Applicability of phenological indices for mapping of understory invasive species using machine learning algorithms

Forests provide crucial ecosystem services and are increasingly threatened by invasive plant species. The spread of these invasive species has affected biodiversity and has become a trending topic due to its impact on both endemic species and biodiversity. Therefore, it is imperative to implement conservation measures to protect native species such as mapping and monitoring invasive plant species in the forest realm. Mapping understory herb invasive plant species within forest categories is challenging, for example species such as Ageratum conyzoides and Cassia tora do not occur in distinct clusters, making them difficult to distinguish from the surrounding forest. In this paper, phenology plays a vital role for analysing the separability of both inter and intra-species discrimination to examine temporal curves for different vegetation indices that affect plant growth during the green and senescence periods. Machine learning algorithms, including regression tree-based algorithms, decision tree-based algorithms, and probabilistic algorithms, were used to determine the most effective algorithm for pixel-based classification. Support Vector Machine (SVM) classifier was the most effective method, with an overall accuracy of this classifier was calculated as 90.28% and a kappa of 0.88. The findings indicate that machine learning algorithms remain effective for pixel-based classification of understory invasive plant species from forest class. Thus, this study shows a technical method to distinguish invasive plant species from forest class which can help forest managers to locate invasion sites to eradicate them and conserve native biodiversity.