Self-organizing maps for multi-scale morphometric feature identification using shuttle radar topography mission data

Geocarto International

Volumn 24, Issue 5, 2009, Pages 335-355

  Ehsani, Amir Houshang ^a,b   Quiel, Friedrich ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Landform; Morphometric feature; Multi scale; Neural network; Self organizing map

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DIGITAL ELEVATION MODEL; LAND SURFACE; MORPHOMETRY; SATELLITE DATA; SHUTTLE RADAR TOPOGRAPHY MISSION; TERRAIN; THEMATIC MAPPING;

EID: 70349567236     PISSN: 10106049     EISSN: None     Source Type: Journal    
DOI: 10.1080/10106040802642577     Document Type: Article

References (60)

1. Adediran, A.O., et al., 2004. Computer-assisted discrimination of morphological units on north-central Crete (Greece) by applying multivariate statistics to local relief gradients. Geomorphology, 58, 357-370.
2. Bacao, F., Lobo, V., and Painho, M., 2005. The self-organizing map, the Geo-SOM, and relevant variants for geosciences. Computers & Geosciences, 31, 155-163.
3. Blumberg, D.G., 2006. Analysis of large Aeolian (wind-blown) bedforms using the Shuttle Radar Topography Mission (SRTM) digital elevation data. Remote Sensing of Environment, 100, 179-189.
4. Brown, D.G., Lusch, D.P., and Duda, K.A., 1998. Supervised classification of types of glaciated landscapes using digital elevation data. Geomorphology, 21 (3-4), 233-250.
5. Bue, B.D. and Stepinski, T.F., 2006. Automated classification of landforms on Mars. Computers & Geosciences, 32, 604-661.
6. Burnett, C. and Blaschke, T., 2003. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168, 233-249.
7. Burrough, P.A., van Gaans, P.F.M., and MacMillan, R.A., 2000. High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems, 113, 37-52.
8. Crevenna, A.B., et al., 2005. Geomorphometric analysis for characterizing landforms in Morelos State, Mexico. Geomorphology, 67 (3-4), 407-422.
9. Dehn, M., Gartner, H., and Dikau, R., 2001. Principles of semantic modeling of landform structures. Computers & Geosciences, 27, 1005-1010.
10. Deng, Y., Wilson, J.P., and Bauer, B.O., 2007a. DEM resolution dependencies of terrain attributes across a landscape. International Journal of Geographical Information Science, 21, 187-213.
11. Deng, Y., Wilson, J.P., and Gallant, J.C., 2007b. Part V: Spatial analysis. In: J.P. Wilson and A.S. Fotheringham, eds. Terrain analysis. The handbook of geographic information science. USA: Blackwell.
12. Dikau, R., 1989. The application of a digital relief model to landform analysis in geomorphology. In: J. Raper, ed. Three dimensional applications in geographical information systems. London: Taylor & Francis, 51-77.
13. Dragut, L. and Blaschke, T., 2006. Automated classification of landform elements using object-based image analysis. Geomorphology, 81 (3-4), 330-344.
14. Ehsani, A.H. and Quiel, F., 2008. Geomorphometric feature analysis using morphometric parameterization and artificial neural networks. Geomorphology, 99 (1-4), 1-12,
15. Evans, I.S., 2003. Scale specific landforms and aspects of the land surface. In: I.S. Evans et al., Concept sand modeling in geomorphology: international perspectives,. Tokyo, Japan: Terrapub, 61-84.
16. Evans, I.S., 1972. General geomorphology, derivatives of altitude and descriptive statistics. In: R.J. Chorley, ed. Spatial analysis in geomorphology. London: Methuen & Co. Ltd, 17-90.
17. Falorni, G., et al., 2005. Analysis and characterization of the vertical accuracy of digital elevation models from the shuttle radar topography mission. Journal of Geophysical Research, 110, F0205.
18. Fisher, P., Wood, J., and Cheng, T., 2004. Where is Helvellyn? Fuzziness of multiscale landscape morphometry. Transactions of the Institute of British Geographers, 29, 106-128.
19. Gallant, J.C. and Dowling, T.I., 2003. A multiresolution index of valley bottom flatness form mapping depositional areas. Water Resources Research, 39, 1347-1359.
20. Giles, P.T., 1998. Geomorphological signatures: classification of aggregated slope unit objects from digital elevation and remote sensing data. Earth Surface Processes and Landforms, 23, 581-594.
21. Giles, P.T. and Franklin, S.E., 1998. An automated approach to the classification of the slope units using digital data. Geomorphology, 21 (3-4), 251-264.
22. GRASS Development Team, 2005. Geographic Resources Analysis Support System (GRASS). GNU General Public License. Available from: http://grass.itc.it. [Accessed March 2007].
23. Grodzinska, K., et al., 2004. Vegetation of the selected forest stands and land use in the Carpathian Mountains. Environmental Pollution, 130, 17-32.
24. Grohmann, C.H., Riccomini, C., and Alves, F.M., 2006. SRTM-based morphotectonic analysis of the Pocos de Caldas Alkaline Massif, southeastern Brazil. Computers & Geosciences, 33, 10-19.
25. Guth, P.L., 2006. Geomorphometry from SRTM: comparison to NED. Photogrammetric Engineering and Remote Sensing, 72, 269-278.
26. Hammond, E.H., 1964. Analysis of properties in landform geography: an application to broadscale landform mapping. Annals of Association of American Geographers, 54, 11-19.
27. Hengl, T. and Rossiter, D.G., 2003. Supervised landform classification to enhance and replace photo-interpretation in semi-detailed soil survey. Soil Science Society of America Journal, 67, 1810-1822.
28. Huisenga, J., 2005. Impact of SRTM data on geospatial support to US Army In: operations. Abstracts Volume, The Shuttle Radar Topography Mission Data Validation and Applications Workshop. Reston, VA: Abstract, 43.
29. Irvin, B.J., Ventura, S.J., and Slater, B.K., 1997. Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin. Geoderma, 77 (2-4), 137- 154.
30. Iwahashi, J. and Pike, R.J., 2007. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, 86, 409-440.
31. Jarvis, A., et al., 2006. Hole-filled SRTM for the globe Version 3. Available from the CGIAR-CSI SRTM 90m Database: http://srtm.csi.cgiar.org [Accessed December 2006].
32. Jianwen, M. and Bagan, H., 2005. Land-use classification using ASTER data and self-organized neutral networks. International Journal of Applied Earth Observation and Geoinformation, 7, 183-188.
33. Kellndorfer, J., et al., 2004. Vegetation height estimation from shuttle radar topography mission and national elevation datasets. Remote Sensing of Environment, 93, 339-358.
34. Kienzle, S., 2004. The effect of DEM raster resolution on first order, second order and compound terrain derivatives. Transactions in GIS, 8, 83-112.
35. Kohonen, T., 2001. Self organizing maps. New York: Springer, 160-161.
36. Kuemmerle, T., et al., 2006. Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique. Remote Sensing of Environment, 103 (4), 449-464.
37. Li, Z. and Eastman, J.R., 2006. The nature and classification of unlabelled neurons in the use of Kohonen's self-organizing map for supervised classification. Transactions in GIS, 10, 599-613.
38. Ludwig, R. and Schneider, P., 2006. Validation of digital elevation models from SRTM X-SAR for applications in hydrologic modeling. ISPRS Journal of Photogrammetry and Remote Sensing, 60, 339-358.
39. Luoto, M. and Hjort, J., 2006. Scale matters-A multi-resolution study of the determinants of patterned ground activity in subarctic Finland. Geomorphology, 80 (3-4), 282- 294.
40. Miliaresis, G.C. and Argialas, D.P., 1999. Segmentation of physiographic features from the global digital elevation model/GTOPO30. Computers & Geosciences, 25, 715-728.
41. Miliaresis, G.C., 2001. Geomorphometric mapping of Zagros Ranges at regional scale. Computers & Geosciences, 27, 775-786.
42. Pike, R.J., 2000. Geomorphology: diversity in quantitative surface analysis. Progress in Physical Geography, 24, 1-20.
43. Prima, O.D.A., et al., 2006. Supervised landform classification of Northeast Honshu from DEM-derived thematic maps. Geomorphology, 78 (3-4), 373-386.
44. Rabus, B., et al., 2003. The shuttle radar topography mission - a new class of digital elevation models acquired by spaceborne radar. ISPRS Journal of Photogrammetry and Remote Sensing, 57, 241-262.
45. Richardson, A.J., Risien, C., and Shillington, F.A., 2003. Using self-organizing maps to identify patterns in satellite imagery. Progress In Oceanography, 59, 223-239.
46. Schaale, M. and Furrer, R., 1995. Land surface classification by neural networks. International Journal of Remote Sensing, 16, 3003-3031.
47. Shary, P.A., Sharaya, S.L., and Mitusov, A.V., 2002. Fundamental quantitative methods of land surface analysis. Geoderma, 107 (1-2), 1-32.
48. Stepinski, T.F. and Collier, M.L., 2004. Extraction of Martian valley networks from digital topography. Journal of Geophysical Research, 109, E11005.
49. Stepinski, T.F. and Vilalta, R., 2005. Digital topography models for Martian surfaces. IEEE Geoscience and Remote Sensing Letters, 2, 260-264.
50. Sueli, A.M. and Lima, J.O., 2006. Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research, 174, 1742-1759.
51. Tate, N. and Wood, J., 2001. Fractals and scale dependencies in topography. In: N. Tate and P. Atkinson, eds. Modelling scale in geographical information science. Chichester: Wiley, 35-51.
52. Vesanto, J. and Alhoniemi, E., 2000. Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks, 11, 586-600.
53. Vesanto, J., et al., 2000. SOM toolbox for Matlab 5. SOM toolbox team, Finland. Helsinki University of Technology, Laboratory of Computer and Information Science. Available from: http://www.cis.hut.fi/projects/somtoolbox.
54. Walsh, S.J., Butler, D.R., and Malanson, G., 1998. An overview of scale, pattern, process relationships in geomorphology: a remote sensing and GIS perspective. Geomorphology, 21 (3-4), 183-205.
55. Wilson, J.P. and Gallant, J.C. eds. 2000. Terrain analysis: principles and applications. New York: Wiley 479.
56. Wood, J., 1996a. The geomorphological characterization of digital elevation models. Ph.D. Thesis. Department of Geography, University of Leicester: UK, 112-124.
57. Wood, J., 1996b. Scale-based characterisation of digital elevation models. In: D. Parker, ed. Innovations in GIS 3: selected papers from the third national conference on GIS Research UK (GISUK). London: Taylor & Francis, 163-175.
58. Wood, J., 1999. Visualization of scale dependencies in the surface models. 19th International Cartographic Association Annual Conference. Proceedings, Ottawa: Available from: http://www.soi.city.ac.uk/~jwo/ica99 [Accessed December 2006].
59. Wood, J., 2002. Visualizing the structure and scale dependency of landscapes. In: P. Fisher and D. Unwin, eds. Virtual reality in geography. London: Taylor & Francis, 163-174.
60. Wright, R., et al., 2006. An assessment of shuttle radar topography mission digital elevation data for studies of volcano morphology. Remote Sensing of Environment, 105, 41-53.