Filiz Sunar

The Artificial Bee Colony algorithm in training Artificial Neural Network for oil spill detection

Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

Multi-criteria spatial decision support system for valuation of open spaces for urban planning

One of the major accompaniments of the globalization is the rapid growing of urban areas. At the end of the 1970th only 38% of world lived in cities, this number increased to more than 50% by 2008. In 2030 two third of all people worldwide are expected to live in cities, many of them in megacities. Urban sprawl is a major environmental concern affecting cities and urban. Urban sprawl depends on the socio-economic situation in the cities. Thus, reducing migration, sustainable handling of the limited resources and “smart growth” are acknowledged as key tasks for urban planning. Coping with these tasks requires precise and adaptive planning instruments. The presented study is part of the research project GAUS (Gaining Additional Urban Space) aiming at inventorying the open space available in urban environments and, moreover, providing flexible multi-criteria spatial decision support system for its development. The method is based on VHR (Very high resolution) optical satellite data (QuickBird (QB) and IKONOS (IK)) which is applied on three study areas: Berlin, Istanbul, and Ruhr Area. Object-based image analysis is applied to map land cover and land use and derive metrics describing urban form and inner-urban structure on multiple scales. The workflow has been standardized and leads to comparable results across different test sites and datasets. In intersection with available GIS (Geographical Information System) and local ancillary data, the outputs of image analysis serve as input for a multi-criteria spatial decision support system. Flexible multi-criteria spatial decision support (MC-SDSS) tool has been created by using MATLAB (Matrix Laboratory) software and its tools (Mapping toolbox etc.). Users can change their weights and parameters with this tool for their different study areas. Urban planners can use final suitability maps of this tool. Thus complex decisions are supported by numerical calculation and spatial visualization in order to come to objective solutions. This work contribute to close the gap between remote sensing methods and applied urban planning.

Integration of remote sensing and GIS for archaeological investigations

The western hinterland of the modern city of Istanbul contains some of the most remarkable monuments of ancient and medieval hydraulic engineering. Until recently fieldwork has been limited and only within the last two decades have there been serious attempts to map the complexity of the monuments and water lines. A GPS‐based archaeological survey has been undertaken by the authors and has been integrated with high resolution (IKONOS) and multi‐spectral spatial data giving the opportunity to view the system in its wider setting and also to identify major urban and landscape changes impacting on the long‐term conservation and management of the ancient remains.

Monitoring Loggerhead Sea Turtle (Caretta caretta) Nests in Turkey Using GIS

Both the loggerhead sea turtle (Caretta caretta) and the green turtle (Chelonia mydas) live, feed, and nest in the Mediterranean, mostly on the beaches of Turkey and Greece. The greatest threat to the survival of Caretta caretta has been found to be on the sandy beaches where their lives begin. In this study, the digital terrain model (DTM) of the Iztuzu Beach in Turkey where the Caretta caretta sea turtles lay their eggs was prepared using a global positioning system (GPS) and a geographic information system (GIS). A flexible coastal geographic information system was prepared integrating the DTM with digitally processed satellite images and ground truth of the nesting area and its surroundings. Researchers, decision makers, other end users, or sea turtle experts can find and query any environmental information having a possible impact on the nesting area using a computer-based GIS database. A multidate monitoring of the nesting area allows users to extract and compare much information about the location ...Both the loggerhead sea turtle (Caretta caretta) and the green turtle (Chelonia mydas) live, feed, and nest in the Mediterranean, mostly on the beaches of Turkey and Greece. The greatest threat to the survival of Caretta caretta has been found to be on the sandy beaches where their lives begin. In this study, the digital terrain model (DTM) of the Iztuzu Beach in Turkey where the Caretta caretta sea turtles lay their eggs was prepared using a global positioning system (GPS) and a geographic information system (GIS). A flexible coastal geographic information system was prepared integrating the DTM with digitally processed satellite images and ground truth of the nesting area and its surroundings. Researchers, decision makers, other end users, or sea turtle experts can find and query any environmental information having a possible impact on the nesting area using a computer-based GIS database. A multidate monitoring of the nesting area allows users to extract and compare much information about the location and condition of the nests and will help researchers develop better strategies for protecting sea turtles.

Evaluation of image fusion methods using PALSAR, RADARSAT-1 and SPOT images for land use/ land cover classification

This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.

Remote Sensing of Urban Land Use Change in Developing Countries: An Example from Büyükçekmece, Istanbul, Turkey

This application chapter discusses rural–urban land use changes in developing countries using Buyukcekmece, a suburb of Istanbul, Turkey, as an example. Specifically, this chapter demonstrates the utility of remote sensing techniques, multitemporal satellite data, a 1984–1997 population database, and ground data to illustrate the impact of urban growth on land cover and land use changes in general and on agricultural land in particular.

Monitoring vegetation biomass of a coastal ecosystem using multidate optical satellite data

In order to effectively protect coastal ecosystems, objective information on the structural and functional characteristics of these systems must be obtained through regular monitoring. In recent years, more advanced technologies, such as satellite remote sensing, have arisen which are being utilized for the monitoring and management of coastal ecosystems in general, and vegetation in particular. This paper presents the results from monitoring the vegetation biomass of the Koycegiz Lagoon area, declared as a Specially Protected Area by the Turkish Government, using multi-temporal satellite imagery. Several problems need to be overcome when using imagery from different dates or from different sensors. These include; image registration, atmospheric variability, and, often, the lack of historical ground data. Furthermore, for monitoring purposes, techniques to overcome these problems should be robust and automatic allowing the database to be upgraded easily. The procedures we have developed include automatic registration (to subpixel accuracy), atmospheric normalization, and vegetation index (VI) calibration components. This was tested on multidate (1984, 1988, 1991, 1995 and 1996) LANDSAT-TM data. From this adjusted data set the performance of different vegetation indices, in this coastal environment was examined, and the vegetation trends analyzed.