Browsing by Author "Sanli, FB"

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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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    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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    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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    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.