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Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea 总被引：3，自引：1，他引：3
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.The online version of the original article can be found at 相似文献
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Due to a particularly favourable geologic and tectonic setting in the Eastern Province, Saudi Arabia has the world largest oil reserves and is the third largest oil-producing nation in the world. Based on oil and gas revenues, the Kingdom experienced an overwhelming development during the last few decades, changing a desert country into a modern industrialized nation. Main characteristics of spatial changes are based on processes of urbanization and industrialization, on agricultural reclamations and infra-structural developments. Since the natural environment of deserts and semi-deserts in the Eastern Province is determined by an extremely sensitive equilibrium, environmental impacts, shortages in water resources, as well as limited human resources are causing basic problems, which will influence future developments. 相似文献
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After World War II Saudi Arabia has experienced spectacular changes. Based on huge oil and gas revenues a particularly rapid
growth of population and a spectacular urbanization took place. One of the most booming cities in the desert environment of
the arabian peninsula is the Kingdoms capital city of Riyadh. With a built-up area of 3.5 km2 only before 1950 the city's present-day expansion reaches more than 500 km2. During the same period of time the population of Riyadh grew from less than 50,000 in 1945 to more than 1.4 Mio. in 1985.
Such an explosive development can be documented best by a satellite image. In this paper the process of urbanization of Riyadh
is presented by the interpretation of a MOMS image taken in 1984, documenting the geographic situation, urban structures,
and the city's development. 相似文献
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Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea 总被引：2，自引：1，他引：2
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.An erratum to this article can be found at 相似文献
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Geomorphometric feature analysis using morphometric parameterization and artificial neural networks 总被引：3，自引：0，他引：3
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°N and 57°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. 相似文献
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