Spectral discrimination of invasive Lantana camara L. From co-occurring species

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