Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR. Issue 4 (25th February 2021)

• Record Type: Journal Article
• Authors: Peerbhay, Kabir; Mutanga, Onisimo; Lottering, Romano; Agjee, Na'eem; Ismail, Riyad
• Is Part Of: Geocarto international. Volume 36:Issue 4(2021)
• Page Start: 465
• Page End: 480
• Publication Date: 2021-02-25
• Subjects: Unsupervised random forest -- anomaly detection -- hyperspectral -- LiDAR; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2019.1614101
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: 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.