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Detecting canopy damage caused by Uromycladium acaciae on South African Black Wattle forest compartments using moderate resolution satellite imagery
Abstract
Uromycladium acaciae, also known as wattle rust, is a rust fungus that has adversely impacted black wattle (Acacia mearnsii) in South Africa. This study assessed the potential of the Landsat 8 multispectral sensor to detect canopy damage caused by wattle rust on two plantation farms near Richmond, KwaZulu-Natal. The Landsat 8 bands and vegetation indices detected forest canopy damage caused by Uromycladium acaciae with an accuracy of 88.24% utilising seven bands and the Partial Least Squares Discriminate Analysis (PLS-DA) algorithm. Additionally, the model was optimised using the Variable Importance in Projection (VIP) method which only selected the most influential bands in the model. The coastal aerosol band (430nm-450nm), red band (640nm-670nm), near infrared (850nm-880nm) and NDVI were exclusively used in the optimised model and an accuracy of 82.35% was produced. The study highlighted the potential of remote sensing to detect canopy damage caused by a rust fungus and contributes towards a monitoring framework for analysing trends using freely available Landsat 8 imagery
