Aktaş, Gülten

The aim of this study is to use EMAG2 magnetic data to analyze and interpret the boundaries of important structural features in and around Lake Van using various advanced edge detection techniques. The reduction pole was applied to the magnetic data. Edge detection is an important step to interpret the boundaries of geological structural elements with higher precision. A comprehensive lineament analysis was conducted by applying traditional and advanced edge-detection filtering techniques, including the Tilt angle (TA), the Tilt Angle of the Total Horizontal Gradient Amplitude (TAHG), and Total Horizontal Gradient-Based Edge Detection (THGED). With these methods, the boundaries of magnetic anomaly sources were tried to be interpreted more clearly. In these comparative results, lineations were obtained in the south of the lake and it was shown that it was affected by faults in the N-S, E-W main directions. In the north of the lake, NE-SW trending lineations were obtained.

Çatal Reis, Hatice; Yılancı, Gulsena

As a result of the development of cities and inclination towards urbanization, natural areas decreased while urban areas increased. In this respect, determination of impermeable surfaces is important for the problems covering; effects of urbanization on natural environment, global environmental variation, urban atmospheric process, human activities and the effects of urbanization on the environment. Remote sensing images are used to examine and classify land cover/uses. Traditional classification methods are mainly divided into supervised classification and unsupervised classification. The aim of this study is to classify land cover/use and to state temporal change using the Support Vector Machines (SVM) approach, which is a supervised classification method. Arnavutkoy district of Istanbul was chosen as the study area for land use and change detection analysis. Landsat 5/7/8 satellite images of Arnavutkoy district were obtained and SVM process was applied to obtain these images. Firstly, four classes were created for each image: urban areas, vegetation, bare soils and wetlands SVM was applied and accuracy analysis was performed to the images classes of which were created before. CAD software and GIS software were used for image processing. The classification accuracy for SVM was found to be 98.66%, 98.31%, 98.95%, 97.99%, 96.37%, 97.90% (from 1995 to 2019). In addition, ROC analysis was used for comparison of accuracy analysis. As a result of this study, land cover/use change of Arnavutkoy district in the last 20 years has been determined. The urban area of the district was 40.99 km2in 1995 and 93.76 km2in 2019. In addition, the impact of the Europe's largest airport on land cover / use has been examined. The results showed that the accuracy of using SVM to classify land use/cover is high. Therefore, it has been proposed that this algorithm is used as an optimal classifier for land use/cover maps.