Small-volume monogenetic igneous landforms and edifices statistics (SMILES): A catalog of representative mafic volcanic landforms to enable quantitative remote identification

Abstract

Accurate classification of terrestrial and non-terrestrial volcanic landforms requires a robust suite of morphometric parameters. The Small-volume Monogenetic Igneous Landforms and Edifices Statistics (SMILES) catalog contains the morphometric characterizations of mafic small-volume volcanic landforms and was created using uncrewed aerial system photogrammetry, open-source LiDAR, and digital elevation model repositories. This study analyzed 20 simple maars, 22 lava collapse features, 24 ring scoria cones, and 24 spatter landforms (fissure and point source spatter ramparts), using high-resolution (<0.1–5 m/pixel) digital elevation models to establish what dimensionless morphometric parameters enable remote identification of the studied landforms. Parameters include isoperimetric circularity, depth ratio (crater depth/major chord), interior slope angles, as well as crater to base ratios for the area, perimeter, and major chord lengths. Landforms were limited to a basal width of <2 km and <1 km3 for scoria cones and spatter landforms, and a major chord of 2 km or less for lava collapse features and maars. Simple maars have an aspect ratio (AR) (>0.74), isoperimetric circularity (IC) (>0.90), interior slope angle (<47°), and depth ratio (<0.26) creating a distinct range of morphometric parameters. Lava collapse features exhibit wider variability in AR (0.26–0.95), IC (0.46–0.98), interior slope angle (up to 16–86°), and depth ratio (0.25–0.52). Scoria cone craters have a distinct range of AR (>0.54), IC (>0.81), interior slope angle (<34°), and lower depth ratio (<0.25). Spatter landforms have a wider range of variability in AR (0.25–0.94), IC (0.43–0.98), interior slope angle (<63°), and depth ratio (0.04–0.37). Scoria cones have lower crater/base area ratios and lower crater/base perimeter ratios than spatter landforms. This study demonstrates that while an individual parameter is not diagnostic for recognizing small-volume mafic volcanic landforms remotely, a suite of parameters is. The SMILES catalog demonstrates the value of evaluating populations of similar landforms using higher-resolution datasets to establish diagnostic suites of dimensionless parameters, to enable accurate and positive remote identification of volcanic landforms. The technique used in this study can be applied to other volcanic and non-volcanic landforms on Earth, as well as non-terrestrial targets.