Stable terrain clusters, limited lithological discrimination: Evaluating DEM-based morphometric classification in an eroded paleo-volcanic landscape

Rizqi Prastowo; Ignatius Adi Prabowo

doi:10.58524/jograv.v1i2.193

Authors

Rizqi Prastowo Department of Mining Engineering, Faculty of Engineering and Planning, Institut Teknologi Nasional Yogyakarta, Sleman, Yogyakarta 55281, Indonesia Author
Ignatius Adi Prabowo Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia Author

DOI:

https://doi.org/10.58524/jograv.v1i2.193

Keywords:

Digital elevation model (DEM), Terrain classification, Unsupervised clustering, Lithological discrimination, Eroded volcanic landscapes

Abstract

Morphometric classification of digital elevation models (DEMs) offers a potential approach to characterizing deeply eroded volcanic landscapes where original volcanic edifices and lithological boundaries are obscured by long-term denudation. However, the extent to which statistically stable terrain clusters correspond to geological units remains insufficiently understood. This study evaluates the geomorphological and lithological significance of unsupervised terrain classification in the eroded paleovolcanic complex of the Kulon Progo Mountains, Indonesia. Eight morphometric attributes derived from the Indonesian National Digital Elevation Model (DEMNAS; approximately 8 m spatial resolution) were aggregated into 26,372 analysis cells at 120 m resolution, standardized, transformed using principal component analysis, and classified using K-means clustering. Cluster selection, reproducibility, and geological correspondence were evaluated using internal clustering indices, conventional and spatial block bootstrapping, geological agreement metrics, and comparisons with single-attribute classifications. Four terrain clusters were identified, representing relatively weakly dissected upper surfaces, deeply incised valley slopes, transitional footslopes, and low-relief alluvial plains and Sentolo hills. The classification exhibited high reproducibility under cell-based bootstrapping (adjusted Rand index, ARI = 0.968) and spatial block bootstrapping (ARI = 0.878). Nevertheless, agreement with the regional geological map was moderate (Cramér's V = 0.454) and lower than that obtained from slope-only (0.511) and relief-only (0.551) classifications. Furthermore, mapped andesite intrusions and the Kebobutak Formation exhibited weak morphometric separability (|Cliff's δ| ≤ 0.27 across individual attributes). These findings demonstrate that stable multivariate terrain partitions do not necessarily improve lithological discrimination. DEM-based morphometric clustering primarily captures landscape position and erosional dissection in this setting, highlighting the need for independent geological validation and complementary subsurface observations when interpreting terrain clusters as lithological boundaries.

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