Globally, biological invasions are considered as one of the major contributing factors for the loss of indigenous biological diversity. Hyperspectral remote sensing plays an important role in the detection and mapping of invasive plant species. The main objective of this study was to discriminate invasive plant species from adjacent native species using a ground-based hyperspectral sensor in two protected areas, Lehri Reserve Forest and Jindi Reserve Forest in Punjab, Pakistan. Field spectral measurements were collected using an ASD FieldSpec handheld2^TM spectroradiometer (325–1075 nm) and the discrimination between native and invasive plant species was evaluated statistically using hyperspectral indices as well as leaf wavelength spectra. Finally, spectral separability was calculated using Jeffries Matusita distance index, based on selected wavebands. The results reveal that there were statistically significant differences (p < 0.05) between the different spectral indices of most of the plant species in the forests. However, the red-edge parameters showed the highest potential (p < 0.001) to discriminate different plant species. With leaf spectral signatures, the mean reflectance between all plant species was significantly different (p < 0.05) at 562 (75%) wavelength bands. Among pairwise comparisons, invasive Leucaena leucocephala showed the best discriminating ability, with Dodonaea viscosa having 505 significant wavebands showing variations between them. Jeffries Matusita distance analysis revealed that band combinations of the red-edge region (725, 726 nm) showed the best spectral separability (85%) for all species. Our findings suggest that it is possible to identify and discriminate invasive species through field spectroscopy for their future monitoring and management. However, the upscaling of hyperspectral measurements to airborne and satellite sensors can provide a reliable estimation of invasion through mapping inside the protected areas and can help to conserve biodiversity and environmental ecosystems in the future.