Önder Gürsoy

Detecting of Lithological Units by Using Terrestrial Spectral Data and Remote Sensing Image

The objective of the study was to carry out an automatic classification of the lithological units of interest using the integration of remote sensing image, in which various objects are spread on, and terrestrial spectral measurement data. Only endmembers of interest are classified using spectral classification methods such as Spectral Angle Mapper. Following the identification of the types of rock and minerals, integration of remote sensing images and spectral measurement data enable spectral classification. In this study, Short Wave Infrared detector images of Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite and spectroradiometer measurements were used. The study area, Gölova with its geological diversity is located in the Kelkit Valley section of the North Anatolian Fault Zone in Northeast of Turkey. Seventeen rock samples were collected and their coordinates were recorded. The samples were categorized via spectral measurements on their thin sections through petrographic analyses. Marble and Meta lava with different lithological were selected as endmembers. SAM was used as the classification method that enables the analysis of the endmember with the threshold value of 0.009 radian for marble and 0.010 radian for metalava. SAM analysis was compared by visual analysis to principle component analysis, decorrelation stretch, band ratio (R: 4/7, G: 4/1, B (2/3) x (4/3)) and band combination analysis (R: 9, G: 4 and B: 5). This study demonstrates that the SAM method can be successfully used in both the integration of remote sensing image and terrestrial spectral measurement data in lithological classification. Both the endmembers of metalava and marbles were detected in the SAM results at the GPS coordinates noted down whilst collecting the rock samples for accuracy assessment.

Determining lateral offsets of rocks along the eastern part of the North Anatolian Fault Zone (Turkey) using spectral classification of satellite images and field measurements

ABSTRACT Fault displacements are being measured by geological observations using the method of detecting and evaluating marker rocks. Thus, the length of total displacement in a fault zone relates to position detection of marker rocks. Due to limits of human eye, we used remote sensing data and terrestrial spectral measurements at 229 locations for measuring the total offset along the Kelkit Valley segment of the North Anatolian Fault Zone (NAFZ). We examined the lithology, especially ophiolites that are older than the fault zone and can be a good marker for detecting the total offset in the region. The Advanced Spaceborne Thermal Emission and Reflection Radiometer images are subjected to Spectral Angle Mapper (SAM) method. Principal component analysis, decorrelation stretching and geological map were used to compare the SAM results. Ophiolites on either side of the fault zone were clearly classified and identified with the SAM analysis. As a result of comparison of SAM with image enhancement methods and the geological map, we measured the total fault displacement on the NAFZ in the part of the Kelkit Valley. Along the fault zone to the north and south of the ophiolites providing a right lateral offset was measured as 90 ± 5 km.

INTEGRATION OF UNMANNED AERIAL VEHICLE DATA WITH GEOGRAPHICAL INFORMATION SYSTEMS

4th International Scientific Conference on Geobalcanica -- MAY 15-16, 2018 -- Ohrid, MACEDONIA

Detecting Clay Minerals in Hydrothermal Alteration Areas with Integration of ASTER Image and Spectral Data in Kösedag-Zara (Sivas), Turkey

Remote sensing technology and its terrestrial components are more useful than classical geological investigation in mineral exploration and mapping the hydrothermal alteration areas and help to investigate larger areas in short time. Intrusive and volcanic rocks, namely Kösedag syenite and Karatas volcanics in Kösedag (Zara) area shows argillic alteration zones. Two different test area were chosen and sampled for mineralogical studies. XRD-CF investigations showed that kaolinite and illite are the dominant clay minerals in test areas of A and B respectively. The spectroradiometer measurements were carried out 5 times on different surfaces of clayey samples with self-illuminated contact-probe lens within the wavelength range of 350-2500 nm. The spectroradiometer measurements used as endmember were resampled to ASTER short wave infrared (SWIR) bandwidths. Band ratio, principal component and decorrelation stretching analysis were performed to visualize the distribution of clay minerals. In spectral classification method, matched filtering (MF) was used for integrating the satellite image and spectroradiometer measurement data. It was concluded that co-interpretations of the band ratio, principal component, decorrelation stretching analysis, MF results and geological map are very useful in determining, classifying and mapping of the argillic alteration zones related to hydrothermal processes on ASTER image and they seem to be very useful to identify the target areas for mineral exploration.

Su Kalitesinin Sınıflandırılmasında Spektral Sınıflandırma Yöntemlerinin Karşılaştırılması

Gunumuzde, uzaktan algilama ve yersel bilesenleri kullanilarak su kalitesi ve su kirliliginin tespiti yapilabilmektedir. Uzaktan algilama, su kalitesini ve kirligini tespit etmede hizli bir cozum sunmakla kalmaz ayni zamanda dusuk maliyetli de olabilmektedir. Calisma kapsaminda Sivas bolgesinin en onemli su kaynaklarindan biri olan Kizilirmak nehrinin Imranli bolgesinin ve nehir uzerinde bulunan Imranli barajinin su kalitesi spektral siniflandirma yontemleri ile incelenmistir. Nehir ve barajda cesitli noktalardan su numuneleri alinip bunlarin laboratuvarda kimyasal oksijen icerikleri tespit edilmistir. Buna ek olarak alinan su numunelerinin yersel spektral olcumlerle yansitim degerlerine bakilmistir. Olculen yansitimlar uc uye olarak alinip spektral siniflandirmada referans olarak kullanilmistir. Uydu goruntusu olarak ise CHRIS Proba kullanilmistir. Spektral siniflandirma yontemleri olarak ise eslesen filtreleme (MF), spektral aci haritalama (SAM) ve spektral bilgi ayrimi (SID) yontemleri kullanilmis olup hangi yontemin su kalitesi tespitinde daha iyi sonuc verdigi irdelenmistir. Sonuclara gore SAM yonteminin diger yontemlere gore daha iyi siniflandirma dogrulugu sagladigi anlasilmistir.