Small-volume monogenetic igneous landforms and edifices statistics (SMILES) of Earth and Mars: one morphometric framework to classify them all

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.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Geosciences (UMKC)

Grantor

University of Missouri-Kansas City

Year dc:date.issued

2025

Author dc:creator

• Nolan, Joseph Ashton

Advisor dc:contributor.advisor

• Graettinger, Alison H.

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/10355/110234

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/110234