Supervised classification of Buxus hyrcana plant communities using artificial neural network

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.

Khabazi F.; ESMAILZADEH O.; NAJAFI A.

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

Journal Paper

Paper Information

Journal: Iranian journal of Forest Year:2019 | Volume:11 | Issue:3 Page(s): 387-400

Download Full-Text

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

Persian Verion

View:

442

Download:

Cites:

Information Journal Paper

Title

Supervised classification of Buxus hyrcana plant communities using artificial neural network

Author(s)

Khabazi F. | ESMAILZADEH O. | NAJAFI A. | Issue Writer Certificate

Keywords

AssignmentQ1

Phytosociological dataQ1

Braun-Blanquet methodQ1

MLPQ1

TWINSPANQ3

Abstract

In this research, the application of Artificial Neural Network or MLP method in the process Assignment of relevé-groups/ plant communities allocation was evaluated using Buxus hyrcana forests database. For this purpose, firstly, the ecological and sociological groups of B. hyrcana were determined using TWINSPAN and Braun-Blanquet method, respectively. The results of both numerical and expert based classification dendrogram of the B. hyrcana communities, which included seven levels of classification as primary groups/plant communities, were introduced to MLP. Then, with Assignments in three sets of training (70%), test (15%) and validation (15%), the MLP classification was performed on each level of the two dendrograms. The results showed that by increasing the level of classification, the degree of adaptation of the MLP result with primary ecological and sociological groups of TWINSPAN (99% to 60%) and Braun-Blanquet (98% to 68%) decreased from the cutoff level of 1 to 7. Results of sensitivity and kappa cross tab coefficients, except in 7 cut level, imply that the quality of MLP groups based on TWINSPAN primary ecological group is better than the primary Braun-Blanquet groups. The MLP results in Buxus hyrcana plant communities classification were consistent with the results of TWINSPAN (90%) and Braun-Blanquet (89%) ecological/syntaxa groups at the fifth cut level of both dendrograms concluding the reliability of MLP application for classification of plant communities. So, our result confirms that MLP can be introduced as a suitable method for the Assignment of releves to plant communities.

Cites

No record.

References

No record.

Cite

APA: Copy

Khabazi, F., ESMAILZADEH, O., & NAJAFI, A.. (2019). Supervised classification of Buxus hyrcana plant communities using artificial neural network. IRANIAN JOURNAL OF FOREST, 11(3 ), 387-400. SID. https://sid.ir/paper/123181/en

Vancouver: Copy

Khabazi F., ESMAILZADEH O., NAJAFI A.. Supervised classification of Buxus hyrcana plant communities using artificial neural network. IRANIAN JOURNAL OF FOREST[Internet]. 2019;11(3 ):387-400. Available from: https://sid.ir/paper/123181/en

IEEE: Copy

F. Khabazi, O. ESMAILZADEH, and A. NAJAFI, “Supervised classification of Buxus hyrcana plant communities using artificial neural network,” IRANIAN JOURNAL OF FOREST, vol. 11, no. 3 , pp. 387–400, 2019, [Online]. Available: https://sid.ir/paper/123181/en

Related Journal Papers