Volume 1, Issue 2 (1-2021)                   Degrad Rehabil Nat Land 2021, 1(2): 80-88 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Moradi H, Rezaei V. Comparison of Land use Type Classification Algorithms in the Land use Mappreparation Inzenouzchai Watershed. Degrad Rehabil Nat Land 2021; 1 (2) :80-88
URL: http://drnl.sanru.ac.ir/article-1-170-en.html
Tarbiat Modares University
Abstract:   (1417 Views)
Land use identification and determination of their spreading level in the region is one of important factors in the natural resources and environmental studies. Preparing land use map from satellite data is one of the fastest and most cost-effective methods. This study’s aim is to determine the best algorithm for TM satellite images classification between 6 supervised classification methods including maximum likelihood, mahalanovis distance, minimum distance, parallel pipe, support vector machines and binary codes to extract land use map of Zenouz Chai watershed.For grassland, wasteland and abandoned and agricultural land use, 30 training samples and for badland land use 45 training samples were prepared separately using ground control points. Results were assessed for satellite image classified using by overall accuracy index, kappa coefficient, producer accuracy and user accuracy. Investigation of TM image classification accuracies showed that the maximum likelihood algorithm with overall accuracy coefficient (84/73 %) and kappa coefficient 65/0, have the higher efficiency for image classification into four land use classes. The results showed that maximum likelihood method has more capabilities than other methods for land use map preparing. Therefore, the results of this study can be used to provide land use maps with higher accuracy using maximum likelihood method to assessing the environment and natural resources works in the areas with the same situations.
Full-Text [PDF 1657 kb]   (546 Downloads)    
Type of Study: Research |
Received: 2021/04/29 | Accepted: 2021/09/4 | Published: 2021/09/11

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Degradation and Rehabilitation of Natural Land

Designed & Developed by : Yektaweb