Terrain Landform Recognition Based on Kernel Pattern Modeling (with Focus on Glacial and Subglacial Landforms), in Alborz Mountainous belt

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SOLHI SINA; SEIF ABDOLLAH

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Journal: Physical Geography Research Year:2020 | Volume:52 | Issue:2 Page(s): 271-294

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Information Journal Paper

Title

Terrain Landform Recognition Based on Kernel Pattern Modeling (with Focus on Glacial and Subglacial Landforms), in Alborz Mountainous belt

Author(s)

SOLHI SINA | SEIF ABDOLLAH | Issue Writer Certificate

Keywords

Kernel PatternQ1

landform recognitionQ1

Terrain LandformQ1

Object-based modelingQ1

GlacialQ1

sub-glacial landformQ1

Abstract

Terrain morphology, provides a lot information for researchers in the field of environmental science. One of the goals in geomorphology is identification, and analyzing Terrain Landforms. In the past, the identification of landforms was performing field-based or using topographical maps. Manually, which was time-consuming and difficult, and in vast areas, it was facing many problems. In this article we had attempted to identify Glacial and sub Glacial landforms including: Glacial cirque, Glacial sinkhole (Tarn Lake), summit, saddle, ridgeline, drainage line, and sedimentary fans. For recognizing these landforms, two modelling approaches have followed. First is a conceptual modeling, which uses Kernel Pattern modeling. This modeling level, provides the condition in which, terrain morphology compares to the reference pattern. Second is an Object-based modeling, which uses reference object to recognize landforms. Above mentioned landforms, considered in this research, recognizes, using both modeling approaches, and the results represents in the form of maps. Some typical landforms considered to make accuracy assessment and performance control, for each model output possible. To automate modeling procedures, Python programming language used widely. Eventually, all codes and scripts prepared in the build-in Graphical User Interface (GUI) programming environment of python (Tkinter), and the software, named: Landform Detector V. 1 prepared. Accuracy assessment, shows that landform recognition process had performed with 60% in average, which respect to the landform complexity, is acceptable. Average accuracy of the considered models are equal to 51. 58 % and 50. 60 % for conceptual and object-based approaches, respectively. In result, object-based approach had a better performance overall.

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APA: Copy

SOLHI, SINA, & SEIF, ABDOLLAH. (2020). Terrain Landform Recognition Based on Kernel Pattern Modeling (with Focus on Glacial and Subglacial Landforms), in Alborz Mountainous belt. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, 52(2 ), 271-294. SID. https://sid.ir/paper/375119/en

Vancouver: Copy

SOLHI SINA, SEIF ABDOLLAH. Terrain Landform Recognition Based on Kernel Pattern Modeling (with Focus on Glacial and Subglacial Landforms), in Alborz Mountainous belt. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2020;52(2 ):271-294. Available from: https://sid.ir/paper/375119/en

IEEE: Copy

SINA SOLHI, and ABDOLLAH SEIF, “Terrain Landform Recognition Based on Kernel Pattern Modeling (with Focus on Glacial and Subglacial Landforms), in Alborz Mountainous belt,” PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, vol. 52, no. 2 , pp. 271–294, 2020, [Online]. Available: https://sid.ir/paper/375119/en

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