SERDAR GÖNCÜ

Dr. SERDAR GÖNCÜ

sgoncu
orcid

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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Verified
Journal Article
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ZAZehra Yigit Avdan
Zehra Yigit Avdan
Gordana KaplanGordana Kaplan
Gordana Kaplan
SERDAR GÖNCÜSERDAR GÖNCÜ
SERDAR GÖNCÜ
Uğur AvdanUğur Avdan
Uğur Avdan
Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r2 = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas.

3 June 2009 in Water Resources Management
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SERDAR GÖNCÜSERDAR GÖNCÜ
SERDAR GÖNCÜ
EAErdem Albek
Erdem Albek

13 February 2012 in Hydrological Processes
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ÖGÖmer Güngör
Ömer Güngör
SERDAR GÖNCÜSERDAR GÖNCÜ
SERDAR GÖNCÜ

1 August 2017 in Afyon Kocatepe University Journal of Sciences and Engineering
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Journal Article
Open access
SERDAR GÖNCÜSERDAR GÖNCÜ
SERDAR GÖNCÜ
EAErdem Ahmet Albek
Erdem Ahmet Albek
MAMine Albek
Mine Albek

11 November 2019 in Environmental Science and Pollution Research
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MAMine Albek
Mine Albek
EAErdem Ahmet Albek
Erdem Ahmet Albek
SERDAR GÖNCÜSERDAR GÖNCÜ
SERDAR GÖNCÜ
BUBurcu Şimşek Uygun
Burcu Şimşek Uygun

12 November 2019 in Proceedings
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Conference Paper
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Gordana KaplanGordana Kaplan
Gordana Kaplan
ZAZehra Yigit Avdan
Zehra Yigit Avdan
SERDAR GÖNCÜSERDAR GÖNCÜ
SERDAR GÖNCÜ
Uğur AvdanUğur Avdan
Uğur Avdan
In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements that require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here, the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the normalized difference turbidity index (NDTI). The turbidity results were then compared with data from 21 in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data.