Evaluation of indicators of remote sensing measurement in quantitative and qualitative studies of surface water with Landsat-8 satellite images (Case study: South of Khuzestan province)

Subject Areas : Geospatial systems development

Seyed Reza  Mir Alizadehfard ¹ (Lecturer, Department of Natural Resources and Environment, Faculty of Agriculture, Payam Noor University)
Shahrooz  Mansouri ² (MSc. Graduated of Range Management, Faculty of Natural Resources, Gorgan University of Agricultural Sciences and Natural Resources)

Received: 2018-09-21 Accepted : 2019-06-26 Published : 2019-07-23

Keywords: Landsat, Surface water, Karun river, Jarahi-Zohreh river, Khuzestan,

Abstract :

Water as one of the most basic needs of our present life and the extent of our use in drinking, agriculture, industry, economic, social, and political-security politics make us to identify with minimal cost savings and time characteristics of the watersheds, rivers and water levels by various methods, including the use of satellite imagery. The purpose of this research was to evaluate the methods of detecting zones, water levels and rivers with indicators; Normalized difference vegetation index, Enhanced vegetation index, Soli  adjusted  vegetation index, Normalized difference water index, Modified normalized difference water index, Automated water extraction index, Automated water extraction index and Unsupervised IsoClusterc and supervised Maximum likelihood classification methods to identification waters basin and the Optimum factor index for identifying the quality of water in terms of salinity, as well as determination infiltrate tabs water entering the larger zones in the part of the basins of the Karun river, Jarahi-Zohreh in the southern province of Khuzestan, with Landsat-8 satellite Land Earth Observations sensor. The results of the study showed that the automatic indicators of the extraction of water in shadow and urban areas are more effective than other indicators because of the consideration of short-range infrared wavelengths in water identification. With the results of the Supervised classification method, they were Maximum likelihood to the Kappa coefficient of the same 94% and the same performance. The results of the Optimum factor Index indicator for the detection of salinity water and the determination infiltrate tab water Show the most useful information and remove duplicate image banding data the Landsat-8 satellite Earth Observation Sensor was 79.10% for the color combination RGB of 651.

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