Document Type : پژوهشی
Authors
1 -Associate Professor, Department of Physical Geography, Mohaghegh Ardabili University
2 MSc. Student of Remote Sensing and GIS, University of Mohaghegh Ardabili
3 Department of physical geography, University of Mohaghegh Ardabili
Abstract
Identification of landforms and the way of their distribution is one of the basic needs in applied geomorphology and other environmental sciences and landform maps show the shapes of the earth's surface. This project aims to identify the landforms of the Qaranqu catchment area using object-oriented classification methods including nearest neighbor algorithm and thresholding. Landsat satellite imagery for 1990 (TM) and 2020 (OLI) was used for this purpose. First, to apply the classification, atmospheric and radiometric corrections were applied to the images, then to better identify and extract the phenomena, principal component analysis (PCA), and MNF algorithms were used to classify satellite images using classification methods. Object-oriented, which included the nearest neighbor method and thresholding was used. For the accuracy of the maps produced using the two methods of Kappa index and the overall accuracy of the use, the results revealed that the nearest neighbor method is more accurate than the thresholding method. The classification results showed that the highest rate of decreasing changes during 1990-2020 is related to dense rangeland because it has decreased by 12.49 percent and the highest rate of incremental changes is related to irrigate agriculture which is 10.83 percent. The most important reason for this increase is the construction of Sahand Dam over time. In the absence of well-organized planning and the adoption of appropriate policies, the destruction of rangeland will continue and turning it into arable land, which leads to irreparable environmental and economic losses in the region.
Keywords
study: Azerbaijan). Geographical Information "Sepehr", 17(67), 62-68.
Vesanto, J., & Alhoniemi, E. (2000). Clustering of the Self-Organizing Map. IEEE Transactions
on Neural Networks, 11(3), 586-600.
Dehn, M., Gartner, H., & Dikau, R. (2001). Principles of Modeling of Landform Structure.
Computers and Geosciences, 27, 1005-1010.
Saeedzadeh, F., Sahebi, M.R., Ebadi, H., & Sadeghi, V. (2016). Change Detection of Multi
temporal Satellite Images by Comparison of Binary Mask and Most Classification
Comparison Methods. Journal of Geomatics Science and Technology, 5(3), 111-128.
Ara, H. (2013). Landforms and their classification in geomorphology science (Case study: Jajroud
catchment northeast of Tehran). Geographical Information "Sepehr", 22, 17-22.
Karimi, K., Zehtabian, Gh., Faramarzi, M., & Khosravi, H. (2018). Investigating the effect of
Karkheh dam irrigation networks on land use changes using satellite images (Case study:
Dasht-e Abbas semi-arid region). Journal of Geographical Information "Sepehr", 27(106),
129-140.
)