Browsing by Author "Yilmaz O.S."

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    Using remote sensing to calculate floating photovoltaic technical potential of a dam's surface
    (Elsevier Ltd, 2020) Ates A.M.; Yilmaz O.S.; 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 Demirköprü 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 m3 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. © 2020 Elsevier Ltd
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    Corrigendum to “Using remote sensing to calculate floating photovoltaic technical potential of a dam's surface” [Sustain. Energy Technol. Assess. 41 (2020) 100799] (Sustainable Energy Technologies and Assessments (2020) 41, (S2213138820312261), (10.1016/j.seta.2020.100799))
    (Elsevier Ltd, 2021) Ates A.M.; Yilmaz O.S.; Gulgen F.
    The authors regret that Table 3 was incorrect in the published version. Table 3 is revised and given below. [Table presented] © 2020 Elsevier Ltd
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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, Türkiye
    (Springer Science and Business Media Deutschland GmbH, 2022) Yilmaz O.S.
    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 Türkiye 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 km2 and 6.89 km2, 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 km2 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 km2 and 3697.30 km2, 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. © 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.
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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
    (Institute for Ionics, 2023) Yilmaz O.S.; Gulgen F.; Balik Sanli F.; Ates A.M.
    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. © 2023, King Fahd University of Petroleum & Minerals.
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    Spatiotemporal statistical analysis of water area changes with climatic variables using Google Earth Engine for Lakes Region in Türkiye
    (Springer Science and Business Media Deutschland GmbH, 2023) Yilmaz O.S.
    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 Türkiye 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 Türkiye, provide valuable information in determining the lakes’ organizational strategies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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    A novel approach suggestion for assessing the impact of topographic shading on the estimation of the floating photovoltaic technical potential
    (Elsevier Ltd, 2023) Yilmaz O.S.; Ateş A.M.; Gülgen F.
    This study presents a novel approach for the implementation of floating photovoltaic (FPV) systems at the Ayvalı hydroelectric power plant (HPP) in Türkiye. 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 algorithms 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 electricity 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. © 2023
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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
    (Springer, 2023) Yilmaz O.S.; Akyuz D.E.; Aksel M.; Dikici M.; Akgul M.A.; Yagci O.; Balik Sanli F.; 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. © 2023, The Author(s) under exclusive licence to International Consortium of Landscape and Ecological Engineering.
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    Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform
    (Springer Science and Business Media Deutschland GmbH, 2023) Yilmaz O.S.; Acar U.; Sanli F.B.; Gulgen F.; Ates A.M.
    This study investigated forest fires in the Mediterranean of Türkiye 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, Gündoğmuş, Marmaris, Bodrum and Köyceğiz 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/m2 in all regions burned before the fire, it was observed that this value increased to 0.14 mol/m2 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/m2. 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. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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    A new method for fully automated detection of algae blooms in Antarctica using Sentinel-2 satellite images
    (Elsevier Ltd, 2024) Acar U.; Yilmaz O.S.; Balik Sanli F.; 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. © 2023 COSPAR