Identification of volcanic rootless cones, ice mounds, and impact craters on Earth and Mars: Using spatial distribution as a remote sensing tool

This study aims to quantify the spatial distribution of terrestrial volcanic rootless cones and ice mounds for the purpose of identifying analogous Martian features. Using a nearest neighbor (NN) methodology, we use the statistics R (ratio of the mean NN distance to that expected from a random distribution) and c (a measure of departure from randomness). We interpret R as a measure of clustering and as a diagnostic for discriminating feature types. All terrestrial groups of rootless cones and ice mounds are clustered ( R : 0.51–0.94) relative to a random distribution. Applying this same methodology to Martian feature fields of unknown origin similarly yields R of 0.57–0.93, indicating that their spatial distributions are consistent with both ice mound or rootless cone origins, but not impact craters. Each Martian impact crater group has R ≥ 1.00 (i.e., the craters are spaced at least as far apart as expected at random). Similar degrees of clustering preclude discrimination between rootless cones and ice mounds based solely on R values. However, the distribution of pairwise NN distances in each feature field shows marked differences between these two feature types in skewness and kurtosis. Terrestrial ice mounds (skewness: 1.17–1.99, kurtosis: 0.80–4.91) tend to have more skewed and leptokurtic distributions than those of rootless cones (skewness: 0.54–1.35, kurtosis: −0.53–1.13). Thus NN analysis can be a powerful tool for distinguishing geological features such as rootless cones, ice mounds, and impact craters, particularly when degradation or modification precludes identification based on morphology alone.