Comparison and Evaluation of Supervised Classification Methods for Extraction and Detection of Changes in Geomorphologic Landforms of Sojasrood Basin
Document Type : Original Article
Authors
mohaghegh ardabili university
Abstract
Abstract:
Understanding geomorphologic landforms and studying its developments and changes in all regions, especially mountainous areas, in order to manage the environment in different fields is the important and essential needs of geomorphology. The internal and external dynamics of the Earth constantly cause changes in geomorphologic landforms. Therefore, recognizing these changes and developments for better management in various natural and human contexts is an essential issue. Mountainous areas due to their difficulty in traffic challenge field studies. Geomorphology also uses the latest technologies of the world, such as remote sensing, to accelerate the advancement of goals and needs, in line with other sciences. Landsat TM and OLI satellite imagery were used to identify the surface landforms of Sojasrood basin and to investigate the trend of changes during the years 1986 to 2018. To identify the landforms, field surveys were performed using Google Earth images and topographic maps.Then, by maximum Likeihood Supervised Classification Methods Neural network and Support vector machine have been extracted from the original and main landforms. The results of classification accuracy assessment showed that the maximum Likeihood method with total accuracy of 97.70 Kappa coefficients 96 Percent in 1986 and 2018 has a better performance in geomorphologic mapping and change process than the other two methods. In order to investigate landform variations and detect changes over a period of 32 years, the maximum Likelihood category and MNF algorithm were used in the ENVI software environment. The final results showed that the Vegetation zones and alluvial plain had 159/479 and 26/572% increase in area, respectively. Mountains and hills, alluvial terraces, alluvial cone and new alluvial were reduced by area. Also, the results of MNF algorithm show that the maximum intensity and speed of changes are related to alluvial plains, mountains and hills, and the least speed of changes related to the alluvial cone.
Understanding geomorphologic landforms and studying its developments and changes in all regions, especially mountainous areas, in order to manage the environment in different fields is the important and essential needs of geomorphology. The internal and external dynamics of the Earth constantly cause changes in geomorphologic landforms. Therefore, recognizing these changes and developments for better management in various natural and human contexts is an essential issue. Mountainous areas due to their difficulty in traffic challenge field studies. Geomorphology also uses the latest technologies of the world, such as remote sensing, to accelerate the advancement of goals and needs, in line with other sciences. Landsat TM and OLI satellite imagery were used to identify the surface landforms of Sojasrood basin and to investigate the trend of changes during the years 1986 to 2018. To identify the landforms, field surveys were performed using Google Earth images and topographic maps.Then, by maximum Likeihood Supervised Classification Methods Neural network and Support vector machine have been extracted from the original and main landforms. The results of classification accuracy assessment showed that the maximum Likeihood method with total accuracy of 97.70 Kappa coefficients 96 Percent in 1986 and 2018 has a better performance in geomorphologic mapping and change process than the other two methods. In order to investigate landform variations and detect changes over a period of 32 years, the maximum Likelihood category and MNF algorithm were used in the ENVI software environment. The final results showed that the Vegetation zones and alluvial plain had 159/479 and 26/572% increase in area, respectively. Mountains and hills, alluvial terraces, alluvial cone and new alluvial were reduced by area. Also, the results of MNF algorithm show that the maximum intensity and speed of changes are related to alluvial plains, mountains and hills, and the least speed of changes related to the alluvial cone.
Keywords
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