Comparison of three visual, object-based, and supervised classification methods of land use/cover mapping in Mollah-Ahmad Watershed, Ardabil

Subject Areas : KARBARI ARAZI

Azad  Kakehmami ¹ (Graduated MSc of Range Management, University of Mohaghegh Ardabili)
Ardavan  Ghorbani ² (Associate Professor at the Department of Range & Watershed Management, University of Mohaghegh Ardabili)

Received: 2017-07-01 Accepted : 2018-01-06 Published : 2018-02-20

Keywords: Supervised classification, Visual interpretation, OLI Sensor, Object-based Classification, QuickBird Satellite,

Abstract :

Due to the growing population and consequently the degradation and uncontrolled use of land, awareness of the state of the land and its use is necessary. In this study, the main purpose was land use /cover mapping of the Mollah Ahmad Watershed. Two types of Google Earth images (Quickbird images, 2013) and Operational Land Imager (OLI) sensor of Landsat 8 image (2014) with three methods of visual interpretation (Google Earth images), object-based classification (Google Earth images), and supervised classification (Landsat 8) were used. In order to evaluate the land use /cover map produced from the methods, 51 points were selected as control points for the accuracy assessment. The results showed that the overall accuracy of the map generated from visual interpretation, object-based classification, and supervised classification were 100, 90, and 72 percent, respectively and their kappa coefficients were 1, 0.85 and 0.6, respectively, which the high accuracy of the maps generated from two methods of visual and object-based interpretation. The method of interpreting the visually using the high-resolution images (Quickbird with 0.65 to 2.9 m resolutions) of Google Earth is the most accurate method; however, the object-based classification method due to its low cost in terms of time in large environment has relatively acceptable accuracy.

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