Invasive plant species pose a significant threat to native ecosystems, and mapping their distribution is essential for effective management and conservation efforts. This study compared the performance of different machine learning techniques in the classification of multispectral signature of the invasive plant species Spathodea campanulata using remote sensing imagery. The ground-based locality data of vegetation was collected using a random sampling technique, and satellite imagery was acquired from Sentinel-2. The results showed that the blue, green, red, red- edge, and near-infrared bands were effective in distinguishing between invasive S. campanulata, non-vegetation, and other vegetation using machine learning techniques. The support vector machine (SVM) technique achieved the highest overall accuracy of 80%, followed by random forest (RF) with 73% and K-nearest neighbor (KNN) with 66%. The Gaussian mixture model (GMM) technique had the lowest overall accuracy of 53%. SVM and RF showed substantial agreement between predicted and observed classes, whereas KNN showed moderate agreement and GMM showed poor agreement. The maps generated by SVM depict a distribution of the invasive plant species that is characterized by isolated patches in the northern region of the study area. In contrast, the southern region – including the protected area of Mount Timolan – shows a dense presence of S. campanulata, indicating an ongoing invasion by the species. This highlights the need for effective management and conservation efforts to mitigate the negative impact of invasive plant species on native ecosystems.
Read full abstract