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.
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Acknowledgments
The LOLA DEM data and lunar geologic map used in this study were provided by the USGS. We also thank Professor Tomasz F. Stepinski for his considerable work on the recognition approach used on Mars; his contribution significantly influenced this paper.
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Foundation: National Natural Science Foundation of China, No.41571388; National Special Basic Research Fund, No.2015FY210500
Author: Wang Jiao (1990–), PhD, specialized in planetary geomorphology and spatial analysis.
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Wang, J., Cheng, W., Zhou, C. et al. Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters. J. Geogr. Sci. 27, 1413–1427 (2017). https://doi.org/10.1007/s11442-017-1443-z
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DOI: https://doi.org/10.1007/s11442-017-1443-z