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

  • SCI-E
作者: Kabir Peerbhay;Onisimo Mutanga;Romano Lottering;Na’eem Agjee;Riyad Ismail
作者机构: School of Agricultural, Earth and Environmental Sciences, Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa
语种: 英文
关键词: Unsupervised random forest;anomaly detection;hyperspectral;LiDAR
期刊: GEOCARTO INTERNATIONAL
ISSN: 1010-6049
年: 2021
摘要: 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. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

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