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Information Journal Paper

Title

ANALYSIS OF MEGA-LANDFORMS IN DESERT ENVIRONMENT USING ARTIFICIAL NEURAL NETWORK, IRAN’S LUT AND CHINA’S QAIDAM DESERTS

Pages

  515-527

Abstract

 Introduction: Morphological segmentation of land surface is commonly used for land surface allocation in management and environmental sciences. The first step in assigning a piece of land to a specific application is to divide it into homogeneous morphological segments. Traditional field-based geomorphological mapping can be time consuming, costly, and challenging when large areas, particularly in remote areas where terrain is difficult to access and where the topography and LANDFORMs can be highly variable over short distances. Like other geomorphometric studies DEM is used as the basic input and SRTM DEM with 90 m resolution is actually suitable for mega LANDFORMs analysis. That is one of the most reliable elevation models worldwide. ...

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    APA: Copy

    EHSANI, AMIR HOUSHANG, & FOROUTAN, MARZIEH. (2014). ANALYSIS OF MEGA-LANDFORMS IN DESERT ENVIRONMENT USING ARTIFICIAL NEURAL NETWORK, IRAN’S LUT AND CHINA’S QAIDAM DESERTS. JOURNAL OF ENVIRONMENTAL STUDIES, 40(2), 515-527. SID. https://sid.ir/paper/2865/en

    Vancouver: Copy

    EHSANI AMIR HOUSHANG, FOROUTAN MARZIEH. ANALYSIS OF MEGA-LANDFORMS IN DESERT ENVIRONMENT USING ARTIFICIAL NEURAL NETWORK, IRAN’S LUT AND CHINA’S QAIDAM DESERTS. JOURNAL OF ENVIRONMENTAL STUDIES[Internet]. 2014;40(2):515-527. Available from: https://sid.ir/paper/2865/en

    IEEE: Copy

    AMIR HOUSHANG EHSANI, and MARZIEH FOROUTAN, “ANALYSIS OF MEGA-LANDFORMS IN DESERT ENVIRONMENT USING ARTIFICIAL NEURAL NETWORK, IRAN’S LUT AND CHINA’S QAIDAM DESERTS,” JOURNAL OF ENVIRONMENTAL STUDIES, vol. 40, no. 2, pp. 515–527, 2014, [Online]. Available: https://sid.ir/paper/2865/en

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