Coastal management requires rapid, up-to-date, and correct information. Thus, coastal movements have primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data are some of the most important information and are utilized for differentiating any detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from satellite images. In the literature most of the algorithms developed for optical images have been discussed in detail. In this study, an algorithm which extracts coastlines efficiently and automatically by processing SAR (Synthetic Aperture Radar) satellite images has been developed. A data set of ALOS Palsar image of Fine Beam Double (FBD) HH-HV polarized data has been used. PALSAR image has L-band data, and has a 14 MHz bandwidth and 34.3 degrees look angle. Data were acquired in ascending geometry. Ground resolution of PALSAR image was resampled to 15 m to amplitude image. Zonguldak city, lies on the northwest costs of Turkey, has been selected as the test area. An algorithm was developed for automatic coastline extraction from SAR images. The algorithm is encoded in a C__ environment. To verify the results the algorithm was applied on two PALSAR images gathered in two different date as 2007 and 2010. The results of automatic coastline extraction obtained from SAR images were compared to the results derived from manual digitizing. Random control points which are seen on each image were used. The average differences of selected points were calculated.

In this study, the effects of algal blooms occurring in Izmir Bay in the summer of 2024 on marine ecosystems were investigated using remote sensing techniques on Google Earth Engine platform. The normalized difference chlorophyll index (NDCI) was calculated from January to the end of September and the chlorophyll‐a density was analyzed. Additionally, an NDCI time series analysis was conducted between September 2018 and 2024 at the designated points. The values, which fluctuated narrowly until 2022, showed a sharp increase in 2024. NDCI, which vary between −0.4 and 0.2 in January 2024 and increase up to 0.8 toward the summer months, indicate that algal blooms are occurring, concentrated in critical areas such as Karşıyaka, Bayraklı, and Alsancak Port. These findings revealed a connection between the sudden fish deaths in the bay during the summer of 2024 and algal blooms, as well as the deterioration of water quality.

Indoor navigation applications have become widespread in recent years with the ability of mobile phones which determine the position. Due to the inefficiency of global positioning system (GPS) indoors, other positioning methods have been developed based on local networks using technologies such as Bluetooth, wireless networks, ultra-wideband signals, ultrasonic signals, and radio frequency identification modules. Various technologies yield high or medium accuracy. Combining data from multiple sources via fusion enhances location precision. In this study, indoor positions were estimated using trilateration with Bluetooth devices, and the accuracy was improved by applying filters to the data from inertial measurement unit (IMU) sensors on the phone. As a result of combining Bluetooth and IMU data with data fusion, submeter accuracy was achieved. The results obtained were tested at Yildiz Technical University-Istanbul Türkiye. It was determined that 92% of the data was obtained with submeter accuracy.