In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification.

A principal and independent component analysis (PCA and ICA) and a minimum noise fraction analysis (MNFA) were applied in this study to Landsat 8 Operational Land Imager (OLI) images along the Adıyaman fault zone in Eastern Turkey. These analyses indicated that the lithologic units, fault patterns, and the morphological and structural features can be mapped highly accurately by using spectral-matching techniques in regions where rocks are well exposed. An inspection of all possible band combinations indicated that the PCA 134 and 231 and the ICA 132 band combinations give the best false color composite images for identifying the lithological units and contacts. The findings of the MNFA band combinations show that the MNFA 521 band combination also is robust for discriminating the lithological units, particularly Quaternary clastic units (colluvium/alluvium). MNFA band 1 alone provides the best image for tracing the tectonic and structural elements in the study area. The new up-to-date lithologic map of the Adıyaman fault zone we produced upon the interpretation of the processed OLI images reveals several river channels that are offset and beheaded by the Adıyaman fault, which verifies its Quaternary activity. This study demonstrated that, when used with the OLI data, the PCA, ICA, and MNFA are very powerful for lithological and structural mapping in actively deforming tectonic zones and hence can be applied to other regions elsewhere in the world where the climate is arid to semiarid, and the vegetation cover is scarce.

Pixel based and object based classification methods are used for the determination of land cover. Pixel based classification methods have salt and pepper effect. So pixel based classification methods cannot reach the accuracy of the object based classification. In order to eliminate the salt and pepper effect on the remote sensing classification accuracy and improve the result maps created as a result of the classification and further improve the classification accuracy in pixel based classification, it is recommended that the sieve class, clump class and majority analyses -which are ordinarily applied to high resolution images in this study by using the pixel based classification method. So the effect of these analyzes on low and medium resolution satellite images are unknown. With the SPOT 5 satellite image, this study will investigate how much this analysis affects the accuracy of classification. The SPOT 5 satellite image was taken in May 2013 in order to determine the land cover of Kadirli region in Turkey. The classification includes the following categories: sun flowers, corns, peanuts, trees, roads, residential areas and water resources. In this study, the object based classification method was compared with three pixel based classification methods, namely the support vector machines, maximum likelihood method and spectral angle mapper method. The following general accuracy and kappa values were obtained from the methods in question: Object based classification method (96% accuracy, kappa value of 0,949), maximum likelihood method (90.99% accuracy, kappa value of 0,67), support vector machines (92.06 accuracy, kappa value of 0.70), spectral angle mapper method (93.88% accuracy, kappa value of 0,78). Following the pixel based classification process, the total accuracy and kappa values of the classified image was improved through the application of sieve class, clump class and majority analyses. As a result of the analyses conducted on the pixel based classification methods, the following general accuracy and kappa values were obtained for the following pixel based classification methods: maximum likelihood method (92.91% accuracy, kappa value of 0,73), support vector machines (93.13% accuracy, kappa value of 0.74) and spectral angle mapper method (95.62% accuracy, kappa value of 0,88). As a result of the analyses applied to the pixel based classification method, the classification accuracy produced similar results to that of the object based classification accuracy. To the best knowledge our author this is the first study dealing with this study area. So the authors think that this paper present a different point of view for interested researchers in this study area.

Flood risk, in the sense of damage that a ‘flood event’ can cause, was globally indexed and quantified to show the relationship between various natural and social factors. The Pressure and Release (PAR) model consisting of five key indexes; hazard, exposure, vulnerability, capacity soft countermeasures, and capacity hard countermeasures along with the entropy method was used to measure the uncertainty in information gathered. In this study, flood risk index was calculated for Yesilirmak Basin of Turkey based on PAR. The basin is one of the coastal watersheds of Turkey experiencing frequent flood events. Damage data covering years from 2000 to 2015 have been obtained from the Ministry of Agriculture and Forestry for Turkey. Candidate data for hazard, vulnerability, counter-measures and exposure were collected in accordance with temporal and spatial scales, and the collected data were compiled on a watershed basis. When grouping the sub-indicators, AWDO (Asian Water Development Outlook) was used and selected from a group of parameters covering a wide range of characteristics such as economy, health, vegetation, population, river according to their global presence, and data consistency. As long-term data accessibility was highly limited under basin conditions, data was obtained from relatively more reliable global sources such as United Nations’ databases on which data has been collected annually. The correlation among the collected parameter values was calculated based on the amount of damage that had occurred, and data with high correlation was included in the index account. Parameters that were not screened were rather weighted by using the entropy method and their effect on flood damage were determined. The calculated flood risk index based on PAR model was named as Yesilirmak Basin Flood Risk Index (Y-FRI). The resulting Y-FRI radar charts indicated that the most important variation occurred for the soft countermeasure index. In addition, the five main indices and the flood risk index was positively correlated with hazard, exposure and vulnerability indices, while negatively correlated with the countermeasures. This study showed that not only the hazard parameters directly contribute to flood formation, but also the vulnerability, exposure and countermeasure parameters that reflected the conditions of the region where the flood occurred have quantitatively influenced the flood damage.