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Remote sensing, evaluation of digital elevation models (DEM), geographic information systems (GIS) and fieldwork techniques were combined to study the groundwater conditions in Eritrea. Remote sensing data were interpreted to produce lithological and lineament maps. DEM was used for lineament and geomorphologic mapping. Field studies permitted the study of structures and correlated them with lineament interpretations. Hydrogeological setting of springs and wells were investigated in the field, from well logs and pumping test data. All thematic layers were integrated and analysed in a GIS. Results show that groundwater occurrence is controlled by lithology, structures and landforms. Highest yields occur in basaltic rocks and are due to primary and secondary porosities. High yielding wells and springs are often related to large lineaments, lineament intersections and corresponding structural features. In metamorphic and igneous intrusive rocks with rugged landforms, groundwater occurs mainly in drainage channels with valley fill deposits. Zones of very good groundwater potential are characteristic for basaltic layers overlying lateritized crystalline rocks, flat topography with dense lineaments and structurally controlled drainage channels with valley fill deposits. The overall results demonstrate that the use of remote sensing and GIS provide potentially powerful tools to study groundwater resources and design a suitable exploration plan. RésuméTélédétection Resumen Se combinó el uso de sensores remotos, la evaluación de modelos de elevación digitales (MED), sistemas de información geográfico (SIG), y técnicas de trabajo de campo para estudiar las condiciones del agua subterránea Hydrogeology Journal (2006) en Eritrea. Se interpretaron los datos de sensores remotos para producir mapas de lineamientos y litológicos. Los MED se usaron para el mapeo geomorfológico y de lineamientos. Los estudios de campo permitieron estudiar las estructuras y correlacionarlas con interpretaciones de lineamientos. Se investigó el marco hidrogeológico de manantiales y pozos en el campo a partir de registros de pozos y datos de pruebas de bombeo. Todas las capas temáticas se integraron y analizaron en un SIG. Los resultados muestran que la presencia de agua subterránea es controlada por litología, estructuras, y paisajes. Los rendimientos más altos ocurren en rocas basálticas y se deben a porosidades primarias y secundarias. Los pozos con altos rendimientos frecuentemente están relacionados con lineamientos grandes, intersecciones de lineamientos y sus características estructurales correspondientes. En rocasígneas intrusivas y metamórficas con paisajes accidentados, el agua subterránea ocurre principalmente en canales de drenaje con depósitos de relleno en valles. Zonas con muy buen potencial de agua subterránea son características de capas basálticas que sobreyacen rocas cristalinas lateritizadas, topografía plana con lineamientos densos, y canales de drenaje con control estructural con depósitos de relleno de valle. Los res...

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In this study, we present a semi-automatic procedure using Neural NetworksSelf 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 Geoinformatica (2010) 14:405-424 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.

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic features is a challenging task and could provide useful information for landscape studies at different scales.In this study the 3 arc second SRTM digital elevation model was projected on a UTM grid with 90 meter spacing for a mountainous terrain at the Polish -Ukrainian border. Terrain parameters (morphometric parameters) such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 corresponding to 450 meters on the ground. These morphometric parameters are strongly related to topographic features and geomorphological processes. Such data allow us to enumerate topographic features in a way meaningful for landscape analysis.Kohonen Self Organizing Map (SOM) as an unsupervised neural network algorithm is used for classification of these morphometric parameters into 10 classes representing landforms elements such as ridge, channel, crest line, planar and valley bottom. These classes were analyzed and interpreted based on spectral signature, feature space, and 3D presentations of the area. Texture contents were enhanced by separating the 10 classes into individual maps and applying occurrence filters with 9×9 window to each map. This procedure resulted in 10 new inputs to the SOM. Again SOM was trained and a map with four dominant landforms, mountains with steep slopes, plane areas with gentle slopes, dissected ridges and lower valleys with moderate to very steep slopes and main valleys with gentle to moderate slopes was produced. Both landform maps were evaluated by superimposing contour lines.Results showed that Self Organizing Map is a very promising and efficient tool for such studies. There is a very good agreement between identified landforms and contour lines. This new procedure is encouraging and offers new possibilities in the study of both type of terrain features, general landforms and landform elements.