Two different methods of Bayesian segmentation algorithm were used with different band combinations. Sequential Maximum a Posteriori (SMAP) is a Bayesian image segmentation algorithm which unlike the traditional Maximum likelihood (ML) classification attempts to improve accuracy by taking contextual information into account, rather than classifying pixels separately. Landsat 7 ETM+ data with Path/Row 18626, dated 30 September 2000 for a mountainous terrain at the Polish - Ukrainian border is acquired. In order to study the role of thermal band with these methods, two data sets with and without the thermal band were used. Nine band combinations including ETM+ and Principal Component (PC) data were selected based on the highest value of Optimum Index Factor (OIF). Using visual and digital analysis, field observation data and auxiliary map data like CORINE land cover, 14 land cover classes are identified. Spectral signatures were derived for every land cover. Spectral signatures as well as feature space analysis were used for detailed analysis of efficiency of the reflective and thermal bands. The result shows that SMAP as the superior method can improve Kappa values compared with ML algorithm for all band combinations with on average 17%. Using all 7 bands both SMAP and ML classifications algorithm achieved the highest Kappa accuracy of 80.37% and 64.36% respectively. Eliminating the thermal band decreased the Kappa values by about 8% for both algorithms. The band combination including PC1, 2, 3, and 4 (PCA calculated for all 7 bands) produced the same Kappa as bands 3, 4, 5 and 6. The Kappa value for band combination 3, 4, 5 and 6 was also about 4% higher than using 6 bands without the thermal band for both algorithms. Contextual classification algorithm like SMAP can significantly improve classification results. The thermal band bears complementary information to other spectral bands and despite the lower spatial resolution improves classification accuracy.

Over than three_ forth of lands in Iran are located in arid or semi arid environment. There are 60 playa in Iran which covers about 67250 square kilometers. Major part of human settlements in such a areas are subjected to the problems such as desertification, sand blowing, salinization, vegetation deterioration and erosion. Sustainable management of such lands especially for areas which are near the playa due to inaccessibility and limitations in resources is a challenging task which is need up-to-data and precise information for land covers situations. In this context, remotely sensed data, especially because of clear sky, shortage of rainfall and thus low moisture contents, has been showed very promising and valuable tools in arid lands. In most of studies, due to lower resolution (60 m) of thermal bands comparing to other SWIR and VIR bands (30 m), this band, despite of bearing valuable information are being eliminated from processing and classifications. In this research, the data of Landsat 7 (ETM+) Path/Row 163-35, dated 20 July 2000 acquired. Using Visual-Digital Analysis (VDA) such as color composite, feature space and spectral signature analysis, homogeneous classes are defined. Then based on field works, auxiliary data and GPS points, 19 classes were defined. In order to study the role of thermal bands and contribution to accuracy improvement, four combination bands feed to maximum likelihood classifiers. The result showed, for such a studies in arid lands, thermal bands despite of lower resolution, is essential which including valuable information and could improve accuracy.

This thesis presents a semi-automatic method to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as an unsupervised Artificial Neural Network algorithm in two completely different environments: 1) the Man and Biosphere Reserve “Eastern Carpathians” (Central Europe) as a complex mountainous humid area and 2) Lut Desert, Iran, a hyper arid region characterized by repetition of wind-eroded features.

In 2003, the National Aeronautics and Space Administration (NASA) released the SRTM/ SIR-C band data with 3 arc seconds (approx. 90 m resolution) grid for approximately 80 % of Earth’s land surface. The X-band SRTM data were processed with a 1 arc second (approx. 30 m resolution) grid by the German space agency, DLR and the Italian space agency ASI, but due to the smaller X-SAR ground swath, large areas are not covered. The latest version 3.0 SRTM/C DEM and SRTM/X band DEM were re-projected to 90 and 30 m UTM grid and used to generate morphometric parameters of first order (slope) and second order (cross-sectional curvature, maximum curvatures and minimum curvature) by using a bivariate quadratic surface. The morphometric parameters are then used in a SOM to identify morphometric features (or landform elements) e.g. planar, channel, ridge in mountainous areas or yardangs (ridge) and corridors (valley) in hyper-arid areas.

Geomorphic phenomena and features are scale-dependent and the characteristics of features vary when measured over different spatial extents or different spatial resolution. Morphometric parameters were derived for nine window sizes of the 90 m DEM ranging from 5 × 5 to 55 ×55. Analysis of the SOM output represents landform entities with ground areas from 450 m to 4950 m that is local to regional scale features. Effect of two SRTM resolutions, C and X bands is studied on morphometric feature identification. The difference change analysis revealed the quantity of resolution dependency of morphometric features. Increasing the DEM spatial resolution from 90 to 30 m (corresponding to X band) by interpolation resulted in a significant improvement of terrain derivatives and morphometric feature identification.

Integration of morphometric parameters with climate data (e.g. Sum of active temperature above 10 ° C) in SOM resulted in delineation of morphologically homogenous discrete geo-ecological units. These units were reclassified to produce a Potential Natural Vegetation map. Finally, we combined morphometric parameters and remotely sensed spectral data from Landsat ETM+ to identify and characterize landscape elements. The single integrated data set of geo-ecosystems shows the spatial distribution of geomorphic, climatic and biotic/cultural properties in the Eastern Carpathians.

The results demonstrate that a SOM is a very efficient tool to analyze geo-morphometric features under diverse environmental conditions and at different scales and resolution. Finer resolution and decreasing window size reveals information that is more detailed while increasing window size and coarser resolution emphasizes more regional patterns. It was also successfully applied to integrate climatic, morphometric parameters and Landsat ETM+ data for landscape analysis. Despite the stochastic nature of SOM, the results are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible with consistent results.

In this paper, we demonstrate artificial neural networks-self-organizing map (SOM)-as a semi-automatic method for extraction and analysis of landscape elements in the man and biosphere reserve "Eastern Carpathians". The Shuttle Radar Topography Mission (SRTM) collected data to produce generally available digital elevation models (DEM). Together with Landsat Thematic Mapper data, this provides a unique, consistent and nearly worldwide data set.

To integrate the DEM with Landsat data, it was re-projected from geographic coordinates to UTM with 28.5 m spatial resolution using cubic convolution interpolation. To provide quantitative morphometric parameters, first-order (slope) and second-order derivatives of the DEM-minimum curvature, maximum curvature and cross-sectional curvature-were calculated by fitting a bivariate quadratic surface with a window size of 9 x 9 pixels. These surface curvatures are strongly related to landform features and geomorphological processes.

Four morphometric parameters and seven Landsat-enhanced thematic mapper (ETM +) bands were used as input for the SOM algorithm. Once the network weights have been randomly initialized, different learning parameter sets, e.g. initial radius, final radius and number of iterations, were investigated. An optimal SOM with 20 classes using 1000 iterations and a final neighborhood radius of 0.05 provided a low average quantization error of 0.3394 and was used for further analysis. The effect of randomization of initial weights for optimal SOM was also studied. Feature space analysis, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of landfonn, based on morphometric analysis, and land use, based on spectral properties.

Results were displayed as thematic map of landscape elements according to form, cover and slope. Spectral and morphometric signature analysis with corresponding zoom samples superimposed by contour lines were compared in detail to clarify the role of morphometric parameters to separate landscape elements. The results revealed the efficiency of SOM to integrate SRTM and Landsat data in landscape analysis. Despite the stochastic nature of SOM, the results in this particular study are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible for the same application with consistent results.

Yardangs, an exclusive landform due to intensive wind erosion, cover a large area in the hyper-arid Lut desert of Iran. This paper presents a new approach using Self Organizing Map (SOM) as unsupervised algorithm of artificial neural networks for analysis and characterization of yardangs.

Nowadays, the Shuttle Radar Topography Mission (SRTM) with 3 arc sec data (approximately 90 m resolution) and nearly world wide coverage provides uniform good quality data.

The SRTM 3 arc sec data were re-projected to a 90 m UTM grid. Bivariate quadratic surfaces with moving window size of 5 x 5 were fitted to this DEM. The first derivative, slope steepness and the second derivatives minimum, maximum curvature and cross-sectional curvatures were calculated as geomorphometric parameters used as input to the SOMs. 42 SOMs with different learning parameter settings, e.g. initial and final radius, number of iterations, and the effect of random initial weights on average quantization error were investigated. A SOM with a low average quantization error (0.1040) was used for further analysis. Feature space analysis, morphometric signatures, three-dimensional inspection, auxiliary data like Landsat ETM+ and high resolution satellite imagery from QuickBird facilitated the assignment of semantic meaning to the output classes in terms of geomorphometric features. Results are provided in a geographic information system as thematic maps of landform entities based on form and slope, e.g. yardangs (ridge), corridors (valley) or planar areas.

The results showed that all yardangs and corridors were clearly recognized and classified by this method when their width was larger than the DEM resolution but became unrecognizable if their width is much smaller than the grid resolution. The identified yardangs and corridors are aligned NNW-SSE parallel to the prevailing direction of the strong local 120 days wind and cover about 31% and 42% of the study area respectively. The results demonstrate that SOM is a very efficient tool for analyzing aeolian landforms in hyper-arid environments that provides very useful information for terrain feature analysis in remote regions.

In this paper we propose a semi-automatic method for landscape analysis with both spectral and morphometric constituents. SRTM data are used to calculate first derivatives (slope) and second derivatives of elevation such as minimum curvature, maximum curvatures and cross-sectional curvature by fitting a bivariate quadratic surface with a window size 9 by 9. Together with multi-spectral remote sensing data like Landsat 7 ETM+ with 28.5 meter raster elements, these data provide comprehensive information for the analysis of the landscape in the study area. Unsupervised neural network algorithm –Self Organizing map- divided all input vectors into inclusive and exhaustive classes on the basis of similarity between attribute vectors. Morphometric analysis, spectral signature analysis, feature space analysis are used to assign semantic meaning to the classes as landscape elements according to form, cover and slope e.g. deciduous forest on ridge (convex landform) with steep slopes.

The Shuttle Radar Topography Mission (SRTM) was launched on 11 February 2000 and 3 arc second data were publicly released in July 2004. Easy availability of SRTM 3 arc second data, covering almost 80% of the land surface on earth, has resulted in great advances in morphometric studies and numerical description of landscape features.

In this study we introduce a new procedure using Neural Network - Self Organizing Map - to characterize morphometric features of landscapes.. We also investigate the effect of two resolutions for morphometric feature identification. Specifically we investigate how the SRTM 3arc second latitude / longitude data projected to UTM coordinates with 90 meter respectively 28.5 m grid, corresponding to Landsat TM data resolution, affect the morphometric characterization. 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 5x5 for the 90 m data (450 m on the ground) and 9x9 for the 28.5 m data (about 250 m).

Kohonen Self Organizing Map as an unsupervised neural network algorithm is employed for the classification of these morphometric parameters into 10 exclusive and exhaustive classes. These classes were analyzed and interpreted as morphometric features such as ridge, channel, crest line, planar and valley bottom for both data sets based on morphometric signatures, feature space and 3D inspection of the area. The difference change detection technique was used between two DEMs (DEM-90 and DEM-28.5 m) to analyze differences in morphometric features identification.

The results showed that the introduced method is very useful for identification of morphometric features. Increasing spatial resolution from 90 meter to 28.5 meter, can produce digital elevation models (DEMs) allowing more precise identification of morphometric features and landforms. Increasing spatial resolution overcomes the main constrains for morphometric analysis with SRTM 90 m data, such as artifacts, unrealistic feature presentations and isolated single elements in the output map. Increased spatial resolution together with the smaller window size emphasized local conditions but main morphometric features were preserved. An overall change of 66.36 % is observed for morphometric features in the 28.5 meter DEM. The most and least frequent changes occurred for class no.6 (moderate slopes, channel) with 82.74% and class no.7 (Gentle slope to flat, valley bottom, planar) with 43.31 % respectively. Increasing spatial resolution can be applied also to watersheds studies like drainage modeling.

Landsat Thermal band measures the emitted radiation of the earth surface. In many studies the ETM+ thermal band with 60 meter resolution is excluded from processing and classification despite the valuable information content.

Two different methods of Bayesian segmentation algorithm were used with different band combinations. Sequential Maximum a Posteriori (SMAP) is a Bayesian image segmentation algorithm which unlike the traditional Maximum likelihood (ML) classification attempts to improve accuracy by taking contextual information into account, rather than classifying pixels separately.

Landsat 7 ETM+ data with Path/Row 186-26, dated 30 September 2000 were used. In order to study the role of thermal band with these methods, two data sets with and without the thermal band were used. Nine band combinations including ETM+ and Principal Component (PC) data were selected based on the highest value of Optimum Index Factor (OIF). Using visual and digital analysis, field observation data and auxiliary map data like CORINE land cover, 14 land cover classes are identified. Spectral signatures were derived for every land cover. Spectral signatures as well as feature space analysis were used for detailed analysis of efficiency of the reflective and thermal bands.

The result shows that SMAP as the superior method can improve Kappa values compared with ML algorithm for all band combinations with on average 17%. Using all 7 bands both SMAP and ML classifications algorithm achieved the highest Kappa accuracy of 80.37 % and 64.36 % respectively. Eliminating the thermal band decreased the Kappa values by about 8% for both algorithms. The band combination including PCI, 2, 3, and 4 (PCA calculated for all 7 bands) produced the same Kappa as bands 3, 4, 5 and 6. The Kappa value for band combination 3, 4, 5 and 6 was also about 4% higher than using 6 bands without the thermal band for both algorithms.

Contextual classification algorithm like SMAP can significantly improve classification results. The thermal band bears complementary information to other spectral bands and despite the lower spatial resolution improves classification accuracy.

This paper presents a semi-automatic method using an unsupervised neural network to analyze geomorphometric features as landform elements. The Shuttle Radar Topography Mission (SRTM) provided detailed digital elevation models (DEMs) for all land masses between 60 degrees N and 57 degrees S. Exploiting these data for recognition and extraction of geomorphometric features is a challenging task. Results obtained with two methods, Wood's morphometric parameterization and the Self Organizing Map (SOM), are presented in this paper.

Four morphometric parameters (slope, minimum curvature, maximum curvature and cross-sectional curvature) were derived by fitting a bivariate quadratic surface with a window size of 5 by 5 to the SRTM DEM. These parameters were then used as input to the two methods. Wood's morphometric parameterization provides point-based features (peak, pit and pass), line-based features (channel and ridge) and area-based features (planar). Since point-based features are defined as having a very small slope when their neighbors are considered, two tolerance values (slope tolerance and curvature tolerance) are introduced. Selection of suitable values for the tolerance parameters is crucial for obtaining useful results.

The SOM as an unsupervised neural network algorithm is employed for the classification of the same morphometric parameters into ten classes characterized by morphometric position (crest, channel, ridge and plan area) subdivided by slope ranges. These terrain features are generic landform element and can be used to improve mapping and modeling of soils, vegetation, and land use, as well as ecological, hydrological and geomorphological features. These landform elements are the smallest homogeneous divisions of the land surface at the given resolution. The result showed that the SOM is an efficient scalable tool for analyzing geomorphometric features as meaningful landform elements, and uses the, full potential of morphometric characteristics.

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 5x5 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 9x9 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.

This paper presents a robust new approach using artificial neural networks in the form of a Self Organizing Map (SOM) as a semi-automatic method for analysis and identification of morphometric features in two completely different environments, the Man and Biosphere Reserve “Eastern Carpathians” (Central Europe) in a complex mountainous humid area and Yardangs in Lut Desert, Iran, a hyper arid region characterized by homogeneous repetition of wind-eroded landforms.

The NASA Shuttle Radar Topography Mission (SRTM) has provided Digital Elevation Models (DEM) for over 80% of the land surface. Version 3.0 SRTM data provided by the CGIAR-CSI GeoPortal are the result of substantial editing effort on the SRTM DEM produced by NASA. Easy availability of SRTM 3 arc second data promoted great advances in morphometric studies and numerical description of terrain surface features as shown by many literature references. The goal of this study was to develop a new semi-automatic DEM-based method for geomorphometric feature recognition and to explore the potential and limitation of SRTM 90 meter data in such studies.

The 3 arc seconds data were re-projected to a 90 m UTM grid. Bivariate quadratic surfaces with moving window size of 5×5 were fitted to this DEM. The first derivative, slope steepness and the second derivatives minimum curvature, maximum curvature and cross-sectional curvature were calculated as geomorphometric parameters and were used as input to the SOMs. Different learning parameter setting, e.g. initial radius, final radius, number of iterations, and the effect of the random initial weights on average quantization error were investigated. A SOM with a low average quantization error was used for further analysis. Feature space analysis, morphometric signatures, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of geomorphometric features. Results are provided in a geographic information system as thematic maps of landform entities based on form and slope.

Geomorphometric features are scale-dependent and the resolution of the DEM limits the information, which can be derived. Mega-Yardangs, an aeolian landform due to intensive wind erosion, cover a large area in the hyper-arid Lut desert. They form elongated depressions and ridges with a width ranging from a few meters to hundreds of meters. These features can clearly be recognized and classified when their width is significantly larger than the DEM resolution but become unrecognizable if their width is less than the grid resolution.

Eastern Carpathians are characterized by elongated ridges and valleys. These ridges are dissected resulting in small patches of landforms like ridge with very steep slope, channels with moderate slopes and plains with gentle slope. These local landform patches are large enough to be recognized in morphometric parameters calculated with a 5×5 window, corresponding to 450 m on the ground. With much larger window sizes, these local features disappear and replaced by features describing regional

patterns and ridges. Therefore, size, shape and pattern of identified geomorphometric objects depend on the underlying DEM resolution and the selected window size.

The results demonstrate that a SOM is an efficient scalable tool for analyzing geo-morphometric features as meaningful landforms under diverse environmental conditions. This method provides additional information for geomorphologic and landscape analysis even in inaccessible regions and uses the full potential of morphometric characteristics.

This article presents a new procedure using artificial neural networks in the form of a self-organizing map (SOM), as a semi-automatic method for analysis and identification of morphometric features in the Man and Biosphere Reserve 'Eastern Carpathians' with nine spatial scales. The NASA Shuttle Radar Topography Mission (SRTM) has provided digital elevation models (DEM) for over 80% of the land surface on earth. The latest version 3.0 SRTM data provided by the Consultative Group for International Agricultural Research-Consortium for Spatial Information GeoPortal is the result of substantial editing effort on the SRTM digital elevation data produced by NASA. Easy availability of SRTM 3 arc second data has resulted in great advances in morphometric studies and numerical description of terrain surface features as shown by many literature references. The first derivative, slope steepness and second derivatives, minimum curvature, maximum curvature and cross-sectional curvature of elevation were derived by fitting bivariate quadratic surfaces with nine window sizes ranging from 5 to 55 cells to the processed SRTM DEM 90 m Version 3.0. These analyses represent landform entities with ground distances from 450 to 4950 m, which are local to regional features. The four morphometric parameters were used as input for the SOM algorithm. Forty-two SOMs with different learning parameter sets, e.g. initial radius, final radius and number of iterations were investigated. An optimal SOM with 10 classes based on 1000 iteration and a final neighbourhood radius of 0.01 provide a low average quantization error of 0.1780 and was used for further analysis. The effect of the random initial weights for optimal SOM was also studied. The results in this particular study are not sensitive to the randomization of initial weight vectors if many iterations are used. Feature space analysis, morphometric signatures, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of morphometric features. Results are provided as thematic maps of landform entities based on form and slopes. The result showed that a SOM is an efficient scalable tool for analysing geomorphometric features as meaningful landforms over different spatial extents, and uses the full potential of morphometric characteristics. This procedure is reproducible for the same application with consistent results.

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 x 5 (corresponding to 450 m on the ground) over SRTM- 90 m. To cover the same ground area, a moving window size of 15 x 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 > 10A degrees. 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.

Lineaments in the central highlands of Eritrea are mapped using various Red-Green-Blue colour combinations and panchromatic Landsat Thematic Mapper (TM) images. Six sets trending N-S, NNE-SSW, NE-SW, ENE-WSW, WNW-ESE and NW-SE are identified in these images. Field studies generally reveal similar orientations. Field and remote sensing studies indicate that most of the lineaments are extensional fractures that correspond to either dikes emplacement or normal faults. Most of these were subsequently reactivated into strike-slip shear fractures. The NW-SE and NNE-SSW lineaments represent dilatational fractures. The NNE-SSW trending lineaments are the oldest. The N-S and WNW-ESE lineaments form conjugate shear fractures and are younger than the NNE-SSW lineaments. These conjugate shear fractures are also older than another set of conjugate shear fractures oriented NE-SW and ENE-WSW. The evolution of all these fractures is attributed to the episodic Red Sea/Danakil rifting because they either displace or locally reactivate the pre-existing late Neoproterozoic structures. Kinematic and dynamic analyses of the two, older and younger, pairs of conjugate strike-slip fractures revealed, respectively, broadly NW-SE and NNW-SSE oriented transpressional stress (sigma(1)) with corresponding transtensional stress (sigma(3)) oriented NE-SW and ENE-WSW. The analysis further enabled us to trace the continuation of a major Red Sea/Danakil rift-related transform fault into the central highlands of Eritrea.

Remote sensing, evaluation of digital elevation models (DEM), geographic information systems (GIS) and fieldwork techniques were combined to study the ground-water 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.