Predicting Sphaeropsis sapinea damage on Pinus radiata stands from CASI-2 using spectral mixture analysis

Within Australian Pinus radiata plantations a diverse range of damaging agents are present. A significant issue is the presence of a fungal pathogen Sphaeropsis sapinea which is present in many softwood plantations. In this research we investigate the use of CASI-2 imagery to detect Sphaeropsis sapinea infestation using linear spectral mixture analysis approaches. Results indicate that four fraction endmember images could be reliably extracted from the 12 channel CASI-2 imagery with sunlit canopy, soil, shadow, and nonphotosynthetic vegetation (NPV) all well estimated. Using multiple linear stepwise regression, models were developed using mixed fractional abundances with model predictions found to be highly significant. The NPV and shadow endmembers, in order, were consistently identified as important in the regression models and confirm their importance in crown condition modelling.