Estimation of Suspended Sediments Using Remote Sensing Technique Sensor in North-West Coasts of Persian Gulf
Subject Areas :
Environmental pollutions (water, soil and air)
Heeva Elmizadeh
1
,
Khosro Fazelpour
2
1 - Assistant professor, Faculty of Natural Resources, Khorramshahr University of Marine Sciences and Technology* (Corresponding Author).
2 - Master Student., Faculty of Natural Resources, Khorramshahr University of Marine Sciences and Technology
Received: 2016-03-11
Accepted : 2016-04-27
Published : 2018-03-21
Keywords:
Remote sensing,
Suspended Sediments,
Suspended Solids (SS),
Total Suspended Sediment (TSS),
MODIS,
Abstract :
Abstract
Background and Objective: This paper aims to estimate suspended sediment using Remote Sensing Technique in the North-west coasts of the Persian Gulf, the two indices which have been used are SS and TSS. Data used in this study are collected from MODIS sensor data through Aqua satellite.
Method: Several different algorithms are used for creating these images and they are used to detect and determine Suspended Sediments. In this regard, by introducing the variables required to extract data, Radiometric and atmospheric correction coding is based on MATLAB programming, Finally, entering the matrix file and apply filters ArcGIS software is proportional to its level in the final maps and points of view have been achieved.
Findings: In general, RMSE results illustrate that using combinational regression method and employing satellite imagery with 500-meter bands and reaching a higher coefficient of determination (=0.82) and also the lowest RMSE (RMSE=0.88 mg/L) and 250 and 500-meter bands is more suitable for designing SS maps in Persian Gulf.
Discussion and Conclusion: These results indicate a very high linear relationship between dependent variable (TSS and SS field data) and data from 1 to 7 bands and sensor angle (depending on the used algorithm and model) are considered as independent variables and it was observed that there is a significant relationship between field data and extracted data from satellite imagery. All these results indicate the potential of remote sensing technique for sensing and considering marine parameters.
References:
Reference
Smith, G. M. and E. J. Milton, 1999. The use of the empirical line method to calibrate remotely sensed data to reflectance, International Journal of remote sensing, 20(13): 2653-2662.
Justice, C. O., J. R. G. Townshend, E. F. Vermote, E. Masuoka, R. E Wolfe, N. Saleous, D. P. Roy, and J. T. Morisette, 2002. An overview of MODIS land data processing and product status, Remote Sensing of Environment, 83: 3-15.
Mueller, J. L., Morel, A., Frouin, 2003. Ocean optics protocols for Satellite Ocean Color validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Protocols, (Eds.). NASA/TM 2003-21621 (pp. 28-29) Greenbelt, MD: NASA Goddard Space Flight Center.
Aghighi, H.,1973. Assessment of MODIS data for mapping water turbidity in the Caspian Sea South Coasts , a master's thesis, Tehran, Tarbiat Modares University, 144 p. (in Persian)
Dekker, A. G., Vos, R. J., Peters, S. W. M., 2001. Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. Science of the Total Environment, 268, 197−214.
Yang, M.D., Merry S.M, 1996. Adaptive short_term water quality forecasts using remote sensing and GIS; RWRA symposium on GIS and water resources research center symposium procedings, september 22-26, Fort Lauderdale Florida, pp. 109-118, 1996.
Miller, R. L. & McKee, B. A., 2004. Using MODIS Terra 250 m imagery to map concentration of total suspended matter in coastal waters, Remote Sensing of Environment, Vol. 93, 259-366.
Chen, X. L., Li, Z. G. Liu, K.D. Yin, Z. Li, and W. H. King, 2004. Integration of multi-source data for water quality classification in the Pearl River Korean Journal of Remote Sensing, Vol.23, No.3, 2007–168–estuary and its adjacent coastal waters of Hong Kong, Continental Shelf Research, 24:1827-1843.
Mohammed, F., Khattab, O. And Border, J. M. 2014. Application of Landsat 5 and Landsat 7 Images Data for Water Quality Mapping in Mosul Dam Lake, Northern Iraq. Arabian Journal of Geosciences. September 2014. 7(9): 3557-3573
Chang, N., Imen, S. And Vannah, B. 2015. Remote Sensing for Monitoring Surface Water Quality Status and Ecosystem State in Relation to the Nutrient Cycle: A 40-Year Perspective. Critical Reviews in Environmental Science and Technology. 45(2): 101-166.
Roberts, D., K. Halligan, and P. Dennison, 2007. Combined use of remote sensing and continuous monitoring to analyze the variability of suspended-sediment concentrations in San Francisco Bay, California, Estuarine, Coastal Shelf Sci., 53, 801–812.
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Reference
Smith, G. M. and E. J. Milton, 1999. The use of the empirical line method to calibrate remotely sensed data to reflectance, International Journal of remote sensing, 20(13): 2653-2662.
Justice, C. O., J. R. G. Townshend, E. F. Vermote, E. Masuoka, R. E Wolfe, N. Saleous, D. P. Roy, and J. T. Morisette, 2002. An overview of MODIS land data processing and product status, Remote Sensing of Environment, 83: 3-15.
Mueller, J. L., Morel, A., Frouin, 2003. Ocean optics protocols for Satellite Ocean Color validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Protocols, (Eds.). NASA/TM 2003-21621 (pp. 28-29) Greenbelt, MD: NASA Goddard Space Flight Center.
Aghighi, H.,1973. Assessment of MODIS data for mapping water turbidity in the Caspian Sea South Coasts , a master's thesis, Tehran, Tarbiat Modares University, 144 p. (in Persian)
Dekker, A. G., Vos, R. J., Peters, S. W. M., 2001. Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. Science of the Total Environment, 268, 197−214.
Yang, M.D., Merry S.M, 1996. Adaptive short_term water quality forecasts using remote sensing and GIS; RWRA symposium on GIS and water resources research center symposium procedings, september 22-26, Fort Lauderdale Florida, pp. 109-118, 1996.
Miller, R. L. & McKee, B. A., 2004. Using MODIS Terra 250 m imagery to map concentration of total suspended matter in coastal waters, Remote Sensing of Environment, Vol. 93, 259-366.
Chen, X. L., Li, Z. G. Liu, K.D. Yin, Z. Li, and W. H. King, 2004. Integration of multi-source data for water quality classification in the Pearl River Korean Journal of Remote Sensing, Vol.23, No.3, 2007–168–estuary and its adjacent coastal waters of Hong Kong, Continental Shelf Research, 24:1827-1843.
Mohammed, F., Khattab, O. And Border, J. M. 2014. Application of Landsat 5 and Landsat 7 Images Data for Water Quality Mapping in Mosul Dam Lake, Northern Iraq. Arabian Journal of Geosciences. September 2014. 7(9): 3557-3573
Chang, N., Imen, S. And Vannah, B. 2015. Remote Sensing for Monitoring Surface Water Quality Status and Ecosystem State in Relation to the Nutrient Cycle: A 40-Year Perspective. Critical Reviews in Environmental Science and Technology. 45(2): 101-166.
Roberts, D., K. Halligan, and P. Dennison, 2007. Combined use of remote sensing and continuous monitoring to analyze the variability of suspended-sediment concentrations in San Francisco Bay, California, Estuarine, Coastal Shelf Sci., 53, 801–812.