Yazar "Hatipoglu, Cansu Besel" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    Optimizing GNSS-IR for sea level monitoring: a case study along the Eastern Black Sea coast of Türkiye
    (Walter De Gruyter Gmbh, 2026) Hatipoglu, Cansu Besel
    Determining the optimal parameter conditions based on satellite elevation angle and azimuth ranges allows the examination of the reflection surface at the GNSS site locations. This study aims to assess whether the GNSS site locations along the Trabzon coast in the Eastern Black Sea region of T & uuml;rkiye meet the optimal parameter conditions for monitoring of sea level changes using GNSS-IR. Additionally, the impact of different topographical features and sea conditions was assessed for parameter selection. For this purpose, an experimental area was selected along the coast, and GNSS receivers were installed at four experimental points (NOK1, NOK2, NOK3, NOK4) with different topographic characteristics. A 3-day measurement campaign was conducted to determine the optimal parameter conditions. Signal-to-noise ratio (SNR) data from these four experimental points were used in the study. Satisfactory results could not be obtained at NOK1 and NOK2. Only the NOK4 could be compared with the nearby tide gauge. A relatively high root mean square error of 20 cm was obtained between the two datasets. The results indicate that the data collected from the experimental points were affected by the optimal parameter conditions. Moreover, the GNSS-IR-derived sea level was influenced by the environmental circumstances in locations with optimum parameters.
  • [ X ]
    Öğe
    Quality control of the GNSS-IR sea level measurements by using K-means clustering
    (Taylor & Francis Ltd, 2025) Hatipoglu, Cansu Besel; Kayikci, Emine Tanir
    Quality control is a crucial step in GNSS-IR data processing and is performed in this study using two methods: the peak-to-noise ratio and K-means clustering. Both quality control methods are applied to the SNR data at the MERS, TRBZ, and SNOP sites. K-means clustering shows better performance for the MERS GPS L1, Galileo L1, and SNOP GPS L2, while the peak-to-noise ratio shows better performance for the TRBZ GPS L1. The correlation coefficient between the GNSS-IR sea levels from the L1 signal and tide gauge is greater than 85%. These results demonstrate that K-means clustering is promising for quality control.