Peak extraction and classification from digital elevation models based on the relationship between morphological characteristics and spatial position

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis, digital geomorphological mapping, and other fields. Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area. This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines: convexity mean index (CM-index), convexity standard deviation (CSD-index), and convexity imbalance index (CIB-index). We develop computation methods to extract peaks from digital elevation model (DEM). Subsequently, the initial peaks extracted by neighborhood statistics are classified using the proposed indices. The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China. An ASTER Global DEM (ASTGTM2 DEM) with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions. DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau. The mountain peak extraction and classification results obtained from the different resolution DEM are compared. The experimental results show that: (1) The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices. (2) The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak (SR-Peak) and multiple ridge intersection peak (MRI-Peak). The visual inspection results show that the classification accuracy in the different study areas exceeds 75%. (3) The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.