| Summary: | Invasive alien plants are considered as one of the major causes of loss of native biodiversity around the world. Remote sensing provides an opportunity to identify and map native and invasive species using accurate spectral information. The current study was aimed to evaluate PlanetScope (3 m) and Sentinel (10 m) datasets for mapping the distribution of native and invasive species in two protected areas in Pakistan, using machine learning (ML) algorithms. The multispectral data were analysed with the following four ML algorithms (classifiers)—random forest (RF), Gaussian mixture model (GMM), k-nearest neighbour (KNN), and support vector machine (SVM)—to classify two invasive species, <i>Lantana camara</i> L. (common lantana) and <i>Leucaena leucocephala</i> L. The (Ipil-ipil) Dzetsaka plugin of QGIS was used to map these species using all ML algorithms. RF, GMM, and SVM algorithms were more accurate at detecting both invasive species when using PlanetScope imagery rather than Sentinel. Random forest produced the highest accuracy of 64% using PlanetScope data. <i>Lantana camara</i> was the most dominating plant species with 23% cover, represented in all thematic maps. <i>Leucaena leucocpehala</i> was represented by 7% cover and was mainly distributed in the southern end of the Jindi Reserve Forest (Jhelum). It was not possible to discriminate native species <i>Dodonea viscosa</i> Jacq. (Snatha) using the SVM classifier for Sentinel data. Overall, the accuracy of PlanetScope was slightly better than Sentinel in term of species discrimination. These spectral findings provide a reliable estimation of the current distribution status of invasive species and would be helpful for land managers to prioritize invaded areas for their effective management.
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