Effect of SRTM resolution on morphometric feature identification using neural networkself organizing map
In this study, we present a semi-automatic procedure using Neural NetworksSelf Organizing Mapand 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 30m interpolated grid, corresponding to SRTM/X band 30m, 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 90m, 30m and interpolated 30m 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 30m grid as benchmark, a change detection technique was used to quantify differences in morphometric features and to assess the scale effect going from a 90m (C-band) DEM to an interpolated 30m 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 450m on the ground) over SRTM- 90m. To cover the same ground area, a moving window size of 15??15 is used for the 30m 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 90m to 30m reveals considerably more detailed information emphasizing local conditions. Comparison between results from DEM-30m as reference data set and interpolated 30m, 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 90m data, such as unrealistic features and isolated single elements in the output map. So in case of lack of high resolution data, the SRTM 90m data could be interpolated and used for further geomorphic analysis.
srtm resolution morphometric features self organizing map neural network