Automated mapping of landforms through the application of supervised classification to lidAR-derived DEMs and the identification of earthquake ruptures

ABSTRACT

The objective of this article was to apply supervised classification to accomplish automated landform mapping using four morphometric parameters. The approach was tested on high-resolution light detection and ranging (lidar) elevation data from the northern flank of the Dushanzi Anticline, western China. The morphometric parameters were calculated by applying a moving window to the lidar-derived digital elevation models (DEMs). The results obtained from using the Jeffries–Matusita distance and standard deviation ellipses for the training areas show that the main landforms in the study area can be distinguished using the four morphometric parameters. Compared with field surveying and image interpretation, the automated landform classification technique has advantages in terms of its efficiency and reproducibility, and it is capable of accurately reconstructing a detailed geomorphological map covering the study area with a classification accuracy of 72.9% and a kappa coefficient (κ) of 0.66. The geomorphological map derived using the automated classification approach revealed an obvious east–west zone composed of alluvial landforms. The close spatial relationship between this zone and mapped thrust faults indicates that this east–west zone represents a belt of seismic risks associated with the thrust faults, which should be avoided in major engineering projects. Due to its accuracy and efficacy, an automated landform classification has considerable prospects for its application in geomorphological mapping and landform characterization studies in the future, especially given the increasing availability of high-resolution digital terrain data.

Acknowledgements

We are especially grateful to two anonymous reviewers for helpful suggestions and comments that substantially improved the original manuscript. This research was supported by the National Natural Science Foundation of China (grant numbers: 41672194, 41302173).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 41302173, 41672194].