Spectral Discrimination of Invasive Lantana Camara L. From Co-Occurring Species
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Date
2023
Authors
Waititu, Julius Maina
Mundia, Charles Ndegwa
Sichang, Arthur W.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Lantana 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.
Description
Article
Keywords
Lantana camara, Invasive species, Species discrimination, Hyperspectral, Sentinel-2, Machine learning algorithms
Citation
Waititu, J. M., Mundia, C. N., & Sichangi, A. W. (2023). Observation and Geoinformation. International Journal of Applied Earth Observation and Geoinformation, 119, 103307.