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dc.contributor.author Masemola, Cecilia R
dc.contributor.author Cho, Moses A
dc.contributor.author Ramoelo, Abel
dc.date.accessioned 2019-10-04T06:34:55Z
dc.date.available 2019-10-04T06:34:55Z
dc.date.issued 2019-08
dc.identifier.citation Masemola, C.R., Cho, M.A. and Ramoelo, A. 2019. Assessing the effect of seasonality on leaf and canopy spectra for the discrimination of an alien tree species, Acacia Mearnsii, from co-occurring native species using parametric and nonparametric classifiers. IEEE Transactions on Geoscience and Remote Sensing, v.57(8), pp 5853-5867. en_US
dc.identifier.issn 0196-2892
dc.identifier.issn 1558-0644
dc.identifier.uri https://ieeexplore.ieee.org/document/8688643
dc.identifier.uri DOI: 10.1109/TGRS.2019.2902774
dc.identifier.uri http://hdl.handle.net/10204/11142
dc.description Copyright: 2019 IEEE. Due to copyright restrictions, the attached PDF file contains the accepted version of the published item. For access to the published version, please consult the publisher's website. en_US
dc.description.abstract The tree Acacia mearnsii is native to south-eastern Australia but has become an aggressive invader in many countries. In South Africa, it is a significant threat to the conservation of biomes. Detecting and mapping its early invasion is critical. The current ground-based methods to map A. mearnsii are accurate but are neither economical nor practical. Remote sensing (RS) provides accurate and repeatable spatial information on tree species. The potential of RS technology to map A. mearnsii distributions remains poorly understood, mainly due to a lack of knowledge on the spectral properties of A. mearnsii relative to co-occurring native plants. We investigated the spectral uniqueness of A. mearnsii compared to co-occurring native plant species within the South African landscape. We explored full-range (400-2500 nm), leaf and canopy hyperspectral reflectance of the species. The spectral reflectance was collected biweekly from December 23, 2016 and May 31, 2017. We conducted a time series analysis, to assess the effect of seasonality on species discrimination. For comparison, two classification models were employed: parametric interval extended canonical variate discriminant (iECVA-DA) and nonparametric random forest discriminant classifiers (RF-DA). The results of this paper suggest that phenology plays a crucial role in discriminating between A. mearnsii and sampled species. The RF classifier discriminated A. mearnsii with slightly higher accuracies (from 92% to 100%) when compared with the iECVA-DA (from 85% to 93%). The study showed the potential of RS to discriminate between A. mearnsii and co-occurring plant species. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;22474
dc.subject Acacia mearnsii en_US
dc.subject Extended canonical variates analysis en_US
dc.subject Invasive tree species classification en_US
dc.subject Leaf and canopy reflectance en_US
dc.subject Linear discriminant analysis en_US
dc.subject Random forest en_US
dc.title Assessing the effect of seasonality on leaf and canopy spectra for the discrimination of an alien tree species, Acacia Mearnsii, from co-occurring native species using parametric and nonparametric classifiers en_US
dc.type Article en_US


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