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Effect of SRTM resolution on morphometric feature identification using neural network—self organizing map

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Abstract

In this study, we present a semi-automatic procedure using Neural Networks—Self Organizing Map—and Shuttle Radar Topography Mission DEMs to characterize morphometric features of the landscape in the Man and Biosphere Reserve “Eastern Carpathians”. We investigate specially the effect of two resolutions, SIR-C with 3 arc seconds and X-SAR with 1 arc second for morphometric feature identification. Specifically we investigate how the SRTM/C band data with 30 m interpolated grid, corresponding to SRTM/X band 30 m, affect the morphometric characterization and topography derivatives. To reduce misregistration between the DEMs, spatial co-registration was performed and a RMSE of 0.48 pixel was achieved. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived using a bivariate quadratic approximation on 90 m, 30 m and interpolated 30 m DEMs. Self Organizing Map (SOM) is used for the classification of morphometric parameters into ten exclusive and exhaustive classes. These classes were analyzed as morphometric features such as ridge, channel, crest line and planar for all data sets based on feature space (scatter plot), morphometric signatures and 3D inspection of the area. The map quality is analyzed by oblique views with contour lines overlaid. Using the X band DEM with 30 m grid as benchmark, a change detection technique was used to quantify differences in morphometric features and to assess the scale effect going from a 90 m (C-band) DEM to an interpolated 30 m DEM. The same procedure is used to study the effect of different resolutions on morphometric features. Morphometric parameters were computed by a moving window size 5 × 5 (corresponding to 450 m on the ground) over SRTM- 90 m. To cover the same ground area, a moving window size of 15 × 15 is used for the 30 m DEM. The change analysis showed the amount of resolution dependency of morphometric features. Overall, the results showed that the introduced method is very useful for identification of morphometric features based on SRTM resolution. Decreasing the grid size from 90 m to 30 m reveals considerably more detailed information emphasizing local conditions. Comparison between results from DEM-30 m as reference data set and interpolated 30 m, showed a rate of change of 31.5% which is negligible. About 17% of this rate correspond to classes with mean slope > 10°. Of the morphometric parameters, the cross sectional curvature is most sensitive to DEM resolution. Increasing spatial resolution reduces the main constrains for morphometric analysis with SRTM 90 m data, such as unrealistic features and isolated single elements in the output map. So in case of lack of high resolution data, the SRTM 90 m data could be interpolated and used for further geomorphic analysis.

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Acknowledgement

We are grateful to the Swedish Institute for funding all travel expenses in the framework of the Visby program. We like to thank all our colleagues especially Docent Ivan Kruhlov, Department of Physical Geography, Ivan Franko University in Lvov, Ukraine and Dr. Mieczyslaw Sobik, Institute of Geography and Regional Development, University Wroclaw, Poland for interesting discussions and for providing facilities and support.

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  1. Amir Houshang Ehsani

    Present address: , P.O. Box 14185-354, Tehran, Iran

Authors and Affiliations

  1. International Research Center For Living With Desert, University of Tehran, Tehran, Iran

    Amir Houshang Ehsani

  2. Division of Environmental and Natural Resources Information Systems, Department of Civil and Architectural Engineering, Royal Institute of Technology (KTH), Stockholm, Sweden

    Friedrich Quiel

  3. International Research Center for Living with Desert, University of Tehran, P.O. Box 14185-354, Tehran, Iran

    Arash Malekian

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  1. Amir Houshang Ehsani
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Correspondence to Amir Houshang Ehsani.

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Ehsani, A.H., Quiel, F. & Malekian, A. Effect of SRTM resolution on morphometric feature identification using neural network—self organizing map. Geoinformatica 14, 405–424 (2010). https://doi.org/10.1007/s10707-009-0085-4

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