Being presented by Fernando Vanegas AlvarezĀ
Myrtle rust is an invasive fungal disease that pose a significant threat to the native Australian myrtaceae ecosystems. Original initiative to eradicate myrtle rust had been unsuccessful as the spread of the spores occur too rapidly by wind, water and physical contact. This caused the shift in biosecurity efforts to focus on management rather than eradication. Management measures begin with the identification and assessment of infected plants. This can prove difficult in environments where ground access is limited or unavailable. Remote sensing using unmanned aerial systems (UAS) are increasingly adopted for such applications. Continual developments in UAS technology have allowed the platforms and sensors to be more advanced, intelligent, compact and accurate. In this work, aerial data collection of high-resolution RGB, multispectral and hyperspectral imageries over a myrtle rust infected tea tree forest ecosystem was carried out. The high-resolution RGB imagery inherits 4mm/px resolution, which shows not only the stages of infection (transitioning from small purple spots to bright yellow spores to faded dull yellow to grey) but also the severity and spread. The multispectral imagery contains several wavelength bands (red, green, blue, red edge and near-infrared) for calculating the normalised difference vegetation index (NDVI). The NDVI provides estimates to vegetation properties such as biomass, leaf area, chlorophyll concentration, plant productivity, fractional vegetation cover, accumulated rainfall, etc. The hyperspectral imagery includes narrower and more numerous spectral bands than the multispectral, specifically, 274 bands as compared to the 5 bands in the multispectral. The continuous spectral features captured within the hyperspectral data provide measurements for the unique spectral signature of myrtle rust. The spectral signature can be used to populate spectral libraries for benefiting ongoing research and development into myrtle rust studies. UAS remote sensing has demonstrated its potentials for myrtle rust management, particularly in remote forested environments.