Identifying the sediment source zones using maximum likelihood, minimum distance and parallelepiped algorithms (Case Study: South Roudbar, Kerman)

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MAHDAVI R.; Alievazi A.; GHOLAMI H.; KAMALI A.

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Journal: IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH Year:2017 | Volume:24 | Issue:3 (68) Page(s): 610-622

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Title

Identifying the sediment source zones using maximum likelihood, minimum distance and parallelepiped algorithms (Case Study: South Roudbar, Kerman)

Author(s)

MAHDAVI R. | Alievazi A. | GHOLAMI H. | KAMALI A. | Issue Writer Certificate

Keywords

Remote sensingQ2

Landsat8Q1

OLIQ1

wind erosionQ2

source zoneQ1

south RoudbarQ1

Abstract

Integrating Remote sensing techniques (RS) and GIS (GIS) is an important tool to identify sources of sand. This can reduce the time and cost of identifying the location of sand resources. In this study, wind sediment source was identified using the image processing techniques as well as digital data of Landsat 8 and OLI in south Rudbar city. To do so, first the radiometric correction was applied on data and the best band combination was identified by the use of optimal index factor (OIF) techniques, so that the band combination (5, 6, 7) was desirable. Then, the images were classified in three ways including parallelepiped, the minimum distance and maximum likelihood. To assess the accuracy of classification, ground observations were recorded using GPS. Finally, the four criteria including overall accuracy, Kappa coefficient, producers' accuracy and users' accuracy were used to express the accuracy of classification. The results of the three classification methods show that more than 50% of the study area is located in the class of sediment source zones, mainly consisting of agricultural lands, dried-bed rivers and saline lands as well as parabola-shaped surfaces in the playa unit. The results of the accuracy assessment showed that the maximum likelihood algorithm with an overall accuracy of 95. 54 % and Kappa coefficient of 0. 9 were more accurate as compared with other algorithms. To extract the maps of sediment source zones with higher accuracy the use of high spatial resolution images such as Ikonos and Quick Bird is recommended.

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APA: Copy

MAHDAVI, R., Alievazi, A., GHOLAMI, H., & KAMALI, A.. (2017). Identifying the sediment source zones using maximum likelihood, minimum distance and parallelepiped algorithms (Case Study: South Roudbar, Kerman). IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH, 24(3 (68) ), 610-622. SID. https://sid.ir/paper/107255/en

Vancouver: Copy

MAHDAVI R., Alievazi A., GHOLAMI H., KAMALI A.. Identifying the sediment source zones using maximum likelihood, minimum distance and parallelepiped algorithms (Case Study: South Roudbar, Kerman). IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH[Internet]. 2017;24(3 (68) ):610-622. Available from: https://sid.ir/paper/107255/en

IEEE: Copy

R. MAHDAVI, A. Alievazi, H. GHOLAMI, and A. KAMALI, “Identifying the sediment source zones using maximum likelihood, minimum distance and parallelepiped algorithms (Case Study: South Roudbar, Kerman),” IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH, vol. 24, no. 3 (68) , pp. 610–622, 2017, [Online]. Available: https://sid.ir/paper/107255/en

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