ASSESSING AIR POLLUTANT EMISSIONS IN THE AFTERMATH OF THE 2021 FOREST FIRES IN MARMARIS AND MANAVGAT, TÜRKİYE: INSIGHTS FROM SATELLITE-BASED MONITORING

In recent years, the occurrence of forest fires has become almost inevitable worldwide due to the severe impact of climate change. Consequently, it is imperative to examine the consequences of these fires, pinpoint the affected areas, and take required measures following post-fire incidents. With the advent of modern remote sensing satellites, which allow for the rapid monitoring of vast expanses, they offer a valuable data source for addressing such emergency scenarios. Therefore, the main objective of this study is to evaluate the extent of air pollutant gas emissions resulting from the forest fires that occurred in Marmaris and Manavgat, Türkiye, in the summer of 2021. To this aim, using the data obtained from the TROPOspheric Monitoring Instrument (TROPOMI) sensor onboard the Sentinel-5P satellite, the amount of major air pollutants nitrogen dioxide (NO2), carbon monoxide (CO) and aerosol were investigated after the forest fires considering three different non-fire time periods. The findings of the experiments indicated that in Manavgat, there was a substantial rise in NO2 and average CO levels by 260.43% and 107.07%, respectively, when compared to the 10-day period preceding the forest fire event. Similarly, in Marmaris, there was an increase of 203.63% in NO2 levels and a 102.47% rise in average CO levels during the same period. Positive absorbing aerosol index (AAI) values were also observed during the events, which means that the amount of UV-absorbing aerosols increased due to the fire. The differenced Normalized Burn Ratio (dNBR) maps derived from the Sentinel-2 MSI imagery were also used to investigate the severity of the forest fires, and to observe the relationship between the fire severity level and the air pollutants investigated

DETERMINING STAND PARAMETERS FROM UAS-BASED POINT CLOUDS

In Turkey, forest management plans are produced by terrestrial surveying techniques for 10 or 20 year periods, which can be considered quite long to maintain the sustainability of forests. For a successful forest management plan, it is necessary to collect accurate information about the stand parameters and store them in dynamic and robust databases. The position, number, height and closure of trees are among the most important stand parameters required for a forest management plan. Determining the position of each single tree is challenging in such an area consisting of too many interlocking trees. Hence, in this study, an object-based tree detection methodology has been developed in MATLAB programming language to determine the position of each tree top in a highly closed area. The developed algorithm uses the Canopy Height Model (CHM), which is computed from the Digital Terrain Model (DTM) and Digital Surface Model (DSM) generated by using the point cloud extracted from the images taken from a UAS (Unmanned Aerial System). The heights of trees have been determined by using the CHM. The closure of the trees has been determined with the written MATLAB script. The results show that the developed tree detection methodology detected more than 70% of the trees successfully. It can also be concluded that the stand parameters may be determined by using the UAS-based point clouds depending on the characteristics of the study area. In addition, determination of the stand parameters by using point clouds reduces the time needed to produce forest management plans

PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS

Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data

Cide Archaeological Project: An Archaeological Survey on the Turkish Black Sea Coast

This archives orginates in the Cide Archaeological Project, an archaeological surface survey undertaken between 2009 - 2011 in the coastal Black Sea district of Cide and the adjacent inland district of Şenpazar, Kastamonu province, Turkey. This project was co-directed by Bleda Düring, Claudia Glatz and Emre Şerifoğlu. It was the first multi-period, systematic survey undertaken in the Western Turkish Black Sea Region, and was set up to investigate an archaeological terra incognita. The project fills an important gap in our knowledge of the archaeology of the region. The results have been published open access with De Gruyter open. That publication presents the main culture-historical results concerning the settlement history of the investigated region, embeds these results in the broader archaeological context of the surrounding regions, and discusses the methodological problems of surveying a highly challenging landscape such as the Western Turkish Black Sea Region. In this archive the primary data are presented upon which the book is based. The archive was prepared by Dr. Toby Wilkinson

The role of computerized tomography in the assessment of perivesical invasion in bladder cancer

Background: The aim of the present study was to identify the contrast patterns of a tumor, and to evaluate the possibility of assessing the invasion of the perivesical fatty tissue in bladder cancer. Material/Methods: In this study, 26 patients with bladder cancer were included. Multiphasic CT examination was performed to determine the stage of the disease before radical cystectomy. Results: There were statistically significant differences in tumor and perivesical fatty tissue densities between pre- and post-contrast phases (p<0.05). Conclusions: Increases in focal density suspected of being invasion of the perivesical fatty tissue can show perivesical invasion with high specificity. © Pol J Radiol

Full-scale CFD Analysis of Double-M Craft Seakeeping Performance in Regular Head Waves

This study presents the full-scale resistance and seakeeping performance of an awarded Double-M craft designed as a 15 m next-generation Emergency Response and Rescue Vessel (ERRV). For this purpose, the Double-M craft is designed by comprising the benchmark Delft 372 catamaran with an additional center and two side hulls. First, the resistance and seakeeping analyses of Delft 372 catamaran are simulated on the model scale to verify and compare the numerical setup for Fr = 0.7. Second, the seakeeping performance of the full-scale Double-M craft is examined at Fr = 0.7 in regular head waves (λ/L = 1 to 2.5) for added resistance and 2-DOF motion responses. The turbulent flow is simulated by the unsteady RANS method with the Realizable Two-Layer k-ε scheme. The calm water is represented by the flat VOF (Volume of Fluid) wave, while the incident long waves are represented by the fifth-order Stokes wave. The residual resistance of the Double-M craft is improved by 2.45% compared to that of the Delft 372 catamaran. In the case of maximum improvement (at λ/L = 1.50), the relative added resistance of the Double-M craft is 10.34% lower than the Delft 372 catamaran; moreover, the heave and pitch motion responses were 72.5% and 35.5% less, respectively

Automated detection of damaged buildings in post-disaster scenarios: a case study of Kahramanmaraş (Türkiye) earthquakes on February 6, 2023

This study develops a novel approach for identifying buildings that were damaged in the aftermath of the Kahramanmaraş earthquakes on February 6, 2023, which were among the most devastating in the history of Türkiye. The approach involves using two pre-event and one post-event Sentinel-1 and Sentinel-2 images to detect changes in the varying-sized and shaped buildings following the earthquakes. The approach is based on the hypothesis that the radiometric characteristics of building pixels should change after an earthquake, and these changes can be detected by analysing the spectral distance between the building pixel vectors before and after the earthquake. The proposed approach examines the changes in building pixel vectors on pre-event and post-event Sentinel-2 MultiSpectral Instrument images. It also incorporates the backscattering features of Sentinel-1 Synthetic Aperture Radar images, as well as the variance image, a feature that is derived from a Grey-Level Co-occurrence Matrix, and the Normalized Difference Built-up Index image, which were derived from the optical data. The approach was tested on three sites, two of which were in Kahramanmaraş and the third in Hatay city. The results showed that the proposed method was able to accurately identify damaged and undamaged buildings with an overall accuracy of 75%, 84.4%, and 73.8% in test sites 1, 2, and 3, respectively. These findings demonstrate the potential of the proposed approach to effectively identify damaged buildings in post-disaster situations.</p

An Archaeological Survey on the Turkish Black Sea Coast

This archives orginates in the Cide Archaeological Project, an archaeological surface survey undertaken between 2009 - 2011 in the coastal Black Sea district of Cide and the adjacent inland district of Şenpazar, Kastamonu province, Turkey. This project was co-directed by Bleda Düring, Claudia Glatz and Emre Şerifoğlu. It was the first multi-period, systematic survey undertaken in the Western Turkish Black Sea Region, and was set up to investigate an archaeological terra incognita. The project fills an important gap in our knowledge of the archaeology of the region. The results have been published open access with De Gruyter open. That publication presents the main culture-historical results concerning the settlement history of the investigated region, embeds these results in the broader archaeological context of the surrounding regions, and discusses the methodological problems of surveying a highly challenging landscape such as the Western Turkish Black Sea Region. In this archive the primary data are presented upon which the book is based. The archive was prepared by Dr. Toby Wilkinson

Stochastic Local Search for Multiprocessor Scheduling for Minimum Total Tardiness

The multi-processor total tardiness problem (MPTTP) is an NP-hard scheduling problem, in which the goal is to minimise the tardiness of a set of jobs that are processed on a number of processors. Exact algorithms like branch and bound have proven to be impractical for the MPTTP, leaving stochastic local search (SLS) algorithms as the main alternative to find high-quality schedules. Among the available..