A heuristic DEM generalization by combining catchments

Abstract

Multi-scale digital elevation model (DEM) is an important content of digital terrain analysis. To generate multi-scale DEM, this study reports a heuristic DEM generalization method. The key of DEM generalization is to maintain the main topographic features and remove the minor topographic features. Our method takes catchment as generalizing object, not single grid pixel as generalizing object. In this way, DEM simplification can be achieved on the basis of maintaining the main terrain characteristics. Compared with the resample method, VIP method and low pass filter method, the proposed method can preserve the main topographic features well when the minor topographic features are deleted. Our main contribution lies in: taking terrain information rather than raw data as the generalizing object, which improves the rationality of the generalizing results; and the heuristic method proposed in this paper has good operability and high automation.

Highlights

• A heuristic DEM generalization method by combining catchments is developed;

• The proposed DEM simplification can maintain the main terrain characteristics;

• The heuristic method proposed in this paper has good operability and high automation;

Keywords:

• Digital elevation model
• DEM generalization
• combination of catchments
• valley line
• virtual modeling
• cartography
• topography
• terrain

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The original data can be downloaded from here: https://github.com/Cartography0706/DEM

Additional information

Funding

This work was supported by the National Key Research and Development Program of China under Grant 2017YFB0503601 and 2017YFB0503502; the National Natural Science Foundation of China under Grant 41671448; the Key Research and Development Program of Sichuan Province under Grant 19ZDYF0839; Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources; the National Natural Science Foundation of China under Grant 42001402; the China Postdoctoral Science Foundation under Grant 2021M692464 and 2021T140521, and the China Scholarship Council (CSC) under Grant 202006275019.