COMPARISON OF THREE COMMON METHODS IN SUPERVISED CLASSIFICATION OF SATELLITE DATA FOR VEGETATION STUDIES

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

AHMADPOUR A.; SOLAIMANI K.; SHOKRI M.; GHORBANI J.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of RS and GIS for Natural Resources Year:2011 | Volume:2 | Issue:2 Page(s): 69-80

Download Full-Text

Persian Verion

View:

4,394

Download:

Cites:

Information Journal Paper

Title

COMPARISON OF THREE COMMON METHODS IN SUPERVISED CLASSIFICATION OF SATELLITE DATA FOR VEGETATION STUDIES

Author(s)

AHMADPOUR A. | SOLAIMANI K. | SHOKRI M. | GHORBANI J. | Issue Writer Certificate

Keywords

KAPPA COEFFICIENTQ1

IMAGE CLASSIFICATIONQ2

SUPERVISED CLASSIFICATIONQ2

REMOTE SENSINGQ2

SANS-SERIF"> ETM+ IMAGEQ2

IRS IMAGEQ2

Abstract

Usage of modern technologies such as GIS and RS in plant ecosystems studies and especially land cover mapping is needed to recognition of these instruments efficiency and also identification of the best methods for applying them. This study aimed to compare the efficiency of three common SUPERVISED CLASSIFICATION methods of satellite data (Minimum to Distance, Parallelepiped and Maximum Likelihood) to identification of plant groups in Goloul-via-Sarani protected area, Northern Khorasan Province, Iran. In order to this, 143 training samples (>30m2) were collected from areas that shown a homogenous plant species composition. These data recorded by GPS device and so transported to a GIS database. Satellite data included Landsat ETM+, and IRS-P6 LISS III that were prepared and analyzed by ENVI 4.2 software. Amount of efficiency for each method was evaluated by measurement of overall accuracy (OA) and KAPPA COEFFICIENT (KC) criteria. Results showed that ML method makes the highest accuracy for two data series (OA=90.35, 82.19 and KC=0.878, 0.772 for Landsat and IRS data respectively). In the face, PP method showed the worst results (OA=67.09, 58.76 and KC=0.593, 0.478). These results suggested that collection of sufficient training samples from natural classes and surveying probability of image pixel's dependency on these classes can be useful for classification of plant groups.

Cites

References

No record.

Cite

APA: Copy

AHMADPOUR, A., SOLAIMANI, K., SHOKRI, M., & GHORBANI, J.. (2011). COMPARISON OF THREE COMMON METHODS IN SUPERVISED CLASSIFICATION OF SATELLITE DATA FOR VEGETATION STUDIES. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 2(2), 69-80. SID. https://sid.ir/paper/189627/en

Vancouver: Copy

AHMADPOUR A., SOLAIMANI K., SHOKRI M., GHORBANI J.. COMPARISON OF THREE COMMON METHODS IN SUPERVISED CLASSIFICATION OF SATELLITE DATA FOR VEGETATION STUDIES. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2011;2(2):69-80. Available from: https://sid.ir/paper/189627/en

IEEE: Copy

A. AHMADPOUR, K. SOLAIMANI, M. SHOKRI, and J. GHORBANI, “COMPARISON OF THREE COMMON METHODS IN SUPERVISED CLASSIFICATION OF SATELLITE DATA FOR VEGETATION STUDIES,” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 2, no. 2, pp. 69–80, 2011, [Online]. Available: https://sid.ir/paper/189627/en

Related Journal Papers