How much the Remote Sensing Indices can Improve Suspended Sediment Predictions?

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FATHZADEH ALI; ASADI MARYAM; TAGHIZADEH MEHRJARDI RUHOLLAH

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Journal: Physical Geography Research Year:2017 | Volume:49 | Issue:1 Page(s): 21-24

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Information Journal Paper

Title

How much the Remote Sensing Indices can Improve Suspended Sediment Predictions?

Author(s)

FATHZADEH ALI | ASADI MARYAM | TAGHIZADEH MEHRJARDI RUHOLLAH | Issue Writer Certificate

Keywords

Data miningQ1

Digital Elevation ModelQ1

Geomorphometry ParametersQ1

Satellite ImagesQ2

Abstract

Introduction: In the recent decades, the prediction of suspended sediment load was highly regarded by water resources management and engineering researches, particularly in flood prone areas. Nowadays, the methods and artificial intelligence techniques to predict hydrologic properties have become very popular. In recent studies, we have used various parameters such as the spectral reflection bands of Satellite Images, land use and geology maps and climatic data. Landsat Satellite Images have good spatial resolution. Da Silvia (2015) also used spectral calibration of multispectral Satellite Images to assess suspended sediment concentration. Their results showed that the concentration of suspended sediment has been strongly influenced by seasonal rainfall. The yellow river sediment using Landsat Satellite Images were evaluated by Zhang et al (2014)....

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APA: Copy

FATHZADEH, ALI, ASADI, MARYAM, & TAGHIZADEH MEHRJARDI, RUHOLLAH. (2017). How much the Remote Sensing Indices can Improve Suspended Sediment Predictions?. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, 49(1 ), 21-24. SID. https://sid.ir/paper/367075/en

Vancouver: Copy

FATHZADEH ALI, ASADI MARYAM, TAGHIZADEH MEHRJARDI RUHOLLAH. How much the Remote Sensing Indices can Improve Suspended Sediment Predictions?. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2017;49(1 ):21-24. Available from: https://sid.ir/paper/367075/en

IEEE: Copy

ALI FATHZADEH, MARYAM ASADI, and RUHOLLAH TAGHIZADEH MEHRJARDI, “How much the Remote Sensing Indices can Improve Suspended Sediment Predictions?,” PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, vol. 49, no. 1 , pp. 21–24, 2017, [Online]. Available: https://sid.ir/paper/367075/en

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