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Identifying faults and revealing their characteristics are of great importance in terms of determining natural disaster risks, safe city infrastructure, stability of large engineering structures, groundwater and mineral zones. Faults form distinctive linear structures on the earth and these are usually easily distinguishable from the surface. Identification, mapping and related field studies of faults require a labor-intensive work. Aerial photographs, digital elevation models and aerial images are used to reduce the intensity of fieldwork and to detect the structural integrity of the larger area. There are not only faults in the land, but also many different linear structures such as roads, railroads and man-made fences that form a contrast. Remote sensing images are widely used in the detection and analysis of these lineaments. In this study, lineament analyzes were carried out using filtering, principal component analysis (PCA), band ratio and false color composite displaying, result linearity map and automatic lineament extraction methods on Landsat-7 (ETM+) satellite images as the basis. The results obtained were compared with the data obtained from field studies. [ABSTRACT FROM AUTHOR]

The mechanical properties are the most important factors determining the strength and usage areas of materials. It can be known from the mechanical properties that a wood material of which material group is in and which wood species it is. In this study, the mechanical properties of some important wood species such as alder (Alnus glutinosa subsp. Barbata), oriental beech (Fagus orientalis Lipsky), black poplar (Populus nigra), walnut (Juglans regia), oak (Quercus robur), chestnut (Castena sativa), cedar (Cedrus libani) and pine (Pinus) were modelled by using artificial neural networks. This study will assist in the determination of ideal wood material that meets the mechanical properties required for any construction. Thus, classifications related to wood species can be made more effectively in scientific studies and in trade. [ABSTRACT FROM AUTHOR]