Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

Abstract

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing (ISO) cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients (K) as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.

Progress and prospects for research on Martian topographic features and typical landform identification

“…However, a limitation was misclassification for certain features, such as craters. Wang et al (2017) classified lunar landforms into highrelief, highlands, lowlands, impact craters and other landform types. Comparably, Wang et al (2017) added relief to topographic parameters and replaced Ward clustering with ISO clustering to achieve an overall accuracy of 83.34% and a kappa coefficient of up to 0.77, despite certain limitations influenced by aggradation, degradation, and complex landform types.…”

“…At present, studies on impact craters (Lei, 2017), sand dunes (Li et al, 2020), and yardangs (Liu, 2021) have established a detailed morphological index system, but index systems for describing other geomorphic types has been less studied. Most of the existing geomorphological classification studies (Bue and Stepinski, 2006;Wang et al, 2017;Deng et al, 2022;Liu et al, 2022) have divided geomorphological units into subtypes based on morphology. However, there have been few studies of descriptive indexes reflecting the formation or material composition.…”

Toward geomorphometry of plains - Country-level unsupervised classification of low-relief areas (Poland)

“…So clustering works best in searching for new patterns leading to better classification schemes. For this reason, unsupervised methods have gained some popularity primarily in the study of extraterrestrial morphometric systems (Stepinski et al, 2006;Bue and Stepinski, 2006;Stepinski et al, 2007;Dan Capitan and Van De Wiel, 2012;Wang et al, 2017) and soil science (De Bruin and Stein, 1998). The weakest point of clustering is the dependency of results on numerous algorithms, free parameters, and variable selection, which makes unsupervised methods unsuitable for creating target cartographic works (Minar and Evans, 2008).…”

Morphological differentiation characteristics and classification criteria of lunar surface relief amplitude

“…The study of lunar geomorphology is younger than that of research into the Earth's geomorphology. Wang et al (2017) proposed an automatic classification method of lunar geomorphology based on the Iterative Self Organizing Data Analysis Technique Algorithm (ISODATA) by using three topographic indices, including elevation, slope, and relief amplitude. This method mainly uses the similarity principle for clustering to achieve automatic segmentation of lunar topography units.…”