Spectral Discrimination of Invasive Lantana Camara L. From Co-Occurring Species

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dc.contributor.authorWaititu, Julius Maina
dc.contributor.authorMundia, Charles Ndegwa
dc.contributor.authorSichang, Arthur W.
dc.date.accessioned2023-09-14T06:39:42Z
dc.date.available2023-09-14T06:39:42Z
dc.date.issued2023
dc.descriptionArticleen_US
dc.description.abstractLantana Camara L. (LC) invasive species has not been successfully mapped due to inadequate spectral information. This study aimed at assessing the performance of leaf-level in-situ hyperspectral data and derived indices in discriminating LC among co-occurring species during the dry and wet seasons. In addition, the performance of simulated Sentinel-2 bands, Sentinel-2 derived indices and machine learning algorithms in discriminating it was explored. Spectrally distinct features for species discrimination were selected using the guided regularized random forest (GRRF) and their separability quantified with Jeffries–Matusita distance method. We found that ratio-based and difference indices constructed with first and second-order derivative hyperspectral reflectance wavelengths perfectly separated LC from co-occurring species in the dry and wet seasons with ≥ 97% of separability accuracy. Similarly, a set of derived ratio-based and difference Sentinel-2 indices yielded > 95% and < 80% of LC separability accuracy in wet and dry seasons respectively. The SVM with radial basis function algorithm fitted with selected continuum removed derivative reflectance (band depth) narrow-bands yielded the highest overall accuracy (OA) of 84% and a Kappa of 0.75 for the dry season while the same algorithm fitted with selected first derivative narrow-bands yielded an OA of 82% and Kappa of 0.66 for the wet season. Conversely, the regularized logistic regression yielded the highest performance (OA of 77% and Kappa of 0.62) when fitted with combined selected Sentinel-2 variables for the dry season while the gradient boosting machine (GBM) fitted with combined Sentinel-2 variables had the highest performance (OA of 75% and Kappa of 0.51) for the wet season. These findings have important implications on the upscaling of LC’s derived leaf-level indices to canopylevel and subsequent LC classification with hyperspectral and Sentinel-2 imagery datasets over heterogeneous environments.en_US
dc.description.sponsorshipInternational Foundation for Scienceen_US
dc.identifier.citationWaititu, J. M., Mundia, C. N., & Sichangi, A. W. (2023). Observation and Geoinformation. International Journal of Applied Earth Observation and Geoinformation, 119, 103307.en_US
dc.identifier.urihttps://doi.org/10.1016/j.jag.2023.103307
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/26952
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectLantana camaraen_US
dc.subjectInvasive speciesen_US
dc.subjectSpecies discriminationen_US
dc.subjectHyperspectralen_US
dc.subjectSentinel-2en_US
dc.subjectMachine learning algorithmsen_US
dc.titleSpectral Discrimination of Invasive Lantana Camara L. From Co-Occurring Speciesen_US
dc.typeArticleen_US
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