نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
2 گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.
3 استاد، گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Examining the changes in the water quality parameters of the Karun River using satellite data and determining the modified quality indicators can be used to assess the vulnerability of irrigation water. In this research, the data of seven qualitative parameters: Na+, SAR, pH, Cl-, HCO3-, EC and TDS were used in six water measuring stations of Abbaspur, Ahvaz, Farsiat, Gotvand, Molasani and Soosan of the Karun River (1392-1398) and NSFWQI general water quality index was calculated. Correlation relationship and estimation errors were determined for two principal component reduction (PCA) and multivariate linear correlation methods. The results showed that multivariate correlation method is more suitable. The coefficient of determination for the seven water quality parameters was calculated as 0.44, 0.43, 0.03, 0.43, 0.09, 0.45 and 0.46 respectively, and for the NSFWQI index it was calculated as 0.46. The statistical correlation for pH and HCO3- is at a very low level; but for the other five parameters and the NSFWQI index, it is in average state. The significance level of estimation for Na+, SAR, Cl-, HCO3-, EC and TDS parameters is acceptable (P<0.001), and for pH is unacceptable (P<0.122). In general, there is an acceptable correlation between the bands extracted through the Landsat 8 satellite and most of the quality parameters of the Karun River, but necessary actions are proposed to improve the correlation relationships. Also, the NSFWQI index shows that the quality of water in the Karun River along the Khuzestan plain is not good enough.
کلیدواژه‌ها [English]
Investigating changes in water quality of the Karun River using landsat 8 satellite data
EXTENDED ABSTRACT
The purpose of this research is to find a suitable relationship between the water quality parameters of the Karun River, which were collected in the field at specific times, with the spectral bands extracted from the Landsat 8 satellite. Also, by using NSFWQI quality index, it is possible to express the quality status of river water in different sampling stations.
To conduct the research, the data of qualitative parameters Na+, SAR, pH, Cl-, HCO3-, EC and TDS of the water of the Karun River in the sampling stations of Abbaspoor, Ahvaz, Farsiat, Gotvand, Mollasani and Soosan from 2012 to 2018 were used. became For the quality data of the river water that was collected in the field at the mentioned stations, at the same time and date of field sampling, Landsat 8 satellite images were downloaded from the USGS website and the spectral bands of the downloaded images extracted by ARC-GIS software were compared to Each of the quality parameters of the river water was placed in each station on the same date of field sample collection. Because the numbers of spectral bands are large, they should be converted to numbers between zero and one using the Scale Factor formula related to Collection 2-Level 2 of Landsat 8 satellite. Finally, the relationship between 7 water quality parameters of Karun River and 7 spectral bands extracted from Landsat 8 satellite was obtained using different regression methods. Also, using the NSFWQI quality index, the quality status of the river water in each of the sampling stations of the Karun River was determined by averaging the quality indicators obtained during the 6-year statistical period.
To express the relationship between the qualitative parameters of the river water and the spectral bands extracted from the Landsat 8 satellite, the multivariate linear regression method was used by calculating the coefficient of determination and the standard error.
The relationship between the qualitative parameters of the river water in each of the sampling stations during the 6-year statistical period, with the spectral bands extracted from the Landsat 8 satellite, provides a suitable relationship. In this research, linear multivariate regression was used. Finally, the relationship between each of the qualitative parameters of the river with the spectral bands extracted from the Landsat 8 satellite has been expressed in the form of mathematical relationships. Also, NSFWQI index was used to express the water quality status of Karun River in each of the mentioned hydrometric stations. The NSFWQI index shows that the quality of water in the Karun River along the Khuzestan plain is not good enough.
The coloration between the bands extracted from the Landsat 8 satellite and the quality parameters of the Karun River was very poor with the method of Principal Component Analysis (PCA), but was found to be acceptable using Linear Multivariate Regression method. The coefficient of determination for the seven parameters of Na+, SAR, pH, Cl-, HCO3-, EC and TDS was 0.44, 0.43, 0.03, 0.43, 0.09, 0.45 and 0.46, respectively, and for the NSFWQI was calculated to be 0.46. The standard error for the seven parameters was calculated as 3.84, 1.43, 0.26, 3.58, 0.39, 598.70 and 393.87, respectively, and for the NSFWQI was about 60.73. The statistical correlation for pH and HCO3- is at a very low level; but for the other five parameters and the NSFWQI index, it is in average state. The significance level of estimation for Na+, SAR, Cl-, HCO3-, EC and TDS parameters is acceptable (P<0.001), and for pH is unacceptable (P<0.122). In general, the correlation is very poor for the pH and HCO3-, but is good enough for other parameters. The results showed that there is an acceptable relationship between the bands extracted from the Landsat 8 satellite and most of the quality parameters of the Karun River. However, further investigation is proposed to detect an optimum correlation using the Landsat 8 and Sentinel satellites data and intelligent analysis methods (such as machine learning and artificial intelligence).
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