Detection of Physical and Chemical Parameters Using Water Indices (NDWI, MNDWI, NDMI, WRI, and AWEI) for Al-Abbasia River in Al-Najaf Al-Ashraf Governorate Using Remote Sensing and Geographic Information System (GIS) Techniques
Article Sidebar
Main Article Content
Abstract
The purpose of this study was to find out the connection between the water parameters that were examined in the laboratory and the water index acquired from the examination of the satellite image of the study area. This was accomplished by analysing the Landsat-8 satellite picture results as well as the geographic information system (GIS). The primary goal of this study is to develop a model for the chemical and physical characteristics of the Al-Abbasia River in Al-Najaf Al-Ashraf Governorate. The water parameters employed in this investigation are as follows: (PH, EC, TDS, TSS, Na, Mg, K, SO4, Cl, and NO3). To collect the samples, ten sampling locations were identified, and the satellite image was obtained on the same day. Geographical information systems (GIS) are commonly used for the process of projecting the coordination often Stations Along Al-Abbasia River in the image of the satellite (Landsat-8) to then analyze the spectral reflections of the items and then treat the data obtained after the analysis process by using (SPSS) Software to find the correlation coefficient and regression equations. Because of the high connections between water metrics and the water index, four regression models were discovered. These models can be used to predict the four water variables (EC, SO4, Cl, and NO3) at any point along the Al-Abbasia River directly from the satellite image.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
© 2023 The Author(s). Published by College of Science, University of Baghdad. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License.
References
Usali N. and Ismail M.H., Use of remote sensing and GIS in monitoring water quality. Journal of Sustainable Development, 2010. 3(3): pp.228-238.
Panda S., Garg V., and Chaubey I., Artificial neural networks application in lake water quality estimation using satellite imagery. Journal of Environmental Informatics, 2004. 4(2): pp.65-74.
Al-Bahrani H., Spatial prediction and classification of water quality parameters for irrigation use in the Euphrates River (Iraq) using GIS and satellite image analyses. International Journal of Sustainable Development Planning, 2014. 9(3): pp.389-399.
Abdullah H.S., Water quality assessment for Dokan Lake using landsat 8 Oli satellite images, Thesis, University of Sulaimani, 2015.
McFeeters S.K., The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 1996. 17(7): pp.1425-1432.
Ko B.C., Kim H.H., and Nam J.Y., Classification of Potential water bodies using landsat 8 Oli and a combination of two boosted random forest classifiers. Sensors, 2015. 15(6): pp.13763-13777.
Zhang K., Thapa B., Ross M., and Gann D., Remote sensing of seasonal changes and disturbances in mangrove forest: a case study from South Florida. Ecosphere, 2016. 7(6): pp.1-23.
Feyisa G.L., Meilby H., Fensholt R., and Proud S.R., Automated water extraction index: a new technique for surface water mapping using landsat imagery. Remote Sensing of Environment, 2014. 140: pp.23-35.
Mukherjee N.R. and Samuel C., Assessment of the temporal variations of surface water bodies in and around chennai using landsat imagery. Indian Journal of Science Technology, 2016. 9(18): pp.1-7.
Mustafa M., Hassoon K.I., Hussain H., and Abd M., Using water indices (NDWI, MNDWI, NDMI, WRI AND AWEI) to detect physical and chemical Parameters by apply remote sensing and GIS techniques. International Journal of Research-Granthaalayah, 2017. 5(10): pp.117-128.