Browsing by Author "Gulgen, F"

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
  • No Thumbnail Available
    Item
    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.