Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR

Accurate spatial information on the location of invasive alien plants (IAPs) in riparian environments is critical to fulfilling a comprehensive weed management regime. This study aimed to automatically map the occurrence of riparian bugweed (Solanum mauritianum) using airborne AISA Eagle hyperspectral data (393 nm-994 nm) in conjunction with LiDAR derived height. Utilising an unsupervised random forest (RF) classification approach and Anselin local Moran's I clustering, results indicate that the integration of LiDAR with minimum noise fraction (MNF) produce the best detection rate (DR) of 88%, the lowest false positive rate (FPR) of 7.14% and an overall mapping accuracy of 83% for riparian bugweed. In comparison, utilising the original hyperspectral wavebands with and without LiDAR produced lower DRs and higher FPRs with overall accuracies of 79% and 68% respectively. This research demonstrates the potential of combining spectral information with LiDAR to accurately map IAPs using an automated unsupervised RF anomaly detection framework.

Automated summary

This study successfully mapped riparian bugweed in riparian environments using a combination of hyperspectral data and LiDAR technology, achieving a detection rate of 88%, a false positive rate of 7.14%, and an overall accuracy of 83%. Compared to using original hyperspectral wavebands, integrating LiDAR can more accurately map the locations of invasive alien plants.