Evaluation of supervised classification capability of Landsat-8 and Sentinel-2A Satellite images in determining type and area of Pistachio Cultivars

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.

ZARE KHORMIZIE H.; GHAFARIAN MALAMIRI H.R.; Mortaz M.

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:2020 | Volume:11 | Issue:1 (38) Page(s): 84-103

Download Full-Text

Persian Verion

View:

754

Download:

Cites:

Information Journal Paper

Title

Evaluation of supervised classification capability of Landsat-8 and Sentinel-2A Satellite images in determining type and area of Pistachio Cultivars

Author(s)

ZARE KHORMIZIE H. | GHAFARIAN MALAMIRI H.R. | Mortaz M. | Issue Writer Certificate

Keywords

Supervised classificationQ1

Maximum likelihood algorithmQ2

Pistachio cultivarsQ1

Kappa coefficientQ1

Remote sensingQ1

Abstract

Remote sensing technique is one of the most effective tools for monitoring, studying and determining the cultivation area of agricultural and horticultural crops, especially on a large scale. Planners, managers, and farmers, with knowledge of the type and extent of crop cultivation, can adopt appropriate management and enforcement policies. The purpose of the present study was to evaluate the Supervised classification ability to classify Landsat 8 and Sentinel-2A multi-band satellite imagery in determining the cultivated area and type of four varieties of Pistachio namely such as; Akbari, Kalle Ghuchi, Ahmad Aghaei and Fandooki in an orchard in the Yazd province. In the present study, the accuracy of four classification algorithms, namely: Parallelepiped classification, Minimum distance, Mahalanobis distance and Maximum likelihood, as well as the optimum time in the separation of Pistachio cultivars, were investigated. According to the classification results of a Landsat-8 image, on June 12, 2018, the Maximum likelihood algorithm with a final accuracy and Kappa coefficient of 76. 8% and 0. 67% and Parallelepiped classification algorithm with the final and Kappa coefficients of 64. 7 and 0. 47, were of highest and lowest accuracy among others, respectively. Also, according to the results, the best time for the separation of Pistachio cultivars was in late June. The Kappa coefficient of maximum likelihood classification algorithm on June 22, July 23, August 24 and September 25 of 2018 were 0. 67, 0. 64, 0. 63 and 0. 63, respectively. The final accuracy and Kappa coefficient of maximum likelihood classification algorithm on the Sentinel-2A Satellite images on 12 June 2018, were 80% and 0. 71, respectively. By applying the median filter with a 3×3 dimensional kernel window size on the classified image, the final accuracy and Kappa coefficient was increased to 82. 6% and 0. 75, respectively. The final accuracy and Kappa coefficient of classification and separation of Pistachio cultivars in Sentinel-2A images were higher than in Landsat-8 images. Overall, based on our results, the Remote sensing classification techniques, as well as multi-spectral satellite imagery, are suitable for agricultural and horticultural mapping.

Cites

No record.

References

No record.

Cite

APA: Copy

ZARE KHORMIZIE, H., GHAFARIAN MALAMIRI, H.R., & Mortaz, M.. (2020). Evaluation of supervised classification capability of Landsat-8 and Sentinel-2A Satellite images in determining type and area of Pistachio Cultivars. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 11(1 (38) ), 84-103. SID. https://sid.ir/paper/358259/en

Vancouver: Copy

ZARE KHORMIZIE H., GHAFARIAN MALAMIRI H.R., Mortaz M.. Evaluation of supervised classification capability of Landsat-8 and Sentinel-2A Satellite images in determining type and area of Pistachio Cultivars. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2020;11(1 (38) ):84-103. Available from: https://sid.ir/paper/358259/en

IEEE: Copy

H. ZARE KHORMIZIE, H.R. GHAFARIAN MALAMIRI, and M. Mortaz, “Evaluation of supervised classification capability of Landsat-8 and Sentinel-2A Satellite images in determining type and area of Pistachio Cultivars,” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 11, no. 1 (38) , pp. 84–103, 2020, [Online]. Available: https://sid.ir/paper/358259/en

Related Journal Papers