[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 16, Issue 55 (12-2016) ::
جغرافیایی 2016, 16(55): 65-85 Back to browse issues page
determine the best algorithm for land use and land cover extraction and changes detecting from Landsat satellite images(Case Study: Sufi chay Basin of Maragheh)
Mohamad Hosein Rezaei Moghadam * 1, Soghra Andaryani1 , Khalil Valizadeh Kamran1 , Farhad Almaspor2
1- scince and reserches
2- az water
Abstract:   (8112 Views)

Using remote sensing data due to providing updated information, cover repetitive, low-cost assessment of natural resources have a special place. Also change detection in the management and evaluation of natural resources is one of the basic needs. Thus the value of change of land use /land cover (LULC) is the result of the change detection process can obtain on multi-temporal remote sensing images. Therefore, in this study, both of the Landsat satellite images 8 (OLI&TIRS) the year 2013 and 7(ETM+) the year 2000 were used as input data for land cover/ use mapping level 1 and 2. In the meantime, because of the new images OLI, radiometric corrections was formulation with existing equation with using in Erdas software model maker.also from Normalize Difference Vegetation Index (NDVI), Bare Soil Index (BI) and three main components from Principal Component Analyze (PCA) as input alongside other bands were used to increase the accuracy of classification. The polynomial 5 degree from SVM method compared with artificial neural network (ANN) and maximum likelihood classification (MLC). Results showed that support vector machine method using Polynomial kernel and degree 5 (accuracy 92%) gives overall accuracy higher than artificial neural network method (accuracy 89% ) and maximum likelihood method (accuracy 91.8%) . Also SVM method shows better performance where classes exhibit similar spectral behavior. Post classification method used for detect changes in the timeframe of 13 years. The results show large changes in (LULC) was occurred thus need monitoring and proper management is needed for this watershed.

Keywords: Land use /Land cover, post classification, Support Vector Machine, Artificial Neural Network, Maximum Likelihood Classification, Sufi chay basin
Full-Text [PDF 1115 kb]   (2916 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2014/04/15 | Accepted: 2015/01/11 | Published: 2016/12/4
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


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

Rezaei Moghadam M H, Andaryani S, Valizadeh Kamran K, Almaspor F. determine the best algorithm for land use and land cover extraction and changes detecting from Landsat satellite images(Case Study: Sufi chay Basin of Maragheh). جغرافیایی 2016; 16 (55) :65-85
URL: http://geographical-space.iau-ahar.ac.ir/article-1-802-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 16, Issue 55 (12-2016) Back to browse issues page
فضای جغرافیایی Geographic Space
Persian site map - English site map - Created in 0.18 seconds with 37 queries by YEKTAWEB 4642