Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR
作者：
Peerbhay, Kabir;Mutanga, Onisimo;Lottering, Romano;Agjee, Na'eem;Ismail, Riyad
通讯作者：
Kabir Peerbhay
作者机构：
School of Agricultural, Earth and Environmental Sciences, Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg, South Africa
通讯机构：
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
卷：
36
期：
4
页码：
465-480
基金类别：
Applied Centre for Climate Change and Earth Systems Science (ACCESS); National Research Foundation of South AfricaNational Research Foundation - South Africa [114898
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 accura...
