نمایش مختصر رکورد

dc.contributor.authorRashidi Tazhan, Oen_US
dc.contributor.authorPir Bavahgar, Men_US
dc.contributor.authorGhazanfari, Hen_US
dc.date.accessioned1399-07-09T11:45:40Zfa_IR
dc.date.accessioned2020-09-30T11:45:40Z
dc.date.available1399-07-09T11:45:40Zfa_IR
dc.date.available2020-09-30T11:45:40Z
dc.date.issued2019-03-01en_US
dc.date.issued1397-12-10fa_IR
dc.date.submitted2018-06-27en_US
dc.date.submitted1397-04-06fa_IR
dc.identifier.citationRashidi Tazhan, O, Pir Bavahgar, M, Ghazanfari, H. (2019). Detecting pollarded stands in Northern Zagros forests, using artificial neural network classifier on multi-temporal lansat-8(OLI) imageries (case study: Armarde, Baneh). Caspian Journal of Environmental Sciences, 17(1), 83-96. doi: 10.22124/cjes.2019.3347en_US
dc.identifier.issn1735-3033
dc.identifier.issn1735-3866
dc.identifier.urihttps://dx.doi.org/10.22124/cjes.2019.3347
dc.identifier.urihttps://cjes.guilan.ac.ir/article_3347.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/408551
dc.description.abstractLocal economy, based on animal husbandry in Northern Zagros forest leads to increase employing leaves and branches (pollarding) compared to the other parts of Zagros. Pollarding is a convenient method in forest utilization to supply fodder and it has been always trying to obtain its stable production by proper management skills. One of the most important forest management tools in a given forest is to provide up-to-date spatial maps of pollarded regions. The objective of this study was to investigate the capability of multi-temporal Landsat 8 OLI sensor for mapping pollarding areas of Northern Zagros forests. So that, we employed Landsat 8-OLI single and multi-date images acquired on 2014 and 2015. To assess the accuracy of output maps, a complete ground-truth of the study area was used to calculate the accuracy heuristics for the output maps. Different classification approaches were applied including minimum distance and maximum likelihood classifiers, artificial neural networks and fuzzy method. The classification accuracy was calculated on the basis of overall accuracy and kappa coefficient. The results indicated that artificial neural network and fuzzy classifier present the highest accuracy than the other classifiers. It was also found that utilizing the multi-temporal OLI imageries improves the accuracy over employing a single date. The results indicate that the multi-temporal imagery is moderately capable of mapping pollarded stands and classifying pollarding types, using ANN and Fuzzy classifiers.en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Guilanen_US
dc.relation.ispartofCaspian Journal of Environmental Sciencesen_US
dc.relation.isversionofhttps://dx.doi.org/10.22124/cjes.2019.3347
dc.subjectOLIen_US
dc.subjectPollardingen_US
dc.subjectZagros forestsen_US
dc.subjectANNen_US
dc.titleDetecting pollarded stands in Northern Zagros forests, using artificial neural network classifier on multi-temporal lansat-8(OLI) imageries (case study: Armarde, Baneh)en_US
dc.typeTexten_US
dc.typeResearch Paperen_US
dc.contributor.departmentDepartment of Forestry, Center for Research & Development of Northern Zagros Forests, University of Kurdistan, Sanandaj, Iranen_US
dc.contributor.departmentDepartment of Forestry, Center for Research & Development of Northern Zagros Forests, University of Kurdistan, Sanandaj, Iranen_US
dc.contributor.departmentDepartment of Forestry, Center for Research & Development of Northern Zagros Forests, University of Kurdistan, Sanandaj, Iranen_US
dc.citation.volume17
dc.citation.issue1
dc.citation.spage83
dc.citation.epage96


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