Mapping invasive strawberry guava (Psidium cattleianum) in tropical forests of Mauritius with Sentinel-2 and machine learning

Invasive flora alien species (IAS) pose a serious threat to biodiversity in tropical forests. This study evaluated different classification approaches for remote sensing of the target IAS, strawberry guava (Psidium cattleianum), using open-access satellite imagery to aid research and understanding of IAS impacts in tropical forests. This is likely the first study using freely available open-access imagery to map strawberry guava, and the first mapping of invasive species in Mauritius using remote sensing. Ground-based observations in Black River Gorges National Park (BRGNP), Mauritius, resulted in 4670 ‘ground truth’ samples across three land cover classes representing different levels of strawberry guava canopy cover; 70% of these ground truth data were used for training and model tuning, and 30% were held-out for testing. Classification was performed using Sentinel-2 MSI images and Google Earth Engine. Computation of Jeffries-Matusita distance supported the addition of Grey Level Co-Occurrence Matrix texture measures, and spectral indices (ARI1, and ReNDVI) for classification, since these features increased separability of strawberry guava cover classes substantially over using spectral bands alone. Higher than 80% overall and individual class accuracies were achieved with both Random Forest (RF) and Support Vector Machine (SVM) classification of strawberry guava in BRGNP. RF is recommended for similar future applications for its higher overall accuracy (97.60% ±.20% at 95% confidence), lower variation, and prediction of more homogenous class regions. This study found that strawberry guava canopy cover in BRGNP was highest in central regions and correlated to steeper slopes, with little overall change from 2016 to 2020.