Browsing by Author "Acar, U"

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    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.
  • 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
    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.