Browsing by Author "Yilmaz, OS"

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    Educational intervention for the awareness improvement and control programme design on Echinococcosis in Izmir, Turkey
    Altintas, NA; Altintas, NU; Yilmaz, OS; Akil, M; Ozturk, EA; Unver, A
    In Turkey, cyst hydatid disease (CHD) or cystic echinococcosis (CE) is publicly known as dog cyst, a fatal and serious disease not only affects livestock husbandry and human health but also brings about economic loss to our country. According to the data of the Ministry of Health; number of annual cases was 408 in 2008, and this number reached 1,867 by the end of 2019. Cystic echinococcosis is especially taken up during childhood and emerged at an older age. They become exposed to the eggs of the tapeworm after close contact with an infected dog or its contaminated environment. The infected dogs also pass in their feces E. granulosus eggs that adhere to the dogs' hairs, and pass on to the children who are in the course of playful and intimate contact with the infected dogs. This study was to create the awareness of risk factors of CE among 10 different districts of Izmir province. Awareness raising seminars are essential component of this study because local people living in CE endemic areas, are crucial to continue and sustain the long-time effort that is needed to tackle this disease. In each district, 3 awareness raising seminars were held to the target groups: (a) in schools for students, teachers, administrators, (b) for general public, (c) for healthcare professionals. 4090 students attended to the trainings, 242 administrators and teachers who attended to the presentations together with the students, 524 people were attended to the trainings and 327 health workers attended to the trainings from different institutions. This study helped improving this situation by organising educational events for the rural populations for preventing transmission of the disease. This is the first educational intervention study regarding creating awareness on CE in Izmir Province which includes 10 districts between January 2019 to January 2020.
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    The Performance Analysis of Different Water Indices and Algorithms Using Sentinel-2 and Landsat-8 Images in Determining Water Surface: Demirkopru Dam Case Study
    Yilmaz, OS; Gulgen, F; Sanli, FB; Ates, AM
    In this study, the most appropriate algorithm and water index to determine the boundaries of the dam water surface using remote sensing (RS) techniques were investigated. Water surface boundaries of Demirkopru Dam were determined using Sentinel-2 L2A (MSI) and Landsat-8 (OLI) satellite images. Demirkopru Dam was chosen as the study area as it is suitable for floating photovoltaic (FPV) solar power plant installation. Normalized difference water index (NDWI) and modified NDWI indices were used to determine the water surface boundaries of the dam. Thirty-six classification results were obtained using K-means, maximum likelihood classification (MLC), and random forest (RF) algorithms. The best classification accuracies of the produced maps have been calculated as 80.3%, 73.1%, and 73.2% by RF, MLC, and K-means, respectively. In addition, the water coastlines determined by classifications were compared with the continuously operating reference station (CORS-TR) data in a local area by calculating the root-mean-square error (RMSE). Compared with the CORS-TR measurements of the dam coastline obtained from the images classified by the RF algorithm, the minimum RMSE values were calculated as 13.8 m and 10.1 m for Landsat and Sentinel images, respectively. While the minimum RMSE value for coastlines obtained with various layer stacks of Landsat images classified by the MLC algorithm is 36.7 m, it could not be calculated in Sentinel images due to poorer classification results. For the coastlines obtained from the images classified by the K-means algorithm, the minimum RMSE values were calculated as 14.5 m and 9.6 m for Landsat and Sentinel images, respectively. According to the comparisons based on classification accuracy and CORS-TR measurements, it is concluded that the RF algorithm performs better than others for the dam water surface. Moreover, it was determined that the NDWI presented better results when the water level was the lowest for Demirkopru Dam. Also, in this study, the MLC algorithm has better results in detecting water surfaces using Landsat images. It was concluded that the K-means algorithm is also very effective in water surface detection. In this study, various water extraction indices, algorithms and free Landsat and Sentinel images were used to extract the water surface in a selected reservoir for the FPV installation. This study guides a series of algorithms and indexes used to detect water surfaces. In addition, it has been shown that the use of RS techniques, which are more practical than classical approaches in determining water boundaries, will be more effective in planning and design in terms of engineers, investors and various organizations who will realize the FPV installation.
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    Investigation of Water Quality in Izmir Bay With Remote Sensing Techniques Using NDCI on Google Earth Engine Platform
    Yilmaz, OS; Acar, U; Sanli, FB; Gülgen, F; Ates, AM
    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 Kar & scedil;& imath;yaka, Bayrakl & imath;, 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.
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    Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies
    Yilmaz, OS; Gülgen, F; Ates, AM
    This study aims to reveal suitable places where floating photovoltaic-solar power plants (FPVSPPs) can be installed on the dam surface using the possibilities of remote sensing (RS) and geographical information science (GISc) technologies. Past satellite images from Landsat and Sentinel platforms allow researchers to analyse shoreline changes in the dam surface. Shoreline extraction is a crucial process for the FPV-SPP to stay afloat despite external constraints. In this study, changes in dam water levels were determined by classifying 20-year satellite images and analysing a 32-year global surface water dynamics dataset. The water surface area was calculated as 1,562.40 ha using the random forest (RF) algorithm and the normalized differences water index (NDWI) on Google Earth Engine (GEE) cloud platform. In addition, solar analysis was carried out with GISc using annual solar radiation maps shuttle radar topography mission (SRTM) data, which directly affects the energy production of FPVSPPs. It has been calculated that the solar radiation on the water surface varies between 1,554 kWh/m2-year and 1,875 kWh/m2-year. These calculated values were divided into five different classes, and it was observed that 88.5% of the dam surface had a very high level of solar radiation compared to other areas. Higher efficiency will be obtained from the FPV-SPP to be installed in this region compared to the systems to be installed in other regions. It has been observed that the radiation values in other parts of the water surface are lower due to topographic shading. These analyses revealed energy zones with high production potential, thereby easing the decision-making process for investors planning to establish FPV-SPPs.
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    Mapping burn severity and monitoring CO content in Turkiye's 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform
    Yilmaz, OS; Acar, U; Sanli, FB; Gulgen, F; Ates, AM
    This study investigated forest fires in the Mediterranean of Turkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gundogmus, Marmaris, Bodrum and Koycegiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03 mol/m(2) in all regions burned before the fire, it was observed that this value increased to 0.14 mol/m(2) after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333 mol/m(2). The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.
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    Using remote sensing to calculate floating photovoltaic technical potential of a dam's surface
    Ates, AM; Yilmaz, OS; Gulgen, F
    A dam with a hydroelectric power plant (HEPP) prevents flooding while generating electricity and providing controlled irrigation of agricultural land. An open dam surface causes a substantial loss in water resources over the course of a year due to evaporation. In this paper, the authors propose to occupy the idle dam area with a floating photovoltaic (FPV) solar power plant (SPP) to generate electrical energy and to conserve water by minimizing evaporation. Since the shoreline of a dam used for agricultural irrigation continually changes, the most critical challenge in installing a SPP is to determine the suitable area to be covered with FPV panels. In this study, the shoreline changes of the Demirkopru Dam in Manisa, Turkey, were monitored over 20 years from Landsat and Sentinel satellite images using the supervised classification in the Google Earth Engine. The minimum surface area of the dam was found to be 1,562.45 ha. Installing a 2.03 GWp FPV SPP horizontally on this surface and obtaining 3,328.33 GWh annual energy is feasible. Moreover, the FPV panels can prevent 28,231,026.90 m(3) of water from evaporating. Approximately 7.82% of the water used for electricity production in 2019 can be recovered with the installation of this SPP.
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    A novel approach suggestion for assessing the impact of topographic shading on the estimation of the floating photovoltaic technical potential
    Yilmaz, OS; Ates, AM; Gülgen, F
    This study presents a novel approach for the implementation of floating photovoltaic (FPV) systems at the Ayvali hydroelectric power plant (HPP) in Turkiye. The method proposed in this study accounts for dynamic changes in water levels to accurately calculate the shading effects induced by topography. First, the minimum reservoir surface for the FPV system was calculated using remote sensing (RS). The minimum reservoir surface area, which was determined as 504.69 ha using 60 Sentinel-2 satellite images, was calculated using machine learning al-gorithms on the Google Earth Engine (GEE) platform, support vector machines (SVM) and automatic water extraction index (AWEI). In the second stage, new digital elevation model (DEM) maps were produced by overlapping monthly changes in water height with ALOS PALSAR data and solar analysis was performed on them. An annual global horizontal irradiance (GHI) map was produced using these maps, and it was divided into five classes to emphasize differences in production potential. The results revealed that 1083.45 GWh of elec-tricity can be produced annually by installing FPV in very high and high potential areas. However, as the moderate, low, and very low regions represent only 5.02% of the reservoir surface and there is a 1.68-fold difference in production potential between the highest and lowest areas due to topography-induced shading near the coastline, it was concluded that FPV installation would not be efficient in those regions. This study highlights the significance of incorporating topography-induced shading and emphasizes the importance of employing RS and geographic information system (GIS) techniques to achieve this objective.
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    Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye
    Kadi, F; Yilmaz, OS
    Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems.
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    Detection of temporal changes of treeless forest areas using remote sensing techniques and Google Earth Engine platform: A case study of Trabzon Duzkoy District
    Kadi, F; Yilmaz, OS
    This study aims to detect temporal changes in treeless forest areas using remote sensing techniques on the Google Earth Engine Platform. In this direction, ten treeless forest areas were determined from stand maps. A general study area was determined to include these areas, and the status of treeless forest areas was obtained by classifying the study area with a random forest algorithm on Sentinel-2 images. Then, normalized difference vegetation index (NDVI) time series analyses were performed on Landsat images to reveal vegetation changes in these treeless forest areas. In the classification study conducted with Sentinel-2 images, the land was divided into three classes: forest, treeless forest areas, and bare land. The overall accuracy of the classification study was calculated as 89.46%, and the Kappa value as 0.810. When the obtained treeless forest areas were compared with the areas on the stand map, it was seen that there was an average change of about 52.56% in terms of forest cover for ten regions. As a result of the analyses made with NDVI time series, it was observed that vegetation in treeless forest areas increased and therefore these areas tended to close.
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    Determining highway slope ratio using a method based on slope angle calculation
    Yilmaz, OS; Özkan, G; Gülgen, F
    Geographic Information System (GIS) is a vital tool used in numerous areas related to natural science and engineering studies. Managing complex data and obtaining accurate results from the analysis are essential functions of GIS. It is also efficiently used in highway designing both in project and application phases. This study proposes a new calculation method of slope angles to determine the suitable slope modal of a road by using topographic and geological datasets in a GIS environment. Using this method in the preparation phase of the project enables a more accurate calculation of earthwork volume. The proposed method was applied to a highway to prove this idea. The selected road is a significant tertiary of which project was completed by the Turkish General Directorate of Highways. In this study, the calculated values of the project were considered as references. Comparing both results obtained from the proposed method and application project, the accuracy of the slope modal of the proposed method is 71%, and the accuracy of its earthwork volume is 99%. The proposed approach will enable project managers and designers to determine more reliable earthwork volume during project feasibility studies without any application in the field.
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    A new method for fully automated detection of algae blooms in Antarctica using Sentinel-2 satellite images
    Acar, U; Yilmaz, OS; Sanli, FB; Ozcimen, D
    The melting of Antarctic glaciers has become a significant issue as a result of global climate change. Algae on the Antarctic ice/snow is an important part of terrestrial photosynthetic organisms. Monitoring and tracking these algal blooms is crucial for understanding the melting of glaciers in the region. Due to the climatic and natural conditions of the region, traveling to and arranging logistics for monitoring and observing snow algae in the Antarctic continent becomes extremely challenging. To overcome these challenges, a novel algorithm has been developed and designed to automatically detect and analyze green algae (Chlorella sp.) from satellite images. Leveraging the vast and free available data from the Sentinel -2 satellite, the algorithm utilizes its high spectral resolution capabilities, capturing invaluable information from various spectral bands. The algorithm was formulated based on the image obtained on February 28, 2017, where green algae formations were intensively seen in the Ryder Bay. The algorithm was developed based on rule -based detection of algae, with the usage of reflection values from the areas where ground truth was established on this date. The developed algorithm was coded and tested using Python version 3.9. The accuracy analysis of the algorithm was conducted using overall accuracy (OA), F1 score, and Kappa statistical test. As a result of the analysis, the OA, F1 score, and Kappa statistic values were calculated as %91, %88.82-% 95.27, and 0.901, respectively. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.
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    Spatiotemporal statistical analysis of water area changes with climatic variables using Google Earth Engine for Lakes Region in Turkiye
    Yilmaz, OS
    In this study, trend analysis of the lake surface areas was performed on the Google Earth Engine (GEE) platform in the period of 1985-2022 with Landsat 5/7/8/9 (TM) (ETM +), and (OLI) satellite images. The study analyzed 10 lakes, including Acigol, Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli in the Turkiye Lakes Region. In this analysis, the normalized differentiated water index was calculated for each of the 3147 satellite images, and water surfaces were extracted from other details using Otsu's threshold method. In the study's accuracy, the overall accuracy and F1-score values were calculated to be over 90% for all lakes. Moreover, the relationship between the changes in the surface areas of the lakes was evaluated using correlation analysis, with the sea surface temperature obtained from the NOAA satellite and the evaporation, temperature, and precipitation parameters obtained from the Era-5 satellite being used. In addition, the change of the area on the lake surface was analysed using Mann-Kendall (MK), Sen's slope, and sequential MK test statistics. During the 37 years between 1985 and 2022, there was no significant change in the Acigol surface area, but a slight increasing trend was observed. Decreases of 76.07, 4.68, 41.77, 5.44, 37.56, 28.97, 78.65, 7.26, and 81.02% were determined in the lakes of Aksehir, Beysehir, Burdur, Egirdir, Ilgin, Isikli, Karatas, Salda, and Yarisli, respectively. The application of this method in the lakes region and monitoring these lakes, which are of great importance for Turkiye, provide valuable information in determining the lakes' organizational strategies.
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    Production of landslide susceptibility maps in geographic information system by frequency ratio method: Example of Demirci Tekeler Village, Demirci, Manisa
    Yilmaz, OS
    In this study, the landslide susceptibility map of Tekeler village, located in the borders of Demirci district of Manisa province, which occurred in 2009 and was declared as a disaster area, was produced using geographic information systems-based frequency ratio method. In landslide susceptibility analysis, precipitation, slope, aspect, height, distance to stream, distance to road, land use, lithology, curvature, topographic wetness index, normalized difference vegetation index conditioning was selected as factors. Sample random points were determined using Google Earth images from the landslide area, and the determined points were divided into two classes, 70% for training and 30% for testing. As a result, the landslide susceptibility map is divided into five classes: very low, low, medium, high, and very high. It was observed that the areas within these classes covered 11.36%, 39.61%, 34.32%, 12.89%, and 1.81% of the entire area, respectively. The accuracy of the landslide susceptibility map is calculated by considering the area under the receiver operating characteristic curve. AUC value success rate was calculated as 95.14% and prediction rate as 94.11%. With this study, it has been shown that landslide susceptibility maps can be produced successfully with the frequency ratio method. In addition, it was concluded that the resulting map is a prediction for possible landslides and can be integrated into disaster management and planning studies.
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    Flood hazard susceptibility areas mapping using Analytical Hierarchical Process (AHP), Frequency Ratio (FR) and AHP-FR ensemble based on Geographic Information Systems (GIS): a case study for Kastamonu, Turkiye
    Yilmaz, OS
    Global climate change brings with it various natural disasters. In particular, natural disasters such as floods destroy nature and human resources. The flood disaster in Kastamonu province, primarily striking Bozkurt district and many other districts in Turkiye on August 11, 2021, causing both life and material losses, has been one of the most devastating disasters in the Black Sea region. In this study, various geospatial and statistical methods were used to produce flood hazard susceptibility maps of Kastamonu province. In order to evaluate the flood risk in Kastamonu, eleven different variables, i.e. rainfall, slope, elevation, distance from stream, land-use-land cover, lithology, curvature plan, curvature profile, Topographic Wetness Index, Stream Power Index and Normalised Differences Vegetation Index were used. Flooded areas were determined by the Modified Normalised Water Index (MNDWI) on the Google Earth Engine platform using Remote Sensing techniques. Flood points determined on the calculated MNDWI image are divided into 70% training and 30% testing dataset. Geographical Information Systems-based Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and ensemble AHP-FR were used in the creation of flood hazard susceptibility maps. The maps were divided into five classes: very low, low, moderate, high, and very high. On the map classified using AHP-FR, areas in high and very high sensitivity classes were calculated as 128.72 km(2) and 6.89 km(2), respectively. These calculated areas constitute 0.99% and 0.05% of the entire region. On the other hand, part of Kastamonu province with an area of 484.07 km(2) was determined as a moderate-risk area. This area covers 3.71% of the entire province. The remaining part of the province, with an area of 8729.39 km(2) and 3697.30 km(2), is classified as very low and low, respectively. These areas cover 66.91% and 28.34% of the entire province, respectively. The study's accuracy was tested using the receiver operating characteristic curves method. Area under curve values for AHP, FR, and AHP-FR were calculated as 0.965, 0.989, and 0.992, respectively. According to these values, using the AHP-FR ensemble gave more successful results than the other two methods.
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    Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey
    Yilmaz, OS; Akyuz, DE; Aksel, M; Dikici, M; Akgul, MA; Yagci, O; Sanli, FB; Aksoy, H
    Wildfires are regarded as one of the devastating natural disturbances to natural ecosystems, and threatening the lives of many species. In July 2021, a wildfire took place in the Mediterranean region of Turkey in multiple areas. In Bodrum, a town with high touristic value and attraction, approximately 17,600 hectares of forest have been affected by the wildfire. In this study, the fire-affected areas were determined using an analytical hierarchy process (AHP) and geographical information system (GIS). Rainfall, slope, distance from the stream, pre- and post-fire land use and land cover, elevation, curvature, topographic wetness index, and lithology were selected as the governing variables for the AHP model. The contribution of each variable was determined from the literature. Based on the model, it was found that the area with a very high flood risk increased from 8.6 to 18.4%, implying flood risk in a particular region doubled following the wildfire. Immediately after the forest fire, floods occurred in Mazikoy in the region and its surroundings. The model accuracy was tested by using randomly selected 61 points in and around the flooded area. The model accuracy was quantified by the receiver operating characteristic (ROC) curves method. Pre- and post-fire areas under curve (AUC) values were found 0.925 and 0.933, respectively, which implies that the prediction ability of the model is acceptably accurate. The study revealed that the model could quantify the increased flood risk for vulnerable areas after a forest fire. Such knowledge may aid local authorities in determining the priorities of the precautions that need to be taken after a forest fire.
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