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Interferometric synthetic aperture radar (InSAR) decorrelation that creates great challenges to phase unwrapping has been a critical issue for mapping large earthquake deformation. Some studies have proposed a 'remove-and-return model' solution to tackle this problem, but it has not been fully validated yet, and therefore has rarely been applied to real earthquake scenarios. In this study, we use the 2023 M w 7.8 and 7.6 earthquake doublet in Turkey and Syria as a case example to develop an iterative modelling method for InSAR-based coseismic mapping. We first derive surface deformation fields using Sentinel-1 offset tracking and Sentinel-2 optical image correlation, and invert them for an initial coseismic slip model, based on which we simulate InSAR coseismic phase measurements. We then remove the simulated phase from the actual Sentinel-1 phase and conduct unwrapping. The simulated phase is added back to the unwrapped phase to produce the final phase measurements. Comparing to the commonly used unwrapping method, our proposed approach can significantly improve coherence and reduce phase gradients, enabling accurate InSAR measurements. Combining InSAR, offset tracking and optical image correlation, we implement a joint inversion to obtain an optimal coseismic slip model. Our model shows that slip on the Çardak Fault is concentrated on a ∼100 km segment; to both ends, slip suddenly diminished. On the contrary, rupture on the East Anatolian Fault Zone propagated much longer as its geometry is fairly smooth. The iterative coseismic modelling method is proven efficient and can be easily applied to other continental earthquakes. [ABSTRACT FROM AUTHOR]

Multiple choice exams appear as the most common method used to measure student success in education systems in Turkey and around the world. Evaluation of multiple choice exams is usually done by optical form reading systems using expensive optical forms and optical scanners. The most important reason for using optical form reading systems is to reduce the error rate to zero in the evaluation process. In this study, an alternative system is proposed for the evaluation of multiple choice exams. The designed system is a web-based software with high accuracy on evaluating that contains optical form design module, session planning module and evaluation module. The form in which the designed template will be printed on A4 paper instead of a special optical form and the system in which standard scanners will be used instead of the optical scanner, uses the image processing algorithms in the OpenCV library in the evaluation process which is an intermediate library developed in C#. The proposed system is coded to run in parallel to speed up the evaluation process. In order to determine the performance of the proposed system, the optical forms filled by 208 students studying at Karamanoğlu Mehmetbey University, Department of Computer Engineering were evaluated. The accuracy rate of the system has been determined as 99.97%. It has been determined that 1 optical evaluation time, calculated by dividing the total time obtained by running in parallel, by the number of evaluated optical forms, varies between 1.7 seconds and 15 seconds, depending on the scanning resolution. [ABSTRACT FROM AUTHOR]

SUMMARY: Large continental earthquakes are infrequent, but they are important for understanding active tectonics. Many pre-1990, magnitude greater than 7 earthquakes have not been well documented due to a lack of data. The declassification of KeyHole (KH) imagery provides opportunities for investigating those historical events. In this study, we present new geodetic observations of surface deformation from the 1976 November 24 Ms 7.3 Chaldiran earthquake in eastern Turkey. We use various historical KH imagery and modern Pleiades stereo imagery to determine the coseismic deformation, providing new constraints on the 1976 rupture. Based on the image measurements, we resolve the coseismic slip at depth. The inverted slip from a simple elastic dislocation model, ∼4.0 m at shallow depth of the Hidirmentes segment, is larger than the surface displacement, around 3 m from previous field surveys, suggesting that the coseismic deformation is a combination of slip on the primary fault with 30 ± 16 per cent off-fault deformation. We also measure cumulative offsets at two sites with well-preserved features, and find a constant ratio of horizontal to vertical offsets throughout the past few earthquake cycles. Both the observed constant slip ratio in this study and offset clusters from previous field measurements support that the Chaldiran fault exhibits a characteristic slip behaviour in the late Quaternary. By combining all the geodetic and geological observations in the broader region, we also present a brief analysis of the slip rate of the Chaldiran fault. [ABSTRACT FROM AUTHOR]

The east-west extended Sivas Basin in central-eastern Anatolia is a foreland basin that formed after the obduction of the Tethyan ophiolite during the late Cretaceous and is also a north-verging fold-and-thrust belt. The basement rocks of the basin represent mainly sedimentary (Mesozoic platform-type carbonates) and late Cretaceous Divriği ophiolitic complex. As the ophiolitic rocks are affected by intense tectonic processes, field-based mapping studies require long processes and costs. The study tests to reveal the lithological features of the ophiolitic complex outcropping around the Ulaş district of the Sivas Province using remote sensing methods and techniques. Due to arid climatic conditions and rare vegetation cover in the region, almost all outcrops of the basin rocks can be separated by spectral enhancement methods easily. Band ratio (BR), spectral indices (SI), decorrelation stretch (DS), principal component analysis (PCA), and support vector machine (SVM) on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used in this research. BR, PCA, DC, and SI techniques clearly distinguish the Divriği Ophiolitic Complex from the basement and cover sedimentary rocks. SVM distinguishes the chromite-bearing dunites from the other ophiolite-related rock units. According to image analysis performances, it has been observed that the rocks of the Divriği ophiolitic complex can be differentiated in more detail compared to a 1/100.000 scaled geological map of the region. [ABSTRACT FROM AUTHOR]

3D Modelling of gravity data is generally performed as a multi-objective optimisation, trying to minimise observed-calculated data misfit while providing models with certain properties, such as smooth or sharp boundaries. We demonstrated that controlling the model properties in global optimisation schemes is possible through basic image processing filters, and developed a Particle Swarm Optimisation (PSO) algorithm that benefits from Gaussian and Percentile filters to avoid ambiguous boundaries that are generally seen in 3D smooth inversions of gravity data. The effectiveness of the algorithm is shown on a synthetic model consisting of two dipping structures with anomalous density contrasts. Thereafter, the algorithm is implemented to recover subsurface density distribution from a field data set. The field data is collected at the south-western part of the Sinanpaşa graben, Turkey. Due to the lack of previous geophysical studies in the area, 3D Euler decomposition, tilt angle, and 3D smooth inversion methods are also implemented to help interpretation and to compare to the model recovered using the PSO algorithm. The developed approach is observed to be resulted with a model, which is more compatible with the known geology of the region. [ABSTRACT FROM AUTHOR]

This paper reports a study which utilizes deep learning for automated detection of the symptoms of diseases on vine leaves. Vine fruits or grapes are very important and have existed in Syria and surrounding areas (e.g., Turkey) for many years. Quality of vine fruits is also very important in grape production as it is consumed in these areas every day. The aim of this study is to improve diseasedetection accuracy in vine leaves and to develop a system to help Syrian and Turkish farmers and agricultural engineers to maintain the quality of grape production. In this study, over 1000 images of vine leaves have been collected from vine yards in Syria and the internet. These images are processed using MATLAB 2018B, Deep Learning Toolbox including convolutional neural networks (CNNs) with AlexNet, GoogleNet and ResNet-18. A standard transfer learning (TL) algorithm is also used with CNNs, whereas a multiclass support vector machine (SVM) is used with AlexNet, whilst GPU and CUDA are used for accelerating the process of the disease detection for vine leaves. A software system has been created that enables the automatic and efficient detection of nine types of leaf diseases and the identification of healthy leaves. Experimental studies showed that the total detection accuracy of this system reaches 92.5%, 87.4% and 85.0%, 85.1% when AlexNet+TL, ResNet-18+TL, GoogleNet+TL and AlexNet+SVM are used respectively. This smart agricultural application can provide early identification of the symptoms of grape diseases on leaves and thus help maintain the quality of vine fruits. [ABSTRACT FROM AUTHOR]

The Late Cretaceous Pınarbaşı Ophiolite (PO), located at central Turkey is represented by distinct rock types (e.g., harzburgite, dunite, pyroxenite, gabbro) that can be observed in many ophiolites around the world. In this study, the classification of rock types of PO in the Eastern Tauride Belt (Turkey) was made using the advanced spaceborne thermal emission and reflection radiometer and Landsat‐8 satellite images. The developed colour–band ratio composite images together with principal component analysis and minimum noise fraction transformations implemented to the remote sensing data efficiently delineate the spatial distribution of pyroxenites, metamorphic sole rocks, and gabbros in the study area. In addition, a body of serpentinized dunites, pyroxenites, metamorphic sole rocks, and listwaenites that has not been recognized before was identified within PO using remote sensing techniques. The promising results of the image processing were verified by detailed field observations and petrographic analysis. The satellite‐based remote sensing approaches of this work can be applicable for mapping ophiolitic rock types in PO in particular as well as other ophiolites from similar geographical regions around the world. [ABSTRACT FROM AUTHOR]

Hazelnut is one of the most popular nuts consumed by people; it has different cultivars in Turkey. The aim of the current study was to characterize some physicomechanical characteristics, shape features, color, and biochemical properties of 6 standard and 3 local hazelnut cultivars grown in Turkey. The shape and size properties of the samples were determined using image processing techniques as an alternative to conventional measurement methods. Additionally, principal component analysis (PCA) was used to classify the hazelnut samples in terms of the biochemical parameters of the hazelnut cultivars. According to the findings, the highest crude oil (63.25%) and lowest protein contents (13.63%) were observed in the Kalınkara cultivar. Oleic and linoleic acids were the major fatty acids for all hazelnut samples. While local Devedişi and standard Çakıldak cultivars had the highest oleic acid levels, the highest linoleic acid level was observed for the Dağ fındığı cultivar. The cultivars of Foşa had the highest Zn and Mn, while the highest Cu was found in the Tombul cultivar. The greatest surface and projected areas were calculated for the Kara fındık and Dağ fındığı samples, while the greatest hardness value was measured for the Devedişi cultivar. PCA revealed some positive and negative correlations between the physicomechanical and biochemical parameters. The present analyses revealed significant correlations between hardness and internal shell b* values and between Cu content and internal L*. Such correlations should be taken into consideration in food processing applications and machine design for these hazelnut cultivars. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) is commonly used in Earth Sciences to study surface displacements or construct high resolution topographic maps. Recent satellites such as those of the Sentinel-1 constellation allow to derive dense deformation maps with millimetric precision with high revisit frequency. However, InSAR is still limited by interferometric coherence. Interferometric phase noise resulting from a loss of coherence, due to changes in scattering properties between repeated SAR acquisitions, may lead to unwrapping errors, which then in turn lead to centimetric errors in time-series reconstruction. We present an algorithm based on interferometric phase closure to automatically correct unwrapping errors. We describe the algorithm and highlight its performances with two case studies, in Lebanon with Envisat satellite data and in Central Turkey with Sentinel-1 data. The first data set is particularly affected by unwrapping errors because of long spatial (500 m) and temporal baseline interferograms (6 yr) and decorrelation due, in particular, to vegetation. The second data set contains unwrapping errors because of temporal changes in the scattering properties of the ground. For these two examples, the algorithm allows the correction of almost all detectable unwrapping errors, without requiring visual inspection or manual deletions. Our algorithm is efficient especially on large data sets, such as with Sentinel-1 constellation, where interferometric phase is redundant and improves eventually the reconstruction of time-series. [ABSTRACT FROM AUTHOR]

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Remote sensing satellite images plays a significant role in mapping land use/land cover LULC. Machine learning ML provide robust functions for satellite image classification. The objective of this paper is to extend the capability of GIS specialists in geospatial area with minimum knowledge in computer science to easily perform ML satellite image classification. A framework consisting 7 stages established. Tools of steps developed in two programing environments, which are ArcGIS for geospatial datasets structuring and Anaconda for ML training and classification. During the development, authors constrained to reduce the complexity of big data of satellite images and limited memory of computers to make tools available for implementation in PC. In addition, automation and improving the performance accuracy. TensorFlow-Keras library employed to perform the classification using neural networks. A case study using RASAT satellite image in Ankara-Turkey utilized to perform the analysis. The developed classifier gained 80% performance accuracy. The complete RASAT satellite image processed and smoothly classified based on blocks methods. The developed tools successfully tested and applied in geospatial area and can be effectively execute in PC by GIS specialist. [ABSTRACT FROM AUTHOR]

The need for timely, accurate, and interoperable geospatial information is steadily increasing. In this context, process-based image processing systems will be the initial segment for future’s automatic systems. A process-based system is believed to be a good approach for agricultural purpose because agricultural activities are carried out according to a periodic (annual) cycle. Therefore, a process-based image analysis procedure was designed for routine crop classification for an agricultural region in Kırklareli, Turkey. The process tree developed uses a multi-temporal image data set as an input and gives the final crop classification as an output by using an incremental rule set. The test data set was composed of five images of Satellite Pour l’Observation de la Terre 4 (SPOT 4) data acquired in 2007. Basically, image objects were first extracted and then classified. A rule set was structured depending on class definitions. A decision-based process was executed and formed a multilevel image classification system. The final classification was obtained by merging classes from the appropriate levels where they were extracted. To evaluate the success of the application the accuracy of the classification was assessed. The overall accuracy and kappa index of agreement was found to be 80% and 0.78, respectively. At the end of the study, problems of segmentation and classification operations were discussed and solution approaches were outlined. To assess the process in terms of its scope for automation, the efficiency and success of the rule set were also discussed. [ABSTRACT FROM AUTHOR]

Human activities in many parts of the world have greatly affected natural areas. Therefore, monitoring and forecasting of land-cover changes are important components for sustainable utilization, conservation, and development of these areas. This research has been conducted on Igneada, a legally protected area on the northwest coast of Turkey, which is famous for its unique, mangrove forests. The main focus of this study was to apply a land use and cover model that could quantitatively and graphically present the changes and its impacts on Igneada landscapes in the future. In this study, a Markov chain-based, stochastic Markov model and cellular automata Markov model were used. These models were calibrated using a time series of developed areas derived from Landsat Thematic Mapper (TM) imagery between 1990 and 2010 that also projected future growth to 2030. The results showed that CA Markov yielded reliable information better than St. Markov model. The findings displayed constant but overall slight increase of settlement and forest cover, and slight decrease of agricultural lands. However, even the slightest unsustainable change can put a significant pressure on the sensitive ecosystems of Igneada. Therefore, the management of the protected area should not only focus on the landscape composition but also pay attention to landscape configuration. [ABSTRACT FROM AUTHOR]

There is a growing scholarly interest in monitoring and assessment of the impacts of land use and land cover changes (LULCC) on the environment. Recent technological developments and tools of geographic information systems (GIS) and remote sensing (RS) facilitate the researches on this interest. This research aims at monitoring LULCC in Bandırma, Turkey over 30 years. This research utilizes GIS and RS techniques. Landsat satellite images of the years 1987, 2003, and 2019 are used in supervised classification methods with the maximum likelihood technique. The classified images show artificial surfaces, agricultural areas, forests, wetlands, and water bodies based on the first level classes of the Coordination of Information on the Environment (CORINE) land cover legend. Accuracy assessment is performed both before and after the correction process by using ArcGIS (10.6 Edition) software. The final overall Kappa values are above 0.95 for 3 years. These images are compared by using the Land Change Modeler tool of IDRISI (Selva Edition) software. The results represent that forest areas are increased and that wetlands are decreased in both 1987-2003 and 2003-2019 periods. Agricultural lands are decreased in the later period. Comparison maps demonstrate that land change from agricultural to artificial occurs (i) around existing settlements; (ii) along transportation axes; and (iii) in the locations of large urban land uses such as industrial areas, mines, and wind energy plants. The land change trend analysis shows that change from agricultural to artificial is the largest in the middle and southeastern parts of the area neighboring the Manyas Lake Ramsar conservation site.