Earth and Mars: one morphometric framework to classify them all"]}]}],"canonical_facts":{"dc:contributor.advisor":["Graettinger, Alison H."],"dc:creator":["Nolan, Joseph Ashton"],"dc:date.accessioned":["2026-01-16T23:07:38Z"],"dc:date.available":["2026-01-16T23:07:38Z"],"dc:date.issued":["2025"],"dc:description":["Title from PDF of title page viewed January 22, 2026","Dissertation advisor: Alison H. Graettinger","Vita","Includes bibliographical references (pages 152-177)","Dissertation (Ph.D.)--Department of Earth and Environmental Sciences and Division of Energy, Matter, and Systems. University of Missouri--Kansas City, 2025"],"dc:description.abstract":["Small-volume (<1 km3) monogenetic volcanoes are the most abundant volcanic landforms on Earth and Mars. The remote classification of these volcanoes remained limited in scope and number due to methodological limitations. This dissertation develops, validates, and applies a dimensionless morphometric framework, known as SMILES (Small-volume Monogenetic Igneous Landform and Edifice Statistics), to improve the classification of these landforms on Earth and to evaluate the transferability of the SMILES method from terrestrial targets to planetary contexts, specifically Mars. By emphasizing dimensionless parameters, the database provides a standardized method to distinguish scoria cones, spatter landforms, and related landforms. SMILES improves the classification of volcanic landforms by distinguishing them from morphologically similar features of non-volcanic origin, such as mud volcanoes.The reliability of terrestrial Digital Elevation Models (DEMs) derived from uncrewed aerial system (UAS) photogrammetry was evaluated by an iterative approach and by comparing these DEM to LiDAR datasets. UAS DEMs are reasonably accurate and reproducible for morphometric analysis. These results reinforce the usage of terrestrial DEMs and further validate the SMILES methodology, while also identifying the limitations of photogrammetry when working with small landforms. The validated SMILES framework was applied to Martian volcanic landforms using Digital Terrian Models created from the High-Resolution Imaging Science Experiment and Context Camera imagery. The SMILES framework successfully classifies Martian scoria cones and spatter landforms from each other and non-volcanic features. The results demonstrate the utility of the SMILES framework for planetary classification. Additionally, the results infer that eruptive style is the primary control on morphology, while gravity and atmosphere function as secondary modifiers. Future studies can adapt this method and apply it to additional targets."],"dc:identifier.uri":["https://hdl.handle.net/10355/110234"],"dc:language.iso":["en_US"],"dc:title":["Small-volume monogenetic igneous landforms and edifices statistics (SMILES) of Earth and Mars: one morphometric framework to classify them all"],"dc:type":["Thesis"],"thesis:degree_discipline":["Geosciences (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. (Doctor of Philosophy)"],"thesis:institution_name":["University of Missouri-Kansas City"]},"updated_at":"2026-07-24T05:17:53Z"}