Sutanto Trijuni Putro, Nucifera Fitria


Landform is an essential aspect for environmental and disaster studies. Automated landform classification has been developed due to the importance role of landrom for many studies. Automated landform classification can be applied for general purposes.

TPI (Topographic Position Index) is one of automated landform classification method. TPI measures the difference between center elevation and mean elevation in its surroundings within certain radius. This study used SRTM data with 90 meters resolution and ASTER GDEM data with 30 meters resolution for the south part of Yogyakarta. Data processing is conducted by using SAGA GIS. The research documented here aims to clarify how TPI support the landform classification thus for practical use can be utilized effectively for analysis any aspect related to landform classification.

Generally, automated landform classification for two datas results the same spatial pattern. Study area is mostly classified as plains. But feature number of landform in ASTER GDEM data is larger than STRM data. Because ASTER GDEM data has higher spatial resolution so that the result is more detail. Based on Tobler`s Law, ASTER GDEM work best for 1:50.000 scale, while SRTM fit for 1:180.000 scale.


classification, landform, topographic position index

Full Text:




Bishop, M. P., James, L. A., Jr., J. F., & Walsh, S. J. (2012). Geospatial Technologies and Digital Geomorphological Mapping: Concepts, Issues and Research. Geomorphology, 5(26), 5-26.

Gallant, J., & Wilson, J. (2000). Primary topographic attributes. i J. Wilson, & J. Gallant, Terrain Analysis: Principles and Applications (ss. 51-85). New York: Wiley.

Hugget, R. J. (2007). Fundamentals of Geomorphology. Second Edition. New York: Routledge.

Józsa, E., & Fábián, S. Á. (2016). Landforms And Geomorphological Landscapes Of Hungary Using Gis Techniques. Studia Geomorphologica Carpatho-Balcanica, 19-31.

K. G. Nikolakopoulos; E. K. Kamaratakis; N. Chrysoulakis. (2006). International Journal of Remote Sensing, 27(21), 4819-4838.

Piloyan, A., & Konečný, M. (2017). Semi-Automated Classification Of Landform Elements In Armenia Based On Srtm Dem Using K-Means Unsupervised Classification. Quaestiones Geographicae, 36(1), 93-103.

Reu, J. D., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., Smedt, P. D., Crombé, P. (2013). Application Of The Topographic Position Index To Heterogeneous Landscapes. Geomorphology, 39-49.

Samodra, G., Chen, G., Sartohadi, J., Hadmoko, D. S., & Kasama, K. (2014). Automated Landform Classification in a Rockfall-prone Area, Gunung Kelir, Java. Earth Surface Dynamics, 2(1), 239-348.

Seif, A. (2014). Topography Position Index for Landform Classification. Case study: Grain Mountain. Bulletin of Environment, Pharmacology and Life Sciences, 33-39.

Straumann, R. (2010). Extraction And Characterisation Of Landforms From Digital Elevation Models: Fiat Parsing The Elevation Field (Dissertation uppl.). Zürich: Universität Zürich.

Tobler, Waldo. 1987. “Measuring Spatial Resolution”, Proceedings, Land Resources Information Systems Conference, Beijing, pp. 12-16.

Weiss, A. (2001). Topographic position and landforms analysis. ESRI Users Conference. San Diego, CA: ESRI.


  • There are currently no refbacks.