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dc.contributor.authorFranceschi S
dc.contributor.authorAntonello A
dc.contributor.authorFloreancig V
dc.contributor.authorGianelle D
dc.contributor.authorComiti F
dc.contributor.authorTonon G
dc.date.accessioned2019-07-01T08:04:49Z
dc.date.available2019-07-01T08:04:49Z
dc.date.issued2018
dc.identifier.issn2279-7254
dc.identifier.urihttp://dx.doi.org/10.1080/22797254.2018.1521707
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/22797254.2018.1521707
dc.identifier.urihttps://bia.unibz.it/handle/10863/10319
dc.description.abstractIn this study, the particle swarming optimization procedure was applied to parametrize two Local Maxima (LM) algorithms in order to extract treetops from LiDAR-data in a test area (10 km2) of heterogeneous forest structures of conifers in the Alps. The obtained results were compared with those of a widely used variable-size window LM algorithm calibrated using literature values. Quantitative statistical parameters like matching, extraction, omission, and commission rates were calculated. The experimental results showed the effectiveness of the proposed method, which was capable to identify the 91% of the trees and estimate the 92% of the real above ground biomass with a total extraction rate close to 1. Almost all the dominant and codominant trees were extracted, while the extraction rate of the dominated trees averaged over 50%.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation
dc.rights
dc.subjectforest inventoryen_US
dc.subjectsingle tree extractionen_US
dc.subjectautomatic calibrationen_US
dc.subjectairborne laser scanningen_US
dc.subjectMatchingen_US
dc.subjectLidaren_US
dc.titleIdentifying treetops from aerial laser scanning data with particle swarming optimizationen_US
dc.typeArticleen_US
dc.date.updated2019-06-23T03:01:35Z
dc.language.isiEN-GB
dc.journal.titleEuropean Journal of Remote Sensing
dc.description.fulltextopenen_US


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