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Automated Landform Classification using SRTM DEM and Satellite Imagery in Belihuloya, Sri Lanka Cover

Automated Landform Classification using SRTM DEM and Satellite Imagery in Belihuloya, Sri Lanka

Open Access
|Jan 2026

Abstract

Belihuloya is a steep and rugged landscape characterized by pronounced elevation gradients and deeply incised valleys. The area is highly susceptible to environmental hazards, particularly landslides and erosion, due to its terrain and high rainfall. The landforms in the Belihuloya area are diverse, highlighting the importance of accurate identification and classification for effective land management and environmental risk assessment. The main objective of this study is to identify and classify landforms in the Belihuloya area using STRM DEM, which aims to gain a comprehensive understanding of the land morphology and landscape diversity of the study area. An automated GIS-based method is used to create a landform classification map using the Topographic Position Index (TPI). TPI is used at different scales for this classification. This study identified and classified the landforms of the Belihuloya area as follows: deep valleys or basins: 6.87 km² (11.18%), lower valleys: 15.97 km² (25.99%), flat or constant slopes: 19.13 km² (31.14%), mountain ranges: 14.16 km² (23.05%), and high mountain ranges or peaks: 5.31 km² (8.64%). The resulting map was validated through field verification, where four of the five GPS points showed complete agreement with the observed ground conditions. Combining different terrain maps and calculating TPI provide valuable insights into terrain morphology across multiple spatial scales. The study demonstrated that this method provides more accurate results in classifying landforms in mountainous areas compared to traditional techniques. The findings highlight the importance of considering topographical attributes in land management and spatial planning efforts that can facilitate informed decision-making and sustainable land management practices in the Belihuloya region, as well as determine the level of vulnerability to landslides.

DOI: https://doi.org/10.4038/jgs.v6i1.74 | Journal eISSN: 2792-1239
Language: English
Page range: 12 - 20
Published on: Jan 17, 2026
Published by: Faculty of Geomatics, Sabaragamuwa University of Sri Lanka
In partnership with: Paradigm Publishing Services

© 2026 D. S. Munasinghe, published by Faculty of Geomatics, Sabaragamuwa University of Sri Lanka
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.